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Data Analysis Techniques in Research – Methods, Tools & Examples

Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

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A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

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Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

  • Inspecting : Initial examination of data to understand its structure, quality, and completeness.
  • Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
  • Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
  • Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

  • Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
  • Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
  • Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

  • Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
  • Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
  • Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

  • Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
  • ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

  • Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
  • Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

  • Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
  • Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

  • Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
  • Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
  • Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
  • Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
  • Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

  • Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
  • Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

  • Calculate the mean, median, and mode of academic scores for both groups.
  • Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

  • Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
  • Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

  • Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
  • Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

  • Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
  • Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

  • Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
  • Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
  • Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

  • Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
  • Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

  • Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
  • Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

  • Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
  • Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

  • Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
  • Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

  • Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
  • Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

  • Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
  • Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

  • Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
  • Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

  • Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
  • Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

  • Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
  • Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

  • Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
  • Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

  • Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
  • Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

  • Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
  • Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

  • Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
  • Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

  • Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
  • Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

  • Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
  • Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

  • Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
  • Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

  • Description: An open-source programming language specifically designed for statistical computing and data visualization.
  • Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

  • Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
  • Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

  • Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
  • Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

  • Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
  • Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

  • Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
  • Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

  • Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
  • Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

  • Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
  • Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

  • Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
  • Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

  • Description: A data mining software application used for building predictive models and conducting advanced analytics.
  • Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

  • Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
  • Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
  • Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
  • In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
  • Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
  • In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
  • In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
  • Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Data Analysis in Research: Types & Methods


Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

  • Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
  • Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
  • Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

  • Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
  • Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
  • Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
  • Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

  • Fraud: To ensure an actual human being records each response to the survey or the questionnaire
  • Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
  • Procedure: To ensure ethical standards were maintained while collecting the data sample
  • Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

  • Count, Percent, Frequency
  • It is used to denote home often a particular event occurs.
  • Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

  • Mean, Median, Mode
  • The method is widely used to demonstrate distribution by various points.
  • Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

  • Range, Variance, Standard deviation
  • Here the field equals high/low points.
  • Variance standard deviation = difference between the observed score and mean
  • It is used to identify the spread of scores by stating intervals.
  • Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

  • Percentile ranks, Quartile ranks
  • It relies on standardized scores helping researchers to identify the relationship between different scores.
  • It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

  • Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
  • Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

  • Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
  • Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
  • Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
  • Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
  • Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
  • Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
  • Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection  methods, and choose samples.

LEARN ABOUT: Best Data Collection Tools

  • The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
  • Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
  • The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Research Methods Guide: Data Analysis

  • Introduction
  • Research Design & Method
  • Survey Research
  • Interview Research
  • Resources & Consultation

Tools for Analyzing Survey Data

  • R (open source)
  • Stata 
  • DataCracker (free up to 100 responses per survey)
  • SurveyMonkey (free up to 100 responses per survey)

Tools for Analyzing Interview Data

  • AQUAD (open source)
  • NVivo 

Data Analysis and Presentation Techniques that Apply to both Survey and Interview Research

  • Create a documentation of the data and the process of data collection.
  • Analyze the data rather than just describing it - use it to tell a story that focuses on answering the research question.
  • Use charts or tables to help the reader understand the data and then highlight the most interesting findings.
  • Don’t get bogged down in the detail - tell the reader about the main themes as they relate to the research question, rather than reporting everything that survey respondents or interviewees said.
  • State that ‘most people said …’ or ‘few people felt …’ rather than giving the number of people who said a particular thing.
  • Use brief quotes where these illustrate a particular point really well.
  • Respect confidentiality - you could attribute a quote to 'a faculty member', ‘a student’, or 'a customer' rather than ‘Dr. Nicholls.'

Survey Data Analysis

  • If you used an online survey, the software will automatically collate the data – you will just need to download the data, for example as a spreadsheet.
  • If you used a paper questionnaire, you will need to manually transfer the responses from the questionnaires into a spreadsheet.  Put each question number as a column heading, and use one row for each person’s answers.  Then assign each possible answer a number or ‘code’.
  • When all the data is present and correct, calculate how many people selected each response.
  • Once you have calculated how many people selected each response, you can set up tables and/or graph to display the data.  This could take the form of a table or chart.
  • In addition to descriptive statistics that characterize findings from your survey, you can use statistical and analytical reporting techniques if needed.

Interview Data Analysis

  • Data Reduction and Organization: Try not to feel overwhelmed by quantity of information that has been collected from interviews- a one-hour interview can generate 20 to 25 pages of single-spaced text.   Once you start organizing your fieldwork notes around themes, you can easily identify which part of your data to be used for further analysis.
  • What were the main issues or themes that struck you in this contact / interviewee?"
  • Was there anything else that struck you as salient, interesting, illuminating or important in this contact / interviewee? 
  • What information did you get (or failed to get) on each of the target questions you had for this contact / interviewee?
  • Connection of the data: You can connect data around themes and concepts - then you can show how one concept may influence another.
  • Examination of Relationships: Examining relationships is the centerpiece of the analytic process, because it allows you to move from simple description of the people and settings to explanations of why things happened as they did with those people in that setting.
  • << Previous: Interview Research
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  • Last Updated: Aug 21, 2023 10:42 AM

Your Modern Business Guide To Data Analysis Methods And Techniques

Data analysis methods and techniques blog post by datapine

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

  • Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
  • Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
  • Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
  • Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

  • Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
  • Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
  • Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

Data analysis process graphic

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

  • Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
  • Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
  • Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
  • Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
  • Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

Top 17 data analysis methods

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

Cohort analysis chart example from google analytics

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

Example on how to use predictive analytics tool from datapine

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

Example on how to use intelligent alerts from datapine

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Example of multidimensional scaling analysis

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

17 top data analysis techniques by datapine

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

data connectors from datapine

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

Transportation costs logistics KPIs

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

An executive dashboard example showcasing high-level marketing KPIs such as cost per lead, MQL, SQL, and cost per customer.

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

  • Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
  • Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
  • Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

  • Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
  • Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
  • SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
  • Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

  • Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
  • External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
  • Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
  • Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

  • Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
  • Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
  • Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
  • Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
  • Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
  • Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
  • Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
  • Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

  • Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
  • Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
  • Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
  • SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
  • Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

  • By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
  • 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
  • Companies that exploit the full potential of their data can increase their operating margins by 60% .
  • We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

  • Cluster analysis
  • Cohort analysis
  • Regression analysis
  • Factor analysis
  • Neural Networks
  • Data Mining
  • Text analysis
  • Time series analysis
  • Decision trees
  • Conjoint analysis 
  • Correspondence Analysis
  • Multidimensional Scaling 
  • Content analysis 
  • Thematic analysis
  • Narrative analysis 
  • Grounded theory analysis
  • Discourse analysis 

Top 17 Data Analysis Techniques:

  • Collaborate your needs
  • Establish your questions
  • Data democratization
  • Think of data governance 
  • Clean your data
  • Set your KPIs
  • Omit useless data
  • Build a data management roadmap
  • Integrate technology
  • Answer your questions
  • Visualize your data
  • Interpretation of data
  • Consider autonomous technology
  • Build a narrative
  • Share the load
  • Data Analysis tools
  • Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

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Handbook of Data Analysis

  • Edited by: Melissa Hardy & Alan Bryman
  • Publisher: SAGE Publications, Ltd
  • Publication year: 2004
  • Online pub date: January 01, 2011
  • Discipline: Anthropology
  • Methods: Statistical modelling , Confirmatory factor analysis
  • DOI: https:// doi. org/10.4135/9781848608184
  • Keywords: content analysis , data analysis , dependent variables , estimates , measurement , population , regression Show all Show less
  • Print ISBN: 9780761966524
  • Online ISBN: 9781848608184
  • Buy the book icon link

Subject index

This book provides an excellent reference guide to basic theoretical arguments, practical quantitative techniques and the methodologies that the majority of social science researchers are likely to require for postgraduate study and beyond. Diagrams and tables are used effectively throughout the text and snippets of sample code provide useful additions to chapters for those of us who are less familiar with statistical software packages. Where equations are used to they are explained and documented with careful explanation of statistical notation. Each of the chapters in the book references a representative range of key authors and seminal texts, making it an ideal springboard for further and more advanced reading the book provides an excellent reference of quantitative methodology and would provide a very useful addition to the shelves of researches and university libraries - Environment and Planning. This is a book that will rapidly be recognized as the bible for social researchers. It provides a first-class, reliable guide to the basic issues in data analysis, such as the construction of variables, the characterization of distributions and the notions of inference. Scholars and students can turn to it for teaching and applied needs with confidence. However, the book also seeks to enhance debate in the field by tackling more advanced topics such as models of change, causality, panel models and network analysis. Specialists will find much food for thought in these chapters. A distinctive feature of the book is the breadth of coverage. No other book provides a better one-stop survey of the field of data analysis. In 30 specially commissioned chapters the editors aim to encourage readers to develop an appreciation of the range of analytic options available, so they can choose a research problem and then develop a suitable approach to data analysis. `The book provides researchers with guidance in, and examples of, both quantitative and qualitative modes of analysis, written by leading practitioners in the field. The editors give a persuasive account of the commonalities of purpose that exist across both modes, as well as demonstrating a keen awareness of the different things that each offers the practising researcher' - Clive Seale, Brunel University. `With the appearance of this handbook, data analysts no longer have to consult dozens of disparate publications to carry out their work. The essential tools for an intelligent telling of the data story are offered here, in thirty chapters written by recognized experts. While quantitative methods are treated, from basic statistics through the general linear model and beyond, qualitative methods are by no means neglected. Indeed, a unique feature of this volume is the careful integration of quantitative and qualitative approaches. Undoubtedly, this integration succeeds because of the research strengths of the editors, leading social researchers who themselves employ both quantitative and qualitative methods' - Michael Lewis-Beck, F Wendell Miller Distinguished Professor of Political Science, University of Iowa and Editor of the SAGE `Quantitative Applications in the Social Sciences' series. `This is an excellent guide to current issues in the analysis of social science data. I recommend it to anyone who is looking for authoritative introductions to the state of the art. Each chapter offers a comprehensive review and an extensive bibliography and will be invaluable to researchers wanting to update themselves about modern developments' - Professor Nigel Gilbert, Pro Vice-Chancellor and Professor of Sociology, University of Surrey

Front Matter

  • Notes on Contributors
  • Introduction: Common Threads among Techniques of Data Analysis
  • Constructing Variables
  • Summarizing Distributions
  • Strategies for Analysis of Incomplete Data
  • Feminist Issues in Data Analysis
  • Historical Analysis
  • Multiple Regression Analysis
  • Incorporating Categorical Information into Regression Models: The Utility of Dummy Variables
  • Analyzing Contingent Effects in Regression Models
  • Regression Models for Categorical Outcomes
  • Log-Linear Analysis
  • Modeling Change
  • Analyzing Panel Data: Fixed- and Random-Effects Models
  • Longitudinal Analysis for Continuous Outcomes: Random Effects Models and Latent Trajectory Models
  • Event History Analysis
  • Sequence Analysis and Optimal Matching Techniques for Social Science Data
  • Sample Selection Bias Models
  • Structural Equation Modeling
  • Multilevel Modelling
  • Causal Inference in Sociological Studies
  • The Analysis of Social Networks
  • Tools for Qualitative Data Analysis
  • Content Analysis
  • Semiotics and Data Analysis
  • Conversation Analysis
  • Discourse Analysis
  • Grounded Theory
  • The Uses of Narrative in Social Science Research
  • Qualitative Research and the Postmodern Turn

Back Matter

  • Appendix: Areas of the Standard Normal Distribution

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Data Analysis

  • Introduction to Data Analysis
  • Quantitative Analysis Tools
  • Qualitative Analysis Tools
  • Mixed Methods Analysis
  • Geospatial Analysis
  • Further Reading

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What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

  • Photographs 
  • Hand-written notes from field observation
  • Machine learning training data sets
  • Ethnographic interview transcripts
  • Sheet music
  • Scripts for plays and musicals 
  • Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

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The 7 Most Useful Data Analysis Methods and Techniques

Data analytics is the process of analyzing raw data to draw out meaningful insights. These insights are then used to determine the best course of action.

When is the best time to roll out that marketing campaign? Is the current team structure as effective as it could be? Which customer segments are most likely to purchase your new product?

Ultimately, data analytics is a crucial driver of any successful business strategy. But how do data analysts actually turn raw data into something useful? There are a range of methods and techniques that data analysts use depending on the type of data in question and the kinds of insights they want to uncover.

You can get a hands-on introduction to data analytics in this free short course .

In this post, we’ll explore some of the most useful data analysis techniques. By the end, you’ll have a much clearer idea of how you can transform meaningless data into business intelligence. We’ll cover:

  • What is data analysis and why is it important?
  • What is the difference between qualitative and quantitative data?
  • Regression analysis
  • Monte Carlo simulation
  • Factor analysis
  • Cohort analysis
  • Cluster analysis
  • Time series analysis
  • Sentiment analysis
  • The data analysis process
  • The best tools for data analysis
  •  Key takeaways

The first six methods listed are used for quantitative data , while the last technique applies to qualitative data. We briefly explain the difference between quantitative and qualitative data in section two, but if you want to skip straight to a particular analysis technique, just use the clickable menu.

1. What is data analysis and why is it important?

Data analysis is, put simply, the process of discovering useful information by evaluating data. This is done through a process of inspecting, cleaning, transforming, and modeling data using analytical and statistical tools, which we will explore in detail further along in this article.

Why is data analysis important? Analyzing data effectively helps organizations make business decisions. Nowadays, data is collected by businesses constantly: through surveys, online tracking, online marketing analytics, collected subscription and registration data (think newsletters), social media monitoring, among other methods.

These data will appear as different structures, including—but not limited to—the following:

The concept of big data —data that is so large, fast, or complex, that it is difficult or impossible to process using traditional methods—gained momentum in the early 2000s. Then, Doug Laney, an industry analyst, articulated what is now known as the mainstream definition of big data as the three Vs: volume, velocity, and variety. 

  • Volume: As mentioned earlier, organizations are collecting data constantly. In the not-too-distant past it would have been a real issue to store, but nowadays storage is cheap and takes up little space.
  • Velocity: Received data needs to be handled in a timely manner. With the growth of the Internet of Things, this can mean these data are coming in constantly, and at an unprecedented speed.
  • Variety: The data being collected and stored by organizations comes in many forms, ranging from structured data—that is, more traditional, numerical data—to unstructured data—think emails, videos, audio, and so on. We’ll cover structured and unstructured data a little further on.

This is a form of data that provides information about other data, such as an image. In everyday life you’ll find this by, for example, right-clicking on a file in a folder and selecting “Get Info”, which will show you information such as file size and kind, date of creation, and so on.

Real-time data

This is data that is presented as soon as it is acquired. A good example of this is a stock market ticket, which provides information on the most-active stocks in real time.

Machine data

This is data that is produced wholly by machines, without human instruction. An example of this could be call logs automatically generated by your smartphone.

Quantitative and qualitative data

Quantitative data—otherwise known as structured data— may appear as a “traditional” database—that is, with rows and columns. Qualitative data—otherwise known as unstructured data—are the other types of data that don’t fit into rows and columns, which can include text, images, videos and more. We’ll discuss this further in the next section.

2. What is the difference between quantitative and qualitative data?

How you analyze your data depends on the type of data you’re dealing with— quantitative or qualitative . So what’s the difference?

Quantitative data is anything measurable , comprising specific quantities and numbers. Some examples of quantitative data include sales figures, email click-through rates, number of website visitors, and percentage revenue increase. Quantitative data analysis techniques focus on the statistical, mathematical, or numerical analysis of (usually large) datasets. This includes the manipulation of statistical data using computational techniques and algorithms. Quantitative analysis techniques are often used to explain certain phenomena or to make predictions.

Qualitative data cannot be measured objectively , and is therefore open to more subjective interpretation. Some examples of qualitative data include comments left in response to a survey question, things people have said during interviews, tweets and other social media posts, and the text included in product reviews. With qualitative data analysis, the focus is on making sense of unstructured data (such as written text, or transcripts of spoken conversations). Often, qualitative analysis will organize the data into themes—a process which, fortunately, can be automated.

Data analysts work with both quantitative and qualitative data , so it’s important to be familiar with a variety of analysis methods. Let’s take a look at some of the most useful techniques now.

3. Data analysis techniques

Now we’re familiar with some of the different types of data, let’s focus on the topic at hand: different methods for analyzing data. 

a. Regression analysis

Regression analysis is used to estimate the relationship between a set of variables. When conducting any type of regression analysis , you’re looking to see if there’s a correlation between a dependent variable (that’s the variable or outcome you want to measure or predict) and any number of independent variables (factors which may have an impact on the dependent variable). The aim of regression analysis is to estimate how one or more variables might impact the dependent variable, in order to identify trends and patterns. This is especially useful for making predictions and forecasting future trends.

Let’s imagine you work for an ecommerce company and you want to examine the relationship between: (a) how much money is spent on social media marketing, and (b) sales revenue. In this case, sales revenue is your dependent variable—it’s the factor you’re most interested in predicting and boosting. Social media spend is your independent variable; you want to determine whether or not it has an impact on sales and, ultimately, whether it’s worth increasing, decreasing, or keeping the same. Using regression analysis, you’d be able to see if there’s a relationship between the two variables. A positive correlation would imply that the more you spend on social media marketing, the more sales revenue you make. No correlation at all might suggest that social media marketing has no bearing on your sales. Understanding the relationship between these two variables would help you to make informed decisions about the social media budget going forward. However: It’s important to note that, on their own, regressions can only be used to determine whether or not there is a relationship between a set of variables—they don’t tell you anything about cause and effect. So, while a positive correlation between social media spend and sales revenue may suggest that one impacts the other, it’s impossible to draw definitive conclusions based on this analysis alone.

There are many different types of regression analysis, and the model you use depends on the type of data you have for the dependent variable. For example, your dependent variable might be continuous (i.e. something that can be measured on a continuous scale, such as sales revenue in USD), in which case you’d use a different type of regression analysis than if your dependent variable was categorical in nature (i.e. comprising values that can be categorised into a number of distinct groups based on a certain characteristic, such as customer location by continent). You can learn more about different types of dependent variables and how to choose the right regression analysis in this guide .

Regression analysis in action: Investigating the relationship between clothing brand Benetton’s advertising expenditure and sales

b. Monte Carlo simulation

When making decisions or taking certain actions, there are a range of different possible outcomes. If you take the bus, you might get stuck in traffic. If you walk, you might get caught in the rain or bump into your chatty neighbor, potentially delaying your journey. In everyday life, we tend to briefly weigh up the pros and cons before deciding which action to take; however, when the stakes are high, it’s essential to calculate, as thoroughly and accurately as possible, all the potential risks and rewards.

Monte Carlo simulation, otherwise known as the Monte Carlo method, is a computerized technique used to generate models of possible outcomes and their probability distributions. It essentially considers a range of possible outcomes and then calculates how likely it is that each particular outcome will be realized. The Monte Carlo method is used by data analysts to conduct advanced risk analysis, allowing them to better forecast what might happen in the future and make decisions accordingly.

So how does Monte Carlo simulation work, and what can it tell us? To run a Monte Carlo simulation, you’ll start with a mathematical model of your data—such as a spreadsheet. Within your spreadsheet, you’ll have one or several outputs that you’re interested in; profit, for example, or number of sales. You’ll also have a number of inputs; these are variables that may impact your output variable. If you’re looking at profit, relevant inputs might include the number of sales, total marketing spend, and employee salaries. If you knew the exact, definitive values of all your input variables, you’d quite easily be able to calculate what profit you’d be left with at the end. However, when these values are uncertain, a Monte Carlo simulation enables you to calculate all the possible options and their probabilities. What will your profit be if you make 100,000 sales and hire five new employees on a salary of $50,000 each? What is the likelihood of this outcome? What will your profit be if you only make 12,000 sales and hire five new employees? And so on. It does this by replacing all uncertain values with functions which generate random samples from distributions determined by you, and then running a series of calculations and recalculations to produce models of all the possible outcomes and their probability distributions. The Monte Carlo method is one of the most popular techniques for calculating the effect of unpredictable variables on a specific output variable, making it ideal for risk analysis.

Monte Carlo simulation in action: A case study using Monte Carlo simulation for risk analysis

 c. Factor analysis

Factor analysis is a technique used to reduce a large number of variables to a smaller number of factors. It works on the basis that multiple separate, observable variables correlate with each other because they are all associated with an underlying construct. This is useful not only because it condenses large datasets into smaller, more manageable samples, but also because it helps to uncover hidden patterns. This allows you to explore concepts that cannot be easily measured or observed—such as wealth, happiness, fitness, or, for a more business-relevant example, customer loyalty and satisfaction.

Let’s imagine you want to get to know your customers better, so you send out a rather long survey comprising one hundred questions. Some of the questions relate to how they feel about your company and product; for example, “Would you recommend us to a friend?” and “How would you rate the overall customer experience?” Other questions ask things like “What is your yearly household income?” and “How much are you willing to spend on skincare each month?”

Once your survey has been sent out and completed by lots of customers, you end up with a large dataset that essentially tells you one hundred different things about each customer (assuming each customer gives one hundred responses). Instead of looking at each of these responses (or variables) individually, you can use factor analysis to group them into factors that belong together—in other words, to relate them to a single underlying construct. In this example, factor analysis works by finding survey items that are strongly correlated. This is known as covariance . So, if there’s a strong positive correlation between household income and how much they’re willing to spend on skincare each month (i.e. as one increases, so does the other), these items may be grouped together. Together with other variables (survey responses), you may find that they can be reduced to a single factor such as “consumer purchasing power”. Likewise, if a customer experience rating of 10/10 correlates strongly with “yes” responses regarding how likely they are to recommend your product to a friend, these items may be reduced to a single factor such as “customer satisfaction”.

In the end, you have a smaller number of factors rather than hundreds of individual variables. These factors are then taken forward for further analysis, allowing you to learn more about your customers (or any other area you’re interested in exploring).

Factor analysis in action: Using factor analysis to explore customer behavior patterns in Tehran

d. Cohort analysis

Cohort analysis is a data analytics technique that groups users based on a shared characteristic , such as the date they signed up for a service or the product they purchased. Once users are grouped into cohorts, analysts can track their behavior over time to identify trends and patterns.

So what does this mean and why is it useful? Let’s break down the above definition further. A cohort is a group of people who share a common characteristic (or action) during a given time period. Students who enrolled at university in 2020 may be referred to as the 2020 cohort. Customers who purchased something from your online store via the app in the month of December may also be considered a cohort.

With cohort analysis, you’re dividing your customers or users into groups and looking at how these groups behave over time. So, rather than looking at a single, isolated snapshot of all your customers at a given moment in time (with each customer at a different point in their journey), you’re examining your customers’ behavior in the context of the customer lifecycle. As a result, you can start to identify patterns of behavior at various points in the customer journey—say, from their first ever visit to your website, through to email newsletter sign-up, to their first purchase, and so on. As such, cohort analysis is dynamic, allowing you to uncover valuable insights about the customer lifecycle.

This is useful because it allows companies to tailor their service to specific customer segments (or cohorts). Let’s imagine you run a 50% discount campaign in order to attract potential new customers to your website. Once you’ve attracted a group of new customers (a cohort), you’ll want to track whether they actually buy anything and, if they do, whether or not (and how frequently) they make a repeat purchase. With these insights, you’ll start to gain a much better understanding of when this particular cohort might benefit from another discount offer or retargeting ads on social media, for example. Ultimately, cohort analysis allows companies to optimize their service offerings (and marketing) to provide a more targeted, personalized experience. You can learn more about how to run cohort analysis using Google Analytics .

Cohort analysis in action: How Ticketmaster used cohort analysis to boost revenue

e. Cluster analysis

Cluster analysis is an exploratory technique that seeks to identify structures within a dataset. The goal of cluster analysis is to sort different data points into groups (or clusters) that are internally homogeneous and externally heterogeneous. This means that data points within a cluster are similar to each other, and dissimilar to data points in another cluster. Clustering is used to gain insight into how data is distributed in a given dataset, or as a preprocessing step for other algorithms.

There are many real-world applications of cluster analysis. In marketing, cluster analysis is commonly used to group a large customer base into distinct segments, allowing for a more targeted approach to advertising and communication. Insurance firms might use cluster analysis to investigate why certain locations are associated with a high number of insurance claims. Another common application is in geology, where experts will use cluster analysis to evaluate which cities are at greatest risk of earthquakes (and thus try to mitigate the risk with protective measures).

It’s important to note that, while cluster analysis may reveal structures within your data, it won’t explain why those structures exist. With that in mind, cluster analysis is a useful starting point for understanding your data and informing further analysis. Clustering algorithms are also used in machine learning—you can learn more about clustering in machine learning in our guide .

Cluster analysis in action: Using cluster analysis for customer segmentation—a telecoms case study example

f. Time series analysis

Time series analysis is a statistical technique used to identify trends and cycles over time. Time series data is a sequence of data points which measure the same variable at different points in time (for example, weekly sales figures or monthly email sign-ups). By looking at time-related trends, analysts are able to forecast how the variable of interest may fluctuate in the future.

When conducting time series analysis, the main patterns you’ll be looking out for in your data are:

  • Trends: Stable, linear increases or decreases over an extended time period.
  • Seasonality: Predictable fluctuations in the data due to seasonal factors over a short period of time. For example, you might see a peak in swimwear sales in summer around the same time every year.
  • Cyclic patterns: Unpredictable cycles where the data fluctuates. Cyclical trends are not due to seasonality, but rather, may occur as a result of economic or industry-related conditions.

As you can imagine, the ability to make informed predictions about the future has immense value for business. Time series analysis and forecasting is used across a variety of industries, most commonly for stock market analysis, economic forecasting, and sales forecasting. There are different types of time series models depending on the data you’re using and the outcomes you want to predict. These models are typically classified into three broad types: the autoregressive (AR) models, the integrated (I) models, and the moving average (MA) models. For an in-depth look at time series analysis, refer to our guide .

Time series analysis in action: Developing a time series model to predict jute yarn demand in Bangladesh

g. Sentiment analysis

When you think of data, your mind probably automatically goes to numbers and spreadsheets.

Many companies overlook the value of qualitative data, but in reality, there are untold insights to be gained from what people (especially customers) write and say about you. So how do you go about analyzing textual data?

One highly useful qualitative technique is sentiment analysis , a technique which belongs to the broader category of text analysis —the (usually automated) process of sorting and understanding textual data.

With sentiment analysis, the goal is to interpret and classify the emotions conveyed within textual data. From a business perspective, this allows you to ascertain how your customers feel about various aspects of your brand, product, or service.

There are several different types of sentiment analysis models, each with a slightly different focus. The three main types include:

Fine-grained sentiment analysis

If you want to focus on opinion polarity (i.e. positive, neutral, or negative) in depth, fine-grained sentiment analysis will allow you to do so.

For example, if you wanted to interpret star ratings given by customers, you might use fine-grained sentiment analysis to categorize the various ratings along a scale ranging from very positive to very negative.

Emotion detection

This model often uses complex machine learning algorithms to pick out various emotions from your textual data.

You might use an emotion detection model to identify words associated with happiness, anger, frustration, and excitement, giving you insight into how your customers feel when writing about you or your product on, say, a product review site.

Aspect-based sentiment analysis

This type of analysis allows you to identify what specific aspects the emotions or opinions relate to, such as a certain product feature or a new ad campaign.

If a customer writes that they “find the new Instagram advert so annoying”, your model should detect not only a negative sentiment, but also the object towards which it’s directed.

In a nutshell, sentiment analysis uses various Natural Language Processing (NLP) algorithms and systems which are trained to associate certain inputs (for example, certain words) with certain outputs.

For example, the input “annoying” would be recognized and tagged as “negative”. Sentiment analysis is crucial to understanding how your customers feel about you and your products, for identifying areas for improvement, and even for averting PR disasters in real-time!

Sentiment analysis in action: 5 Real-world sentiment analysis case studies

4. The data analysis process

In order to gain meaningful insights from data, data analysts will perform a rigorous step-by-step process. We go over this in detail in our step by step guide to the data analysis process —but, to briefly summarize, the data analysis process generally consists of the following phases:

Defining the question

The first step for any data analyst will be to define the objective of the analysis, sometimes called a ‘problem statement’. Essentially, you’re asking a question with regards to a business problem you’re trying to solve. Once you’ve defined this, you’ll then need to determine which data sources will help you answer this question.

Collecting the data

Now that you’ve defined your objective, the next step will be to set up a strategy for collecting and aggregating the appropriate data. Will you be using quantitative (numeric) or qualitative (descriptive) data? Do these data fit into first-party, second-party, or third-party data?

Learn more: Quantitative vs. Qualitative Data: What’s the Difference? 

Cleaning the data

Unfortunately, your collected data isn’t automatically ready for analysis—you’ll have to clean it first. As a data analyst, this phase of the process will take up the most time. During the data cleaning process, you will likely be:

  • Removing major errors, duplicates, and outliers
  • Removing unwanted data points
  • Structuring the data—that is, fixing typos, layout issues, etc.
  • Filling in major gaps in data

Analyzing the data

Now that we’ve finished cleaning the data, it’s time to analyze it! Many analysis methods have already been described in this article, and it’s up to you to decide which one will best suit the assigned objective. It may fall under one of the following categories:

  • Descriptive analysis , which identifies what has already happened
  • Diagnostic analysis , which focuses on understanding why something has happened
  • Predictive analysis , which identifies future trends based on historical data
  • Prescriptive analysis , which allows you to make recommendations for the future

Visualizing and sharing your findings

We’re almost at the end of the road! Analyses have been made, insights have been gleaned—all that remains to be done is to share this information with others. This is usually done with a data visualization tool, such as Google Charts, or Tableau.

Learn more: 13 of the Most Common Types of Data Visualization

To sum up the process, Will’s explained it all excellently in the following video:

5. The best tools for data analysis

As you can imagine, every phase of the data analysis process requires the data analyst to have a variety of tools under their belt that assist in gaining valuable insights from data. We cover these tools in greater detail in this article , but, in summary, here’s our best-of-the-best list, with links to each product:

The top 9 tools for data analysts

  • Microsoft Excel
  • Jupyter Notebook
  • Apache Spark
  • Microsoft Power BI

6. Key takeaways and further reading

As you can see, there are many different data analysis techniques at your disposal. In order to turn your raw data into actionable insights, it’s important to consider what kind of data you have (is it qualitative or quantitative?) as well as the kinds of insights that will be useful within the given context. In this post, we’ve introduced seven of the most useful data analysis techniques—but there are many more out there to be discovered!

So what now? If you haven’t already, we recommend reading the case studies for each analysis technique discussed in this post (you’ll find a link at the end of each section). For a more hands-on introduction to the kinds of methods and techniques that data analysts use, try out this free introductory data analytics short course. In the meantime, you might also want to read the following:

  • The Best Online Data Analytics Courses for 2024
  • What Is Time Series Data and How Is It Analyzed?
  • What is Spatial Analysis?

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  • Indian J Anaesth
  • v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.


Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

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Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.


Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

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Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

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where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

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where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

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where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

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where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

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where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

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Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

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Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

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Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

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If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

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Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

  • The assumption of normality which specifies that the means of the sample group are normally distributed
  • The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

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where X = sample mean, u = population mean and SE = standard error of mean

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where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

  • To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

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where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

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where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

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Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

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A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.


Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

  • – provides links to a number of online power calculators
  • G-Power – provides a downloadable power analysis program that runs under DOS
  • Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
  • SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Data Analysis


Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

  • Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
  • SQL : A programming language used to manage and manipulate relational databases.
  • R : An open-source programming language and software environment for statistical computing and graphics.
  • Python : A general-purpose programming language that is widely used in data analysis and machine learning.
  • Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
  • SAS : A statistical analysis software used for data management, analysis, and reporting.
  • SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
  • Matlab : A numerical computing software that is widely used in scientific research and engineering.
  • RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

  • Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
  • Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
  • Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
  • Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
  • Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
  • Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
  • Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
  • Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

  • Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
  • Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
  • Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
  • Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
  • Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
  • Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
  • Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

  • To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
  • To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
  • To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
  • To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
  • To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

  • Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
  • Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
  • Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
  • Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
  • Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
  • Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
  • Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

  • Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
  • Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
  • Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
  • Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
  • Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
  • Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
  • Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
  • Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

  • Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
  • Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
  • Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
  • Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
  • Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
  • Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
  • Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
  • Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

  • Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
  • Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
  • Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
  • Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
  • Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
  • Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
  • Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

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What is Data Analysis? (Types, Methods, and Tools)

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Data analysis is the process of cleaning, transforming, and interpreting data to uncover insights, patterns, and trends. It plays a crucial role in decision making, problem solving, and driving innovation across various domains. 

In addition to further exploring the role data analysis plays this blog post will discuss common data analysis techniques, delve into the distinction between quantitative and qualitative data, explore popular data analysis tools, and discuss the steps involved in the data analysis process. 

By the end, you should have a deeper understanding of data analysis and its applications, empowering you to harness the power of data to make informed decisions and gain actionable insights.

Why is Data Analysis Important?

Data analysis is important across various domains and industries. It helps with:

  • Decision Making : Data analysis provides valuable insights that support informed decision making, enabling organizations to make data-driven choices for better outcomes.
  • Problem Solving : Data analysis helps identify and solve problems by uncovering root causes, detecting anomalies, and optimizing processes for increased efficiency.
  • Performance Evaluation : Data analysis allows organizations to evaluate performance, track progress, and measure success by analyzing key performance indicators (KPIs) and other relevant metrics.
  • Gathering Insights : Data analysis uncovers valuable insights that drive innovation, enabling businesses to develop new products, services, and strategies aligned with customer needs and market demand.
  • Risk Management : Data analysis helps mitigate risks by identifying risk factors and enabling proactive measures to minimize potential negative impacts.

By leveraging data analysis, organizations can gain a competitive advantage, improve operational efficiency, and make smarter decisions that positively impact the bottom line.

Quantitative vs. Qualitative Data

In data analysis, you’ll commonly encounter two types of data: quantitative and qualitative. Understanding the differences between these two types of data is essential for selecting appropriate analysis methods and drawing meaningful insights. Here’s an overview of quantitative and qualitative data:

Quantitative Data

Quantitative data is numerical and represents quantities or measurements. It’s typically collected through surveys, experiments, and direct measurements. This type of data is characterized by its ability to be counted, measured, and subjected to mathematical calculations. Examples of quantitative data include age, height, sales figures, test scores, and the number of website users.

Quantitative data has the following characteristics:

  • Numerical : Quantitative data is expressed in numerical values that can be analyzed and manipulated mathematically.
  • Objective : Quantitative data is objective and can be measured and verified independently of individual interpretations.
  • Statistical Analysis : Quantitative data lends itself well to statistical analysis. It allows for applying various statistical techniques, such as descriptive statistics, correlation analysis, regression analysis, and hypothesis testing.
  • Generalizability : Quantitative data often aims to generalize findings to a larger population. It allows for making predictions, estimating probabilities, and drawing statistical inferences.

Qualitative Data

Qualitative data, on the other hand, is non-numerical and is collected through interviews, observations, and open-ended survey questions. It focuses on capturing rich, descriptive, and subjective information to gain insights into people’s opinions, attitudes, experiences, and behaviors. Examples of qualitative data include interview transcripts, field notes, survey responses, and customer feedback.

Qualitative data has the following characteristics:

  • Descriptive : Qualitative data provides detailed descriptions, narratives, or interpretations of phenomena, often capturing context, emotions, and nuances.
  • Subjective : Qualitative data is subjective and influenced by the individuals’ perspectives, experiences, and interpretations.
  • Interpretive Analysis : Qualitative data requires interpretive techniques, such as thematic analysis, content analysis, and discourse analysis, to uncover themes, patterns, and underlying meanings.
  • Contextual Understanding : Qualitative data emphasizes understanding the social, cultural, and contextual factors that shape individuals’ experiences and behaviors.
  • Rich Insights : Qualitative data enables researchers to gain in-depth insights into complex phenomena and explore research questions in greater depth.

In summary, quantitative data represents numerical quantities and lends itself well to statistical analysis, while qualitative data provides rich, descriptive insights into subjective experiences and requires interpretive analysis techniques. Understanding the differences between quantitative and qualitative data is crucial for selecting appropriate analysis methods and drawing meaningful conclusions in research and data analysis.

Types of Data Analysis

Different types of data analysis techniques serve different purposes. In this section, we’ll explore four types of data analysis: descriptive, diagnostic, predictive, and prescriptive, and go over how you can use them.

Descriptive Analysis

Descriptive analysis involves summarizing and describing the main characteristics of a dataset. It focuses on gaining a comprehensive understanding of the data through measures such as central tendency (mean, median, mode), dispersion (variance, standard deviation), and graphical representations (histograms, bar charts). For example, in a retail business, descriptive analysis may involve analyzing sales data to identify average monthly sales, popular products, or sales distribution across different regions.

Diagnostic Analysis

Diagnostic analysis aims to understand the causes or factors influencing specific outcomes or events. It involves investigating relationships between variables and identifying patterns or anomalies in the data. Diagnostic analysis often uses regression analysis, correlation analysis, and hypothesis testing to uncover the underlying reasons behind observed phenomena. For example, in healthcare, diagnostic analysis could help determine factors contributing to patient readmissions and identify potential improvements in the care process.

Predictive Analysis

Predictive analysis focuses on making predictions or forecasts about future outcomes based on historical data. It utilizes statistical models, machine learning algorithms, and time series analysis to identify patterns and trends in the data. By applying predictive analysis, businesses can anticipate customer behavior, market trends, or demand for products and services. For example, an e-commerce company might use predictive analysis to forecast customer churn and take proactive measures to retain customers.

Prescriptive Analysis

Prescriptive analysis takes predictive analysis a step further by providing recommendations or optimal solutions based on the predicted outcomes. It combines historical and real-time data with optimization techniques, simulation models, and decision-making algorithms to suggest the best course of action. Prescriptive analysis helps organizations make data-driven decisions and optimize their strategies. For example, a logistics company can use prescriptive analysis to determine the most efficient delivery routes, considering factors like traffic conditions, fuel costs, and customer preferences.

In summary, data analysis plays a vital role in extracting insights and enabling informed decision making. Descriptive analysis helps understand the data, diagnostic analysis uncovers the underlying causes, predictive analysis forecasts future outcomes, and prescriptive analysis provides recommendations for optimal actions. These different data analysis techniques are valuable tools for businesses and organizations across various industries.

Data Analysis Methods

In addition to the data analysis types discussed earlier, you can use various methods to analyze data effectively. These methods provide a structured approach to extract insights, detect patterns, and derive meaningful conclusions from the available data. Here are some commonly used data analysis methods:

Statistical Analysis 

Statistical analysis involves applying statistical techniques to data to uncover patterns, relationships, and trends. It includes methods such as hypothesis testing, regression analysis, analysis of variance (ANOVA), and chi-square tests. Statistical analysis helps organizations understand the significance of relationships between variables and make inferences about the population based on sample data. For example, a market research company could conduct a survey to analyze the relationship between customer satisfaction and product price. They can use regression analysis to determine whether there is a significant correlation between these variables.

Data Mining

Data mining refers to the process of discovering patterns and relationships in large datasets using techniques such as clustering, classification, association analysis, and anomaly detection. It involves exploring data to identify hidden patterns and gain valuable insights. For example, a telecommunications company could analyze customer call records to identify calling patterns and segment customers into groups based on their calling behavior. 

Text Mining

Text mining involves analyzing unstructured data , such as customer reviews, social media posts, or emails, to extract valuable information and insights. It utilizes techniques like natural language processing (NLP), sentiment analysis, and topic modeling to analyze and understand textual data. For example, consider how a hotel chain might analyze customer reviews from various online platforms to identify common themes and sentiment patterns to improve customer satisfaction.

Time Series Analysis

Time series analysis focuses on analyzing data collected over time to identify trends, seasonality, and patterns. It involves techniques such as forecasting, decomposition, and autocorrelation analysis to make predictions and understand the underlying patterns in the data.

For example, an energy company could analyze historical electricity consumption data to forecast future demand and optimize energy generation and distribution.

Data Visualization

Data visualization is the graphical representation of data to communicate patterns, trends, and insights visually. It uses charts, graphs, maps, and other visual elements to present data in a visually appealing and easily understandable format. For example, a sales team might use a line chart to visualize monthly sales trends and identify seasonal patterns in their sales data.

These are just a few examples of the data analysis methods you can use. Your choice should depend on the nature of the data, the research question or problem, and the desired outcome.

How to Analyze Data

Analyzing data involves following a systematic approach to extract insights and derive meaningful conclusions. Here are some steps to guide you through the process of analyzing data effectively:

Define the Objective : Clearly define the purpose and objective of your data analysis. Identify the specific question or problem you want to address through analysis.

Prepare and Explore the Data : Gather the relevant data and ensure its quality. Clean and preprocess the data by handling missing values, duplicates, and formatting issues. Explore the data using descriptive statistics and visualizations to identify patterns, outliers, and relationships.

Apply Analysis Techniques : Choose the appropriate analysis techniques based on your data and research question. Apply statistical methods, machine learning algorithms, and other analytical tools to derive insights and answer your research question.

Interpret the Results : Analyze the output of your analysis and interpret the findings in the context of your objective. Identify significant patterns, trends, and relationships in the data. Consider the implications and practical relevance of the results.

Communicate and Take Action : Communicate your findings effectively to stakeholders or intended audiences. Present the results clearly and concisely, using visualizations and reports. Use the insights from the analysis to inform decision making.

Remember, data analysis is an iterative process, and you may need to revisit and refine your analysis as you progress. These steps provide a general framework to guide you through the data analysis process and help you derive meaningful insights from your data.

Data Analysis Tools

Data analysis tools are software applications and platforms designed to facilitate the process of analyzing and interpreting data . These tools provide a range of functionalities to handle data manipulation, visualization, statistical analysis, and machine learning. Here are some commonly used data analysis tools:

Spreadsheet Software

Tools like Microsoft Excel, Google Sheets, and Apple Numbers are used for basic data analysis tasks. They offer features for data entry, manipulation, basic statistical functions, and simple visualizations.

Business Intelligence (BI) Platforms

BI platforms like Microsoft Power BI, Tableau, and Looker integrate data from multiple sources, providing comprehensive views of business performance through interactive dashboards, reports, and ad hoc queries.

Programming Languages and Libraries

Programming languages like R and Python, along with their associated libraries (e.g., NumPy, SciPy, scikit-learn), offer extensive capabilities for data analysis. They provide flexibility, customizability, and access to a wide range of statistical and machine-learning algorithms.

Cloud-Based Analytics Platforms

Cloud-based platforms like Google Cloud Platform (BigQuery, Data Studio), Microsoft Azure (Azure Analytics, Power BI), and Amazon Web Services (AWS Analytics, QuickSight) provide scalable and collaborative environments for data storage, processing, and analysis. They have a wide range of analytical capabilities for handling large datasets.

Data Mining and Machine Learning Tools

Tools like RapidMiner, KNIME, and Weka automate the process of data preprocessing, feature selection, model training, and evaluation. They’re designed to extract insights and build predictive models from complex datasets.

Text Analytics Tools

Text analytics tools, such as Natural Language Processing (NLP) libraries in Python (NLTK, spaCy) or platforms like RapidMiner Text Mining Extension, enable the analysis of unstructured text data . They help extract information, sentiment, and themes from sources like customer reviews or social media.

Choosing the right data analysis tool depends on analysis complexity, dataset size, required functionalities, and user expertise. You might need to use a combination of tools to leverage their combined strengths and address specific analysis needs. 

By understanding the power of data analysis, you can leverage it to make informed decisions, identify opportunities for improvement, and drive innovation within your organization. Whether you’re working with quantitative data for statistical analysis or qualitative data for in-depth insights, it’s important to select the right analysis techniques and tools for your objectives.

To continue learning about data analysis, review the following resources:

  • What is Big Data Analytics?
  • Operational Analytics
  • JSON Analytics + Real-Time Insights
  • Database vs. Data Warehouse: Differences, Use Cases, Examples
  • Couchbase Capella Columnar Product Blog

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Statistical Methods for Data Analysis: a Comprehensive Guide

In today’s data-driven world, understanding statistical methods for data analysis is like having a superpower.

Whether you’re a student, a professional, or just a curious mind, diving into the realm of data can unlock insights and decisions that propel success.

Statistical methods for data analysis are the tools and techniques used to collect, analyze, interpret, and present data in a meaningful way.

From businesses optimizing operations to researchers uncovering new discoveries, these methods are foundational to making informed decisions based on data.

In this blog post, we’ll embark on a journey through the fascinating world of statistical analysis, exploring its key concepts, methodologies, and applications.

Introduction to Statistical Methods

At its core, statistical methods are the backbone of data analysis, helping us make sense of numbers and patterns in the world around us.

Whether you’re looking at sales figures, medical research, or even your fitness tracker’s data, statistical methods are what turn raw data into useful insights.

But before we dive into complex formulas and tests, let’s start with the basics.

Data comes in two main types: qualitative and quantitative data .

Qualitative vs Quantitative Data - a simple infographic

Quantitative data is all about numbers and quantities (like your height or the number of steps you walked today), while qualitative data deals with categories and qualities (like your favorite color or the breed of your dog).

And when we talk about measuring these data points, we use different scales like nominal, ordinal , interval , and ratio.

These scales help us understand the nature of our data—whether we’re ranking it (ordinal), simply categorizing it (nominal), or measuring it with a true zero point (ratio).

Scales of Data Measurement - an infographic

In a nutshell, statistical methods start with understanding the type and scale of your data.

This foundational knowledge sets the stage for everything from summarizing your data to making complex predictions.

Descriptive Statistics: Simplifying Data

What is Descriptive Statistics - an infographic

Imagine you’re at a party and you meet a bunch of new people.

When you go home, your roommate asks, “So, what were they like?” You could describe each person in detail, but instead, you give a summary: “Most were college students, around 20-25 years old, pretty fun crowd!”

That’s essentially what descriptive statistics does for data.

It summarizes and describes the main features of a collection of data in an easy-to-understand way. Let’s break this down further.

The Basics: Mean, Median, and Mode

  • Mean is just a fancy term for the average. If you add up everyone’s age at the party and divide by the number of people, you’ve got your mean age.
  • Median is the middle number in a sorted list. If you line up everyone from the youngest to the oldest and pick the person in the middle, their age is your median. This is super handy when someone’s age is way off the chart (like if your grandma crashed the party), as it doesn’t skew the data.
  • Mode is the most common age at the party. If you notice a lot of people are 22, then 22 is your mode. It’s like the age that wins the popularity contest.

Spreading the News: Range, Variance, and Standard Deviation

  • Range gives you an idea of how spread out the ages are. It’s the difference between the oldest and the youngest. A small range means everyone’s around the same age, while a big range means a wider variety.
  • Variance is a bit more complex. It measures how much the ages differ from the average age. A higher variance means ages are more spread out.
  • Standard Deviation is the square root of variance. It’s like variance but back on a scale that makes sense. It tells you, on average, how far each person’s age is from the mean age.

Picture Perfect: Graphical Representations

  • Histograms are like bar charts showing how many people fall into different age groups. They give you a quick glance at how ages are distributed.
  • Bar Charts are great for comparing different categories, like how many men vs. women were at the party.
  • Box Plots (or box-and-whisker plots) show you the median, the range, and if there are any outliers (like grandma).
  • Scatter Plots are used when you want to see if there’s a relationship between two things, like if bringing more snacks means people stay longer at the party.

Why Descriptive Statistics Matter?

Descriptive statistics are your first step in data analysis.

They help you understand your data at a glance and prepare you for deeper analysis.

Without them, you’re like someone trying to guess what a party was like without any context.

Whether you’re looking at survey responses, test scores, or party attendees, descriptive statistics give you the tools to summarize and describe your data in a way that’s easy to grasp.

Remember, the goal of descriptive statistics is to simplify the complex.

Inferential Statistics: Beyond the Basics

Statistics Seminar Illustration

Let’s keep the party analogy rolling, but this time, imagine you couldn’t attend the party yourself.

You’re curious if the party was as fun as everyone said it would be.

Instead of asking every single attendee, you decide to ask a few friends who went.

Based on their experiences, you try to infer what the entire party was like.

This is essentially what inferential statistics does with data.

It allows you to make predictions or draw conclusions about a larger group (the population) based on a smaller group (a sample). Let’s dive into how this works.


Inferential statistics is all about playing the odds.

When you make an inference, you’re saying, “Based on my sample, there’s a certain probability that my conclusion about the whole population is correct.”

It’s like betting on whether the party was fun, based on a few friends’ opinions.

The Central Limit Theorem (CLT)

The Central Limit Theorem is the superhero of statistics.

It tells us that if you take enough samples from a population, the sample means (averages) will form a normal distribution (a bell curve), no matter what the population distribution looks like.

This is crucial because it allows us to use sample data to make inferences about the population mean with a known level of uncertainty.

Confidence Intervals

Imagine you’re pretty sure the party was fun, but you want to know how fun.

A confidence interval gives you a range of values within which you believe the true mean fun level of the party lies.

It’s like saying, “I’m 95% confident the party’s fun rating was between 7 and 9 out of 10.”

Hypothesis Testing

This is where you get to be a bit of a detective. You start with a hypothesis (a guess) about the population.

For example, your null hypothesis might be “the party was average fun.” Then you use your sample data to test this hypothesis.

If the data strongly suggests otherwise, you might reject the null hypothesis and accept the alternative hypothesis, which could be “the party was super fun.”

The p-value tells you how likely it is that your data would have occurred by random chance if the null hypothesis were true.

A low p-value (typically less than 0.05) indicates that your findings are significant—that is, unlikely to have happened by chance.

It’s like saying, “The chance that all my friends are exaggerating about the party being fun is really low, so the party probably was fun.”

Why Inferential Statistics Matter?

Inferential statistics let us go beyond just describing our data.

They allow us to make educated guesses about a larger population based on a sample.

This is incredibly useful in almost every field—science, business, public health, and yes, even planning your next party.

By using probability, the Central Limit Theorem, confidence intervals, hypothesis testing, and p-values, we can make informed decisions without needing to ask every single person in the population.

It saves time, resources, and helps us understand the world more scientifically.

Remember, while inferential statistics gives us powerful tools for making predictions, those predictions come with a level of uncertainty.

Being a good data scientist means understanding and communicating that uncertainty clearly.

So next time you hear about a party you missed, use inferential statistics to figure out just how much FOMO (fear of missing out) you should really feel!

Common Statistical Tests: Choosing Your Data’s Best Friend

Data Analysis Research and Statistics Concept

Alright, now that we’ve covered the basics of descriptive and inferential statistics, it’s time to talk about how we actually apply these concepts to make sense of data.

It’s like deciding on the best way to find out who was the life of the party.

You have several tools (tests) at your disposal, and choosing the right one depends on what you’re trying to find out and the type of data you have.

Let’s explore some of the most common statistical tests and when to use them.

T-Tests: Comparing Averages

Imagine you want to know if the average fun level was higher at this year’s party compared to last year’s.

A t-test helps you compare the means (averages) of two groups to see if they’re statistically different.

There are a couple of flavors:

  • Independent t-test : Use this when comparing two different groups, like this year’s party vs. last year’s party.
  • Paired t-test : Use this when comparing the same group at two different times or under two different conditions, like if you measured everyone’s fun level before and after the party.

ANOVA : When Three’s Not a Crowd.

But what if you had three or more parties to compare? That’s where ANOVA (Analysis of Variance) comes in handy.

It lets you compare the means across multiple groups at once to see if at least one of them is significantly different.

It’s like comparing the fun levels across several years’ parties to see if one year stood out.

Chi-Square Test: Categorically Speaking

Now, let’s say you’re interested in whether the type of music (pop, rock, electronic) affects party attendance.

Since you’re dealing with categories (types of music) and counts (number of attendees), you’ll use the Chi-Square test.

It’s great for seeing if there’s a relationship between two categorical variables.

Correlation and Regression: Finding Relationships

What if you suspect that the amount of snacks available at the party affects how long guests stay? To explore this, you’d use:

  • Correlation analysis to see if there’s a relationship between two continuous variables (like snacks and party duration). It tells you how closely related two things are.
  • Regression analysis goes a step further by not only showing if there’s a relationship but also how one variable predicts the other. It’s like saying, “For every extra bag of chips, guests stay an average of 10 minutes longer.”

Non-parametric Tests: When Assumptions Don’t Hold

All the tests mentioned above assume your data follows a normal distribution and meets other criteria.

But what if your data doesn’t play by these rules?

Enter non-parametric tests, like the Mann-Whitney U test (for comparing two groups when you can’t use a t-test) or the Kruskal-Wallis test (like ANOVA but for non-normal distributions).

Picking the Right Test

Choosing the right statistical test is crucial and depends on:

  • The type of data you have (categorical vs. continuous).
  • Whether you’re comparing groups or looking for relationships.
  • The distribution of your data (normal vs. non-normal).

Why These Tests Matter?

Just like you’d pick the right tool for a job, selecting the appropriate statistical test helps you make valid and reliable conclusions about your data.

Whether you’re trying to prove a point, make a decision, or just understand the world a bit better, these tests are your gateway to insights.

By mastering these tests, you become a detective in the world of data, ready to uncover the truth behind the numbers!

Regression Analysis: Predicting the Future

Regression Analysis

Ever wondered if you could predict how much fun you’re going to have at a party based on the number of friends going, or how the amount of snacks available might affect the overall party vibe?

That’s where regression analysis comes into play, acting like a crystal ball for your data.

What is Regression Analysis?

Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest.

Think of it as detective work, where you’re trying to figure out if, how, and to what extent certain factors (like snacks and music volume) predict an outcome (like the fun level at a party).

The Two Main Characters: Independent and Dependent Variables

  • Independent Variable(s): These are the predictors or factors that you suspect might influence the outcome. For example, the quantity of snacks.
  • Dependent Variable: This is the outcome you’re interested in predicting. In our case, it could be the fun level of the party.

Linear Regression: The Straight Line Relationship

The most basic form of regression analysis is linear regression .

It predicts the outcome based on a linear relationship between the independent and dependent variables.

If you plot this on a graph, you’d ideally see a straight line where, as the amount of snacks increases, so does the fun level (hopefully!).

  • Simple Linear Regression involves just one independent variable. It’s like saying, “Let’s see if just the number of snacks can predict the fun level.”
  • Multiple Linear Regression takes it up a notch by including more than one independent variable. Now, you’re looking at whether the quantity of snacks, type of music, and number of guests together can predict the fun level.

Logistic Regression: When Outcomes are Either/Or

Not all predictions are about numbers.

Sometimes, you just want to know if something will happen or not—will the party be a hit or a flop?

Logistic regression is used for these binary outcomes.

Instead of predicting a precise fun level, it predicts the probability of the party being a hit based on the same predictors (snacks, music, guests).

Making Sense of the Results

  • Coefficients: In regression analysis, each predictor has a coefficient, telling you how much the dependent variable is expected to change when that predictor changes by one unit, all else being equal.
  • R-squared : This value tells you how much of the variation in your dependent variable can be explained by the independent variables. A higher R-squared means a better fit between your model and the data.

Why Regression Analysis Rocks?

Regression analysis is like having a superpower. It helps you understand which factors matter most, which can be ignored, and how different factors come together to influence the outcome.

This insight is invaluable whether you’re planning a party, running a business, or conducting scientific research.

Bringing It All Together

Imagine you’ve gathered data on several parties, including the number of guests, type of music, and amount of snacks, along with a fun level rating for each.

By running a regression analysis, you can start to predict future parties’ success, tailoring your planning to maximize fun.

It’s a practical tool for making informed decisions based on past data, helping you throw legendary parties, optimize business strategies, or understand complex relationships in your research.

In essence, regression analysis helps turn your data into actionable insights, guiding you towards smarter decisions and better predictions.

So next time you’re knee-deep in data, remember: regression analysis might just be the key to unlocking its secrets.

Non-parametric Methods: Playing By Different Rules

So far, we’ve talked a lot about statistical methods that rely on certain assumptions about your data, like it being normally distributed (forming that classic bell curve) or having a specific scale of measurement.

But what happens when your data doesn’t fit these molds?

Maybe the scores from your last party’s karaoke contest are all over the place, or you’re trying to compare the popularity of various party games but only have rankings, not scores.

This is where non-parametric methods come to the rescue.

Breaking Free from Assumptions

Non-parametric methods are the rebels of the statistical world.

They don’t assume your data follows a normal distribution or that it meets strict requirements regarding measurement scales.

These methods are perfect for dealing with ordinal data (like rankings), nominal data (like categories), or when your data is skewed or has outliers that would throw off other tests.

When to Use Non-parametric Methods?

  • Your data is not normally distributed, and transformations don’t help.
  • You have ordinal data (like survey responses that range from “Strongly Disagree” to “Strongly Agree”).
  • You’re dealing with ranks or categories rather than precise measurements.
  • Your sample size is small, making it hard to meet the assumptions required for parametric tests.

Some Popular Non-parametric Tests

  • Mann-Whitney U Test: Think of it as the non-parametric counterpart to the independent samples t-test. Use this when you want to compare the differences between two independent groups on a ranking or ordinal scale.
  • Kruskal-Wallis Test: This is your go-to when you have three or more groups to compare, and it’s similar to an ANOVA but for ranked/ordinal data or when your data doesn’t meet ANOVA’s assumptions.
  • Spearman’s Rank Correlation: When you want to see if there’s a relationship between two sets of rankings, Spearman’s got your back. It’s like Pearson’s correlation for continuous data but designed for ranks.
  • Wilcoxon Signed-Rank Test: Use this for comparing two related samples when you can’t use the paired t-test, typically because the differences between pairs are not normally distributed.

The Beauty of Flexibility

The real charm of non-parametric methods is their flexibility.

They let you work with data that’s not textbook perfect, which is often the case in the real world.

Whether you’re analyzing customer satisfaction surveys, comparing the effectiveness of different marketing strategies, or just trying to figure out if people prefer pizza or tacos at parties, non-parametric tests provide a robust way to get meaningful insights.

Keeping It Real

It’s important to remember that while non-parametric methods are incredibly useful, they also come with their own limitations.

They might be more conservative, meaning you might need a larger effect to detect a significant result compared to parametric tests.

Plus, because they often work with ranks rather than actual values, some information about your data might get lost in translation.

Non-parametric methods are your statistical toolbox’s Swiss Army knife, ready to tackle data that doesn’t fit into the neat categories required by more traditional tests.

They remind us that in the world of data analysis, there’s more than one way to uncover insights and make informed decisions.

So, the next time you’re faced with skewed distributions or rankings instead of scores, remember that non-parametric methods have got you covered, offering a way to navigate the complexities of real-world data.

Data Cleaning and Preparation: The Unsung Heroes of Data Analysis

Before any party can start, there’s always a bit of housecleaning to do—sweeping the floors, arranging the furniture, and maybe even hiding those laundry piles you’ve been ignoring all week.

Similarly, in the world of data analysis, before we can dive into the fun stuff like statistical tests and predictive modeling, we need to roll up our sleeves and get our data nice and tidy.

This process of data cleaning and preparation might not be the most glamorous part of data science, but it’s absolutely critical.

Let’s break down what this involves and why it’s so important.

Why Clean and Prepare Data?

Imagine trying to analyze party RSVPs when half the responses are “yes,” a quarter are “Y,” and the rest are a creative mix of “yup,” “sure,” and “why not?”

Without standardization, it’s hard to get a clear picture of how many guests to expect.

The same goes for any data set. Cleaning ensures that your data is consistent, accurate, and ready for analysis.

Preparation involves transforming this clean data into a format that’s useful for your specific analysis needs.

The Steps to Sparkling Clean Data

  • Dealing with Missing Values: Sometimes, data is incomplete. Maybe a survey respondent skipped a question, or a sensor failed to record a reading. You’ll need to decide whether to fill in these gaps (imputation), ignore them, or drop the observations altogether.
  • Identifying and Handling Outliers: Outliers are data points that are significantly different from the rest. They might be errors, or they might be valuable insights. The challenge is determining which is which and deciding how to handle them—remove, adjust, or analyze separately.
  • Correcting Inconsistencies: This is like making sure all your RSVPs are in the same format. It could involve standardizing text entries, correcting typos, or converting all measurements to the same units.
  • Formatting Data: Your analysis might require data in a specific format. This could mean transforming data types (e.g., converting dates into a uniform format) or restructuring data tables to make them easier to work with.
  • Reducing Dimensionality: Sometimes, your data set might have more information than you actually need. Reducing dimensionality (through methods like Principal Component Analysis) can help simplify your data without losing valuable information.
  • Creating New Variables: You might need to derive new variables from your existing ones to better capture the relationships in your data. For example, turning raw survey responses into a numerical satisfaction score.

The Tools of the Trade

There are many tools available to help with data cleaning and preparation, ranging from spreadsheet software like Excel to programming languages like Python and R.

These tools offer functions and libraries specifically designed to make data cleaning as painless as possible.

Why It Matters

Skipping the data cleaning and preparation stage is like trying to cook without prepping your ingredients first.

Sure, you might end up with something edible, but it’s not going to be as good as it could have been.

Clean and well-prepared data leads to more accurate, reliable, and meaningful analysis results.

It’s the foundation upon which all good data analysis is built.

Data cleaning and preparation might not be the flashiest part of data science, but it’s where all successful data analysis projects begin.

By taking the time to thoroughly clean and prepare your data, you’re setting yourself up for clearer insights, better decisions, and, ultimately, more impactful outcomes.

Software Tools for Statistical Analysis: Your Digital Assistants

Diving into the world of data without the right tools can feel like trying to cook a gourmet meal without a kitchen.

Just as you need pots, pans, and a stove to create a culinary masterpiece, you need the right software tools to analyze data and uncover the insights hidden within.

These digital assistants range from user-friendly applications for beginners to powerful suites for the pros.

Let’s take a closer look at some of the most popular software tools for statistical analysis.

R and RStudio: The Dynamic Duo

  • R is like the Swiss Army knife of statistical analysis. It’s a programming language designed specifically for data analysis, graphics, and statistical modeling. Think of R as the kitchen where you’ll be cooking up your data analysis.
  • RStudio is an integrated development environment (IDE) for R. It’s like having the best kitchen setup with organized countertops (your coding space) and all your tools and ingredients within reach (packages and datasets).

Why They Rock:

R is incredibly powerful and can handle almost any data analysis task you throw at it, from the basics to the most advanced statistical models.

Plus, there’s a vast community of users, which means a wealth of tutorials, forums, and free packages to add on.

Python with pandas and scipy: The Versatile Virtuoso

  • Python is not just for programming; with the right libraries, it becomes an excellent tool for data analysis. It’s like a kitchen that’s not only great for baking but also equipped for gourmet cooking.
  • pandas is a library that provides easy-to-use data structures and data analysis tools for Python. Imagine it as your sous-chef, helping you to slice and dice data with ease.
  • scipy is another library used for scientific and technical computing. It’s like having a set of precision knives for the more intricate tasks.

Why They Rock: Python is known for its readability and simplicity, making it accessible for beginners. When combined with pandas and scipy, it becomes a powerhouse for data manipulation, analysis, and visualization.

SPSS: The Point-and-Click Professional

SPSS (Statistical Package for the Social Sciences) is a software package used for interactive, or batched, statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009.

Why It Rocks: SPSS is particularly user-friendly with its point-and-click interface, making it a favorite among non-programmers and researchers in the social sciences. It’s like having a kitchen gadget that does the job with the push of a button—no manual setup required.

SAS: The Corporate Chef

SAS (Statistical Analysis System) is a software suite developed for advanced analytics, multivariate analysis, business intelligence, data management, and predictive analytics.

Why It Rocks: SAS is a powerhouse in the corporate world, known for its stability, deep analytical capabilities, and support for large data sets. It’s like the industrial kitchen used by professional chefs to serve hundreds of guests.

Excel: The Accessible Apprentice

Excel might not be a specialized statistical software, but it’s widely accessible and capable of handling basic statistical analyses. Think of Excel as the microwave in your kitchen—it might not be fancy, but it gets the job done for quick and simple tasks.

Why It Rocks: Almost everyone has access to Excel and knows the basics, making it a great starting point for those new to data analysis. Plus, with add-ons like the Analysis ToolPak, Excel’s capabilities can be extended further into statistical territory.

Choosing Your Tool

Selecting the right software tool for statistical analysis is like choosing the right kitchen for your cooking style—it depends on your needs, expertise, and the complexity of your recipes (data).

Whether you’re a coding chef ready to tackle R or Python, or someone who prefers the straightforwardness of SPSS or Excel, there’s a tool out there that’s perfect for your data analysis kitchen.

Ethical Considerations

Digital Ethics and Privacy Abstract Concept

Embarking on a data analysis journey is like setting sail on the vast ocean of information.

Just as a captain needs a compass to navigate the seas safely and responsibly, a data analyst requires a strong sense of ethics to guide their exploration of data.

Ethical considerations in data analysis are the moral compass that ensures we respect privacy, consent, and integrity while uncovering the truths hidden within data. Let’s delve into why ethics are so crucial and what principles you should keep in mind.

Respect for Privacy

Imagine you’ve found a diary filled with personal secrets.

Reading it without permission would be a breach of privacy.

Similarly, when you’re handling data, especially personal or sensitive information, it’s essential to ensure that privacy is protected.

This means not only securing data against unauthorized access but also anonymizing data to prevent individuals from being identified.

Informed Consent

Before you can set sail, you need the ship owner’s permission.

In the world of data, this translates to informed consent. Participants should be fully aware of what their data will be used for and voluntarily agree to participate.

This is particularly important in research or when collecting data directly from individuals. It’s like asking for permission before you start the journey.

Data Integrity

Maintaining data integrity is like keeping the ship’s log accurate and unaltered during your voyage.

It involves ensuring the data is not corrupted or modified inappropriately and that any data analysis is conducted accurately and reliably.

Tampering with data or cherry-picking results to fit a narrative is not just unethical—it’s like falsifying the ship’s log, leading to mistrust and potentially dangerous outcomes.

Avoiding Bias

The sea is vast, and your compass must be calibrated correctly to avoid going off course. Similarly, avoiding bias in data analysis ensures your findings are valid and unbiased.

This means being aware of and actively addressing any personal, cultural, or statistical biases that might skew your analysis.

It’s about striving for objectivity and ensuring your journey is guided by truth, not preconceived notions.

Transparency and Accountability

A trustworthy captain is open about their navigational choices and ready to take responsibility for them.

In data analysis, this translates to transparency about your methods and accountability for your conclusions.

Sharing your methodologies, data sources, and any limitations of your analysis helps build trust and allows others to verify or challenge your findings.

Ethical Use of Findings

Finally, just as a captain must consider the impact of their journey on the wider world, you must consider how your data analysis will be used.

This means thinking about the potential consequences of your findings and striving to ensure they are used to benefit, not harm, society.

It’s about being mindful of the broader implications of your work and using data for good.

Navigating with a Moral Compass

In the realm of data analysis, ethical considerations form the moral compass that guides us through complex moral waters.

They ensure that our work respects individuals’ rights, contributes positively to society, and upholds the highest standards of integrity and professionalism.

Just as a captain navigates the seas with respect for the ocean and its dangers, a data analyst must navigate the world of data with a deep commitment to ethical principles.

This commitment ensures that the insights gained from data analysis serve to enlighten and improve, rather than exploit or harm.

Conclusion and Key Takeaways

And there you have it—a whirlwind tour through the fascinating landscape of statistical methods for data analysis.

From the grounding principles of descriptive and inferential statistics to the nuanced details of regression analysis and beyond, we’ve explored the tools and ethical considerations that guide us in turning raw data into meaningful insights.

The Takeaway

Think of data analysis as embarking on a grand adventure, one where numbers and facts are your map and compass.

Just as every explorer needs to understand the terrain, every aspiring data analyst must grasp these foundational concepts.

Whether it’s summarizing data sets with descriptive statistics, making predictions with inferential statistics, choosing the right statistical test, or navigating the ethical considerations that ensure our analyses benefit society, each aspect is a crucial step on your journey.

The Importance of Preparation

Remember, the key to a successful voyage is preparation.

Cleaning and preparing your data sets the stage for a smooth journey, while choosing the right software tools ensures you have the best equipment at your disposal.

And just as every responsible navigator respects the sea, every data analyst must navigate the ethical dimensions of their work with care and integrity.

Charting Your Course

As you embark on your own data analysis adventures, remember that the path you chart is unique to you.

Your questions will guide your journey, your curiosity will fuel your exploration, and the insights you gain will be your treasure.

The world of data is vast and full of mysteries waiting to be uncovered. With the tools and principles we’ve discussed, you’re well-equipped to start uncovering those mysteries, one data set at a time.

The Journey Ahead

The journey of statistical methods for data analysis is ongoing, and the landscape is ever-evolving.

As new methods emerge and our understanding deepens, there will always be new horizons to explore and new insights to discover.

But the fundamentals we’ve covered will remain your steadfast guide, helping you navigate the challenges and opportunities that lie ahead.

So set your sights on the questions that spark your curiosity, arm yourself with the tools of the trade, and embark on your data analysis journey with confidence.

About The Author

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Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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Data Analysis: Techniques, Tools, and Processes

Big or small, companies now expect their decisions to be data-driven. The world is growing and relying more on data. There is a greater need for professionals who know data analysis techniques.

Data analysis is a valuable skill that empowers you to make better decisions. This skill serves as a powerful catalyst in your professional and personal life. From personal budgeting to analyzing customer experiences , data analysis is the stepping stone to your career advancement.

So, whether you’re looking to upskill at work or kickstart a career in data analytics, this article is for you. We will discuss the best data analysis techniques in detail. To put all that into perspective, we’ll also discuss the step-by-step data analysis process. 

Let’s begin.

What is Data Analysis?

Data analysis is collecting, cleansing, analyzing, presenting, and interpreting data to derive insights. This process aids decision-making by providing helpful insights and statistics. 

The history of data analysis dates back to the 1640s. John Grant, a hatmaker, started collecting the number of deaths in London. He was the first person to use data analysis to solve a problem. Also, Florence Nightingale, best known as a nurse from 1854, made significant contributions to medicine through data analysis, particularly in public health and sanitation.

This simple practice of data analysis has evolved and broadened over time. “ Data analytics ” is the bigger picture. It employs data, tools, and techniques (covered later in this article) to discover new insights and make predictions.  

Why is Data Analysis so Important Now?

How do businesses make better decisions, analyze trends, or invent better products and services ?

The simple answer: Data Analysis. The distinct methods of analysis reveal insights that would otherwise get lost in the mass of information. Big data analytics is getting even more prominent owing to the below reasons.

1. Informed Decision-making

The modern business world relies on facts rather than intuition. Data analysis serves as the foundation of informed decision-making. 

Consider the role of data analysis in UX design , specifically when dealing with non-numerical, subjective information. Qualitative research delves into the 'why' and 'how' behind user behavior , revealing nuanced insights. It provides a foundation for making well-informed decisions regarding color , layout, and typography . Applying these insights allows you to create visuals that deeply resonate with your target audience.

2. Better Customer Targeting and Predictive Capabilities

Data has become the lifeblood of successful marketing . Organizations rely on data science techniques to create targeted strategies and marketing campaigns. 

Big data analytics helps uncover deep insights about consumer behavior. For instance, Google collects and analyzes many different data types. It examines search history, geography, and trending topics to deduce what consumers want.

3. Improved Operational Efficiencies and Reduced Costs

Data analytics also brings the advantage of streamlining operations and reducing organizational costs. It makes it easier for businesses to identify bottlenecks and improvement opportunities. This enables them to optimize resource allocation and ultimately reduce costs.

Procter & Gamble (P&G) , a leading company, uses data analytics to optimize their supply chain and inventory management. Data analytics helps the company reduce excess inventory and stockouts, achieving cost savings.  

4. Better Customer Satisfaction and Retention

Customer behavior patterns enable you to understand how they feel about your products, services, and brand. Also, different data analysis models help uncover future trends. These trends allow you to personalize the customer experience and improve satisfaction.

The eCommerce giant Amazon learns from what each customer wants and likes. It then recommends the same or similar products when they return to the shopping app. Data analysis helps create personalized experiences for Amazon customers and improves user experience . 

Enhance your knowledge by understanding “when” and “why” to use data analytics.

  • Transcript loading...

Types of Data Analysis Methods

“We are surrounded by data, but starved for insights.” — Jay Baer, Customer Experience Expert & Speaker

The above quote summarizes that strategic analysis must support data to produce meaningful insights. 

Before discussing the top data analytics techniques , let’s first understand the two types of data analysis methods.

1. Quantitative Data Analysis

As the name suggests, quantitative analysis involves looking at the complex data, the actual numbers, or the rows and columns. Let’s understand this with the help of a scenario.

Your e-commerce company wants to assess the sales team’s performance. You gather quantitative data on various key performance indicators (KPIs). These KPIs include

The number of units sold.

Sales revenue.

Conversion rates.

Customer acquisition costs.

By analyzing these numeric data points, the company can calculate:

Monthly sales growth.

Average order value.

Return on investment (ROI) for each sales representative.

How does it help?

The quantitative analysis can help you identify:

Top-performing sales reps

Best-selling products. 

Most cost-effective customer acquisition channels.

The above metrics help the company make data-driven decisions and improve its sales strategy.

2. Qualitative Data Analysis

There are situations where numbers in rows and columns are impossible to fit. This is where qualitative research can help you understand the data’s underlying factors, patterns, and meanings via non-numerical means. Let’s take an example to understand this.

Imagine you’re a product manager for an online shopping app. You want to improve the app’s user experience and boost user engagement. You have quantitative data that tells you what's going on but not why . Here’s what to do:

Collect customer feedback through interviews, open-ended questions, and online reviews. 

Conduct in-depth interviews to explore their experiences. 

Watch this instructional video to elevate your interview preparation to a more professional level.

By reading and summarizing the comments, you can identify issues, sentiments, and areas that need improvement. This qualitative insight can guide you to identify and work on areas of frustration or confusion. 

Learn more about quantitative and qualitative user research in this video.

10 Best Data Analysis and Modeling Techniques

We generate over 120 zettabytes daily. That’s about 120 billion copies of the entire Internet in 2020, daily . Without the best data analysis techniques, businesses of all sizes will never be able to collect, analyze, and interpret data into real, actionable insights .  

Now that you have an overarching picture of data analysis , let’s move on to the nitty-gritty: top data analysis methods .

An infographic showcasing the best quantitative and qualitative data analysis techniques.

© Interaction Design Foundation, CC BY-SA 4.0

Quantitative Methods

1. cluster analysis.

Also called segmentation or taxonomy analysis, this method identifies structures within a dataset. It’s like sorting objects into different boxes (clusters) based on their similarities. The data points within a similar group are similar to each other (homogeneous). Likewise, they’re dissimilar to data points in another cluster(heterogeneous).  

Cluster analysis aims to find hidden patterns in the data. It can be your go-to approach if you require additional context to a trend or dataset.

Let’s say you own a retail store. You want to understand your customers better to tailor your marketing strategies. You collect customer data, including their shopping behavior and preferences. 

Here, cluster analysis can help you group customers with similar behaviors and preferences. Customers who visit your store frequently and shop a lot may form one cluster. Customers who shop infrequently and spend less may form another cluster.

With the help of cluster analysis, you can target your marketing efforts more efficiently.

2. Regression Analysis

Regression analysis is a powerful data analysis technique. It is quite popular in economics, biology, biology, and psychology. This technique helps you understand how one thing (or more) influences another. 

Suppose you’re a manager trying to predict next month’s sales. Many factors, like the weather, promotions, or the buzz about a better product, can affect these figures.

In addition, some people in your organization might have their own theory on what might impact sales the most. For instance, one colleague might confidently say, “When winter starts, our sales go up.” And another insists, “Sales will spike two weeks after we launch a promotion.”

All the above factors are “variables.” Now, the “dependent variable” will always be the factor being measured. In our example—the monthly sales. 

Next, you have your independent variables. These are the factors that might impact your dependent variable.

Regression analysis can mathematically sort out which variables have an impact. This statistical analysis identifies trends and patterns to make predictions and forecast possible future directions. 

There are many types of regression analysis, including linear regression, non-linear regression, binary logistic regression, and more. The model you choose will highly depend upon the type of data you have

3. Monte Carlo Simulation

This mathematical technique is an excellent way to estimate an uncertain event’s possible outcomes. Interestingly, the method derives its name from the Monte Carlo Casino in Monaco. The casino is famous for its games of chance. 

Let’s say you want to know how much money you might make from your investments in the stock market. So, you make thousands of guesses instead of one guess. Then, you consider several scenarios . The scenarios can be a growing economy or an unprecedented catastrophe like Covid-19. 

The idea is to test many random situations to estimate the potential outcomes.

4. Time Series Analysis

The time series method analyzes data collected over time. You can identify trends and cycles over time with this technique. Here, one data set recorded at different intervals helps understand patterns and make forecasts. 

Industries like finance, retail, and economics leverage time-series analysis to predict trends. It is so because they deal with ever-changing currency exchange rates and sales data. 

Using time series analysis in the stock market is an excellent example of this technique in action. Many stocks exhibit recurring patterns in their underlying businesses due to seasonality or cyclicality. Time series analysis can uncover these patterns. Hence, investors can take advantage of seasonal trading opportunities or adjust their portfolios accordingly.

Time series analysis is part of predictive analytics . It can show likely changes in the data to provide a better understanding of data variables and better forecasting. 

5. Cohort Analysis

Cohort analysis also involves breaking down datasets into relative groups (or cohorts), like cluster analysis. However, in this method, you focus on studying the behavior of specific groups over time. This aims to understand different groups’ performance within a larger population.

This technique is popular amongst marketing, product development, and user experience research teams. 

Let’s say you’re an app developer and want to understand user engagement over time. Using this method, you define cohorts based on a familiar identifier. This identifier can be the demographics, app download date, or users making an in-app purchase. In this way, your cohort represents a group of users who had a similar starting point. 

With the data in hand, you analyze how each cohort behaves over time. Do users from the US use your app more frequently than people in the UK? Are there any in-app purchases from a specific cohort?

This iterative approach can reveal insights to refine your marketing strategies and improve user engagement. 

Qualitative Methods

6. content analysis.

When you think of “data” or “analysis,” do you think of text, audio, video, or images? Probably not, but these forms of communication are an excellent way to uncover patterns, themes, and insights. 

Widely used in marketing, content analysis can reveal public sentiment about a product or brand. For instance, analyzing customer reviews and social media mentions can help brands discover hidden insights. 

There are two further categories in this method:

Conceptual analysis: It focuses on explicit data. For example, the number of times a word repeats in a content. 

Relational analysis: It examines the relationship between different concepts or words and how they connect. It's not about counting but about understanding how things fit together. A user experience technique called card sorting can help with this.

This technique involves counting and measuring the frequency of categorical data. It also studies the meaning and context of the content. This is why content analysis can be both quantitative and qualitative. 

How to Improve Your Design with Task Analysis

7. Sentiment Analysis

Also known as opinion mining, this technique is a valuable business intelligence tool. It can assist you to enhance your products and services. The modern business landscape has substantial textual data, including emails, social media comments, website chats, and reviews. You often need to know whether this text data conveys a positive, negative, or neutral sentiment.

Sentiment Analysis tools help scan this text to determine the emotional tone of the message automatically. The insights from sentiment analysis are highly helpful in improving customer service and elevating brand reputation.

8. Thematic Analysis

Whether you’re an entrepreneur, a UX researcher, or a customer relationship manager— thematic analysis can help you better understand user behaviors and needs. 

The thematic technique analyzes large chunks of text data such as transcripts or interviews. It then groups them into themes or categories that come up frequently within the text. While this may sound similar to content analysis, it’s worth noting that the thematic method purely uses qualitative data. 

Moreover, it is a very subjective technique since it depends upon the researcher’s experience to derive insights. 

9. Grounded Theory Analysis

Think of grounded theory as something you, as a researcher, might do. Instead of starting with a hypothesis and trying to prove or disprove it, you gather information and construct a theory as you go along.

It's like a continuous loop. You collect and examine data and then create a theory based on your discovery. You keep repeating this process until you've squeezed out all the insights possible from the data. This method allows theories to emerge naturally from the information, making it a flexible and open way to explore new ideas.

Grounded theory is the basis of a popular user-experience research technique called contextual enquiry .

10. Discourse Analysis

Discourse analysis is popular in linguistics, sociology, and communication studies. It aims to understand the meaning behind written texts, spoken conversations, or visual and multimedia communication. It seeks to uncover:

How individuals structure a specific language

What lies behind it; and 

How social and cultural practices influence it

For instance, as a social media manager, if you analyze social media posts, you go beyond the text itself. You would consider the emojis, hashtags, and even the timing of the posts. You might find that a particular hashtag is used to mobilize a social movement. 

The Data Analysis Process: Step-by-Step Guide

You must follow a step-by-step data analytics process to derive meaningful conclusions from your data. Here is a rundown of five main data analysis steps :

A graphical representation of data analysis steps. 

1. Problem Identification

The first step in the data analysis process is “identification.” What problem are you trying to solve? In other words, what research question do you want to address with your data analysis?

Let’s say you’re an analyst working for an e-commerce company. There has been a recent decline in sales. Now, the company wants to understand why this is happening. Our problem statement is to find the reason for the decline in sales. 

2. Data Collection

The next step is to collect data. You can do this through various internal and external sources. For example, surveys , questionnaires, focus groups , interviews , etc.

Delve deeper into the intricacies of data collection with Ann Blandford in this video:

The key here is to collect and aggregate the appropriate statistical data. By “appropriate,” we mean the data that could help you understand the problem and build a forecasting model. The data can be quantitative (sales figures) or qualitative (customer reviews). 

All types of data can fit into one of three categories:

First-party data : Data that you, or your company, can collect directly from customers.

Second-party data : The first-party data of other organizations. For instance, sales figures of your competition company. 

Third-party data : Data that a third-party organization can collect and aggregate from numerous sources. For instance, government portals or open data repositories. 

3. Data Cleaning

Now that you have acquired the necessary data, the next step is to prepare it for analysis. That means you must clean or scrub it. This is essential since acquired data can be in different formats. Cleaning ensures you’re not dealing with bad data and your results are dependable. 

Here are some critical data-cleaning steps:

Remove white spaces, duplicates, and formatting errors.

Delete unwanted data points.

Bring structure to your data.

For survey data, you also need to do consistency analysis. Some of this relies on good questionnaire design, but you also need to ensure that:

Respondents are not “straight-lining” (all answers in a single column).

Similar questions are answered consistently.

Open-ended questions contain plausible responses.

4. Data Analysis

This is the stage where you’d be ready to leverage any one or more of the data analysis and research techniques mentioned above. The choice of technique depends upon the data you’re dealing with and the desired results. 

All types of data analysis fit into the following four categories:

An illustration depicting the four data analysis processes. These types further represent their respective objectives.

A. Descriptive Analysis

Descriptive analysis focuses on what happened. It is the starting point for any research before proceeding with deeper explorations. As the first step, it involves breaking down data and summarizing its key characteristics.   

B. Diagnostic Analysis

This analysis focuses on why something has happened. Just as a doctor uses a patient’s diagnosis to uncover a disease, you can use diagnostic analysis to understand the underlying cause of the problem. 

C. Predictive Analysis

This type of analysis allows you to identify future trends based on historical data. It generally uses the results from the above analysis, machine learning (ML), and artificial intelligence (AI) to forecast future growth. 

D. Prescriptive Analysis

Now you know what to do, you must also understand how you’ll do it. The prescriptive analysis aims to determine your research’s best course of action.  

5. Data Interpretation

The step is like connecting the dots in a puzzle. This is where you start making sense of all the data and analysis done in the previous steps. You dig deeper into your data analysis findings and visualize the data to present insights in meaningful and understandable ways. 

Explore this comprehensive video resource to understand the complete user research data analysis process:

The Best Tools and Resources to Use for Data Analysis in 2023

You’ve got data in hand, mastered the process, and understood all the ways to analyze data . So, what comes next?

Well, parsing large amounts of data inputs can make it increasingly challenging to uncover hidden insights. Data analysis tools can track and analyze data through various algorithms, allowing you to create actionable reports and dashboards.

We’ve compiled a handy list of the best tools for you with their pros and cons. 

1. Microsoft Excel

The world’s best and most user-friendly spreadsheet software features calculations and graphing functions. It is ideal for non-techies to perform basic data analysis and create charts and reports.

No coding is required.

User-friendly interface.

Runs slow with complex data analysis.

Less automation compared to specialized tools.

2. Google Sheets

Similar to Microsoft Excel, Google Sheets stands out as a remarkable and cost-effective tool for fundamental data analysis. It handles everyday data analysis tasks, including sorting, filtering, and simple calculations. Besides, it is known for its seamless collaboration capabilities. 

Easily accessible .

Compatible with Microsoft Excel.

Seamless integration with other Google Workspace tools.

Lacks advanced features such as in Microsoft Excel.

May not be able to handle large datasets.

3. Google Analytics

Widely used by digital marketers and web analysts, this tool helps businesses understand how people interact with their websites and apps. It provides insights into website traffic, user behavior, and performance to make data-driven business decisions .

Free version available.

Integrates with Google services.

Limited customization for specific business needs.

May not support non-web data sources.

4. RapidMiner

RapidMiner is ideal for data mining and model development. This platform offers remarkable machine learning and predictive analytics capabilities. It allows professionals to work with data at many stages, including preparation, information visualization , and analysis.

Excellent support for machine learning.

Large library of pre-built models.

Can be expensive for advanced features.

Limited data integration capabilities.

Being one of the best commercial data analysis tools, Tableau is famous for its interactive dashboards and data exploration capabilities. Data teams can create visually appealing and interactive data representations through its easy-to-use interface and powerful capabilities. 

Intuitive drag-and-drop interface.

Interactive and dynamic data visualization.

Backed by Salesforce.

Expensive than competition.

Steeper learning curve for advanced features.

6. Power BI

This is an excellent choice for creating insightful business dashboards. It boasts incredible data integration features and interactive reporting, making it ideal for enterprises. 

Short for Konstanz Information Miner, KNIME is an outstanding tool for data mining. Its user-friendly graphical interface makes it accessible even to non-technical users, enabling them to create data workflows easily. Additionally, KNIME is a cost-effective choice. Hence, it is ideal for small businesses operating on a limited budget. 

Visual workflow for data blending and automation.

Active community and user support.

Complex for beginners.

Limited real-time data processing.

8. Zoho Analytics

Fueled by artificial intelligence and machine learning, Zoho Analytics is a robust data analysis platform. Its data integration capabilities empower you to seamlessly connect and import data from diverse sources while offering an extensive array of analytical functions.

Affordable pricing options.

User-friendly interface

Limited scalability for very large datasets.

Not as widely adopted as some other tools.

9. Qlik Sense

Qlik Sense offers a wide range of augmented capabilities. It has everything from AI-generated analysis and insights to automated creation and data prep, machine learning, and predictive analytics. 

Impressive data exploration and visualization features.

Can handle large datasets.

Steep learning curve for new users.

How to Pick the Right Tool?

Consider the below factors to find the perfect data analysis tool for your organization:

Your organization’s business needs.

Who needs to use the data analysis tools?

The tool’s data modeling capabilities.

The tool’s pricing. 

Besides the above tools, additional resources like a Service Design certification can empower you to provide sustainable solutions and optimal customer experiences. 

How to Become a Data Analyst? 

Data analysts are in high demand owing to the soaring data boom across various sectors. As per the US Bureau of Labor Statistics , the demand for data analytics jobs will grow by 23% between 2021 and 2031. What’s more, roles offer excellent salaries and career progression. As you gain experience and climb the ranks, your pay scales up, making it one of the most competitive fields in the job market. 

Learning data analytics methodology can help you give an all-new boost to your career. Here are some tips to become a data analyst:

1. Take an Online Course

You do not necessarily need a degree to become a data analyst. A degree can give you solid foundational knowledge in relevant quantitative skills. But so can certificate programs or university courses. 

2. Gain the Necessary Technical Skills

Having a set of specific technical skills will help you deepen your analytical capabilities. You must explore and understand the data analysis tools to deal with large datasets and comprehend the analysis. 

3. Gain Practical Knowledge

You can work on data analysis projects to showcase your skills. Then, create a portfolio highlighting your ability to handle real-world data and provide insights. You can also seek internship opportunities that provide valuable exposure and networking opportunities. 

4. Keep Up to Date with the Trends

Since data analysis is rapidly evolving, keep pace with cutting-edge analytics tools, methods, and trends. You can do this through exploration, networking, and continuous learning.

5. Search for the Ideal Job

The job titles and responsibilities continue to change and expand in data analytics. Beyond “Data Analyst,” explore titles like Business Analyst, Data Scientist, Data Engineer, Data Architect, and Marketing Analyst. Your knowledge, education, and experience can guide your path to the right data job. 

The Take Away

Whether you’re eager to delve into a personal area of interest or upgrade your skills to advance your data career, we’ve covered all the relevant aspects in this article. 

Now that you have a clear understanding of what data analysis is, and a grasp of the best data analysis techniques , it’s time to roll up your sleeves and put your knowledge into practice.

We have designed The IxDF courses and certifications to align with your intellectual and professional objectives. If you haven’t already, take the initial step toward enriching your data analytics skills by signing up today. Your journey to expertise in data analysis awaits.

Where to Learn More

1. Learn the most sought-after tool, Microsoft Excel, from basic to advanced in this LinkedIn Microsoft Excel Online Training Course .

2. Ensure all the touchpoints of your service are perfect through this certification in Service Design .

3. Learn more about the analytics data types we encounter daily in this video.

Author: Stewart Cheifet. Appearance time: 0:22 - 0:24. Copyright license and terms: CC / Fair Use. Modified: Yes. Link: greatestgames

4. Read this free eBook, The Elements of Statistical Learning , to boost your statistical analysis skills.

5. Check out Python for Data Analysis to learn how to solve statistical problems with Python. 

6. Join this beginner-level course and launch your career in data analytics. Data-Driven Design: Quantitative UX Research Course

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Write code to work with data, working with qualitative data, related guides.

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  • Data Commons Free Google initiative that organizes a wide range of publicly available data into a uniform visualization and download platform. The Explore interface uses generative AI to accept and interpret natural language questions.
  • Deepsheet AI tool for conversational data analysis. Accepts many import formats and allows exporting responses and outputs.
  • Power BI Desktop Note: Windows users at Temple can download Power BI Desktop through Office 365. Power BI Desktop's AI Insights includes a collection of pre-trained machine learning models that can enhance data preparation. Access AI Insights in the Power Query Editor.
  • Wolfram Alpha This link opens in a new window Wolfram|Alpha offers dynamic computations based on a vast collection of built-in data, algorithms and methods. Temple users have free access to Wolfram|Alpha Pro. See Temple ITS' site licensed software page for more details.
  • Coming soon: Tableau AI Brings "trusted generative AI" to the Tableau platform. Tableau Pulse is a reimagined data experience for business users, powered by Tableau AI. Tableau licenses are available through some Temple departments. See Temple ITS' Tableau page for more information.

Standalone, free chatbots excel at generating code, and walking users through how to code a program to accomplish a task. This is useful for working with data. Try using a free chatbot, like ChatGPT, to generate code for retrieving, analyzing, or visualizing data. Then paste it into your preferred development environment. Several free computational notebook platforms also have AI coding assistance built in:

  • Google Colab Google's free computational notebook system, has AI coding and AI chatbot features for users in eligible locations .
  • JupyterLab A web-based development environment for running Jupyter Notebooks. Use Jupyter AI (python magic %%ai) to access a generative AI interface to any Notebook.

Machine learning-based tools for automatic coding are not new in qualitative data analysis software, but some existing platforms and newer apps are introducing more generative AI features. Listed below are some that have either a free tier or are available to some Temple users.

When analyzing qualitative data from human participants, use caution in considering cloud-based tools. If personal or sensitive data is included, offline platforms may be needed to comply with university research security and IRB requirements for responsible data management.

  • AILYZE Lite An AI tool for qualitative research that has a free tier and attempts to generate summaries, identify themes, count participant viewpoints, and answer questions about qualitative data. Data security info page states that your uploaded data is encrypted and not used to train AI models. See this Qeludra Qualitative Research Community blog post about testing AILYZE for more details.
  • ATLAS.ti ATLAS.ti is not free, but some Temple users have free access to a license through their school or college. The latest software version includes generative AI tools including: AI Coding, AI Intentional Coding, AI Suggested Codes, and AI Summaries. See the ATLAS.ti's AI tools YouTube playlist of helpful video tutorials. ATLAS.ti's AI tools send your data over the internet to OpenAI for processing. Their AI data security page describes their security and privacy measures for this transfer . This may make them inappropriate for use with research data that requires offline or certain levels of secure online storage and management.
  • Sentigem Easy-to-use sentiment analysis tool for English language text.
  • Transcription tools See the Temple Libraries' guide to qualitative data analysis and tools, Transcription page for links to many AI-powered tools for transcribing qualitative data.
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  • What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

  • The type of research you conducted
  • How you collected and analyzed your data
  • Any tools or materials you used in the research
  • How you mitigated or avoided research biases
  • Why you chose these methods
  • Your methodology section should generally be written in the past tense .
  • Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
  • Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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data analysis tools in research methodology

Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

  • Aim to describe the characteristics of something?
  • Explore an under-researched topic?
  • Establish a causal relationship?

And what type of data did you need to achieve this aim?

  • Quantitative data , qualitative data , or a mix of both?
  • Primary data collected yourself, or secondary data collected by someone else?
  • Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

  • Why is this the best way to answer your research question?
  • Is this a standard methodology in your field, or does it require justification?
  • Were there any ethical considerations involved in your choices?
  • What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

  • How did you design the questionnaire?
  • What form did your questions take (e.g., multiple choice, Likert scale )?
  • Were your surveys conducted in-person or virtually?
  • What sampling method did you use to select participants?
  • What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

  • How did you design the experiment ?
  • How did you recruit participants?
  • How did you manipulate and measure the variables ?
  • What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

  • Where did you source the material?
  • How was the data originally produced?
  • What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

  • Information bias
  • Omitted variable bias
  • Regression to the mean
  • Survivorship bias
  • Undercoverage bias
  • Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

  • How did you find and select participants?
  • How many participants took part?
  • What form did the interviews take ( structured , semi-structured , or unstructured )?
  • How long were the interviews?
  • How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

  • What group or community did you observe? How long did you spend there?
  • How did you gain access to this group? What role did you play in the community?
  • How long did you spend conducting the research? Where was it located?
  • How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

  • What type of materials did you analyze?
  • How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

  • The Hawthorne effect
  • Observer bias
  • The placebo effect
  • Response bias and Nonresponse bias
  • The Pygmalion effect
  • Recall bias
  • Social desirability bias
  • Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

  • How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
  • Which software you used (e.g., SPSS, Stata or R)
  • Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

  • Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
  • Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
  • Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

  • Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
  • Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
  • Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

  • Show that you followed established practice for your type of research
  • Discuss how you decided on your approach by evaluating existing research
  • Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Measures of central tendency
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles


  • Cluster sampling
  • Stratified sampling
  • Thematic analysis
  • Cohort study
  • Peer review
  • Ethnography

Research bias

  • Implicit bias
  • Cognitive bias
  • Conformity bias
  • Hawthorne effect
  • Availability heuristic
  • Attrition bias

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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  • Published: 07 March 2024

Design and validation of a conceptual model regarding impact of open science on healthcare research processes

  • Maryam Zarghani   ORCID: 1 ,
  • Leila Nemati-Anaraki   ORCID: 2 , 3 ,
  • Shahram Sedghi   ORCID: 2 , 4 ,
  • Abdolreza Noroozi Chakoli   ORCID: 5 &
  • Anisa Rowhani-Farid   ORCID: 6  

BMC Health Services Research volume  24 , Article number:  309 ( 2024 ) Cite this article

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The development and use of digital tools in various stages of research highlight the importance of novel open science methods for an integrated and accessible research system. The objective of this study was to design and validate a conceptual model of open science on healthcare research processes.

This research was conducted in three phases using a mixed-methods approach. The first phase employed a qualitative method, namely purposive sampling and semi-structured interview guides to collect data from healthcare researchers and managers. Influential factors of open science on research processes were extracted for refining the components and developing the proposed model; the second phase utilized a panel of experts and collective agreement through purposive sampling. The final phase involved purposive sampling and Delphi technique to validate the components of the proposed model according to researchers’ perspectives.

From the thematic analysis of 20 interview on the study topic, 385 codes, 38 sub-themes, and 14 main themes were extracted for the initial proposed model. These components were reviewed by expert panel members, resulting in 31 sub-themes, 13 main themes, and 4 approved themes. Ultimately, the agreed-upon model was assessed in four layers for validation by the expert panel, and all the components achieved a score of > 75% in two Delphi rounds. The validated model was presented based on the infrastructure and culture layers, as well as supervision, assessment, publication, and sharing.

To effectively implement these methods in the research process, it is essential to create cultural and infrastructural backgrounds and predefined requirements for preventing potential abuses and privacy concerns in the healthcare system. Applying these principles will lead to greater access to outputs, increasing the credibility of research results and the utilization of collective intelligence in solving healthcare system issues.

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The transformation of information carriers, digital media, and internet tools has created new opportunities for the dissemination and sharing of scientific information, giving rise to the broader concept of open science [ 1 ]. Open science aims to take advantage of diverse methods to remove barriers to sharing scientific research [ 2 , 3 , 4 ], bringing about fundamental changes in how research is conducted, communicated, published, its results evaluated, researchers collaborated, and scientific works shared [ 5 ]. Open science has been recognized as a tool for participatory research management [ 6 ]. European Commission has introduced open science as a new approach to scientific processes, which is based on collaborative work and innovative methods of knowledge dissemination through digital technologies [ 3 ]. Fundamentally, open science aims to enhance public access to data, analyses, and findings with historical roots. David (1994) suggested that open science likely began during the scientific revolution in 17th century, when printed versions of scientific results were intended for public access [ 3 ], implicitly seeking to bridge the gap between science and society through new methods and greater alignment with democratic values and rights as well as promoting access to publicly-funded knowledge and the development of open tools [ 7 ].

Given the importance of open science methods and tools in research processes in various fields, many researches have been conducted in this regard. However, most of these studies have focused only on one dimension of various subject areas or one dimension of open science. The highlighted topics include principles and methods of open science in research teams [ 8 ], the gap between science and practice in open science [ 9 ], open science opportunities in knowledge sharing [ 10 ], the relationship between open science policies and research methods [ 11 ], clinical data sharing [ 12 ], strengthening open science in research process [ 13 ], the concept and aspects of open science [ 3 ]. In addition, some studies have examined a number of approaches for applying these principles to maximize the value of open science and minimize its adverse effects on the progress of science in practice [ 8 ]. For accelerating the dissemination and development of new treatments in neurodegenerative disorders, a new strategy called “Open Science” model has been used experimentally by the Montreal Neurological Institute (MNI) and partners to remove the barriers of many universities and companies [ 9 ]. However, in the mentioned study, it has been attempted to identify all aspects of open science that influence the research process as tools and methods promoting and facilitating the research process in the field of health and determine how to use open science methods and tools at each stage of research process in healthcare, including publication, distribution, evaluation and effectiveness of research.

The methods of open science have been clearly effective in the dissemination and access to information in medicine; studies have often focused on methods such as open data, publication of research details, open refereeing, and open research repositories in the organization [ 12 , 14 , 15 , 16 , 17 ]. To maintain the principles of research, ethics and issues such as privacy in the health system should be taken into account in infrastructure and open publishing laws in research organizations and legislative organizations of different countries [ 18 ]. Despite recognizing the extensive applications of open science tools in scientific processes, the proponents of open science hold diverse viewpoints on how traditional openness to research outputs should be interpreted [ 19 ]. Different definitions, objectives, and commitments have been proposed for utilizing repositories, databases, researcher communication, and open science tools [ 19 ]. Substantial variations exist among scientific fields, countries, and stakeholders’ groups regarding open science methods and concepts in relation to policies and program directions [ 4 ]. Thus, challenges and opportunities for implementing open science policies in various countries require further investigation and study [ 3 ].

Therefore, considering the direct relationship between the method of publishing research outputs, as well as publishing rules, infrastructure and culture governing the subject areas, a specific conceptual framework should be provided to use the open science tools and methods according to the nature of information. The application of open science tools in healthcare system to optimize research outputs for treatment processes, management decisions, and public knowledge enhancement is of high importance [ 20 , 21 ]. Universities and research centers must address approaches to create value for stakeholders at social, national, and international levels by employing modern technology tools similar to that presented in open science practices to tackle multifaceted challenges. Given this gap, our study aims to identify and validate the influential components of open science on research processes of the healthcare system by using a conceptual model enhancing the understanding of dimensions associated with it for benefiting researchers, policymakers, and healthcare managers. Since open science introduces novel concepts of applicable technologies and innovations in research processes, investigating the implementation of open science methods in research processes of the healthcare system necessitates exploring a conceptual model, which could lead to the formulation of relevant policies, legal conditions for publishing and retrieving various research outputs within the framework of open science for universities and research centers related to the healthcare sector. This conceptual framework is based on an exploratory method, which was conducted by interview, expert panel and Delphi method and presented the effective and important factors in the implementation of open science in health system under the conceptual model.


The current study falls under the category of exploratory research in terms of nature and applied research in terms of research type, which adopts an inductive strategy that show in flow chart of the study desing (Fig. 1 ). It also utilizes a qualitative data approach by employing a thematic analysis method. To formulate the model components, a three-step process of framework coding was employed. This process involved structuring organized concepts (main themes and subthemes obtained from combining and summarizing codes) and comprehensive concepts (themes encompassing the impact of open science on research process) within the healthcare system with respect to validation purposes (reliability and credibility of themes), for which two methods were utilized. The first method involved communicative validation, meaning referring back to the participants (interviewees) for verification [ 22 ]. The second method was expert validation, which utilized expert panels and Delphi technique. Furthermore, for validating the stability of themes, two methods were applied: repeatability and generalizability. The former was achieved through an agreement process between the two coders (i.e., the researcher and a collaborator) regarding coding [ 23 ]. This approach aimed to resolve inconsistencies arising from the coding review process. Regarding generalizability, efforts were made to involve various academic and executive stakeholders related to research topic as much as possible. It means that sampling should be done regularly and comprehensively based on the agreement of experts [ 24 ].

figure 1

Flow chart of the study design

To identify influential components and develop an initial model, a qualitative method was employed through semi-structured interviews (Appendix 1 & Appendix 2 ) among academic experts and managers in the field of research and technology within Deputy of Research and Technology of MOHME in different universities. The sampling method was purposeful and snowball, and the individuals needed to meet one of the following criteria: researchers with at least three years of research experience and involvement; academic members or managers who had served in a managerial or executive role in Deputy of Research and Technology within MOHME for at least one semester and were available and willing to cooperate. According to these criteria, interviews continued until data saturation, ultimately resulting in 20 interviews. Data saturation refers to the point where new data on the research topic is no longer obtained during interviews and the data becomes repetitive. The interviews began in early July 2021 and continued until mid-November 2021.

During the first five interviews, initial direction and examination determined the number of questions, timing of interviews, and final interview guidance. Each interview was allotted a time between 40 and 90 min. In the review done by participants to confirm validity, a portion of the text along with initial codes was sent to some of them to compare and validate the coherence of emerging ideas from the data with their own content. In the next step, to control data validity, the method of agreement rate between two coders was employed. Five initial interviews were coded in parallel, and the codes were discussed to reach an agreement. The data analysis method in this phase was the framework analysis. After each interview, the interview was first heard multiple times by the researcher (the one conducting the interview). Then, the text was transcribed using Microsoft Word (version 13) and read multiple times, and initial semantic units were identified. The transcribed files were transferred to MAXQDA software (version 20), and the determination of initial codes and their analysis was performed. The thematic analysis method was used to categorize codes, extract and classify sub-themes and main themes. After analyzing the data, a list of influential components of open science was prepared according to the perspectives of participants, which should be applied in the research processes of healthcare system. This list was used to develop the initial model.

This stage of research was designed according to consensus among experts. The preliminary proposed model was developed based on the components that were extracted from the first stage of the study. Expert panel members were selected using purposeful and available sampling methods. The panel consisted of five research team members, three researchers with research experience in the field of open science and four healthcare system executives related to research and technology. The research was conducted in the workplace of experts using online sessions. In this step, a form designed according to main components and subcomponents was utilized to assess the position of each component in the proposed model considering the experts’ opinions (Appendix 3 ). The expert panel guidelines were sent electronically and in print to panel members. A one-month time frame was allocated for panel members to complete and review the form. After this period, follow-ups were conducted, both in-person and online, to collect the forms. Once all panel members had submitted their forms, the summarized opinions were entered into the data collection form. To maintain the confidentiality of opinions, they were coded and entered into the form, which was subsequently sent to panel members again with a one-week window for review. In an online session using Google Meet, each component was discussed, and a consensus-based approach was used to confirm the results. Through the review of all components listed in the expert panel guidelines regarding the proposed model, the experts’ opinions regarding the acceptance or rejection of each proposed component were evaluated. Final analysis was performed by assessing each component based on consensus through collective agreement and utilizing the Likert scale. If there was unanimity regarding a component, it was incorporated into the final model. In cases of difference of opinions among the experts, the majority opinion prevailed, leading to revisions and corrections of the component in question.

This stage involved the Delphi method, and the participants were managers and researchers of Ministry of Health who had also participated in the first stage, as well as the activists of the field of open science in MOHME who were invited to evaluate the model. The research sample was selected using purposeful and available sampling methods. In addition, the diversity of participants in this stage contributed to better evaluation and improved the quality of the model. The sample size for this stage ranged from 20 to 30 participants. In the first Delphi round, 24 participants took part, and in the second round, there were 21 participants. To qualify as the study sample, individuals needed to meet at least two of the following criteria: being a faculty member and researcher at one of the medical sciences universities under MOHME, having research or managerial experience in research processes, or being specialists in librarianship and medical information with research experience in open science or having at least three years of active record in research management. The research environment was the workplace of research community members. A structured questionnaire based on the main components and sub-components extracted from interview analysis in the second phase was used for data collection (Appendix 4 ).

Implementation process of delphi approach

Selection of experts.

In studies employing the Delphi method, the sample size varies from 10 to 50 people, which was shown in the study of Campbell and Cantrill [ 25 ]. Agumba and Haupt identified 30 experts, out of whom 20 participated in completing the questionnaires [ 26 ]. In Rowe and Wrigh analysis of Delphi studies, it has been shown that the number of experts varies from 4 to 21 [ 27 ], and Woudenberg stated that he considered between 5 and 20 experts [ 28 ]. Based on these references and considering the necessary population size for Delphi studies, the sample size was determined to be between 20 and 30 participants in this study. Experts were selected using purposive sampling. Based on the inclusion criteria explained in the sampling section, the experts at least met two of the conditions for participation. Agumba and Haupt required experts to meet at least three of eight entry criteria [ 26 ], while Rogers and Lopez were satisfied with two out of five inclusion criteria [ 29 ]. Consequently, 30 experts were first identified and provided with the questionnaire, 24 of whom expressed willingness to participate and took part in validating the model components. Ultimately, the research sample included 24 experts, all of whom were educators and researchers with over three years of research and executive experience.

Development and validation of questionnaire

Thirteen main themes and 31 sub-themes that were approved by experts as components of the proposed model in the second step were the basis of the closed structured questionnaire design for this step. The first phase analysis and evaluation by the expert panel in the second phase served as the basis for constructing the structured questionnaire for this stage. According to Hsu and Sandford, the use of a closed questionnaire is more appropriate than an open one because a simpler response process and shorter completion time increases the likelihood of greater expert participation [ 30 ]. If the members participating in the study are representative of the relevant field of knowledge, it can guarantee the validity of the content [ 1 ]. Also, the Delphi approach should not be judged with quantitative methods, but rather transferability, reliability, applicability and confirmability criteria should be considered for the validity and reliability of the results [ 31 ]. Since the structured Delphi questionnaire was prepared based on expert panel in the second phase, including representatives from the healthcare knowledge domain and open science practitioners and had also been reviewed by research team as well as some of the participating experts in the third phase, its face validity was confirmed.

Criterion for achieving consensus

The term “consensus” refers to the agreement on an idea for participants to reach a common ground on a specific topic, rather than finding a correct answer [ 32 ]. Research using Delphi method has also shown that there is no specific criterion for achieving consensus. A common criterion in these studies is that at least 60% of respondents should agree on the component under consideration, which occurs with 50–90% probability [ 32 , 33 ]. Components with agreement levels below this rate are considered not to have reached consensus and move on to the next phase [ 34 ]. However, achieving 100% agreement is not feasible due to diverse political, social, economic, and scientific backgrounds of individuals [ 35 ]. A decision about consensus is made when a certain percentage of votes fall within a specific range [ 30 ]. In previous studies, a consensus range of 51–100% has been reported [ 36 , 37 ]. In this study, the criterion for achieving consensus for each component was based on research, considering that at least 60% of participants should agree on the importance of the component. Accordingly, responses were scored on a five-point Likert scale, ranging from one to five. The acceptance threshold for each component was a score higher than 75% or > 75% agreement based on the total opinions about it (very much and much). Components that scored between 50% and 75% underwent revisions and were re-entered into the validation cycle for reevaluation. Components that scored < 50% were excluded from the study. The Delphi process was conducted in two rounds to confirm the components. Delphi iterations refer to the process of systematically (and in writing) repeating a series of steps using questionnaires with the aim of reaching consensus on opinions [ 38 ]. In terms of the number of iterations, articles have reported 2 to 10 rounds [ 37 ]. The decision about the number of rounds is somewhat practical or empirical and depends on available time and the nature of the initial question [ 37 ]. In this study, a panel of experts was used for validating the components of the proposed model. As a result, Delphi iterations were implemented in two rounds to validate the components.

Data analysis

Analysis methods are determined based on Delphi’s objective, the structure of iterations, the type of questions, and the number of participants [ 30 , 38 ]. Descriptive statistics such as mean, median, and measures of dispersion are commonly used [ 39 ]. In this study, descriptive statistics was utilized for analyzing the results of the first and second rounds, including frequency and percentage for ranking the findings. After collecting questionnaires in the first Delphi round, the proposed components were applied, and the results of the first phase along with the revised questionnaire were sent again to study participants. This process continued until consensus was reached on the options. Data analysis in the validation phase was done using descriptive statistics (frequency, percentage), and the responses were scored on a five-point Likert scale. The acceptance criterion for each component was a score > 75%. Components that scored 50–75% underwent revisions and were re-entered into the validation cycle. Components that scored < 50% were excluded from the study.

Execution of delphi rounds

When necessary information regarding the research topic is available, a structured questionnaire can be used to improve responses [ 30 ]. Since the information regarding tool design was obtained in previous steps of this study, a structured questionnaire was used. In the first Delphi round, a total of 30 questionnaires were sent to the identified individuals through e-mail and in-person channels. After two weeks, 24 questionnaires were returned to the research group following repeated follow-ups. At the end of the first round, responses were collected and summarized. The results of this round indicated that there was a consensus of over 75% on 13 main components and 29 sub-components listed in the questionnaire. Two sub-components did not reach consensus in this stage, so the second round of Delphi was initiated. In the Second Delphi Round, the feedback received from the first round along with revisions of components that were not approved was sent to 24 participants of the first round. They were asked to provide their opinions and reasons for agreement or disagreement with the components. After collecting questionnaires in this round and analyzing them, all 31 sub-components and 13 main components achieved a consensus with a score exceeding 75%.

From the analysis of interviews in the first stage using thematic analysis, a structured collection of 385 codes, 38 sub-themes, 14 main themes, and 3 major themes was extracted. The initial proposed model concerning the impact of open science on health research processes was formed based on the semantic relationship between these components for presentation to the expert panel. Table  1 presents the structured collection of themes, as well as main components, and sub-components extracted from qualitative data related to the interviews.

In the second phase, to review and refine the titles of extracted components and the semantic relationships established between them in the proposed model according to experts’ opinions, the model was evaluated and reviewed by experts using the data collection form (Appendix 3 ). The summary of experts’ opinions that aimed at revising, refining the titles, and establishing semantic relationships between the proposed model’s initial components indicated a collective agreement on most of the components. Furthermore, summarizing experts’ opinions and applying them to the proposed model led to the refinement and enhancement of components. The modified titles of the components were as follows: “Enhancing Factors of Trust in Research Outputs,” “Publishing Peer-Reviewed Results and Other Outputs in Scientific Networks,” “Publishing Research Outputs in the Scientific Language,” “Disseminating Research Outputs to the Public,” “Enhancing Participation in All Research Stages,” “Increasing Public Involvement in Data Collection,” “Strengthening the Knowledge Cycle and Trust in Research,” “Leveraging Innovative Communication Tools,” “Public Participation in Research Funding,” “Mechanisms and Guidelines for Open Research,” “Facilitating Intellectual Property Conditions for Research,” and “Promoting Ethical Principles in the Research Process.”

Semantic congruence according to expert opinions and reevaluation of codes and components led to the integration of eight components as follows: “Publishable Research Topics,” “Publishing Research Outputs in the Scientific Language,” “Infrastructure and Tools for Sharing Outputs,” “Protective Infrastructure and Data Sharing,” “Training in Open Science Principles,” “Ethical Considerations in Publishing Outputs,” “Supportive and Encouraging Policies,” and “Evaluation Indicators.” Based on the revisions suggested, new concepts emerged during the re-review of codes and component meanings: “Transparency of Research’s Scientific and Technical Process,” “Transparency of Research’s Managerial and Financial Process,” “Impact of Open Science on Regulatory Processes,” “Impact of Open Science on Evaluation Processes,” and “Open Peer Review.”

According to the overall opinion, open science is considered an effective factor in reducing research barriers. A uniform research structure cannot be proposed for all organizations. The sub-component “Unified Form of Open Research Structure” was removed. The initial coding was also reviewed again. Applying the suggestions received from experts led to reconsideration of the initial proposed model. Ultimately, the proposed model was selected for final evaluation using Delphi method, which consisted of 31 sub-components, 13 main components, and 4 super-components as shown in (Fig.  2 ).

figure 2

Conceptual model of the impact of open science on research processes in healthcare system of Iran

The results of final stage of the study, which aimed at validating the proposed model regarding the impact of open science on research processes of the healthcare system, were obtained using the classical Delphi method and quantitative descriptive statistics. Participants of this stage consisted of 13 males (54.2%) and 11 females (45.8%). All the participants (100%) had more than five years of research experience. According to the opinions of participants in the first round of Delphi, all sub-components, main components, and super-components (a total of 31 sub-components, 13 main components, and 4 super-components) reached a consensus except for two sub-components, namely “Transparency of Managerial and Financial Process of Research” and “Publishing Research Outputs to the Public”. The acceptance or rejection of each component depended on the total opinions received (very much and much) and required a score of > 75%.

The second round of Delphi was conducted to reevaluate the two components that could not achieve a score > 75%. Accordingly, a questionnaire was designed for the participants to assess the impact of these two components on research process in the second round of Delphi. The questionnaire was sent to 24 participants who took part in the first round. After analyzing the data from this stage, these two components also reached a consensus with a score > 75%.

All components of open science that were effective on research processes of healthcare system reached a consensus in both rounds of the Delphi process. Table  2 categorizes the importance levels of the main components according to opinions of Delphi study participants into three levels. Four components, namely “Enhancing Trust Factors in Research Outputs,” “Mechanisms and Guidelines for Open Research,” “Promoting Ethical Principles in the Research Process,” and “Open Research Evaluation Process” achieved 100% consensus among participants. Additionally, three components, including “Formation of Extensive Scientific Communication,” “Managing Publication Costs,” and “Supportive Policies,” were ranked second with over 90% agreement. Components that ranked third also obtained consensus with over 80% agreement. This indicates the importance of all components in the proposed model and the need for proper implementation of each.

In the presented model, the impact of open science components on research processes is structured in four main layers, forming the foundation for open research policy. This model, which is derived from analysis of interviews and expert opinions relevant to research topic, created a structure leading to open research policy. In the first layer, namely the broadest layer, the necessary hardware and software equipment for implementing open science research methods should be provided, as along with issues such as specialized human resources, technical infrastructure, software, systems, and tools needed for conducting research in an open manner, as well as pathways for sharing, which should be taken into account. The educational principles required for fostering open science culture are considered in this layer, too. The second layer is essential for determining the necessary principles and strategies for implementing open science research. In this layer, the laws, ethical principles in open research and policies are determined; it is a fundamental step towards creating an open research policy and plays a role in all stages of research. The third layer is based on open peer review, research efficiency, and evaluation indicators related to pre- and post-publication evaluation of research results, as well the impact of research from various aspects, which should be measured based on quantitative and qualitative indicators. The fourth layer is related to the process of publishing and sharing of research outputs addressing publishable aspects of research, access principles and conditions, transparency and reproducibility processes of open research. Additionally, pathways for accessing research outputs and participation of citizens are defined in this layer. For the establishment of this layer, previous layers must be systematically and effectively defined and supported. The proper formation of these four layers will lead to an open research policy for health system research, resulting in better issue identification, transparent process execution and responsiveness of research, as well as effective utilization of outputs by relevant stakeholders.

Open science can have a significant impact on various research processes. By providing an integrated digital research structure, it can facilitate broader access to outputs and increase participation in various research stages, fostering interactions among researchers and stakeholders within academic, industrial, and policy-making structures. In some cases, open science can be likened to a double-edged sword. On one hand, it could be constructive and transformative, while on the other, it might create challenges such as privacy concerns and the lack of protection for stakeholders’ rights. Nevertheless, the application of open science methodologies in health system research is both constructive and advantageous, with its benefits potentially outweighing the drawbacks. For this purpose, it is necessary to utilize the unique opportunities of open science to enhance knowledge and science derived from research through a specific perspective and plan. This would lead to knowledge democratization and proper utilization in various societal strata, alongside increased community awareness and appropriate utilization of research outputs. Consequently, open science methodologies play a pivotal role in quality management of science. As a result, this support will lead to a win-win situation [ 40 ].

Whereas the use of these technologies has led to challenges in some cases, the potential and actual benefits have been so impressive that newer measures should be taken to apply these technologies correctly. One of the goals of organizational science is contributing to evidence-based development in problem solving. Since studies such as clinical trials and cohorts in the field of medical sciences are looking for a scientific and practical basis in the direction of evidence-based medicine, the use of open science methods in these research processes to discover and test evidence-based actions can be beneficial for doctors [ 20 ]. One of the most prominent advantages of open science in healthcare system is providing conditions for maximum public access to scientific outputs in an understandable language free from complexity. Utilizing diverse scientific discourse methods through various media outlets should be considered in this regard. Nonetheless, for proper utilization of research outputs to create conducive conditions, the need for a cycle of credible and transparent knowledge circulation arises. And a well-established knowledge cycle based on sharing outputs across different research stages enhances trust in research structures, fosters greater participation, and ultimately amplifies the impact of research across different societal domains. To fulfill these requirements, various dimensions of open science provide this crucial opportunity to researchers and stakeholders, yielding significant cost-effectiveness for institutions and universities [ 13 ]. An open science research policy comprises scientific dissemination channels, participation, university relationships, research quality and coherence, transparency, repeatability, requirements for transparent scientific processes, and a system for alignment and evaluation [ 6 ]. This system is achievable based on values of openness, fair sharing, resource accessibility, education of research outputs, and acceptance of open culture [ 41 ]. Therefore, an open science platform should have several properties, including categorizing multiple versions of data and codes, supporting multiple data access schemes, especially for sensitive data, flexible metadata management and standards in evolution, connecting organizational and external data, supporting object identifiers such as DOI, facilitating internal and external scientific collaboration and participation [ 36 ]. These characteristics enable the digital support of all research steps within the framework of open science.

Models of open access and open data dissemination are rapidly becoming open scientific methods that influence the entire research ecosystem, including production, communication, and reuse of research results. Utilizing technological innovations for the dissemination of scientific content is vital for sustainability of scientific journals and publishers [ 42 ]. Nevertheless, in the current context, these practices are not widely adopted because insufficient knowledge on utilizing these practices, potential misuse of research, imposing high publication costs on researchers, and so forth have led to negative reactions towards the application of these methods. Also, the lengthy process of open peer reviews and the dissemination of evaluation feedback have not been favorable for researchers. Appropriate policies with clear mechanisms are needed to create desirability and confidence among stakeholders for conducting research within the framework of open science; for example, encouraging factors and preventive measures against potential misuse. Transparency and openness in research require cultural transformation. Enhancing transparency and openness should not only be embraced by scientists and researchers, but also by budget-providing institutions and even those beyond the research and innovation sector [ 43 ]. Moreover, the budgetary mechanism for publishing research outputs plays a crucial role in this stage. Most academics support the principle of making knowledge freely available to everyone, but the use of open access publications among academics is still limited due to relevant policies [ 44 ]. Additionally, legal and ethical issues in research have prompted the development of new tools and methods for addressing these matters. European Commission has deemed the implementation of open science processes as a task for universities to free themselves from these conditions [ 45 ].


Due to their nature, qualitative studies have limitations. It has been attempted to reduce these limitations with the measures taken for validity and reliability of the study as follows. Some participants were not willing to cooperate in the interview when the purpose of the research was explained to them and they were assured that their information would remain confidential. The time and place of the interview was determined according to their wishes. In addition, the timetable to conduct this study was arranged according to the communication restrictions imposed by COVID-19, which caused the time to collect and carry out various stages of the study to be longer than usual. In order to solve this limitation, reminders were sent via e-mail, as well as face-to-face and telephone follow-ups to receive comments. The diversity and geographical dispersion of participants was another issue that caused a lot of time to follow up and receive information. An attempt was made to use auxiliary forces in different geographical areas of Iran to follow up and receive information.

The conceptual model presented based on the findings of this study has shown that to apply open science methods in different stages of research in the health system, it is essential to cultivate a culture of open research and ethical issues through formal and informal education or repeated communication within universities and research centers, which reaches various stakeholders. The technical infrastructure should also be established, which has already been provided to a considerable extent in research libraries through monitoring software of research centers and universities. Access conditions should be reconsidered based on the type of research and the target audience. Another important finding based on this model was that the laws and policies for implementing open research in the healthcare system should be formulated through university research councils and ethics committees, so that the support of higher-level organizations and lawmakers, as well as necessary laws are enacted and enforced. Additionally, principles and assessment processes must consider various aspects such as effectiveness, problem-solving, participation and collaboration in different projects, as well as transparency enhancements. Based on the conditions and processes outlined in different layers of this model, maximal dissemination and sharing of various research outputs will result in the greatest degree of research application in healthcare system and various strata of society.

In general, the findings of this research have shown that open science methods can be highly effective in improving the research process and benefiting from its outputs, which requires providing sufficient background, knowledge and skills to apply each of them in different stages of research. In line with the findings of this study, it is suggested that the organizations in charge of health system should review research guidelines and communication processes between research stakeholders. And in connection with the influential factors in cultural processes, infrastructure and supervision help implement the open research process by forming specialized working groups consisting of people active in the field of research, observing ethics in research, evaluation and validation of studies, as well as knowledge translation groups. The principles of transparency and scientific openness by research organizations and universities should be considered as a codified and strategic plan because it will cause positive consequences, including increasing the amount of scientific credibility, widespread participation of different people in research, and benefiting more from the scientific knowledge produced. Also, to create an organizational culture based on the results obtained in the policy department, it is suggested that the principles of scientific openness should be considered as an aspect of research activities of organizations and universities. Considering the importance of the type of data and research outputs in health system and privacy protection, openness and open access to research results can be defined according to the type of studies. And the tools and services that provide the conditions of scientific openness should be defined as one of the strategies of organizations and universities because open science accelerates the conditions for creating a culture of scientific openness in organizations. According to the necessity of open research topic in future studies, open science should focus on the following topics. Compilation of open research evaluation principles based on new indicators in the health system, presenting a user model to apply each of the open science methods in health system research, the effect of teaching necessary skills to apply open science methods by researchers and research supporting organizations, compilation of ethical principles and adjustment of intellectual property in health system researches, compilation of the conditions of access to information and data of health system with an emphasis on privacy and biosecurity issues. With the identification of these factors, the research stakeholders will proceed to widely use open science methods in a safer intellectual environment.

Data availability

The datasets formed and analysed during the current study are available from the corresponding author on reasonable request.

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We appreciate Iran University of Medical Sciences for financial support with grant NO: IUMS/SHMIS_99-2-37-18607.

This work was supported by Partial financial from Iran University of Medical Sciences with grant NO: IUMS/SHMIS_99-2-37-18607.

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The interviews were collected by “M.Z”; implementation, analysis by “L. NA, S.S and A.NC”. The first draft of the manuscript was written by “ M. Z”. Writing - review and editing final of the manuscript was written by"M. Z, A. RF, L. N A” and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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The study procedure was approved by Medical Ethics Committee of Iran University of Medical Sciences [date: Jul 2020, ID: IR.IUMS.REC.1399.462] as a doctoral dissertation titled “Developing an conceptual model for open science in health system research processes”. The current study included only those who supplied their informed consent. For this purpose, informed consent form (Additional file2.ICF) was completed by all participants after explanation of the objectives of study. Information from all the participants was private and nameless; there was no personal information that could link the answers with any of the participants in the present study. All methods in the study were in accordance with relevant regulations and guidelines (General Ethical Guidance for Medical Research with Human Participants in the Islamic Republic of Iran).

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Published on 8.3.2024 in Vol 26 (2024)

Willingness to Use Digital Health Screening and Tracking Tools for Public Health in Sexual Minority Populations in a National Probability Sample: Quantitative Intersectional Analysis

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  • Wilson Vincent, MPH, PhD  

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Background: Little is known about sexual minority adults’ willingness to use digital health tools, such as pandemic-related tools for screening and tracking, outside of HIV prevention and intervention efforts for sexual minority men, specifically. Additionally, given the current cultural climate in the United States, heterosexual and sexual minority adults may differ in their willingness to use digital health tools, and there may be within-group differences among sexual minority adults.

Objective: This study compared sexual minority and heterosexual adults’ willingness to use COVID-19–related digital health tools for public health screening and tracking and tested whether sexual minority adults differed from each other by age group, gender, and race or ethnicity.

Methods: We analyzed data from a cross-sectional, national probability survey (n=2047) implemented from May 30 to June 8, 2020, in the United States during the height of the public health response to the COVID-19 pandemic. Using latent-variable modeling, heterosexual and sexual minority adults were tested for differences in their willingness to use digital health tools for public health screening and tracking. Among sexual minority adults, specifically, associations with age, gender, and race or ethnicity were assessed.

Results: On average, sexual minority adults showed greater willingness to use digital health tools for screening and tracking than heterosexual adults (latent factor mean difference 0.46, 95% CI 0.15-0.77). Among sexual minority adults, there were no differences by age group, gender, or race or ethnicity. However, African American ( b =0.41, 95% CI 0.19-0.62), Hispanic or Latino ( b =0.36, 95% CI 0.18-0.55), and other racial or ethnic minority ( b =0.54, 95% CI 0.31-0.77) heterosexual adults showed greater willingness to use digital health tools for screening and tracking than White heterosexual adults.

Conclusions: In the United States, sexual minority adults were more willing to use digital health tools for screening and tracking than heterosexual adults. Sexual minority adults did not differ from each other by age, gender, or race or ethnicity in terms of their willingness to use these digital health tools, so no sexual orientation-based or intersectional disparities were identified. Furthermore, White heterosexual adults were less willing to use these tools than racial or ethnic minority heterosexual adults. Findings support the use of digital health tools with sexual minority adults, which could be important for other public health-related concerns (eg, the recent example of mpox). Additional studies are needed regarding the decision-making process of White heterosexual adults regarding the use of digital health tools to address public health crises, including pandemics or outbreaks that disproportionately affect minoritized populations.


Despite the economic, health, and mortality impacts of COVID-19 on the population as a whole, there has been great variability in the public’s willingness to participate in public health efforts to address the pandemic, including wearing masks and getting vaccinated [ 1 , 2 ]. Additionally, the COVID-19 pandemic has had an especially devastating impact on specific groups in the United States, with disproportionate mortality affecting older adults, men, and African American, and Hispanic American individuals [ 3 - 5 ]. Studies have examined the willingness to engage in preventive behaviors of American adults by age, gender, and race or ethnicity to curtail the COVID-19 pandemic, including the use of digital health tools for screening and tracking [ 6 - 11 ]. Although the COVID-19 pandemic has had a significant impact on sexual minority populations (ie, people who identify as gay, lesbian, bisexual, or other nonheterosexual sexual orientation identities) [ 12 - 14 ], less is known about sexual minority populations’ preventive responses to the COVID-19 pandemic. In the current climate of increased medical mistrust, the corresponding unwillingness to follow public health recommendations often occurs along demographic lines [ 15 - 17 ].

Some public health efforts for screening and tracking COVID-19, such as mobile health (mHealth), which includes the use of smartphones and related digital technologies to assess or address health, and other digital health tools (eg, patient portals and web-based patient questionnaires), have previously proven effective, acceptable, and feasible for HIV prevention for sexual minority men [ 18 - 21 ]. Generally, research shows that digital technologies such as mHealth applications improve the feasibility of delivering health care to sexual minority patients [ 22 ]. Additionally, studies indicate that tracking mental and physical health-related information through mHealth applications was associated with better mental health status for sexual minority people to a greater extent during the COVID-19 pandemic than before the pandemic [ 23 ]. Despite the potential for significant benefits, research has yet to examine sexual minority populations’ willingness to use mHealth tools for COVID-19.

For COVID-19, mobile apps created specifically in response to the COVID-19 pandemic have been the most frequently used type of mHealth tool [ 24 ]. Such mHealth tools aid in the screening, monitoring, and treatment of COVID-19 [ 24 ]. These mHealth-based approaches have had the advantage of easing the burden of in-person activities on COVID-19 testing and public health infrastructure (eg, avoiding supply chain issues, limiting the possibility of viral exposure) [ 18 , 25 ]. Previous research is mixed on whether there are no demographic differences by age, gender, or race or ethnicity in willingness to use digital health tools for COVID-19 screening and tracking. Although some studies showed no differences [ 26 - 28 ], others found evidence of greater willingness or support among younger adults, women, and racial and ethnic minority people [ 29 - 31 ]. However, these studies did not examine sexual orientation diversity. Heterosexual and sexual minority adults in the United States may differ in their willingness to participate in pandemic-related mHealth approaches, and these differences may vary based on other demographic characteristics such as age, gender, and race or ethnicity.

In addition to potential differences between heterosexual and sexual orientation–diverse populations, there may be notable differences among sexual minority populations. For example, the intersectionality theory asserts that seemingly independent yet intersecting social identities along social hierarchies based on dimensions such as race or ethnicity, gender, and sexual orientation jointly shape human experiences [ 32 - 35 ]. The intersectionality framework suggests that sexual minority people’s multiple intersecting identities must be considered simultaneously (eg, sexual minority people who are also African American), rather than treating each identity as a mutually exclusive category [ 36 , 37 ]. A recent review [ 38 ] and commentaries [ 39 , 40 ] have emphasized the urgent need to use intersectionality theory in the conceptualization and methodology of examining digital health disparities.

Although research has yet to explore sexual minority people’s willingness to use mHealth tools for COVID-19 based on their intersecting identities, such as age, gender, or race or ethnicity, the sexual minority health and HIV literatures illustrate how minoritized identities that intersect with sexual orientation–minoritized identities change the social position of individuals in ways that increase their risk of oppression and resulting adverse health outcomes. For example, Black sexual minority men experience both sexual orientation– and race-based stigma, and they experience more race- or ethnicity-based stigma in gay spaces than other groups [ 41 , 42 ]. Also, although both Black and White sexual minority men experience stereotypes about their sexual behaviors, assumptions may be more extreme for Black sexual minority men given the added layer of stereotypes about Black male sexuality [ 43 , 44 ]. The confluence of racism and antigay attitudes contributes to the increased risk of HIV for Black sexual minority men, including through social marginalization within communities of sexual minority men and late detection of HIV by medical and public health establishments, despite these men having no greater frequency or extent of sexual risk behavior to explain elevated HIV risk [ 42 , 45 , 46 ]. Additionally, Black sexual minority men may not benefit from or see the usefulness of mHealth tools for HIV prevention given that these tools may inadvertently stigmatize them through their “targeted” sexual health messages [ 47 ], or the tools may be viewed as a subpar offering in place of clinicians and public health professions “doing their jobs” [ 48 ]. Thus, although mHealth apps for HIV prevention have been found to be acceptable and feasible for implementation for sexual minority men in general [ 18 - 21 ], results may vary depending on intersecting racial, ethnic, or other identities.

This study examined the extent to which heterosexual adults and sexual minority adults differed in their willingness to participate in public health digital screening and tracking efforts to address the COVID-19 pandemic in a nationally representative sample of adults living in the United States. Also, for sexual minority participants, the author assessed differences within sexual minority populations in mean levels of willingness for COVID-19–related digital screening and tracking based on age group, gender, and race or ethnicity. Thus, the focus was on whether a sexual orientation–based disparity adversely affected sexual minority adults’ willingness to use digital health tools for screening and tracking and whether there were also intersectional disparities based on age, gender, and racial or ethnic categories.

This study was conducted and reported in accordance with the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) guidelines [ 49 , 50 ]. Specific study methods are provided in subsequent sections.

The COVID Impact Survey (CIS) is a national probability survey of US households designed to provide estimates for preventative behaviors and the impact of the COVID-19 pandemic; the data are publicly available [ 51 ]. The author used data from the last of 3 waves of cross-sectional data collection in the CIS, which occurred from May 30 to June 8, 2020 (n=2047). All 3 waves occurred between April 20 and June 8, 2020. These data were collected using the AmeriSpeak Panel, a probability-based panel distributed by NORC (formerly the National Opinion Research Center) at the University of Chicago.

US households were sampled with a known, nonzero probability of selection based on the NORC National Sample Frame, which was extracted from the US Postal Service Delivery Sequence File. Households were contacted by US mail, email, telephone, and field interviewers. The data are representative of noninstitutionalized adults who reside in the United States when weighted using sampling weights provider by the CIS. The CIS was funded by the Data Foundation. The NORC Institutional Review Board approved the CIS protocol to protect human participants (FWA00000142).

Willingness for Public Health Digital Screening and Tracking for COVID-19

Participants responded to questions asking about their likelihood of COVID-19–related testing (ie, “Testing you for COVID-19 infection using a Q-tip to swab your cheek or nose” and “Testing you for immunity or resistance to COVID-19 by drawing a small amount of blood”) and digital screening and tracking (eg, “Installing an app on your phone that asks you questions about your own symptoms and provides recommendations about COVID-19” and “Installing an app on your phone that tracks your location and sends push notifications if you might have been exposed to COVID-19”). Response options ranged from (1) “extremely likely” to (5) “not at all likely.” Items were reverse-coded such that higher scores reflected a greater perceived likelihood for screening and tracking. Participants had the option to respond with (88) “Already done this,” and these cases were excluded using listwise deletion.

In a sample that included mostly heterosexual participants from Wave 2 of the CIS (manuscript under review), the measure showed construct validity in its positive correlations with participants having engaged in other protective behaviors to prevent COVID-19 infection (eg, “worn a face mask” and “avoided public or crowded places”). Additionally, participants who engaged in more frequent digital communications with friends and family before the public health response to the COVID-19 pandemic in the United States in March 2020 scored higher in willingness to use pandemic-related mHealth tools than participants who used digital communications with friends and family less frequently. The measure also showed measurement invariance across age groups, genders, and categories of race or ethnicity based on Wave 3. Based on Wave 1 of the CIS, the measure has demonstrated high internal consistency (Cronbach α=.90).

Demographic Characteristics

Participants self-reported their sexual orientation identity (ie, gay, lesbian, or bisexual, straight, something else, and I don’t know). Sexual orientation identity was dichotomized to reflect heterosexual status and nonheterosexual sexual-minority status, respectively. The following additional demographic characteristics were assessed for measurement invariance: age, gender, and race or ethnicity. Additionally, participants reported their current age, which the CIS categorized (ie, 18-24 years, 25-34 years, 45-54 years, 55-64 years, 65-74 years, and ≥75 years) to help anonymize the data set; gender (female coded 1, male coded 0); and self-identified race or ethnicity (eg, Black or African American, Hispanic or Latino, White, multiple other races and ethnicities, such as Asian, Indian, and Native Hawaiian). Transgender and nonbinary identities were not options on the CIS.

Data Analysis Plan

This study tested the extent to which heterosexual and sexual minority adults differed in their willingness to use digital health tools for public health screening and tracking, a latent variable, and whether sexual minority adults’ willingness to use these COVID-19–related digital health tools was associated with age, gender, and race or ethnicity. Measurement invariance (ie, whether the measure means and assesses the same thing across groups) was tested across heterosexual and sexual minority adults.

Descriptive statistics and Cronbach α were computed using Stata (version 16; StataCorp) [ 52 ], and coefficient ω and all analyses of associations and latent variables were conducted using M plus (version 8; Muthén & Muthén) [ 53 ]. Weighted least squares estimation with Delta parameterization was used to estimate model parameters [ 53 ]. This estimation method uses a diagonal weight matrix with SEs and mean- and variance-adjusted chi-square test statistics that rely on a full weight matrix (ie, ESTIMATOR=WLSMV in M plus ) [ 53 ]. It is particularly appropriate for ordinal and nominal data [ 53 ]. Model fit was assessed with several fit indices based on any 2 of the following 3 criteria: a root-mean-square standard error of approximation (RMSEA) value of 0.06, a comparative fit index (CFI) value of at least 0.95, and a standardized root-mean-square residual (SRMR) criterion of 0.08 or less [ 54 , 55 ].

The author tested the extent to which the 5-item measure was invariant across the 2 sexual orientation categories. The 3 levels of measurement invariance—configural, metric, and scalar—were tested to determine if the 5-item measure was invariant across sexual-orientation categories. For ordinal variables and weighted least squares estimation methods with Delta parameterization, configural invariance (ie, pattern invariance), the least strict form of invariance, shows that each group has the same indicators loading onto the same factors in the same direction (ie, positive versus negative). To model configural invariance: (1) factor loadings are free to vary across groups, (2) thresholds are free to vary across groups, (3) scale factors are fixed to 1 in all groups, (4) factor means are fixed to 0 for all groups, and (5) factor variances are free to vary across groups [ 53 ]. Metric invariance (ie, weak invariance) indicates the invariance of factor loadings across groups, wherein (1) factor loadings are constrained to be equal across groups, (2) the first threshold of each item is constrained to be equal across groups, (3) the second threshold of the item that sets the metric of the factor is constrained to be equal across groups, (4) scale factors are fixed to 1 in 1 group and free to vary in the other groups, (5) factor means a Muthén & Muthénre fixed to 0 in 1 group and free to vary in the other groups, and (6) factor variances remain free to vary across groups [ 53 ]. Scalar invariance (ie, strong invariance) indicates equivalence of item intercepts or thresholds, in the case of categorical or ordinal variables, across groups and is the minimum needed to proceed with using a measure to test for differences in latent factor means between groups [ 56 , 57 ]. Scalar invariance is the same as metric invariance, except that thresholds are constrained to be equal across groups [ 53 ].

To compare invariance models, the author used a difference in CFI (ΔCFI) equal to or greater than 0.01 to indicate noninvariance [ 56 ]. Thus, a lack of worsened model fit with increased constraints indicates measurement invariance. Although scaled chi-square difference tests scaled for the weighted least squares estimator were conducted, this test may detect small discrepancies in ways that are not practically or theoretically meaningful in sample sizes greater than 200 [ 56 - 58 ].

Upon determining measurement invariance, the latent factor mean difference between heterosexual adults and sexual minority adults in the underlying factor of willingness to use digital health tools for public health screening and tracking was tested. Specifically, the latent variable for willingness to use digital health tools was standardized such that its mean was fixed to 0 and SD set to 1. The factor mean remained 0 for the reference group, heterosexual individuals, but the factor mean was freely estimated for the comparison group, sexual minority individuals. Thus, the resulting mean for sexual minority individuals reflected the difference in the mean from the reference group on a standardized metric, or in SD units. To identify correlates of willingness to use digital health tools for public health screening and tracking among sexual minority populations, specifically, willingness to use digital health tools was regressed on sexual minority adults’ age group, gender, and race or ethnicity, respectively.

Within an intersectionality-informed analytic framework, as described by Jackson et al [ 59 ], we can use additive measures of interaction to test for joint, referent, or excess intersectional disparities. Using the present analyses as a guiding example, the outcome variable would be recoded such that higher scores reflect a more adverse or disparity-oriented outcome (ie, less willingness to use COVID-19 screening and tracking tools). The predictor, gender (women coded 1), and the moderator, sexual orientation identity (sexual minority identity coded 1), would be coded such that the reference category (coded 0) is the nonminoritized group in this instance (ie, men and heterosexual adults) and the active category (coded 1) is the minoritized group (ie, women and sexual minority adults). Thus, the code of 1 reflects an adverse social position. For gender and sexual orientation, the original equation in the primary analyses before recoding and not including other covariates would be:

data analysis tools in research methodology

Given that the outcome should reflect a negative outcome to identify a disparity, the analyses would be repeated with the outcome variable recoded to reflect an unwillingness to use digital health tools rather than a willingness to use these tools. For gender and sexual orientation, the equation after recoding and not including other covariates would then be:

data analysis tools in research methodology

The joint disparity compares outcomes from the cell or group at the intersection of 2 minoritized identities, in this case, sexual minority women, to the group at the intersection of the 2 corresponding nonminoritized identities, in this instance, heterosexual men. In our example, b 1 + b 2 + b 3 equals the joint disparity in unwillingness to use COVID-19 screening and tracking tools comparing sexual minority women to heterosexual men. Referent disparities are those that affect only 1 minoritized population or identity, in this case, women compared with men among heterosexual adults or heterosexual adults compared with sexual minority adults among men. It describes the disparity based on gender as a proxy for sexism or sexual minority identity as a proxy for heterosexism or homonegativity, but not both. Specifically, b 1 equals the referent gender disparity in unwillingness to use COVID-19 digital screening among heterosexual adults, and b 2 equals the referent sexual minority disparity among men. Finally, the excess intersectional disparity focuses on the intersection of minoritized identities and describes the extent to which the joint disparity exceeds the 2 individual referent disparities. Suppose it is greater than 0, or statistically significant. In that case, the strength of the association indicates the disparity at the intersection of minoritized gender and sexual orientation, that is, women who are also sexual minority adults, and b 3 equals this excess intersectional disparity. A more detailed explanation can be found in Jackson et al [ 59 ] and VanderWeele and Tchetgen Tchetgen [ 60 ].

Disparities are indicated if the regression coefficients are positive, reflecting direct associations (ie, disadvantages for the minoritized groups) as opposed to inverse associations (ie, advantages for the more minoritized group). An advantage on an outcome for a relatively disadvantaged group that otherwise disproportionately and systematically experiences worse health outcomes and greater health risks would not meet established definitions of a disparity [ 61 , 62 ].

Given the complex nature of these survey data, analyses were adjusted using a sampling weight based on the inverse of the probability of selection in the sample. These analyses also accounted for stratification using pseudostrata based on census tracts. The data producer, NORC, used pseudostrata to preserve confidentiality. Per NORC, they did not include cluster variables because there were negligible cluster effects, and excluding these variables better preserved confidentiality (personal communication; Jennifer Benz, May 14, 2021). Descriptive statistics for the present sample accounted for weighting and stratification to reflect the complex survey design and national representativeness of the sample along key raking variables (ie, age, gender, and race or ethnicity). Latent factor mean differences (ΔM) and regression coefficients ( b ) are presented with their 95% CIs. Missing data, which were up to 3.7% missing across analyses, were handled using listwise deletion.

Ethical Considerations

Temple University’s institutional review board determined that the present analyses, which used deidentified publicly available data, did not require institutional approval for human participants research (contact the corresponding author for documentation).

Sample Characteristics

Of the total sample of 1928 adults, 161 were sexual minority individuals. Other sample characteristics are listed in Tables 1 and 2 . The sample size was reduced from 2047 due to missing data on sexual orientation (6.2%).

a Not available.

Psychometric Properties of Measure of Willingness to Use Digital Health Tools for COVID-19–Related Screening and Tracking

The measure of willingness to use digital health tools for COVID-19–related screening and tracking showed internal consistency and reliability (Cronbach α=.89 and coefficient ω=0.93). Additionally, as shown in Table 3 , the measure was invariant by sexual orientation. Configural invariance was indicated by all factor loadings being significant and in the expected direction for each group. The configural model had no global fit statistics, as it was a fully saturated model. Next, the author tested a metric invariance model with factor loadings constrained to be equal across groups, and metric invariance was evident (ΔCFI<0.01; Δ χ 2 2 =2.30; P =.32). Thus, the metric model had an equivalent model fit with the configural invariance model; the nonsaturated metric model fit the data, per the RMSEA, CFI, and SRMR ( Table 3 ). Finally, the author tested a scalar invariance model, and scalar invariance was shown (ΔCFI<0.01; Δ χ 2 8 =6.44; P =.60).

a RMSEA: root-mean-square standard error of approximation.

b CFI: comparative fit index.

c SRMR: standardized root-mean-square residual.

Mean Difference by Sexual Orientation on Willingness to Use Digital Health Tools for COVID-19–Related Screening and Tracking

Given scalar invariance, factor means for willingness to use COVID-19–related digital screening and tracking tools differed between heterosexual and sexual minority adults. Specifically, willingness to use digital health tools was nearly half an SD greater for sexual minority adults than for heterosexual adults (ΔM=0.46, 95% CI 0.15-0.77).

Associations Between Demographic Characteristics and Willingness to Use Digital Health Tools for COVID-19–Related Screening and Tracking Among Sexual Minority Adults

Within the population of sexual minority adults, no differences were detected by age group, gender, or race or ethnicity in their willingness to use digital health tools for COVID-19–related screening and tracking. Specifically, as detailed in Table 4 , for each increase in age by group, there was no change in willingness to use digital health tools. Also, men and women did not differ in their willingness to use digital health tools. Finally, African American individuals, Hispanic or Latino individuals, and people of other races or ethnicities did not differ from White individuals (the reference group) in their willingness to use digital health tools.

a b =unstandardized regression coefficient.

b R 2 =coefficient of determination.

c RMSEA: root-mean-square standard error of approximation.

d CFI: comparative fit index.

e SRMR: standardized root-mean-square residual.

f Age group is an ordinal variable with levels as follows: (1) 18-24 years, (2) 25-34 years, (3) 35-44 years, (4) 45-54 years, (5) 55-64 years, (6) 65-74 years, and (7) ≥75 years.

Additional models were tested for interactions of sexual minority status as a moderator with the other demographic characteristics as respective predictors in their associations with willingness to use digital health tools for screening and tracking. None of the interaction terms reached statistical significance in the models ( Table 5 ). However, with the inclusion of the interaction terms, the main effects of being African American, Hispanic or Latino, and another racial or ethnic identity reached statistical significance. Specifically, among heterosexual adults, being Black or African American was associated with 41% of an SD greater willingness to use digital health screening and tracking tools ( b =0.41, CI 0.19-0.62), being Hispanic or Latino was associated with 36% of an SD greater willingness to use these tools ( b =0.36, CI 0.18-0.55), and being of another racial or ethnic minority group was associated with 54% of an SD greater willingness to use these tools ( b =0.54, CI 0.31-0.77).

The models were re-run with the outcome variable recoded to identify referent, excess intersectional, and joint disparities. Given the direction of the significant associations for each racial or ethnic minority group (Black or African American: b =–0.41, CI –0.62 to –0.19; Hispanic or Latino: b =–0.36, CI –0.55 to –0.18; and other racial or ethnic minority group: b =–0.54, CI –0.77 to –0.31), referent racial or ethnic or sexual orientation-based disparities were not detected. Also, no excess intersectional disparity was detected (Black or African American × sexual minority: b =–0.35, CI –0.35 to 1.05; Hispanic or Latino × sexual minority: b =0.21, CI –0.35 to 0.77; and other racial or ethnic minority group × sexual minority: b =–0.75, CI –0.07 to 1.57). Overall, no joint disparity was identified.

Studies rarely examine the willingness of sexual minority populations to use mHealth and related digital health tools in the context of pandemic-related or non-HIV prevention. However, such mHealth tools have been acceptable and effective when used to fight the HIV epidemic for sexual minority men, specifically [ 18 - 21 ]. This study examined the use of digital health tools for screening and tracking for the COVID-19 pandemic, focusing on a broader, more diverse range of the sexual minority population in the United States. In particular, the study used the conceptual [ 32 , 33 ] and methodological [ 34 , 35 , 39 ] frameworks of intersectionality theory to determine the presence of disparities between heterosexual and sexual minority adults across various intersections of identity (ie, age, gender, and race or ethnicity) in their willingness to use digital health tools for screening and tracking.

Findings indicated that sexual minority adults were significantly more willing to use digital health tools for screening and tracking than heterosexual adults in the United States. The greater willingness to use digital health tools among sexual minority adults compared with heterosexual adults might be explained partly by the familiarity of many sexual minority individuals with the use of mHealth and other digital health methods for outreach and other public health efforts, particularly sexual minority men in the context of HIV prevention [ 18 - 21 ].

Despite the difference between heterosexual adults and sexual minority adults in this study, there were no within-group demographic differences among sexual minority adults in their willingness to use digital health tools for screening and tracking. These findings from the United States are consistent with findings from a large survey of registered National Health Service users in the United Kingdom, in which there were no differences by age or gender in terms of willingness to participate in contact tracing through a mobile phone app in the adult population as a whole [ 63 ]. Interestingly, although sexual minority men are often more likely to be the focus of mHealth interventions for HIV [ 20 , 21 ], they were no more likely than sexual minority women to express a willingness to use digital health tools for screening and tracking in this study.

Based on the established definitions of a disparity [ 61 , 62 ] and the analytic framework of intersectionality theory [ 32 , 33 , 59 ], this study detected no referent disparity based on sexual orientation and no joint or excess joint disparity at the intersection of sexual orientation identity and other demographic characteristics. A previous study that used the same publicly available data without testing for sexual minority status as a predictor or moderator found no significant associations that would indicate an age-related, gender, or racial or ethnic disparity [ 28 ]. In contrast to other studies, which showed mixed findings for race or ethnicity and other demographics as a predictor without considering sexual minority status [ 26 , 27 , 29 - 31 ], the significant main effects of race or ethnicity in this study occurred among heterosexual adults in the presence of interactions of race or ethnicity with sexual minority status.

The differences between racial or ethnic minority adults and White adults may be explained, in part, by political ideology affecting attitudes toward the public health establishment during COVID-19. For example, a study found that moderate- and conservative-leaning respondents showed less support for using COVID-19–related digital health tools than liberal-leaning respondents in the same model in which racial and ethnic minorities showed greater support for using COVID-19–related digital health tools than White Americans or non-Hispanic Americans [ 31 ]. Additionally, studies indicate that some White Americans may be increasingly voting conservative [ 64 - 66 ]. To the extent that these political ideologies are also tied to public health mistrust, there may be noteworthy consequences. For example, there is evidence of excess deaths for conservative-voting adults compared with less conservative-voting adults in Florida and Ohio during the COVID-19 pandemic [ 67 ]. The CIS did not include questions regarding political beliefs. As such, this study did not test whether demographic factors interacted with political ideology, which would have helped to determine whether political ideology mattered within each racial or ethnic category. Additional studies are needed to examine the decision-making process of White heterosexual adults regarding their use of digital health tools for screening and tracking during public health emergencies.

The present psychometric evaluation indicated that the COVID-19–related psychological distress measure was assessing the same construct in heterosexual participants and sexual minority participants. A previous study has already validated the psychometric properties of the present items (eg, construct validity and internal consistency) and demonstrated measurement invariance across age groups, genders, and races and ethnicities [ 28 ]. Other studies on willingness to use digital health tools have typically used a single item [ 63 ] or several items treated as separate measures [ 68 , 69 ] rather than a single, validated scale. In terms of scales that use any variation on willingness (eg, intentions and perceived usefulness), 1 study used a 15-item measure [ 70 ] and another study used 2 measures of 32 items each [ 71 ]; these are notably longer than the 3-item measure of this study. Some studies have used items with binary yes-or-no responses [ 63 , 68 , 69 ], which may not capture sufficient gradation in response if the goal is to understand the degree of willingness.

Strengths and Limitations

This study has multiple strengths. For example, the study used a national probability sample to represent the population of noninstitutionalized adults in the United States. Additionally, the study used innovative methods to conceptualize and quantitatively identify disparities within the framework of intersectionality theory. In addition, the study established measurement invariance between heterosexual and sexual orientation–diverse adults for a measure of willingness to use a digital health screening and tracking tool that was previously validated by age, gender, and race or ethnicity. The measure can be adapted for screening and tracking in response to future public health events.

Additionally, several limitations must be noted. Specifically, the cross-sectional study design precludes definitive causal conclusions. In addition, the author did not attempt to draw conclusions about the temporal associations among the variables. Moreover, the sample was imbalanced with respect to the proportion of sexual minority adults compared with heterosexual adults in the sample; the number of sexual minority adults was much smaller. Limitations also include a lack of questions measuring sexual and gender identity that follow best practice [ 72 ], including the lack of transgender-inclusive gender questions and the lack of sexual orientation measures that distinguish different sexual orientation groups beyond sexual minority status by gender (ie, sexual minority men, including gay and bisexual men, and women, including lesbians and bisexual women, which were accounted for in this study). As a result, the analyses were not more nuanced regarding gender and sexual orientation identity.


This study has several research and applied implications. For instance, additional research can oversample sexual minority adults to provide balanced samples for comparisons between heterosexual and sexual minority adults. Additionally, studies can examine sexual minority individuals’ willingness to use digital health tools for other non-COVID-19–related health issues beyond HIV, including specific mental health diagnoses (eg, depression and substance use) and chronic illnesses (eg, diabetes and hypertension). These studies should consider intersections of identities among sexual minority people, such as underrepresented racial and ethnic minority people among sexual minority populations. Recently, monkey pox has emerged among sexual minority men, in particular [ 73 ], and digital health approaches may be useful in such circumstances. Additionally, studies are needed to further examine the decision-making process of White heterosexual adults regarding their use of digital health tools in response to public health emergencies.

Regarding applied implications, public health professionals and clinicians should consider screening sexual minority adults for their willingness to use digital health tools as they continue to use telehealth during COVID-19 and post–COVID-19 times. Such screening is particularly needed for sexual minority adults who contend with intersecting systems of oppression and identities and, thus, have elevated levels of medical mistrust (eg, underrepresented ethnic and sexual minority people) [ 15 , 17 , 74 ]. Policy changes and other structural interventions are needed to provide access to digital health technologies in cases in which willingness to use these technologies does not appear to be the issue. In this study, racial and ethnic minority heterosexual adults seemed particularly willing to use these technologies.


This study is responsive to recent calls in the literature to address the pronounced dearth of intersectionality theory-informed research investigating disparities related to digital health [ 38 - 40 ]. As we strive to narrow the digital divide, or the disparities in technology and internet access and use [ 75 - 77 ], we must understand disparities in willingness to use digital technologies for health-related purposes even when these tools are available. This study detected no disparities based on sexual minority status or intersections of identity among sexual minority adults along age, gender, or race or ethnicity. As such, for sexual minority populations, including intersections that are at joint or compound risk of experiencing adverse health outcomes, the issue is not willingness to use digital health tools compared to heterosexual adults. Sexual minority populations require culturally responsive digital health approaches to address their needs, as opposed to motivational enhancement or other interventions to increase their willingness during public health events. The willingness of sexual minority adults across intersecting identities to use pandemic-related digital health tools, including mobile health apps, is noteworthy given the potential promise of digital health tools for other public health-related concerns, such as the recently ended mpox outbreak [ 78 , 79 ], which disproportionately affected sexual minority men [ 80 - 82 ], and obesity [ 83 , 84 ] and cardiovascular disease [ 85 , 86 ], which disproportionately affects sexual minority women [ 87 , 88 ]. Additionally, White heterosexual adults demonstrated a disproportionately low willingness to use digital health tools, and this may become an issue in the event that this population is adversely affected by a public health concern that can benefit from digital health technologies.


The author would like to thank the NORC (formerly the National Opinion Research Center) at the University of Chicago. The data analyzed are freely available to the public through NORC at the University of Chicago. WV was supported by a National Institute of Mental Health grant (K23-MH111402).

Data Availability

The data sets analyzed during this study are available in the NORC at the University of Chicago repository [ 51 ].

Conflicts of Interest

None declared.

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Edited by A Mavragani; submitted 20.03.23; peer-reviewed by A Asadzadeh, J Jabson Tree; comments to author 19.07.23; revised version received 29.11.23; accepted 20.12.23; published 08.03.24.

©Wilson Vincent. Originally published in the Journal of Medical Internet Research (, 08.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.


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