research types by method

15 Types of Research Methods

Research methods refer to the strategies, tools, and techniques used to gather and analyze data in a structured way in order to answer a research question or investigate a hypothesis (Hammond & Wellington, 2020).

Generally, we place research methods into two categories: quantitative and qualitative. Each has its own strengths and weaknesses, which we can summarize as:

• Quantitative research can achieve generalizability through scrupulous statistical analysis applied to large sample sizes.
• Qualitative research achieves deep, detailed, and nuance accounts of specific case studies, which are not generalizable.

Some researchers, with the aim of making the most of both quantitative and qualitative research, employ mixed methods, whereby they will apply both types of research methods in the one study, such as by conducting a statistical survey alongside in-depth interviews to add context to the quantitative findings.

Below, I’ll outline 15 common research methods, and include pros, cons, and examples of each .

Types of Research Methods

Research methods can be broadly categorized into two types: quantitative and qualitative.

• Quantitative methods involve systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques, providing an in-depth understanding of a specific concept or phenomenon (Schweigert, 2021). The strengths of this approach include its ability to produce reliable results that can be generalized to a larger population, although it can lack depth and detail.
• Qualitative methods encompass techniques that are designed to provide a deep understanding of a complex issue, often in a specific context, through collection of non-numerical data (Tracy, 2019). This approach often provides rich, detailed insights but can be time-consuming and its findings may not be generalizable.

These can be further broken down into a range of specific research methods and designs:

Combining the two methods above, mixed methods research mixes elements of both qualitative and quantitative research methods, providing a comprehensive understanding of the research problem . We can further break these down into:

• Sequential Explanatory Design (QUAN→QUAL): This methodology involves conducting quantitative analysis first, then supplementing it with a qualitative study.
• Sequential Exploratory Design (QUAL→QUAN): This methodology goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.

Let’s explore some methods and designs from both quantitative and qualitative traditions, starting with qualitative research methods.

Qualitative Research Methods

Qualitative research methods allow for the exploration of phenomena in their natural settings, providing detailed, descriptive responses and insights into individuals’ experiences and perceptions (Howitt, 2019).

These methods are useful when a detailed understanding of a phenomenon is sought.

1. Ethnographic Research

Ethnographic research emerged out of anthropological research, where anthropologists would enter into a setting for a sustained period of time, getting to know a cultural group and taking detailed observations.

Ethnographers would sometimes even act as participants in the group or culture, which many scholars argue is a weakness because it is a step away from achieving objectivity (Stokes & Wall, 2017).

In fact, at its most extreme version, ethnographers even conduct research on themselves, in a fascinating methodology call autoethnography .

The purpose is to understand the culture, social structure, and the behaviors of the group under study. It is often useful when researchers seek to understand shared cultural meanings and practices in their natural settings.

However, it can be time-consuming and may reflect researcher biases due to the immersion approach.

Example of Ethnography

Liquidated: An Ethnography of Wall Street  by Karen Ho involves an anthropologist who embeds herself with Wall Street firms to study the culture of Wall Street bankers and how this culture affects the broader economy and world.

2. Phenomenological Research

Phenomenological research is a qualitative method focused on the study of individual experiences from the participant’s perspective (Tracy, 2019).

It focuses specifically on people’s experiences in relation to a specific social phenomenon ( see here for examples of social phenomena ).

This method is valuable when the goal is to understand how individuals perceive, experience, and make meaning of particular phenomena. However, because it is subjective and dependent on participants’ self-reports, findings may not be generalizable, and are highly reliant on self-reported ‘thoughts and feelings’.

Example of Phenomenological Research

A phenomenological approach to experiences with technology  by Sebnem Cilesiz represents a good starting-point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

3. Historical Research

Historical research is a qualitative method involving the examination of past events to draw conclusions about the present or make predictions about the future (Stokes & Wall, 2017).

As you might expect, it’s common in the research branches of history departments in universities.

This approach is useful in studies that seek to understand the past to interpret present events or trends. However, it relies heavily on the availability and reliability of source materials, which may be limited.

Common data sources include cultural artifacts from both material and non-material culture , which are then examined, compared, contrasted, and contextualized to test hypotheses and generate theories.

Example of Historical Research

A historical research example might be a study examining the evolution of gender roles over the last century. This research might involve the analysis of historical newspapers, advertisements, letters, and company documents, as well as sociocultural contexts.

4. Content Analysis

Content analysis is a research method that involves systematic and objective coding and interpreting of text or media to identify patterns, themes, ideologies, or biases (Schweigert, 2021).

A content analysis is useful in analyzing communication patterns, helping to reveal how texts such as newspapers, movies, films, political speeches, and other types of ‘content’ contain narratives and biases.

However, interpretations can be very subjective, which often requires scholars to engage in practices such as cross-comparing their coding with peers or external researchers.

Content analysis can be further broken down in to other specific methodologies such as semiotic analysis, multimodal analysis , and discourse analysis .

Example of Content Analysis

How is Islam Portrayed in Western Media?  by Poorebrahim and Zarei (2013) employs a type of content analysis called critical discourse analysis (common in poststructuralist and critical theory research ). This study by Poorebrahum and Zarei combs through a corpus of western media texts to explore the language forms that are used in relation to Islam and Muslims, finding that they are overly stereotyped, which may represent anti-Islam bias or failure to understand the Islamic world.

5. Grounded Theory Research

Grounded theory involves developing a theory  during and after  data collection rather than beforehand.

This is in contrast to most academic research studies, which start with a hypothesis or theory and then testing of it through a study, where we might have a null hypothesis (disproving the theory) and an alternative hypothesis (supporting the theory).

Grounded Theory is useful because it keeps an open mind to what the data might reveal out of the research. It can be time-consuming and requires rigorous data analysis (Tracy, 2019).

Grounded Theory Example

Developing a Leadership Identity   by Komives et al (2005) employs a grounded theory approach to develop a thesis based on the data rather than testing a hypothesis. The researchers studied the leadership identity of 13 college students taking on leadership roles. Based on their interviews, the researchers theorized that the students’ leadership identities shifted from a hierarchical view of leadership to one that embraced leadership as a collaborative concept.

6. Action Research

Action research is an approach which aims to solve real-world problems and bring about change within a setting. The study is designed to solve a specific problem – or in other words, to take action (Patten, 2017).

This approach can involve mixed methods, but is generally qualitative because it usually involves the study of a specific case study wherein the researcher works, e.g. a teacher studying their own classroom practice to seek ways they can improve.

Action research is very common in fields like education and nursing where practitioners identify areas for improvement then implement a study in order to find paths forward.

Action Research Example

Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing   by Ellison and Drew was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

7. Natural Observational Research

Observational research can also be quantitative (see: experimental research), but in naturalistic settings for the social sciences, researchers tend to employ qualitative data collection methods like interviews and field notes to observe people in their day-to-day environments.

This approach involves the observation and detailed recording of behaviors in their natural settings (Howitt, 2019). It can provide rich, in-depth information, but the researcher’s presence might influence behavior.

While observational research has some overlaps with ethnography (especially in regard to data collection techniques), it tends not to be as sustained as ethnography, e.g. a researcher might do 5 observations, every second Monday, as opposed to being embedded in an environment.

Observational Research Example

A researcher might use qualitative observational research to study the behaviors and interactions of children at a playground. The researcher would document the behaviors observed, such as the types of games played, levels of cooperation , and instances of conflict.

8. Case Study Research

Case study research is a qualitative method that involves a deep and thorough investigation of a single individual, group, or event in order to explore facets of that phenomenon that cannot be captured using other methods (Stokes & Wall, 2017).

Case study research is especially valuable in providing contextualized insights into specific issues, facilitating the application of abstract theories to real-world situations (Patten, 2017).

However, findings from a case study may not be generalizable due to the specific context and the limited number of cases studied (Walliman, 2021).

See More: Case Study Advantages and Disadvantages

Example of a Case Study

Scholars conduct a detailed exploration of the implementation of a new teaching method within a classroom setting. The study focuses on how the teacher and students adapt to the new method, the challenges encountered, and the outcomes on student performance and engagement. While the study provides specific and detailed insights of the teaching method in that classroom, it cannot be generalized to other classrooms, as statistical significance has not been established through this qualitative approach.

Quantitative Research Methods

Quantitative research methods involve the systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques (Pajo, 2022). The focus is on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

9. Experimental Research

Experimental research is a quantitative method where researchers manipulate one variable to determine its effect on another (Walliman, 2021).

This is common, for example, in high-school science labs, where students are asked to introduce a variable into a setting in order to examine its effect.

This type of research is useful in situations where researchers want to determine causal relationships between variables. However, experimental conditions may not reflect real-world conditions.

Example of Experimental Research

A researcher may conduct an experiment to determine the effects of a new educational approach on student learning outcomes. Students would be randomly assigned to either the control group (traditional teaching method) or the experimental group (new educational approach).

10. Surveys and Questionnaires

Surveys and questionnaires are quantitative methods that involve asking research participants structured and predefined questions to collect data about their attitudes, beliefs, behaviors, or characteristics (Patten, 2017).

Surveys are beneficial for collecting data from large samples, but they depend heavily on the honesty and accuracy of respondents.

They tend to be seen as more authoritative than their qualitative counterparts, semi-structured interviews, because the data is quantifiable (e.g. a questionnaire where information is presented on a scale from 1 to 10 can allow researchers to determine and compare statistical means, averages, and variations across sub-populations in the study).

Example of a Survey Study

A company might use a survey to gather data about employee job satisfaction across its offices worldwide. Employees would be asked to rate various aspects of their job satisfaction on a Likert scale. While this method provides a broad overview, it may lack the depth of understanding possible with other methods (Stokes & Wall, 2017).

11. Longitudinal Studies

Longitudinal studies involve repeated observations of the same variables over extended periods (Howitt, 2019). These studies are valuable for tracking development and change but can be costly and time-consuming.

With multiple data points collected over extended periods, it’s possible to examine continuous changes within things like population dynamics or consumer behavior. This makes a detailed analysis of change possible.

Perhaps the most relatable example of a longitudinal study is a national census, which is taken on the same day every few years, to gather comparative demographic data that can show how a nation is changing over time.

While longitudinal studies are commonly quantitative, there are also instances of qualitative ones as well, such as the famous 7 Up study from the UK, which studies 14 individuals every 7 years to explore their development over their lives.

Example of a Longitudinal Study

A national census, taken every few years, uses surveys to develop longitudinal data, which is then compared and analyzed to present accurate trends over time. Trends a census can reveal include changes in religiosity, values and attitudes on social issues, and much more.

12. Cross-Sectional Studies

Cross-sectional studies are a quantitative research method that involves analyzing data from a population at a specific point in time (Patten, 2017). They provide a snapshot of a situation but cannot determine causality.

This design is used to measure and compare the prevalence of certain characteristics or outcomes in different groups within the sampled population.

The major advantage of cross-sectional design is its ability to measure a wide range of variables simultaneously without needing to follow up with participants over time.

However, cross-sectional studies do have limitations . This design can only show if there are associations or correlations between different variables, but cannot prove cause and effect relationships, temporal sequence, changes, and trends over time.

Example of a Cross-Sectional Study

Our longitudinal study example of a national census also happens to contain cross-sectional design. One census is cross-sectional, displaying only data from one point in time. But when a census is taken once every few years, it becomes longitudinal, and so long as the data collection technique remains unchanged, identification of changes will be achievable, adding another time dimension on top of a basic cross-sectional study.

13. Correlational Research

Correlational research is a quantitative method that seeks to determine if and to what degree a relationship exists between two or more quantifiable variables (Schweigert, 2021).

This approach provides a fast and easy way to make initial hypotheses based on either positive or  negative correlation trends  that can be observed within dataset.

While correlational research can reveal relationships between variables, it cannot establish causality.

Methods used for data analysis may include statistical correlations such as Pearson’s or Spearman’s.

Example of Correlational Research

A team of researchers is interested in studying the relationship between the amount of time students spend studying and their academic performance. They gather data from a high school, measuring the number of hours each student studies per week and their grade point averages (GPAs) at the end of the semester. Upon analyzing the data, they find a positive correlation, suggesting that students who spend more time studying tend to have higher GPAs.

14. Quasi-Experimental Design Research

Quasi-experimental design research is a quantitative research method that is similar to experimental design but lacks the element of random assignment to treatment or control.

Instead, quasi-experimental designs typically rely on certain other methods to control for extraneous variables.

The term ‘quasi-experimental’ implies that the experiment resembles a true experiment, but it is not exactly the same because it doesn’t meet all the criteria for a ‘true’ experiment, specifically in terms of control and random assignment.

Quasi-experimental design is useful when researchers want to study a causal hypothesis or relationship, but practical or ethical considerations prevent them from manipulating variables and randomly assigning participants to conditions.

Example of Quasi-Experimental Design

A researcher wants to study the impact of a new math tutoring program on student performance. However, ethical and practical constraints prevent random assignment to the “tutoring” and “no tutoring” groups. Instead, the researcher compares students who chose to receive tutoring (experimental group) to similar students who did not choose to receive tutoring (control group), controlling for other variables like grade level and previous math performance.

Related: Examples and Types of Random Assignment in Research

15. Meta-Analysis Research

Meta-analysis statistically combines the results of multiple studies on a specific topic to yield a more precise estimate of the effect size. It’s the gold standard of secondary research .

Meta-analysis is particularly useful when there are numerous studies on a topic, and there is a need to integrate the findings to draw more reliable conclusions.

Some meta-analyses can identify flaws or gaps in a corpus of research, when can be highly influential in academic research, despite lack of primary data collection.

However, they tend only to be feasible when there is a sizable corpus of high-quality and reliable studies into a phenomenon.

Example of a Meta-Analysis

The power of feedback revisited (Wisniewski, Zierer & Hattie, 2020) is a meta-analysis that examines 435 empirical studies research on the effects of feedback on student learning. They use a random-effects model to ascertain whether there is a clear effect size across the literature. The authors find that feedback tends to impact cognitive and motor skill outcomes but has less of an effect on motivational and behavioral outcomes.

Choosing a research method requires a lot of consideration regarding what you want to achieve, your research paradigm, and the methodology that is most valuable for what you are studying. There are multiple types of research methods, many of which I haven’t been able to present here. Generally, it’s recommended that you work with an experienced researcher or research supervisor to identify a suitable research method for your study at hand.

Hammond, M., & Wellington, J. (2020). Research methods: The key concepts . New York: Routledge.

Howitt, D. (2019). Introduction to qualitative research methods in psychology . London: Pearson UK.

Pajo, B. (2022). Introduction to research methods: A hands-on approach . New York: Sage Publications.

Patten, M. L. (2017). Understanding research methods: An overview of the essentials . New York: Sage

Schweigert, W. A. (2021). Research methods in psychology: A handbook . Los Angeles: Waveland Press.

Stokes, P., & Wall, T. (2017). Research methods . New York: Bloomsbury Publishing.

Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact . London: John Wiley & Sons.

Walliman, N. (2021). Research methods: The basics. London: Routledge.

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Research methods--quantitative, qualitative, and more: overview.

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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 3, 2023 3:14 PM
• URL: https://guides.lib.berkeley.edu/researchmethods

Exploring Types of Research Methods: A Comprehensive Guide

Grasping the concept of research method is essential for anyone engaged in research or assessing the outcomes of studies. Whether you're an academic student, a dedicated researcher, or just inquisitive about the world, a thorough understanding of the diverse research methods will assist you in sifting through the extensive array of information at your disposal.

Our detailed guide will walk you through the types of research design, including qualitative and quantitative approaches, as well as descriptive, correlational, experimental, and mixed methods research. We will also touch upon the different types of research methodology, ensuring a comprehensive understanding of the various types of methods in research.

This article will also highlight the pivotal factors to consider when crafting a study and the inherent strengths and limitations of different type of research methods.Whether you're embarking on your own research project or looking to enhance your critical thinking skills Armed with the research methods definition, this guide will equip you with the essential knowledge to make well-informed decisions and formulate significant conclusions in the field of research.

Qualitative vs. Quantitative Research Methods

Qualitative and quantitative research methods represent two fundamentally different approaches to data collection and analysis. Qualitative observation delves into non-numerical data, while quantitative observation involves the scrutiny of data that is numerical and quantifiable.

Qualitative Research:

• Involves gathering and interpreting non-numerical data, such as text, video, photographs, or audio recordings
• Uses sources like interviews, focus groups, documents, personal accounts, cultural records, and observation
• Unstructured or semi-structured format
• Open-ended questions
• Comprehensive perspective on individuals' experiences
• Comparison of participants' feedback and input
• Focus on answering the "why" behind a phenomenon, correlation, or behavior
• Ethnography, for instance, seeks to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified, offering a deep dive into the cultural fabric of a community.
• This method is used to understand how an individual subjectively perceives and imparts meaning to their social reality, often revealing underlying bias that can influence the interpretation of social phenomena.
• Data analysis techniques include content analysis, grounded theory, thematic analysis, or discourse analysis

Quantitative Research:

• Focuses on numerical or measurable data
• Uses sources such as experiments, questionnaires, surveys, and database reports
• Multiple-choice format
• Countable answers (e.g., "yes" or "no")
• Numerical analysis
• Statistical picture of a trend or connection
• To define research methods, one must focus on answering the 'what' or 'how' in relation to a particular phenomenon, correlation, or behavior. This foundational approach is crucial in the realm of empirical inquiry.
• Provides precise causal explanations that can be measured and communicated mathematically
• The objectives of scientific inquiry often include hypothesis testing to examine causal relationships between variables, making accurate predictions, and generalizing findings to broader populations.
• Aims to establish general laws of behavior and phenomenon across different settings/contexts
• Used to test a theory and ultimately support or reject it
• Empirical research in psychology utilizes examples of quantitative data such as standardized psychological assessments, neuroimaging data, and clinical outcome measures to inform its findings.
• Data analysis techniques include descriptive and inferential statistics

When selecting research methodology types, it's important to consider various factors such as the study's primary goal, the nature of the research questions and conceptual framework, the variables involved, the context of the study, ethical issues, and whether the focus is on individuals or groups, or on comparing groups and understanding their relationships.

Research design methods play a pivotal role in determining the appropriateness of qualitative methods for studies involving individuals or groups, while quantitative methods are often chosen for studies aimed at comparing groups or deciphering the relationship between variables.

Descriptive Research

Descriptive research is a methodological approach that aims to accurately and systematically depict a population, situation, or phenomenon. It adeptly addresses 'what', 'where', 'when', and 'how' questions, although it steers clear of exploring 'why'. Employing a descriptive research design means observing and documenting variables without exerting control or manipulation, which is particularly beneficial when exploring new topics or problems to identify characteristics, frequencies, trends, and categories.

Descriptive research methods include:

• Surveys: Survey research is a powerful tool that enables researchers to collect extensive data sets, which can then be meticulously analyzed to uncover frequencies, averages, and emerging patterns.
• Observations: Utilizing observation allows researchers to collect data on behaviors and phenomena, ensuring the gathered information is not tainted by the honesty or accuracy of respondents.
• Case studies: Case study research delves into detailed data to pinpoint the unique characteristics of a narrowly defined subject, providing in-depth insights.

Descriptive research can be conducted in different ways:

• Cross-sectional : Observing a population at a single point in time.
• Longitudinal : Following a population over a period of time.
• Surveys or interviews : When the researcher interacts with the participant.
• Observational studies or data collection using existing records : When the researcher does not interact with the participant.

Advantages of descriptive research include:

• Varied data collection methods
• A natural environment for respondents
• Quick and cheap data collection
• A holistic understanding of the research topic

Limitations of  descriptive research studies:

• They cannot establish cause and effect relationships.
• The reliability and validity of survey responses can be compromised if respondents are not truthful or tend to provide socially desirable answers.
• The choice and wording of questions on a questionnaire may influence the descriptive findings.

Correlational Research

Correlational research, a non-experimental method, delves into the dynamics between two variables, focusing on the strength and direction of their relationship without manipulating any factors, which is pivotal in understanding associations rather than causality.

Researchers may choose correlational research in the following situations

• When manipulating the independent variable is impractical, impossible, or unethical
• When exploring non-causal relationships between variables
• When testing new measurement tools

In correlational research, the correlation coefficient is measured, which can range from -1 to +1, indicating the relationship's direction and strength. A comprehensive meta-analysis can further elucidate these types of correlations.

• Positive correlation: Both variables change in the same direction
• Negative correlation: Variables change in opposite directions
• Zero correlation: No relationship exists between the variables

Data collection methods for correlational research include

• Naturalistic observation
• Archival research or secondary data

Analytical research methods, such as correlation or regression analyses, are employed to analyze correlational data, with the former yielding a coefficient that clarifies the relationship's intensity and direction, and the latter forecasting the impact of variable changes.

Experimental Research

Experimental research, a methodical scientific approach, manipulates variables to observe their effects and is indispensable for establishing cause-and-effect relationships and making informed decisions in the face of inadequate data.

• Pre-experimental research design : Includes One-shot Case Study Research Design, One-group Pretest-posttest Research Design, and Static-group Comparison.
• True experimental research design Statistical analysis, a cornerstone in testing hypotheses, is pivotal in research for its accuracy in proving or disproving a hypothesis. It's uniquely capable of establishing a cause-effect relationship within a group, making it an indispensable tool for researchers.
• Quasi-experimental design : Similar to an experimental design but assigns participants to groups non-randomly.

Experimental research is essential for various fields, such as:

• Developing new drugs and medical treatments
• Understanding human behavior in psychology
• Improving educational outcomes
• Identifying opportunities for businesses and organizations

To conduct experimental research effectively, researchers must consider three key factors:

• A Control Group and an Experimental Group
• A variable that can be manipulated by the researcher
• Random distribution of participants

Experimental research, whether conducted in laboratory settings with high control variables and internal validity or in field settings that boast both internal and external validity, presents a spectrum of advantages and challenges. Researchers must navigate potential threats to internal validity, including history, maturation, testing, instrumentation, mortality, and regression threats.

Mixed Methods Research

Mixed methods research, an approach that synergizes the rigor of quantitative and qualitative research methods, capitalizes on the strengths of each to provide a comprehensive analysis. This integration, which can occur during data collection, analysis, or presentation of results, is a hallmark of mixed methods research designs.

• Convergent design
• Explanatory sequential design
• Exploratory sequential design
• Embedded design

The practice of triangulation in mixed methods research enhances the integration of quantitative and qualitative data, offering multiple perspectives and a more comprehensive understanding. It also allows for a deeper explanation of statistical results, as exemplified by the EQUALITY study's exploratory sequential design for patient-centered data collection.

In mixed methods research, the intricate research design and methodology combine qualitative and quantitative data collection and analysis. This purposeful mixing of methods and data integration at strategic stages of the research process can reveal relationships between complex layers of research questions, although it demands significant resources and specialized training.

• Explanatory
• Exploratory
• Nested (embedded) designs

Mixed methods research, characterized by its diverse research design and methods, integrates quantitative and qualitative approaches within a single study. Grounded in positivism and interpretivism, it provides a multifaceted understanding of research topics, despite the challenges of mastering both methodologies and collaborating with multidisciplinary teams.

In sum, a thorough grasp of the various research methodologies is crucial for conducting robust research and critically assessing others' findings. From qualitative to quantitative, descriptive, correlational, experimental, and mixed methods research, each approach offers distinct strengths and limitations, guiding researchers to the most suitable methods for effective data collection and analysis.

As we navigate the vast landscape of information available, understanding what are research methods empowers us to make informed decisions, draw meaningful conclusions, and contribute to the advancement of knowledge across various fields. Embracing the diversity of research methods, whether you're a student, researcher, or simply curious, will enhance your critical thinking skills and enable you to uncover valuable insights that shape our understanding of the world.

What are the seven most commonly used research methods? The seven most commonly used research methods are:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (a combination of some of the above)

What does comprehensive research methodology entail?

Comprehensive research methodology involves conducting a thorough and exhaustive investigation on a specific topic, subject, or issue. This approach is characterized by the meticulous collection, analysis, and evaluation of a wide array of information, data, and sources, with the objective of achieving a deep and comprehensive understanding of the subject matter.

What are the three primary methods to investigate a specific research question?

To investigate a specific research question, you can use:

• Quantitative methods for measuring something or testing a hypothesis.
• Qualitative methods for exploring ideas, thoughts, and meanings.
• Secondary data analysis for examining a large volume of readily-available data.

What does exploration mean in the context of research methodology?

Exploration in research methodology signifies a research approach that aims to delve into questions that have not been extensively explored before. Exploratory research, often qualitative and primary in nature, is focused on uncovering new insights and understanding. Nonetheless, it can also adopt a quantitative stance, particularly when it involves analyzing a large sample size, to further the scope of exploratory research.

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Research Methods Guide: Research Design & Method

• Introduction
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Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

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• Last Updated: Aug 21, 2023 10:42 AM

Research Methods: What are research methods?

• What are research methods?
• Searching specific databases

What are research methods

Research methods are the strategies, processes or techniques utilized in the collection of data or evidence for analysis in order to uncover new information or create better understanding of a topic.

There are different types of research methods which use different tools for data collection.

Types of research

• Qualitative Research
• Quantitative Research
• Mixed Methods Research

Qualitative Research gathers data about lived experiences, emotions or behaviours, and the meanings individuals attach to them. It assists in enabling researchers to gain a better understanding of complex concepts, social interactions or cultural phenomena. This type of research is useful in the exploration of how or why things have occurred, interpreting events and describing actions.

Quantitative Research gathers numerical data which can be ranked, measured or categorised through statistical analysis. It assists with uncovering patterns or relationships, and for making generalisations. This type of research is useful for finding out how many, how much, how often, or to what extent.

Mixed Methods Research integrates both Q ualitative and Quantitative Research . It provides a holistic approach combining and analysing the statistical data with deeper contextualised insights. Using Mixed Methods also enables Triangulation,  or verification, of the data from two or more sources.

Finding Mixed Methods research in the Databases 

“mixed model*” OR “mixed design*” OR “multiple method*” OR multimethod* OR triangulat*

Data collection tools

Sage research methods.

• SAGE research methods online This link opens in a new window Research methods tool to help researchers gather full-text resources, design research projects, understand a particular method and write up their research. Includes access to collections of video, business cases and eBooks,

Help and Information

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• Last Updated: Apr 18, 2024 11:16 AM
• URL: https://libguides.newcastle.edu.au/researchmethods

Research Methodologies

• What are research designs?
• What are research methodologies?

What are research methods?

Quantitative research methods, qualitative research methods, mixed method approach, selecting the best research method.

• Additional Sources

Research methods are different from research methodologies because they are the ways in which you will collect the data for your research project.  The best method for your project largely depends on your topic, the type of data you will need, and the people or items from which you will be collecting data.  The following boxes below contain a list of quantitative, qualitative, and mixed research methods.

• Closed-ended questionnaires/survey: These types of questionnaires or surveys are like "multiple choice" tests, where participants must select from a list of premade answers.  According to the content of the question, they must select the one that they agree with the most.  This approach is the simplest form of quantitative research because the data is easy to combine and quantify.
• Structured interviews: These are a common research method in market research because the data can be quantified.  They are strictly designed for little "wiggle room" in the interview process so that the data will not be skewed.  You can conduct structured interviews in-person, online, or over the phone (Dawson, 2019).

Constructing Questionnaires

When constructing your questions for a survey or questionnaire, there are things you can do to ensure that your questions are accurate and easy to understand (Dawson, 2019):

• Keep the questions brief and simple.
• Eliminate any potential bias from your questions.  Make sure that they do not word things in a way that favor one perspective over another.
• If your topic is very sensitive, you may want to ask indirect questions rather than direct ones.  This prevents participants from being intimidated and becoming unwilling to share their true responses.
• If you are using a closed-ended question, try to offer every possible answer that a participant could give to that question.
• Do not ask questions that assume something of the participant.  The question "How often do you exercise?" assumes that the participant exercises (when they may not), so you would want to include a question that asks if they exercise at all before asking them how often.
• Try and keep the questionnaire as short as possible.  The longer a questionnaire takes, the more likely the participant will not complete it or get too tired to put truthful answers.
• Promise confidentiality to your participants at the beginning of the questionnaire.

Quantitative Research Measures

When you are considering a quantitative approach to your research, you need to identify why types of measures you will use in your study.  This will determine what type of numbers you will be using to collect your data.  There are four levels of measurement:

• Nominal: These are numbers where the order of the numbers do not matter.  They aim to identify separate information.  One example is collecting zip codes from research participants.  The order of the numbers does not matter, but the series of numbers in each zip code indicate different information (Adamson and Prion, 2013).
• Ordinal: Also known as rankings because the order of these numbers matter.  This is when items are given a specific rank according to specific criteria.  A common example of ordinal measurements include ranking-based questionnaires, where participants are asked to rank items from least favorite to most favorite.  Another common example is a pain scale, where a patient is asked to rank their pain on a scale from 1 to 10 (Adamson and Prion, 2013).
• Interval: This is when the data are ordered and the distance between the numbers matters to the researcher (Adamson and Prion, 2013).  The distance between each number is the same.  An example of interval data is test grades.
• Ratio: This is when the data are ordered and have a consistent distance between numbers, but has a "zero point."  This means that there could be a measurement of zero of whatever you are measuring in your study (Adamson and Prion, 2013).  An example of ratio data is measuring the height of something because the "zero point" remains constant in all measurements.  The height of something could also be zero.

Focus Groups

This is when a select group of people gather to talk about a particular topic.  They can also be called discussion groups or group interviews (Dawson, 2019).  They are usually lead by a moderator  to help guide the discussion and ask certain questions.  It is critical that a moderator allows everyone in the group to get a chance to speak so that no one dominates the discussion.  The data that are gathered from focus groups tend to be thoughts, opinions, and perspectives about an issue.

Advantages of Focus Groups

• Only requires one meeting to get different types of responses.
• Less researcher bias due to participants being able to speak openly.
• Helps participants overcome insecurities or fears about a topic.
• The researcher can also consider the impact of participant interaction.

Disadvantages of Focus Groups

• Participants may feel uncomfortable to speak in front of an audience, especially if the topic is sensitive or controversial.
• Since participation is voluntary, not every participant may contribute equally to the discussion.
• Participants may impact what others say or think.
• A researcher may feel intimidated by running a focus group on their own.
• A researcher may need extra funds/resources to provide a safe space to host the focus group.
• Because the data is collective, it may be difficult to determine a participant's individual thoughts about the research topic.

Observation

There are two ways to conduct research observations:

• Direct Observation: The researcher observes a participant in an environment.  The researcher often takes notes or uses technology to gather data, such as a voice recorder or video camera.  The researcher does not interact or interfere with the participants.  This approach is often used in psychology and health studies (Dawson, 2019).
• Participant Observation:  The researcher interacts directly with the participants to get a better understanding of the research topic.  This is a common research method when trying to understand another culture or community.  It is important to decide if you will conduct a covert (participants do not know they are part of the research) or overt (participants know the researcher is observing them) observation because it can be unethical in some situations (Dawson, 2019).

Open-Ended Questionnaires

These types of questionnaires are the opposite of "multiple choice" questionnaires because the answer boxes are left open for the participant to complete.  This means that participants can write short or extended answers to the questions.  Upon gathering the responses, researchers will often "quantify" the data by organizing the responses into different categories.  This can be time consuming because the researcher needs to read all responses carefully.

Semi-structured Interviews

This is the most common type of interview where researchers aim to get specific information so they can compare it to other interview data.  This requires asking the same questions for each interview, but keeping their responses flexible.  This means including follow-up questions if a subject answers a certain way.  Interview schedules are commonly used to aid the interviewers, which list topics or questions that will be discussed at each interview (Dawson, 2019).

Theoretical Analysis

Often used for nonhuman research, theoretical analysis is a qualitative approach where the researcher applies a theoretical framework to analyze something about their topic.  A theoretical framework gives the researcher a specific "lens" to view the topic and think about it critically. it also serves as context to guide the entire study.  This is a popular research method for analyzing works of literature, films, and other forms of media.  You can implement more than one theoretical framework with this method, as many theories complement one another.

Common theoretical frameworks for qualitative research are (Grant and Osanloo, 2014):

• Behavioral theory
• Change theory
• Cognitive theory
• Content analysis
• Cross-sectional analysis
• Developmental theory
• Feminist theory
• Gender theory
• Marxist theory
• Queer theory
• Systems theory
• Transformational theory

Unstructured Interviews

These are in-depth interviews where the researcher tries to understand an interviewee's perspective on a situation or issue.  They are sometimes called life history interviews.  It is important not to bombard the interviewee with too many questions so they can freely disclose their thoughts (Dawson, 2019).

• Open-ended and closed-ended questionnaires: This approach means implementing elements of both questionnaire types into your data collection.  Participants may answer some questions with premade answers and write their own answers to other questions.  The advantage to this method is that you benefit from both types of data collection to get a broader understanding of you participants.  However, you must think carefully about how you will analyze this data to arrive at a conclusion.

Other mixed method approaches that incorporate quantitative and qualitative research methods depend heavily on the research topic.  It is strongly recommended that you collaborate with your academic advisor before finalizing a mixed method approach.

How do you determine which research method would be best for your proposal?  This heavily depends on your research objective.  According to Dawson (2019), there are several questions to ask yourself when determining the best research method for your project:

• Are you good with numbers and mathematics?
• Would you be interested in conducting interviews with human subjects?
• Would you enjoy creating a questionnaire for participants to complete?
• Do you prefer written communication or face-to-face interaction?
• What skills or experiences do you have that might help you with your research?  Do you have any experiences from past research projects that can help with this one?
• How much time do you have to complete the research?  Some methods take longer to collect data than others.
• What is your budget?  Do you have adequate funding to conduct the research in the method you  want?
• How much data do you need?  Some research topics need only a small amount of data while others may need significantly larger amounts.
• What is the purpose of your research? This can provide a good indicator as to what research method will be most appropriate.
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• Last Updated: Aug 2, 2022 2:36 PM
• URL: https://library.tiffin.edu/researchmethodologies

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Types of Research Methods: Examples and Tips

What are research methods?

Research methods are the techniques and procedures used to collect and analyze data in order to answer research questions and test a research hypothesis . There are several different types of research methods, each with its own strengths and weaknesses. 

Common Types of Research Methods

There are several main types of research methods that are employed in academic articles. The type of research method applied depends on the nature of the data to be collected and analyzed, as well as any restrictions or limitations that dictate the study’s resources and methodology. Surveying articles from your target journal and identifying the methods commonly used in these studies is also recommended before choosing a research method or methods.

It’s important to note that research methods can be combined for a more complete understanding of a research question or hypothesis. For example, an experiment can be followed by a survey to gather more information about participants’ attitudes and behaviors.

Overall, the choice of research method depends on the research question, the type of data needed, and the resources available to the researcher.

Data Collection Methods

Data is information collected in order to answer research questions . The kind of data you choose to collect will depend on the nature of your research question and the aims of your study. There are a few main category distinctions of data a researcher can collect.

Quantitative vs qualitative data

Qualitative and quantitative data are two types of data that are often used in research studies. They are different in terms of their characteristics, how they are collected, and how they are analyzed.

Quantitative data is numerical and is collected through methods such as surveys, polls, and experiments. It is often used to measure and describe the characteristics of a large group of people or objects. This data can be analyzed using statistical methods to find patterns and trends.

Qualitative data, on the other hand, is non-numerical and is collected through methods such as interviews, observations, and focus groups. It is often used to understand the experiences, attitudes, and perceptions of individuals or small groups. This data is analyzed using methods such as content analysis, thematic analysis, and discourse analysis to identify patterns and themes.

Overall, quantitative data provides a more objective and generalizable understanding of a phenomenon, while qualitative data provides a more subjective and in-depth understanding. Both types of data are important and can be used together to gain a more comprehensive understanding of a topic.

You can also make use of both qualitative and quantitative research methods in your study.

Primary vs secondary data

Primary and secondary research are two different types of research methods that are used in the field of academia and market research. Both primary and secondary sources can be applied in most studies.

Primary research is research that is conducted by the individual or organization themselves. It involves collecting original data through methods such as surveys, interviews, or experiments. The data collected through primary research is specific to the research question and objectives, and is not typically available through other sources.

Secondary research, on the other hand, involves the use of existing data that has already been collected by someone else. This can include data from government reports, academic journals, or industry publications. The advantage of secondary research is that it is typically less time-consuming and less expensive than primary research, as the data has already been collected. However, the data may not be as specific or relevant to the research question and objectives.

The choice between using primary and secondary research will depend on the research question, study budget, and time constraints of the project, as well as the target journal to which you are submitting your manuscript.

Experimental vs descriptive data collection

Experimental data is collected through a controlled experiment, in which the researcher manipulates one or more variables to observe the effect on another variable. The goal of experimental data is to determine cause-and-effect relationships. For example, in a study on the effectiveness of a new drug for treating a certain condition, the researchers would randomly assign participants to either a group that receives the drug or a group that receives a placebo, and then compare the outcomes between the two groups. The data collected in this study would be considered experimental data.

Descriptive data, on the other hand, is data that is collected through observation or surveys and is used to describe the characteristics of a population or phenomenon. The goal of descriptive data is to provide a snapshot of the current state of a certain population or phenomenon, rather than to determine cause-and-effect relationships. For example, in a study on the dietary habits of a certain population, the researchers would collect data on what types of food the participants typically eat and how often they eat them. This data would be considered descriptive data.

In summary, experimental data is collected through a controlled experiment to determine cause-and-effect relationships, while descriptive data is collected through observation or surveys to describe the characteristics of a population or phenomenon.

Descriptive data examples:

• A survey that asks people about their favorite type of music
• A census that counts the number of people living in a certain area
• A poll that asks people about their political affiliation

Experimental data examples:

• A study comparing the effectiveness of two different medications for treating a certain condition
• An experiment measuring the effect of different levels of a certain chemical on plant growth
• A clinical trial comparing the side effects of a new treatment to a standard treatment for a disease

Examples of Difference Data Collection Methods

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2.2 Research Methods

Learning objectives.

By the end of this section, you should be able to:

• Recall the 6 Steps of the Scientific Method
• Differentiate between four kinds of research methods: surveys, field research, experiments, and secondary data analysis.
• Explain the appropriateness of specific research approaches for specific topics.

Sociologists examine the social world, see a problem or interesting pattern, and set out to study it. They use research methods to design a study. Planning the research design is a key step in any sociological study. Sociologists generally choose from widely used methods of social investigation: primary source data collection such as survey, participant observation, ethnography, case study, unobtrusive observations, experiment, and secondary data analysis , or use of existing sources. Every research method comes with plusses and minuses, and the topic of study strongly influences which method or methods are put to use. When you are conducting research think about the best way to gather or obtain knowledge about your topic, think of yourself as an architect. An architect needs a blueprint to build a house, as a sociologist your blueprint is your research design including your data collection method.

When entering a particular social environment, a researcher must be careful. There are times to remain anonymous and times to be overt. There are times to conduct interviews and times to simply observe. Some participants need to be thoroughly informed; others should not know they are being observed. A researcher wouldn’t stroll into a crime-ridden neighborhood at midnight, calling out, “Any gang members around?”

Making sociologists’ presence invisible is not always realistic for other reasons. That option is not available to a researcher studying prison behaviors, early education, or the Ku Klux Klan. Researchers can’t just stroll into prisons, kindergarten classrooms, or Klan meetings and unobtrusively observe behaviors or attract attention. In situations like these, other methods are needed. Researchers choose methods that best suit their study topics, protect research participants or subjects, and that fit with their overall approaches to research.

As a research method, a survey collects data from subjects who respond to a series of questions about behaviors and opinions, often in the form of a questionnaire or an interview. The survey is one of the most widely used scientific research methods. The standard survey format allows individuals a level of anonymity in which they can express personal ideas.

At some point, most people in the United States respond to some type of survey. The 2020 U.S. Census is an excellent example of a large-scale survey intended to gather sociological data. Since 1790, United States has conducted a survey consisting of six questions to received demographical data pertaining to residents. The questions pertain to the demographics of the residents who live in the United States. Currently, the Census is received by residents in the United Stated and five territories and consists of 12 questions.

Not all surveys are considered sociological research, however, and many surveys people commonly encounter focus on identifying marketing needs and strategies rather than testing a hypothesis or contributing to social science knowledge. Questions such as, “How many hot dogs do you eat in a month?” or “Were the staff helpful?” are not usually designed as scientific research. The Nielsen Ratings determine the popularity of television programming through scientific market research. However, polls conducted by television programs such as American Idol or So You Think You Can Dance cannot be generalized, because they are administered to an unrepresentative population, a specific show’s audience. You might receive polls through your cell phones or emails, from grocery stores, restaurants, and retail stores. They often provide you incentives for completing the survey.

Sociologists conduct surveys under controlled conditions for specific purposes. Surveys gather different types of information from people. While surveys are not great at capturing the ways people really behave in social situations, they are a great method for discovering how people feel, think, and act—or at least how they say they feel, think, and act. Surveys can track preferences for presidential candidates or reported individual behaviors (such as sleeping, driving, or texting habits) or information such as employment status, income, and education levels.

A survey targets a specific population , people who are the focus of a study, such as college athletes, international students, or teenagers living with type 1 (juvenile-onset) diabetes. Most researchers choose to survey a small sector of the population, or a sample , a manageable number of subjects who represent a larger population. The success of a study depends on how well a population is represented by the sample. In a random sample , every person in a population has the same chance of being chosen for the study. As a result, a Gallup Poll, if conducted as a nationwide random sampling, should be able to provide an accurate estimate of public opinion whether it contacts 2,000 or 10,000 people.

After selecting subjects, the researcher develops a specific plan to ask questions and record responses. It is important to inform subjects of the nature and purpose of the survey up front. If they agree to participate, researchers thank subjects and offer them a chance to see the results of the study if they are interested. The researcher presents the subjects with an instrument, which is a means of gathering the information.

A common instrument is a questionnaire. Subjects often answer a series of closed-ended questions . The researcher might ask yes-or-no or multiple-choice questions, allowing subjects to choose possible responses to each question. This kind of questionnaire collects quantitative data —data in numerical form that can be counted and statistically analyzed. Just count up the number of “yes” and “no” responses or correct answers, and chart them into percentages.

Questionnaires can also ask more complex questions with more complex answers—beyond “yes,” “no,” or checkbox options. These types of inquiries use open-ended questions that require short essay responses. Participants willing to take the time to write those answers might convey personal religious beliefs, political views, goals, or morals. The answers are subjective and vary from person to person. How do you plan to use your college education?

Some topics that investigate internal thought processes are impossible to observe directly and are difficult to discuss honestly in a public forum. People are more likely to share honest answers if they can respond to questions anonymously. This type of personal explanation is qualitative data —conveyed through words. Qualitative information is harder to organize and tabulate. The researcher will end up with a wide range of responses, some of which may be surprising. The benefit of written opinions, though, is the wealth of in-depth material that they provide.

An interview is a one-on-one conversation between the researcher and the subject, and it is a way of conducting surveys on a topic. However, participants are free to respond as they wish, without being limited by predetermined choices. In the back-and-forth conversation of an interview, a researcher can ask for clarification, spend more time on a subtopic, or ask additional questions. In an interview, a subject will ideally feel free to open up and answer questions that are often complex. There are no right or wrong answers. The subject might not even know how to answer the questions honestly.

Questions such as “How does society’s view of alcohol consumption influence your decision whether or not to take your first sip of alcohol?” or “Did you feel that the divorce of your parents would put a social stigma on your family?” involve so many factors that the answers are difficult to categorize. A researcher needs to avoid steering or prompting the subject to respond in a specific way; otherwise, the results will prove to be unreliable. The researcher will also benefit from gaining a subject’s trust, from empathizing or commiserating with a subject, and from listening without judgment.

Surveys often collect both quantitative and qualitative data. For example, a researcher interviewing people who are incarcerated might receive quantitative data, such as demographics – race, age, sex, that can be analyzed statistically. For example, the researcher might discover that 20 percent of incarcerated people are above the age of 50. The researcher might also collect qualitative data, such as why people take advantage of educational opportunities during their sentence and other explanatory information.

The survey can be carried out online, over the phone, by mail, or face-to-face. When researchers collect data outside a laboratory, library, or workplace setting, they are conducting field research, which is our next topic.

Field Research

The work of sociology rarely happens in limited, confined spaces. Rather, sociologists go out into the world. They meet subjects where they live, work, and play. Field research refers to gathering primary data from a natural environment. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In field work, the sociologists, rather than the subjects, are the ones out of their element.

The researcher interacts with or observes people and gathers data along the way. The key point in field research is that it takes place in the subject’s natural environment, whether it’s a coffee shop or tribal village, a homeless shelter or the DMV, a hospital, airport, mall, or beach resort.

While field research often begins in a specific setting , the study’s purpose is to observe specific behaviors in that setting. Field work is optimal for observing how people think and behave. It seeks to understand why they behave that way. However, researchers may struggle to narrow down cause and effect when there are so many variables floating around in a natural environment. And while field research looks for correlation, its small sample size does not allow for establishing a causal relationship between two variables. Indeed, much of the data gathered in sociology do not identify a cause and effect but a correlation .

Sociology in the Real World

Beyoncé and lady gaga as sociological subjects.

Sociologists have studied Lady Gaga and Beyoncé and their impact on music, movies, social media, fan participation, and social equality. In their studies, researchers have used several research methods including secondary analysis, participant observation, and surveys from concert participants.

In their study, Click, Lee & Holiday (2013) interviewed 45 Lady Gaga fans who utilized social media to communicate with the artist. These fans viewed Lady Gaga as a mirror of themselves and a source of inspiration. Like her, they embrace not being a part of mainstream culture. Many of Lady Gaga’s fans are members of the LGBTQ community. They see the “song “Born This Way” as a rallying cry and answer her calls for “Paws Up” with a physical expression of solidarity—outstretched arms and fingers bent and curled to resemble monster claws.”

Sascha Buchanan (2019) made use of participant observation to study the relationship between two fan groups, that of Beyoncé and that of Rihanna. She observed award shows sponsored by iHeartRadio, MTV EMA, and BET that pit one group against another as they competed for Best Fan Army, Biggest Fans, and FANdemonium. Buchanan argues that the media thus sustains a myth of rivalry between the two most commercially successful Black women vocal artists.

Participant Observation

In 2000, a comic writer named Rodney Rothman wanted an insider’s view of white-collar work. He slipped into the sterile, high-rise offices of a New York “dot com” agency. Every day for two weeks, he pretended to work there. His main purpose was simply to see whether anyone would notice him or challenge his presence. No one did. The receptionist greeted him. The employees smiled and said good morning. Rothman was accepted as part of the team. He even went so far as to claim a desk, inform the receptionist of his whereabouts, and attend a meeting. He published an article about his experience in The New Yorker called “My Fake Job” (2000). Later, he was discredited for allegedly fabricating some details of the story and The New Yorker issued an apology. However, Rothman’s entertaining article still offered fascinating descriptions of the inside workings of a “dot com” company and exemplified the lengths to which a writer, or a sociologist, will go to uncover material.

Rothman had conducted a form of study called participant observation , in which researchers join people and participate in a group’s routine activities for the purpose of observing them within that context. This method lets researchers experience a specific aspect of social life. A researcher might go to great lengths to get a firsthand look into a trend, institution, or behavior. A researcher might work as a waitress in a diner, experience homelessness for several weeks, or ride along with police officers as they patrol their regular beat. Often, these researchers try to blend in seamlessly with the population they study, and they may not disclose their true identity or purpose if they feel it would compromise the results of their research.

At the beginning of a field study, researchers might have a question: “What really goes on in the kitchen of the most popular diner on campus?” or “What is it like to be homeless?” Participant observation is a useful method if the researcher wants to explore a certain environment from the inside.

Field researchers simply want to observe and learn. In such a setting, the researcher will be alert and open minded to whatever happens, recording all observations accurately. Soon, as patterns emerge, questions will become more specific, observations will lead to hypotheses, and hypotheses will guide the researcher in analyzing data and generating results.

In a study of small towns in the United States conducted by sociological researchers John S. Lynd and Helen Merrell Lynd, the team altered their purpose as they gathered data. They initially planned to focus their study on the role of religion in U.S. towns. As they gathered observations, they realized that the effect of industrialization and urbanization was the more relevant topic of this social group. The Lynds did not change their methods, but they revised the purpose of their study.

This shaped the structure of Middletown: A Study in Modern American Culture , their published results (Lynd & Lynd, 1929).

The Lynds were upfront about their mission. The townspeople of Muncie, Indiana, knew why the researchers were in their midst. But some sociologists prefer not to alert people to their presence. The main advantage of covert participant observation is that it allows the researcher access to authentic, natural behaviors of a group’s members. The challenge, however, is gaining access to a setting without disrupting the pattern of others’ behavior. Becoming an inside member of a group, organization, or subculture takes time and effort. Researchers must pretend to be something they are not. The process could involve role playing, making contacts, networking, or applying for a job.

Once inside a group, some researchers spend months or even years pretending to be one of the people they are observing. However, as observers, they cannot get too involved. They must keep their purpose in mind and apply the sociological perspective. That way, they illuminate social patterns that are often unrecognized. Because information gathered during participant observation is mostly qualitative, rather than quantitative, the end results are often descriptive or interpretive. The researcher might present findings in an article or book and describe what he or she witnessed and experienced.

This type of research is what journalist Barbara Ehrenreich conducted for her book Nickel and Dimed . One day over lunch with her editor, Ehrenreich mentioned an idea. How can people exist on minimum-wage work? How do low-income workers get by? she wondered. Someone should do a study . To her surprise, her editor responded, Why don’t you do it?

That’s how Ehrenreich found herself joining the ranks of the working class. For several months, she left her comfortable home and lived and worked among people who lacked, for the most part, higher education and marketable job skills. Undercover, she applied for and worked minimum wage jobs as a waitress, a cleaning woman, a nursing home aide, and a retail chain employee. During her participant observation, she used only her income from those jobs to pay for food, clothing, transportation, and shelter.

She discovered the obvious, that it’s almost impossible to get by on minimum wage work. She also experienced and observed attitudes many middle and upper-class people never think about. She witnessed firsthand the treatment of working class employees. She saw the extreme measures people take to make ends meet and to survive. She described fellow employees who held two or three jobs, worked seven days a week, lived in cars, could not pay to treat chronic health conditions, got randomly fired, submitted to drug tests, and moved in and out of homeless shelters. She brought aspects of that life to light, describing difficult working conditions and the poor treatment that low-wage workers suffer.

The book she wrote upon her return to her real life as a well-paid writer, has been widely read and used in many college classrooms.

Ethnography

Ethnography is the immersion of the researcher in the natural setting of an entire social community to observe and experience their everyday life and culture. The heart of an ethnographic study focuses on how subjects view their own social standing and how they understand themselves in relation to a social group.

An ethnographic study might observe, for example, a small U.S. fishing town, an Inuit community, a village in Thailand, a Buddhist monastery, a private boarding school, or an amusement park. These places all have borders. People live, work, study, or vacation within those borders. People are there for a certain reason and therefore behave in certain ways and respect certain cultural norms. An ethnographer would commit to spending a determined amount of time studying every aspect of the chosen place, taking in as much as possible.

A sociologist studying a tribe in the Amazon might watch the way villagers go about their daily lives and then write a paper about it. To observe a spiritual retreat center, an ethnographer might sign up for a retreat and attend as a guest for an extended stay, observe and record data, and collate the material into results.

Institutional Ethnography

Institutional ethnography is an extension of basic ethnographic research principles that focuses intentionally on everyday concrete social relationships. Developed by Canadian sociologist Dorothy E. Smith (1990), institutional ethnography is often considered a feminist-inspired approach to social analysis and primarily considers women’s experiences within male- dominated societies and power structures. Smith’s work is seen to challenge sociology’s exclusion of women, both academically and in the study of women’s lives (Fenstermaker, n.d.).

Historically, social science research tended to objectify women and ignore their experiences except as viewed from the male perspective. Modern feminists note that describing women, and other marginalized groups, as subordinates helps those in authority maintain their own dominant positions (Social Sciences and Humanities Research Council of Canada n.d.). Smith’s three major works explored what she called “the conceptual practices of power” and are still considered seminal works in feminist theory and ethnography (Fensternmaker n.d.).

Sociological Research

The making of middletown: a study in modern u.s. culture.

In 1924, a young married couple named Robert and Helen Lynd undertook an unprecedented ethnography: to apply sociological methods to the study of one U.S. city in order to discover what “ordinary” people in the United States did and believed. Choosing Muncie, Indiana (population about 30,000) as their subject, they moved to the small town and lived there for eighteen months.

Ethnographers had been examining other cultures for decades—groups considered minorities or outsiders—like gangs, immigrants, and the poor. But no one had studied the so-called average American.

Recording interviews and using surveys to gather data, the Lynds objectively described what they observed. Researching existing sources, they compared Muncie in 1890 to the Muncie they observed in 1924. Most Muncie adults, they found, had grown up on farms but now lived in homes inside the city. As a result, the Lynds focused their study on the impact of industrialization and urbanization.

They observed that Muncie was divided into business and working class groups. They defined business class as dealing with abstract concepts and symbols, while working class people used tools to create concrete objects. The two classes led different lives with different goals and hopes. However, the Lynds observed, mass production offered both classes the same amenities. Like wealthy families, the working class was now able to own radios, cars, washing machines, telephones, vacuum cleaners, and refrigerators. This was an emerging material reality of the 1920s.

As the Lynds worked, they divided their manuscript into six chapters: Getting a Living, Making a Home, Training the Young, Using Leisure, Engaging in Religious Practices, and Engaging in Community Activities.

When the study was completed, the Lynds encountered a big problem. The Rockefeller Foundation, which had commissioned the book, claimed it was useless and refused to publish it. The Lynds asked if they could seek a publisher themselves.

Middletown: A Study in Modern American Culture was not only published in 1929 but also became an instant bestseller, a status unheard of for a sociological study. The book sold out six printings in its first year of publication, and has never gone out of print (Caplow, Hicks, & Wattenberg. 2000).

Nothing like it had ever been done before. Middletown was reviewed on the front page of the New York Times. Readers in the 1920s and 1930s identified with the citizens of Muncie, Indiana, but they were equally fascinated by the sociological methods and the use of scientific data to define ordinary people in the United States. The book was proof that social data was important—and interesting—to the U.S. public.

Sometimes a researcher wants to study one specific person or event. A case study is an in-depth analysis of a single event, situation, or individual. To conduct a case study, a researcher examines existing sources like documents and archival records, conducts interviews, engages in direct observation and even participant observation, if possible.

Researchers might use this method to study a single case of a foster child, drug lord, cancer patient, criminal, or rape victim. However, a major criticism of the case study as a method is that while offering depth on a topic, it does not provide enough evidence to form a generalized conclusion. In other words, it is difficult to make universal claims based on just one person, since one person does not verify a pattern. This is why most sociologists do not use case studies as a primary research method.

However, case studies are useful when the single case is unique. In these instances, a single case study can contribute tremendous insight. For example, a feral child, also called “wild child,” is one who grows up isolated from human beings. Feral children grow up without social contact and language, which are elements crucial to a “civilized” child’s development. These children mimic the behaviors and movements of animals, and often invent their own language. There are only about one hundred cases of “feral children” in the world.

As you may imagine, a feral child is a subject of great interest to researchers. Feral children provide unique information about child development because they have grown up outside of the parameters of “normal” growth and nurturing. And since there are very few feral children, the case study is the most appropriate method for researchers to use in studying the subject.

At age three, a Ukranian girl named Oxana Malaya suffered severe parental neglect. She lived in a shed with dogs, and she ate raw meat and scraps. Five years later, a neighbor called authorities and reported seeing a girl who ran on all fours, barking. Officials brought Oxana into society, where she was cared for and taught some human behaviors, but she never became fully socialized. She has been designated as unable to support herself and now lives in a mental institution (Grice 2011). Case studies like this offer a way for sociologists to collect data that may not be obtained by any other method.

Experiments

You have probably tested some of your own personal social theories. “If I study at night and review in the morning, I’ll improve my retention skills.” Or, “If I stop drinking soda, I’ll feel better.” Cause and effect. If this, then that. When you test the theory, your results either prove or disprove your hypothesis.

One way researchers test social theories is by conducting an experiment , meaning they investigate relationships to test a hypothesis—a scientific approach.

There are two main types of experiments: lab-based experiments and natural or field experiments. In a lab setting, the research can be controlled so that more data can be recorded in a limited amount of time. In a natural or field- based experiment, the time it takes to gather the data cannot be controlled but the information might be considered more accurate since it was collected without interference or intervention by the researcher.

As a research method, either type of sociological experiment is useful for testing if-then statements: if a particular thing happens (cause), then another particular thing will result (effect). To set up a lab-based experiment, sociologists create artificial situations that allow them to manipulate variables.

Classically, the sociologist selects a set of people with similar characteristics, such as age, class, race, or education. Those people are divided into two groups. One is the experimental group and the other is the control group. The experimental group is exposed to the independent variable(s) and the control group is not. To test the benefits of tutoring, for example, the sociologist might provide tutoring to the experimental group of students but not to the control group. Then both groups would be tested for differences in performance to see if tutoring had an effect on the experimental group of students. As you can imagine, in a case like this, the researcher would not want to jeopardize the accomplishments of either group of students, so the setting would be somewhat artificial. The test would not be for a grade reflected on their permanent record of a student, for example.

And if a researcher told the students they would be observed as part of a study on measuring the effectiveness of tutoring, the students might not behave naturally. This is called the Hawthorne effect —which occurs when people change their behavior because they know they are being watched as part of a study. The Hawthorne effect is unavoidable in some research studies because sociologists have to make the purpose of the study known. Subjects must be aware that they are being observed, and a certain amount of artificiality may result (Sonnenfeld 1985).

A real-life example will help illustrate the process. In 1971, Frances Heussenstamm, a sociology professor at California State University at Los Angeles, had a theory about police prejudice. To test her theory, she conducted research. She chose fifteen students from three ethnic backgrounds: Black, White, and Hispanic. She chose students who routinely drove to and from campus along Los Angeles freeway routes, and who had had perfect driving records for longer than a year.

Next, she placed a Black Panther bumper sticker on each car. That sticker, a representation of a social value, was the independent variable. In the 1970s, the Black Panthers were a revolutionary group actively fighting racism. Heussenstamm asked the students to follow their normal driving patterns. She wanted to see whether seeming support for the Black Panthers would change how these good drivers were treated by the police patrolling the highways. The dependent variable would be the number of traffic stops/citations.

The first arrest, for an incorrect lane change, was made two hours after the experiment began. One participant was pulled over three times in three days. He quit the study. After seventeen days, the fifteen drivers had collected a total of thirty-three traffic citations. The research was halted. The funding to pay traffic fines had run out, and so had the enthusiasm of the participants (Heussenstamm, 1971).

Secondary Data Analysis

While sociologists often engage in original research studies, they also contribute knowledge to the discipline through secondary data analysis . Secondary data does not result from firsthand research collected from primary sources, but are the already completed work of other researchers or data collected by an agency or organization. Sociologists might study works written by historians, economists, teachers, or early sociologists. They might search through periodicals, newspapers, or magazines, or organizational data from any period in history.

Using available information not only saves time and money but can also add depth to a study. Sociologists often interpret findings in a new way, a way that was not part of an author’s original purpose or intention. To study how women were encouraged to act and behave in the 1960s, for example, a researcher might watch movies, televisions shows, and situation comedies from that period. Or to research changes in behavior and attitudes due to the emergence of television in the late 1950s and early 1960s, a sociologist would rely on new interpretations of secondary data. Decades from now, researchers will most likely conduct similar studies on the advent of mobile phones, the Internet, or social media.

Social scientists also learn by analyzing the research of a variety of agencies. Governmental departments and global groups, like the U.S. Bureau of Labor Statistics or the World Health Organization (WHO), publish studies with findings that are useful to sociologists. A public statistic like the foreclosure rate might be useful for studying the effects of a recession. A racial demographic profile might be compared with data on education funding to examine the resources accessible by different groups.

One of the advantages of secondary data like old movies or WHO statistics is that it is nonreactive research (or unobtrusive research), meaning that it does not involve direct contact with subjects and will not alter or influence people’s behaviors. Unlike studies requiring direct contact with people, using previously published data does not require entering a population and the investment and risks inherent in that research process.

Using available data does have its challenges. Public records are not always easy to access. A researcher will need to do some legwork to track them down and gain access to records. To guide the search through a vast library of materials and avoid wasting time reading unrelated sources, sociologists employ content analysis , applying a systematic approach to record and value information gleaned from secondary data as they relate to the study at hand.

Also, in some cases, there is no way to verify the accuracy of existing data. It is easy to count how many drunk drivers, for example, are pulled over by the police. But how many are not? While it’s possible to discover the percentage of teenage students who drop out of high school, it might be more challenging to determine the number who return to school or get their GED later.

Another problem arises when data are unavailable in the exact form needed or do not survey the topic from the precise angle the researcher seeks. For example, the average salaries paid to professors at a public school is public record. But these figures do not necessarily reveal how long it took each professor to reach the salary range, what their educational backgrounds are, or how long they’ve been teaching.

When conducting content analysis, it is important to consider the date of publication of an existing source and to take into account attitudes and common cultural ideals that may have influenced the research. For example, when Robert S. Lynd and Helen Merrell Lynd gathered research in the 1920s, attitudes and cultural norms were vastly different then than they are now. Beliefs about gender roles, race, education, and work have changed significantly since then. At the time, the study’s purpose was to reveal insights about small U.S. communities. Today, it is an illustration of 1920s attitudes and values.

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Table of Contents

What is data collection, why do we need data collection, what are the different data collection methods, data collection tools, the importance of ensuring accurate and appropriate data collection, issues related to maintaining the integrity of data collection, what are common challenges in data collection, what are the key steps in the data collection process, data collection considerations and best practices, choose the right data science program, are you interested in a career in data science, what is data collection: methods, types, tools.

The process of gathering and analyzing accurate data from various sources to find answers to research problems, trends and probabilities, etc., to evaluate possible outcomes is Known as Data Collection. Knowledge is power, information is knowledge, and data is information in digitized form, at least as defined in IT. Hence, data is power. But before you can leverage that data into a successful strategy for your organization or business, you need to gather it. That’s your first step.

So, to help you get the process started, we shine a spotlight on data collection. What exactly is it? Believe it or not, it’s more than just doing a Google search! Furthermore, what are the different types of data collection? And what kinds of data collection tools and data collection techniques exist?

If you want to get up to speed about what is data collection process, you’ve come to the right place. 

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Data collection is the process of collecting and evaluating information or data from multiple sources to find answers to research problems, answer questions, evaluate outcomes, and forecast trends and probabilities. It is an essential phase in all types of research, analysis, and decision-making, including that done in the social sciences, business, and healthcare.

Accurate data collection is necessary to make informed business decisions, ensure quality assurance, and keep research integrity.

During data collection, the researchers must identify the data types, the sources of data, and what methods are being used. We will soon see that there are many different data collection methods . There is heavy reliance on data collection in research, commercial, and government fields.

Before an analyst begins collecting data, they must answer three questions first:

• What’s the goal or purpose of this research?
• What kinds of data are they planning on gathering?
• What methods and procedures will be used to collect, store, and process the information?

Additionally, we can break up data into qualitative and quantitative types. Qualitative data covers descriptions such as color, size, quality, and appearance. Quantitative data, unsurprisingly, deals with numbers, such as statistics, poll numbers, percentages, etc.

Before a judge makes a ruling in a court case or a general creates a plan of attack, they must have as many relevant facts as possible. The best courses of action come from informed decisions, and information and data are synonymous.

The concept of data collection isn’t a new one, as we’ll see later, but the world has changed. There is far more data available today, and it exists in forms that were unheard of a century ago. The data collection process has had to change and grow with the times, keeping pace with technology.

Whether you’re in the world of academia, trying to conduct research, or part of the commercial sector, thinking of how to promote a new product, you need data collection to help you make better choices.

Now that you know what is data collection and why we need it, let's take a look at the different methods of data collection. While the phrase “data collection” may sound all high-tech and digital, it doesn’t necessarily entail things like computers, big data , and the internet. Data collection could mean a telephone survey, a mail-in comment card, or even some guy with a clipboard asking passersby some questions. But let’s see if we can sort the different data collection methods into a semblance of organized categories.

Primary and secondary methods of data collection are two approaches used to gather information for research or analysis purposes. Let's explore each data collection method in detail:

1. Primary Data Collection:

Primary data collection involves the collection of original data directly from the source or through direct interaction with the respondents. This method allows researchers to obtain firsthand information specifically tailored to their research objectives. There are various techniques for primary data collection, including:

a. Surveys and Questionnaires: Researchers design structured questionnaires or surveys to collect data from individuals or groups. These can be conducted through face-to-face interviews, telephone calls, mail, or online platforms.

b. Interviews: Interviews involve direct interaction between the researcher and the respondent. They can be conducted in person, over the phone, or through video conferencing. Interviews can be structured (with predefined questions), semi-structured (allowing flexibility), or unstructured (more conversational).

c. Observations: Researchers observe and record behaviors, actions, or events in their natural setting. This method is useful for gathering data on human behavior, interactions, or phenomena without direct intervention.

d. Experiments: Experimental studies involve the manipulation of variables to observe their impact on the outcome. Researchers control the conditions and collect data to draw conclusions about cause-and-effect relationships.

e. Focus Groups: Focus groups bring together a small group of individuals who discuss specific topics in a moderated setting. This method helps in understanding opinions, perceptions, and experiences shared by the participants.

2. Secondary Data Collection:

Secondary data collection involves using existing data collected by someone else for a purpose different from the original intent. Researchers analyze and interpret this data to extract relevant information. Secondary data can be obtained from various sources, including:

a. Published Sources: Researchers refer to books, academic journals, magazines, newspapers, government reports, and other published materials that contain relevant data.

b. Online Databases: Numerous online databases provide access to a wide range of secondary data, such as research articles, statistical information, economic data, and social surveys.

c. Government and Institutional Records: Government agencies, research institutions, and organizations often maintain databases or records that can be used for research purposes.

d. Publicly Available Data: Data shared by individuals, organizations, or communities on public platforms, websites, or social media can be accessed and utilized for research.

e. Past Research Studies: Previous research studies and their findings can serve as valuable secondary data sources. Researchers can review and analyze the data to gain insights or build upon existing knowledge.

Now that we’ve explained the various techniques, let’s narrow our focus even further by looking at some specific tools. For example, we mentioned interviews as a technique, but we can further break that down into different interview types (or “tools”).

Word Association

The researcher gives the respondent a set of words and asks them what comes to mind when they hear each word.

Sentence Completion

Researchers use sentence completion to understand what kind of ideas the respondent has. This tool involves giving an incomplete sentence and seeing how the interviewee finishes it.

Role-Playing

Respondents are presented with an imaginary situation and asked how they would act or react if it was real.

In-Person Surveys

The researcher asks questions in person.

Online/Web Surveys

These surveys are easy to accomplish, but some users may be unwilling to answer truthfully, if at all.

Mobile Surveys

These surveys take advantage of the increasing proliferation of mobile technology. Mobile collection surveys rely on mobile devices like tablets or smartphones to conduct surveys via SMS or mobile apps.

Phone Surveys

No researcher can call thousands of people at once, so they need a third party to handle the chore. However, many people have call screening and won’t answer.

Observation

Sometimes, the simplest method is the best. Researchers who make direct observations collect data quickly and easily, with little intrusion or third-party bias. Naturally, it’s only effective in small-scale situations.

Accurate data collecting is crucial to preserving the integrity of research, regardless of the subject of study or preferred method for defining data (quantitative, qualitative). Errors are less likely to occur when the right data gathering tools are used (whether they are brand-new ones, updated versions of them, or already available).

Among the effects of data collection done incorrectly, include the following -

• Erroneous conclusions that squander resources
• Decisions that compromise public policy
• Incapacity to correctly respond to research inquiries
• Bringing harm to participants who are humans or animals
• Deceiving other researchers into pursuing futile research avenues
• The study's inability to be replicated and validated

When these study findings are used to support recommendations for public policy, there is the potential to result in disproportionate harm, even if the degree of influence from flawed data collecting may vary by discipline and the type of investigation.

Let us now look at the various issues that we might face while maintaining the integrity of data collection.

In order to assist the errors detection process in the data gathering process, whether they were done purposefully (deliberate falsifications) or not, maintaining data integrity is the main justification (systematic or random errors).

Quality assurance and quality control are two strategies that help protect data integrity and guarantee the scientific validity of study results.

Each strategy is used at various stages of the research timeline:

• Quality control - tasks that are performed both after and during data collecting
• Quality assurance - events that happen before data gathering starts

Let us explore each of them in more detail now.

Quality Assurance

As data collecting comes before quality assurance, its primary goal is "prevention" (i.e., forestalling problems with data collection). The best way to protect the accuracy of data collection is through prevention. The uniformity of protocol created in the thorough and exhaustive procedures manual for data collecting serves as the best example of this proactive step. 

The likelihood of failing to spot issues and mistakes early in the research attempt increases when guides are written poorly. There are several ways to show these shortcomings:

• Failure to determine the precise subjects and methods for retraining or training staff employees in data collecting
• List of goods to be collected, in part
• There isn't a system in place to track modifications to processes that may occur as the investigation continues.
• Instead of detailed, step-by-step instructions on how to deliver tests, there is a vague description of the data gathering tools that will be employed.
• Uncertainty regarding the date, procedure, and identity of the person or people in charge of examining the data
• Incomprehensible guidelines for using, adjusting, and calibrating the data collection equipment.

Now, let us look at how to ensure Quality Control.

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Quality Control

Despite the fact that quality control actions (detection/monitoring and intervention) take place both after and during data collection, the specifics should be meticulously detailed in the procedures manual. Establishing monitoring systems requires a specific communication structure, which is a prerequisite. Following the discovery of data collection problems, there should be no ambiguity regarding the information flow between the primary investigators and staff personnel. A poorly designed communication system promotes slack oversight and reduces opportunities for error detection.

Direct staff observation conference calls, during site visits, or frequent or routine assessments of data reports to spot discrepancies, excessive numbers, or invalid codes can all be used as forms of detection or monitoring. Site visits might not be appropriate for all disciplines. Still, without routine auditing of records, whether qualitative or quantitative, it will be challenging for investigators to confirm that data gathering is taking place in accordance with the manual's defined methods. Additionally, quality control determines the appropriate solutions, or "actions," to fix flawed data gathering procedures and reduce recurrences.

Problems with data collection, for instance, that call for immediate action include:

• Fraud or misbehavior
• Systematic mistakes, procedure violations 
• Individual data items with errors
• Issues with certain staff members or a site's performance 

Researchers are trained to include one or more secondary measures that can be used to verify the quality of information being obtained from the human subject in the social and behavioral sciences where primary data collection entails using human subjects. 

For instance, a researcher conducting a survey would be interested in learning more about the prevalence of risky behaviors among young adults as well as the social factors that influence these risky behaviors' propensity for and frequency. Let us now explore the common challenges with regard to data collection.

There are some prevalent challenges faced while collecting data, let us explore a few of them to understand them better and avoid them.

Data Quality Issues

The main threat to the broad and successful application of machine learning is poor data quality. Data quality must be your top priority if you want to make technologies like machine learning work for you. Let's talk about some of the most prevalent data quality problems in this blog article and how to fix them.

Inconsistent Data

When working with various data sources, it's conceivable that the same information will have discrepancies between sources. The differences could be in formats, units, or occasionally spellings. The introduction of inconsistent data might also occur during firm mergers or relocations. Inconsistencies in data have a tendency to accumulate and reduce the value of data if they are not continually resolved. Organizations that have heavily focused on data consistency do so because they only want reliable data to support their analytics.

Data Downtime

Data is the driving force behind the decisions and operations of data-driven businesses. However, there may be brief periods when their data is unreliable or not prepared. Customer complaints and subpar analytical outcomes are only two ways that this data unavailability can have a significant impact on businesses. A data engineer spends about 80% of their time updating, maintaining, and guaranteeing the integrity of the data pipeline. In order to ask the next business question, there is a high marginal cost due to the lengthy operational lead time from data capture to insight.

Schema modifications and migration problems are just two examples of the causes of data downtime. Data pipelines can be difficult due to their size and complexity. Data downtime must be continuously monitored, and it must be reduced through automation.

Ambiguous Data

Even with thorough oversight, some errors can still occur in massive databases or data lakes. For data streaming at a fast speed, the issue becomes more overwhelming. Spelling mistakes can go unnoticed, formatting difficulties can occur, and column heads might be deceptive. This unclear data might cause a number of problems for reporting and analytics.

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Duplicate Data

Streaming data, local databases, and cloud data lakes are just a few of the sources of data that modern enterprises must contend with. They might also have application and system silos. These sources are likely to duplicate and overlap each other quite a bit. For instance, duplicate contact information has a substantial impact on customer experience. If certain prospects are ignored while others are engaged repeatedly, marketing campaigns suffer. The likelihood of biased analytical outcomes increases when duplicate data are present. It can also result in ML models with biased training data.

Too Much Data

While we emphasize data-driven analytics and its advantages, a data quality problem with excessive data exists. There is a risk of getting lost in an abundance of data when searching for information pertinent to your analytical efforts. Data scientists, data analysts, and business users devote 80% of their work to finding and organizing the appropriate data. With an increase in data volume, other problems with data quality become more serious, particularly when dealing with streaming data and big files or databases.

Inaccurate Data

For highly regulated businesses like healthcare, data accuracy is crucial. Given the current experience, it is more important than ever to increase the data quality for COVID-19 and later pandemics. Inaccurate information does not provide you with a true picture of the situation and cannot be used to plan the best course of action. Personalized customer experiences and marketing strategies underperform if your customer data is inaccurate.

Data inaccuracies can be attributed to a number of things, including data degradation, human mistake, and data drift. Worldwide data decay occurs at a rate of about 3% per month, which is quite concerning. Data integrity can be compromised while being transferred between different systems, and data quality might deteriorate with time.

Hidden Data

The majority of businesses only utilize a portion of their data, with the remainder sometimes being lost in data silos or discarded in data graveyards. For instance, the customer service team might not receive client data from sales, missing an opportunity to build more precise and comprehensive customer profiles. Missing out on possibilities to develop novel products, enhance services, and streamline procedures is caused by hidden data.

Finding Relevant Data

Finding relevant data is not so easy. There are several factors that we need to consider while trying to find relevant data, which include -

• Relevant Domain
• Relevant demographics
• Relevant Time period and so many more factors that we need to consider while trying to find relevant data.

Data that is not relevant to our study in any of the factors render it obsolete and we cannot effectively proceed with its analysis. This could lead to incomplete research or analysis, re-collecting data again and again, or shutting down the study.

Deciding the Data to Collect

Determining what data to collect is one of the most important factors while collecting data and should be one of the first factors while collecting data. We must choose the subjects the data will cover, the sources we will be used to gather it, and the quantity of information we will require. Our responses to these queries will depend on our aims, or what we expect to achieve utilizing your data. As an illustration, we may choose to gather information on the categories of articles that website visitors between the ages of 20 and 50 most frequently access. We can also decide to compile data on the typical age of all the clients who made a purchase from your business over the previous month.

Not addressing this could lead to double work and collection of irrelevant data or ruining your study as a whole.

Dealing With Big Data

Big data refers to exceedingly massive data sets with more intricate and diversified structures. These traits typically result in increased challenges while storing, analyzing, and using additional methods of extracting results. Big data refers especially to data sets that are quite enormous or intricate that conventional data processing tools are insufficient. The overwhelming amount of data, both unstructured and structured, that a business faces on a daily basis. 

The amount of data produced by healthcare applications, the internet, social networking sites social, sensor networks, and many other businesses are rapidly growing as a result of recent technological advancements. Big data refers to the vast volume of data created from numerous sources in a variety of formats at extremely fast rates. Dealing with this kind of data is one of the many challenges of Data Collection and is a crucial step toward collecting effective data. 

Low Response and Other Research Issues

Poor design and low response rates were shown to be two issues with data collecting, particularly in health surveys that used questionnaires. This might lead to an insufficient or inadequate supply of data for the study. Creating an incentivized data collection program might be beneficial in this case to get more responses.

Now, let us look at the key steps in the data collection process.

In the Data Collection Process, there are 5 key steps. They are explained briefly below -

1. Decide What Data You Want to Gather

The first thing that we need to do is decide what information we want to gather. We must choose the subjects the data will cover, the sources we will use to gather it, and the quantity of information that we would require. For instance, we may choose to gather information on the categories of products that an average e-commerce website visitor between the ages of 30 and 45 most frequently searches for. 

2. Establish a Deadline for Data Collection

The process of creating a strategy for data collection can now begin. We should set a deadline for our data collection at the outset of our planning phase. Some forms of data we might want to continuously collect. We might want to build up a technique for tracking transactional data and website visitor statistics over the long term, for instance. However, we will track the data throughout a certain time frame if we are tracking it for a particular campaign. In these situations, we will have a schedule for when we will begin and finish gathering data. 

3. Select a Data Collection Approach

We will select the data collection technique that will serve as the foundation of our data gathering plan at this stage. We must take into account the type of information that we wish to gather, the time period during which we will receive it, and the other factors we decide on to choose the best gathering strategy.

4. Gather Information

Once our plan is complete, we can put our data collection plan into action and begin gathering data. In our DMP, we can store and arrange our data. We need to be careful to follow our plan and keep an eye on how it's doing. Especially if we are collecting data regularly, setting up a timetable for when we will be checking in on how our data gathering is going may be helpful. As circumstances alter and we learn new details, we might need to amend our plan.

5. Examine the Information and Apply Your Findings

It's time to examine our data and arrange our findings after we have gathered all of our information. The analysis stage is essential because it transforms unprocessed data into insightful knowledge that can be applied to better our marketing plans, goods, and business judgments. The analytics tools included in our DMP can be used to assist with this phase. We can put the discoveries to use to enhance our business once we have discovered the patterns and insights in our data.

Let us now look at some data collection considerations and best practices that one might follow.

We must carefully plan before spending time and money traveling to the field to gather data. While saving time and resources, effective data collection strategies can help us collect richer, more accurate, and richer data.

Below, we will be discussing some of the best practices that we can follow for the best results -

1. Take Into Account the Price of Each Extra Data Point

Once we have decided on the data we want to gather, we need to make sure to take the expense of doing so into account. Our surveyors and respondents will incur additional costs for each additional data point or survey question.

2. Plan How to Gather Each Data Piece

There is a dearth of freely accessible data. Sometimes the data is there, but we may not have access to it. For instance, unless we have a compelling cause, we cannot openly view another person's medical information. It could be challenging to measure several types of information.

Consider how time-consuming and difficult it will be to gather each piece of information while deciding what data to acquire.

3. Think About Your Choices for Data Collecting Using Mobile Devices

Mobile-based data collecting can be divided into three categories -

• IVRS (interactive voice response technology) -  Will call the respondents and ask them questions that have already been recorded. 
• SMS data collection - Will send a text message to the respondent, who can then respond to questions by text on their phone. 
• Field surveyors - Can directly enter data into an interactive questionnaire while speaking to each respondent, thanks to smartphone apps.

We need to make sure to select the appropriate tool for our survey and responders because each one has its own disadvantages and advantages.

4. Carefully Consider the Data You Need to Gather

It's all too easy to get information about anything and everything, but it's crucial to only gather the information that we require. 

It is helpful to consider these 3 questions:

• What details will be helpful?
• What details are available?
• What specific details do you require?

5. Remember to Consider Identifiers

Identifiers, or details describing the context and source of a survey response, are just as crucial as the information about the subject or program that we are actually researching.

In general, adding more identifiers will enable us to pinpoint our program's successes and failures with greater accuracy, but moderation is the key.

6. Data Collecting Through Mobile Devices is the Way to Go

Although collecting data on paper is still common, modern technology relies heavily on mobile devices. They enable us to gather many various types of data at relatively lower prices and are accurate as well as quick. There aren't many reasons not to pick mobile-based data collecting with the boom of low-cost Android devices that are available nowadays.

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1. What is data collection with example?

Data collection is the process of collecting and analyzing information on relevant variables in a predetermined, methodical way so that one can respond to specific research questions, test hypotheses, and assess results. Data collection can be either qualitative or quantitative. Example: A company collects customer feedback through online surveys and social media monitoring to improve their products and services.

2. What are the primary data collection methods?

As is well known, gathering primary data is costly and time intensive. The main techniques for gathering data are observation, interviews, questionnaires, schedules, and surveys.

3. What are data collection tools?

The term "data collecting tools" refers to the tools/devices used to gather data, such as a paper questionnaire or a system for computer-assisted interviews. Tools used to gather data include case studies, checklists, interviews, occasionally observation, surveys, and questionnaires.

4. What’s the difference between quantitative and qualitative methods?

While qualitative research focuses on words and meanings, quantitative research deals with figures and statistics. You can systematically measure variables and test hypotheses using quantitative methods. You can delve deeper into ideas and experiences using qualitative methodologies.

5. What are quantitative data collection methods?

While there are numerous other ways to get quantitative information, the methods indicated above—probability sampling, interviews, questionnaire observation, and document review—are the most typical and frequently employed, whether collecting information offline or online.

6. What is mixed methods research?

User research that includes both qualitative and quantitative techniques is known as mixed methods research. For deeper user insights, mixed methods research combines insightful user data with useful statistics.

7. What are the benefits of collecting data?

Collecting data offers several benefits, including:

• Knowledge and Insight
• Evidence-Based Decision Making
• Problem Identification and Solution
• Validation and Evaluation
• Identifying Trends and Predictions
• Support for Research and Development
• Policy Development
• Quality Improvement
• Personalization and Targeting
• Knowledge Sharing and Collaboration

8. What’s the difference between reliability and validity?

Reliability is about consistency and stability, while validity is about accuracy and appropriateness. Reliability focuses on the consistency of results, while validity focuses on whether the results are actually measuring what they are intended to measure. Both reliability and validity are crucial considerations in research to ensure the trustworthiness and meaningfulness of the collected data and measurements.

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Research Methods (Developmental Psychology)

• September 2024

1 year full-time

Durham City

Course details

The MA in Research Methods (Developmental Psychology) is an ESRC-accredited Social Sciences & Health Faculty degree in which departments from across the University work together to deliver a tailored study programme that explores human development through a research lens.

It is primarily designed for students who plan to continue their graduate studies at PhD level in the broad area of developmental psychology or work as a research psychologist. It is also relevant for students wishing to pursue careers in applied areas, such as clinical or educational psychology.

Students intending to have a career as a research psychologist need to acquire a high level of research skills at postgraduate level. Research methods training, co-ordinated through the Durham Research Methods Centre, therefore forms a central part of the MA course, including both quantitative and qualitative research methods. One third of the course is also devoted to the dissertation which may be carried out in any area of psychology related to development. The taught course modules include both generic and subject level components, providing an introduction to broad issues and methodological approaches in developmental psychology and the social sciences.

Course structure

Core modules:

Interdisciplinary Perspectives and Practices Across Social Research introduces the areas of contemporary social scientific research practice. You will develop an understanding of the intersection where philosophical, methodological and disciplinary contexts meet, including qualitative, quantitative, statistical, computational and historical programmes of research. You will also explore the various issues involved in the application of these methods inside and outside academia and will consider the ethical issues involved such as power and equality.

Qualitative Methods and Analysis supports your preparation for conducting qualitative research equipping you with the knowledge and skills to understand and appraise qualitative social sciences research, including different approaches to research and design and the analysis of qualitative data.

Advanced Developmental Psychology Review teaches the skills to create and deliver literature reviews as well as providing analysis of key topics in the field of developmental psychology, including research skills, data handling, writing styles and methods of presentation.

Research Design in Child and Clinical Psychology provides an introduction to the methods and design used in child and clinical psychology and offers practical training in the transferable skills relevant for professional and personal development.

Current Issues in Developmental Psychology and Psychopathology provides in-depth knowledge of current theoretical stances and methodological issues within the fields of developmental psychology and neurodiversity. The aim is for you to gain a broad understanding of typical development and then consider different development pathways.

Quantitative Methods and Analysis develops your knowledge, critical understanding and skills in quantitative data analysis and teaches you how to use statistical techniques for exploration and description of data sets. You will also learn how to make appropriate statistical inferences about associations between social phenomena.

Advanced Statistics for Psychology and the Behavioural Sciences teaches you a set of advanced statistical methods that are used across psychology and the behavioural sciences, equipping you with the skills to confidently identify appropriate statistical techniques and analyse data using relevant software across a range of different types of research.

The Dissertation enables you to design, implement, analyse and finally discuss an empirical project relevant to your field of study, giving you the opportunity to apply the theoretical knowledge you have acquired and to gain experience of collaborating with other researchers and undertaking research and project management. The Department provides excellent support and opportunities for conducting a significant piece of independent research.

Alternative modules (depending on prior knowledge and understanding) include: 

• Advanced Ethnographic Research
• Multilevel Modelling
• Simulating Data in R
• Systematic Review
• Computational Social Science
• Participatory Action Research
• Statistics in Health and Medicine with R

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The course is delivered through a mixture of lectures, seminars and practical classes. Lectures provide key information on a particular topic, such as social and emotional development. Seminars are held in order that smaller group teaching can take place, with focused discussion on specific topics. Finally, practical and workshop classes allow you to gain direct experience, particularly in how to use statistical tools.

The balance of this type of activity varies as a function of the module. This is a one year course, with students having the summer term to work on dissertation related activities. You will typically attend approximately 12 hours a week comprising lectures, tutorials and seminars. Outside timetabled contact hours, you are also expected to undertake your own independent study to prepare for your classes and broaden your subject knowledge, as well as conduct your dissertation. Independent study is a key element to the course, with complex factors raised in lectures that do assume some prior knowledge of the topic area.

The course is divided into three parts. One third, comprising three modules, is of subject specific topics related to developmental psychology and developmental psychopathology, including issues relevant to clinical/ educational work throughout development. Across these modules, the material is delivered via a combination of lectures, seminars, practical workshops and discussions.

A further three modules focus on placing psychology in the larger framework of social science research and providing generic research skills, for example, qualitative and quantitative analysis skills.

The final third of the course is the dissertation, which reflects the culmination of learning and practical endeavours from throughout the course via the production of an independent and original body of research material. This is performed under supervision with a member of staff, with meetings varying in duration and frequency throughout the year as a function of the needs of the research project and student.

Assessment features examinations, written assignments, oral presentations, and the completion of a dissertation.

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2:1 in Psychology or Psychology related subject (or equivalent).

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The tuition fees shown are for one complete academic year of full time study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated) .

Please also check costs for colleges and accommodation .

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We are committed to supporting the best students irrespective of financial circumstances and are delighted to offer a range of funding opportunities. 

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Our Masters qualifications provide the essential skills and understanding to progress to research at a higher academic level or to launch a professional career.

Our students enter the workplace with knowledge relevant to careers in psychology but also with the transferable skills in analysis and problem-solving that are valued by employers.

Such skills are highly sought after in a range of professions and our postgraduates have progressed to roles that include consultants, advisors, human resources professionals, talent managers, business analysts, market researchers, public relations professionals, marketeers, administrators, managers, civil servants, coaches, trainers and teachers.

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The Department of Psychology provides a world-class teaching and research environment for postgraduates who are interested in understanding more about the thoughts, feelings and motivations that lie behind actions and human behaviours and the processes that trigger them.

Our academic team are leading experts in their specialisms and provide outstanding supervision for postgraduate learning and research in key areas of psychology including Developmental Science, Cognitive Neuroscience and Quantitative Social Psychology.

Our range of taught Masters qualifications offer the opportunity to focus on a specific aspect of psychology be it biological, cognitive, social, developmental or neuroscience-related and to learn key approaches to the discipline including statistical training. For those who are interested in pursuing their own research interests we offer the MA/MSc by Research which offers a direct route to the PhD.

All postgraduate taught courses include a postgraduate dissertation in a chosen subject, supported by a member of staff with expertise in that area. Students also have access to state-of-the art technical, clinical and experimental facilities to support learning and research.

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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When it comes to sustainability and environmental impact, this Northeastern Ph.D. grad is at the ‘forefront of innovation’

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Looking at doctorate programs, Abhijeet Parvatker found a perfect match in Northeastern and Matthew Eckelman, a professor of civil, environmental and chemical engineering.

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Before arriving at Northeastern University to pursue a doctoral degree, Abhijeet Parvatker worked as a research engineer at one of the world’s largest petrochemicals manufacturers. 

He was a part of a team that measured sustainability of the company’s products, primarily produced from natural gas.

“There is generally a lot of pressure from stakeholders to understand the environmental impacts that the products have,” Parvatker says. 

He used one of the most common methodologies — life-cycle assessment — that evaluates how a product or service impacts the environment over the course of its life cycle, from the moment raw materials are extracted to manufacture the product, to when it is discarded.

Although the life-cycle assessment methodology was standardized, Parvatker says, it was challenging to find the data needed to determine consumed energy, carbon dioxide emissions or toxicity. 

“I soon realized that there are a lot of gaps in this,” he says. “The methods to get to the data are not as evolved, and I wanted to do a Ph.D. with that focus.”

Looking at doctorate programs, Parvatker found Northeastern and Matthew Eckelman , a professor of civil, environmental and chemical engineering, who was doing research in the exact area he was interested in. 

From then on, his life’s work started to take on a larger meaning.

“It was a really good fit for a research project that I had just gotten funded from the National Science Foundation,” Eckelman says. “I felt like he had the experience to carry out this project.”

Parvatker’s research looks at developing new methods and models for generating data for life-cycle assessments and evaluating the sustainability of different chemicals and chemical manufacturing processes.

“There’s something like tens of thousands of chemicals in commerce,” Eckelman says. “We have inventory information on maybe a couple thousands.”  

It was a big and ambitious project, he says, but Parvatker has made a lot of progress. And it’s been extremely useful, Eckelman says, to the global community.

“I am always getting questions about papers that Abhijeet and I wrote together,” Eckelman says. “People wanting to use the data, wanting to adapt the techniques that we use to generate the data.”

Another project Parvatker worked on was analyzing the environmental impact of intravenous anesthetic drugs used by medical institutions.The health care sector makes a considerable contribution to America’s GDP, Eckelman says, which means that to reduce the country’s carbon dioxide output into the atmosphere, health care must become more sustainable. 

However, pharmaceutical companies don’t share much data about the inputs of chemicals they use in a drug, Parvatker says, for proprietary or other reasons.

He used chemical engineering principles, process design and physics and chemistry knowledge about these products to calculate inputs and outputs. He looked at 20 intravenous anesthetic drugs and about 140 different chemicals, building life-cycle inventory data for them.

“It’s still the largest study in the pharmaceutical sector for those chemicals,” he says.

Together, Parvatker and Eckelman wrote some of the first papers about the contribution of the health care industry to national and global emissions.

“These numbers are quoted all the time,” Eckelman says. “And then the National Academy of Medicine opened up this big effort called the Action Collaborative on Decarbonizing the U.S. Health Sector. And now we’re invited to be a part of that because of his work.”

Parvatker earned his undergraduate degree in chemical engineering back home in India and his master’s degree in advanced process integration and design from the University of Manchester before being accepted into Northeastern’s Ph.D. program.

Parvatker says the university takes experiential learning to the next level by encouraging students to apply their research in the real world through programs such as LEADERs .

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Short for Leadership Education Advancing Discovery through Embedded Research, LEADERs is a customized internship program for Northeastern doctoral students and postdoctoral researchers.

Parvatker credits the program — and his internship at Wayfair that resulted from it — for introducing him to the world of sustainability consulting. At Wayfair, he was tasked with creating guidelines for vendors on the use of chemicals based on the U.S. and the EU regulations. He also gained experience in chemical safety and supply chain management.

After completing his doctoral degree program, Parvatker was offered a full-time job at Sphera Solutions, a provider of consulting services, data and software solutions for risk management and monitoring of environmental, social and governance performance. 

He currently manages a team of five sustainability consultants who help companies with large product portfolios address their sustainability challenges, automate calculations and reports on ESG with Sphera’s software. 

“Imagine companies that have tens of thousands and millions of products,” Parvatker says. “With some initial effort of six months to one year, you can scale the life-cycle assessment methodology to the entire product portfolio and calculate all the life-cycle assessment results for tens of thousands of products in hours.”

Parvatker loves working with different companies, different processes and different people.

“In this space of the implementation of this methodology, we are always at the forefront of innovation,” he says.

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About 1 in 5 U.S. teens who’ve heard of ChatGPT have used it for schoolwork

Roughly one-in-five teenagers who have heard of ChatGPT say they have used it to help them do their schoolwork, according to a new Pew Research Center survey of U.S. teens ages 13 to 17. With a majority of teens having heard of ChatGPT, that amounts to 13% of all U.S. teens who have used the generative artificial intelligence (AI) chatbot in their schoolwork.

Teens in higher grade levels are particularly likely to have used the chatbot to help them with schoolwork. About one-quarter of 11th and 12th graders who have heard of ChatGPT say they have done this. This share drops to 17% among 9th and 10th graders and 12% among 7th and 8th graders.

There is no significant difference between teen boys and girls who have used ChatGPT in this way.

The introduction of ChatGPT last year has led to much discussion about its role in schools , especially whether schools should integrate the new technology into the classroom or ban it .

Pew Research Center conducted this analysis to understand American teens’ use and understanding of ChatGPT in the school setting.

The Center conducted an online survey of 1,453 U.S. teens from Sept. 26 to Oct. 23, 2023, via Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the  questions used for this analysis , along with responses, and its  methodology .

Teens’ awareness of ChatGPT

Overall, two-thirds of U.S. teens say they have heard of ChatGPT, including 23% who have heard a lot about it. But awareness varies by race and ethnicity, as well as by household income:

• 72% of White teens say they’ve heard at least a little about ChatGPT, compared with 63% of Hispanic teens and 56% of Black teens.
• 75% of teens living in households that make $75,000 or more annually have heard of ChatGPT. Much smaller shares in households with incomes between $30,000 and $74,999 (58%) and less than $30,000 (41%) say the same.

Teens who are more aware of ChatGPT are more likely to use it for schoolwork. Roughly a third of teens who have heard a lot about ChatGPT (36%) have used it for schoolwork, far higher than the 10% among those who have heard a little about it.

When do teens think it’s OK for students to use ChatGPT?

For teens, whether it is – or is not – acceptable for students to use ChatGPT depends on what it is being used for.

There is a fair amount of support for using the chatbot to explore a topic. Roughly seven-in-ten teens who have heard of ChatGPT say it’s acceptable to use when they are researching something new, while 13% say it is not acceptable.

However, there is much less support for using ChatGPT to do the work itself. Just one-in-five teens who have heard of ChatGPT say it’s acceptable to use it to write essays, while 57% say it is not acceptable. And 39% say it’s acceptable to use ChatGPT to solve math problems, while a similar share of teens (36%) say it’s not acceptable.

Some teens are uncertain about whether it’s acceptable to use ChatGPT for these tasks. Between 18% and 24% say they aren’t sure whether these are acceptable use cases for ChatGPT.

Those who have heard a lot about ChatGPT are more likely than those who have only heard a little about it to say it’s acceptable to use the chatbot to research topics, solve math problems and write essays. For instance, 54% of teens who have heard a lot about ChatGPT say it’s acceptable to use it to solve math problems, compared with 32% among those who have heard a little about it.

Note: Here are the  questions used for this analysis , along with responses, and its  methodology .

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Olivia Sidoti is a research assistant focusing on internet and technology research at Pew Research Center

Jeffrey Gottfried is an associate director focusing on internet and technology research at Pew Research Center

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