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Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

  • The  aim of the research
  • The type of data that you will collect
  • The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

  • Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods .
  • Qualitative data is expressed in words and analyzed through interpretations and categorizations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

  • Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
  • Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

  • Experimental research is primarily a quantitative method.
  • Interviews , focus groups , and ethnographies are qualitative methods.
  • Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).

Operationalization

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

  • You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability.
  • You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection.

Standardizing procedures

If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organize and store your data.

  • If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
  • If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion.
  • You can prevent loss of data by having an organization system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

To ensure that high quality data is recorded in a systematic way, here are some best practices:

  • Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
  • Double-check manual data entry for errors.
  • If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval
  • Cluster sampling
  • Stratified sampling
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Likert scale

Research bias

  • Implicit bias
  • Framing effect
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hindsight bias
  • Affect heuristic

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

When conducting research, collecting original data has significant advantages:

  • You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
  • You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

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

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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Data collection in research: Your complete guide

Last updated

31 January 2023

Reviewed by

Cathy Heath

In the late 16th century, Francis Bacon coined the phrase "knowledge is power," which implies that knowledge is a powerful force, like physical strength. In the 21st century, knowledge in the form of data is unquestionably powerful.

But data isn't something you just have - you need to collect it. This means utilizing a data collection process and turning the collected data into knowledge that you can leverage into a successful strategy for your business or organization.

Believe it or not, there's more to data collection than just conducting a Google search. In this complete guide, we shine a spotlight on data collection, outlining what it is, types of data collection methods, common challenges in data collection, data collection techniques, and the steps involved in data collection.

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  • What is data collection?

There are two specific data collection techniques: primary and secondary data collection. Primary data collection is the process of gathering data directly from sources. It's often considered the most reliable data collection method, as researchers can collect information directly from respondents.

Secondary data collection is data that has already been collected by someone else and is readily available. This data is usually less expensive and quicker to obtain than primary data.

  • What are the different methods of data collection?

There are several data collection methods, which can be either manual or automated. Manual data collection involves collecting data manually, typically with pen and paper, while computerized data collection involves using software to collect data from online sources, such as social media, website data, transaction data, etc. 

Here are the five most popular methods of data collection:

Surveys are a very popular method of data collection that organizations can use to gather information from many people. Researchers can conduct multi-mode surveys that reach respondents in different ways, including in person, by mail, over the phone, or online.

As a method of data collection, surveys have several advantages. For instance, they are relatively quick and easy to administer, you can be flexible in what you ask, and they can be tailored to collect data on various topics or from certain demographics.

However, surveys also have several disadvantages. For instance, they can be expensive to administer, and the results may not represent the population as a whole. Additionally, survey data can be challenging to interpret. It may also be subject to bias if the questions are not well-designed or if the sample of people surveyed is not representative of the population of interest.

Interviews are a common method of collecting data in social science research. You can conduct interviews in person, over the phone, or even via email or online chat.

Interviews are a great way to collect qualitative and quantitative data . Qualitative interviews are likely your best option if you need to collect detailed information about your subjects' experiences or opinions. If you need to collect more generalized data about your subjects' demographics or attitudes, then quantitative interviews may be a better option.

Interviews are relatively quick and very flexible, allowing you to ask follow-up questions and explore topics in more depth. The downside is that interviews can be time-consuming and expensive due to the amount of information to be analyzed. They are also prone to bias, as both the interviewer and the respondent may have certain expectations or preconceptions that may influence the data.

Direct observation

Observation is a direct way of collecting data. It can be structured (with a specific protocol to follow) or unstructured (simply observing without a particular plan).

Organizations and businesses use observation as a data collection method to gather information about their target market, customers, or competition. Businesses can learn about consumer behavior, preferences, and trends by observing people using their products or service.

There are two types of observation: participatory and non-participatory. In participatory observation, the researcher is actively involved in the observed activities. This type of observation is used in ethnographic research , where the researcher wants to understand a group's culture and social norms. Non-participatory observation is when researchers observe from a distance and do not interact with the people or environment they are studying.

There are several advantages to using observation as a data collection method. It can provide insights that may not be apparent through other methods, such as surveys or interviews. Researchers can also observe behavior in a natural setting, which can provide a more accurate picture of what people do and how and why they behave in a certain context.

There are some disadvantages to using observation as a method of data collection. It can be time-consuming, intrusive, and expensive to observe people for extended periods. Observations can also be tainted if the researcher is not careful to avoid personal biases or preconceptions.

Automated data collection

Business applications and websites are increasingly collecting data electronically to improve the user experience or for marketing purposes.

There are a few different ways that organizations can collect data automatically. One way is through cookies, which are small pieces of data stored on a user's computer. They track a user's browsing history and activity on a site, measuring levels of engagement with a business’s products or services, for example.

Another way organizations can collect data automatically is through web beacons. Web beacons are small images embedded on a web page to track a user's activity.

Finally, organizations can also collect data through mobile apps, which can track user location, device information, and app usage. This data can be used to improve the user experience and for marketing purposes.

Automated data collection is a valuable tool for businesses, helping improve the user experience or target marketing efforts. Businesses should aim to be transparent about how they collect and use this data.

Sourcing data through information service providers

Organizations need to be able to collect data from a variety of sources, including social media, weblogs, and sensors. The process to do this and then use the data for action needs to be efficient, targeted, and meaningful.

In the era of big data, organizations are increasingly turning to information service providers (ISPs) and other external data sources to help them collect data to make crucial decisions. 

Information service providers help organizations collect data by offering personalized services that suit the specific needs of the organizations. These services can include data collection, analysis, management, and reporting. By partnering with an ISP, organizations can gain access to the newest technology and tools to help them to gather and manage data more effectively.

There are also several tools and techniques that organizations can use to collect data from external sources, such as web scraping, which collects data from websites, and data mining, which involves using algorithms to extract data from large data sets. 

Organizations can also use APIs (application programming interface) to collect data from external sources. APIs allow organizations to access data stored in another system and share and integrate it into their own systems.

Finally, organizations can also use manual methods to collect data from external sources. This can involve contacting companies or individuals directly to request data, by using the right tools and methods to get the insights they need.

  • What are common challenges in data collection?

There are many challenges that researchers face when collecting data. Here are five common examples:

Big data environments

Data collection can be a challenge in big data environments for several reasons. It can be located in different places, such as archives, libraries, or online. The sheer volume of data can also make it difficult to identify the most relevant data sets.

Second, the complexity of data sets can make it challenging to extract the desired information. Third, the distributed nature of big data environments can make it difficult to collect data promptly and efficiently.

Therefore it is important to have a well-designed data collection strategy to consider the specific needs of the organization and what data sets are the most relevant. Alongside this, consideration should be made regarding the tools and resources available to support data collection and protect it from unintended use.

Data bias is a common challenge in data collection. It occurs when data is collected from a sample that is not representative of the population of interest. 

There are different types of data bias, but some common ones include selection bias, self-selection bias, and response bias. Selection bias can occur when the collected data does not represent the population being studied. For example, if a study only includes data from people who volunteer to participate, that data may not represent the general population.

Self-selection bias can also occur when people self-select into a study, such as by taking part only if they think they will benefit from it. Response bias happens when people respond in a way that is not honest or accurate, such as by only answering questions that make them look good. 

These types of data bias present a challenge because they can lead to inaccurate results and conclusions about behaviors, perceptions, and trends. Data bias can be avoided by identifying potential sources or themes of bias and setting guidelines for eliminating them.

Lack of quality assurance processes

One of the biggest challenges in data collection is the lack of quality assurance processes. This can lead to several problems, including incorrect data, missing data, and inconsistencies between data sets.

Quality assurance is important because there are many data sources, and each source may have different levels of quality or corruption. There are also different ways of collecting data, and data quality may vary depending on the method used. 

There are several ways to improve quality assurance in data collection. These include developing clear and consistent goals and guidelines for data collection, implementing quality control measures, using standardized procedures, and employing data validation techniques. By taking these steps, you can ensure that your data is of adequate quality to inform decision-making.

Limited access to data

Another challenge in data collection is limited access to data. This can be due to several reasons, including privacy concerns, the sensitive nature of the data, security concerns, or simply the fact that data is not readily available.

Legal and compliance regulations

Most countries have regulations governing how data can be collected, used, and stored. In some cases, data collected in one country may not be used in another. This means gaining a global perspective can be a challenge. 

For example, if a company is required to comply with the EU General Data Protection Regulation (GDPR), it may not be able to collect data from individuals in the EU without their explicit consent. This can make it difficult to collect data from a target audience.

Legal and compliance regulations can be complex, and it's important to ensure that all data collected is done so in a way that complies with the relevant regulations.

  • What are the key steps in the data collection process?

There are five steps involved in the data collection process. They are:

1. Decide what data you want to gather

Have a clear understanding of the questions you are asking, and then consider where the answers might lie and how you might obtain them. This saves time and resources by avoiding the collection of irrelevant data, and helps maintain the quality of your datasets. 

2. Establish a deadline for data collection

Establishing a deadline for data collection helps you avoid collecting too much data, which can be costly and time-consuming to analyze. It also allows you to plan for data analysis and prompt interpretation. Finally, it helps you meet your research goals and objectives and allows you to move forward.

3. Select a data collection approach

The data collection approach you choose will depend on different factors, including the type of data you need, available resources, and the project timeline. For instance, if you need qualitative data, you might choose a focus group or interview methodology. If you need quantitative data , then a survey or observational study may be the most appropriate form of collection.

4. Gather information

When collecting data for your business, identify your business goals first. Once you know what you want to achieve, you can start collecting data to reach those goals. The most important thing is to ensure that the data you collect is reliable and valid. Otherwise, any decisions you make using the data could result in a negative outcome for your business.

5. Examine the information and apply your findings

As a researcher, it's important to examine the data you're collecting and analyzing before you apply your findings. This is because data can be misleading, leading to inaccurate conclusions. Ask yourself whether it is what you are expecting? Is it similar to other datasets you have looked at? 

There are many scientific ways to examine data, but some common methods include:

looking at the distribution of data points

examining the relationships between variables

looking for outliers

By taking the time to examine your data and noticing any patterns, strange or otherwise, you can avoid making mistakes that could invalidate your research.

  • How qualitative analysis software streamlines the data collection process

Knowledge derived from data does indeed carry power. However, if you don't convert the knowledge into action, it will remain a resource of unexploited energy and wasted potential.

Luckily, data collection tools enable organizations to streamline their data collection and analysis processes and leverage the derived knowledge to grow their businesses. For instance, qualitative analysis software can be highly advantageous in data collection by streamlining the process, making it more efficient and less time-consuming.

Secondly, qualitative analysis software provides a structure for data collection and analysis, ensuring that data is of high quality. It can also help to uncover patterns and relationships that would otherwise be difficult to discern. Moreover, you can use it to replace more expensive data collection methods, such as focus groups or surveys.

Overall, qualitative analysis software can be valuable for any researcher looking to collect and analyze data. By increasing efficiency, improving data quality, and providing greater insights, qualitative software can help to make the research process much more efficient and effective.

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4 Gathering and Analyzing Qualitative Data

Gathering and analyzing qualitative data.

As the role of clinician researchers expands beyond the bedside, it is important to consider the possibilities of inquiry beyond the quantitative approach. In contrast to the quantitative approach, qualitative methodology is highly inductive and relies on the background and interpretation of the researcher to derive meaning from the gathering and analytic processes central to qualitative inquiry.

Chapter 4: Learning Objectives

As you explore the research opportunities central to your interests to consider whether qualitative component would enrich your work, you’ll be able to:

  • Define what qualitative research is
  • Compare qualitative and quantitative approaches
  • Describe the process of creating themes from recurring ideas gleaned from narrative interviews

What Is Qualitative Research?

Quantitative researchers typically start with a focused research question or hypothesis, collect a small amount of numerical data from a large number of individuals, describe the resulting data using statistical techniques, and draw general conclusions about some large population. Although this method is by far the most common approach to conducting empirical research in fields such as respiratory care and other clinical fields, there is an important alternative called qualitative research. Qualitative research originated in the disciplines of anthropology and sociology but is now used to study psychological topics as well. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques, such as grounded theory, thematic analysis, critical discourse analysis, or interpretative phenomenological analysis. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the experience of their research participants.

Consider, for example, a study by researcher Per Lindqvist and his colleagues, who wanted to learn how the families of teenage suicide victims cope with their loss (Lindqvist, Johansson, & Karlsson, 2008). They did not have a specific research question or hypothesis, such as, What percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from their perspectives. To address this question, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected.

The Purpose of Qualitative Research

The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior. This method is how we know that people have a strong tendency to obey authority figures, for example, and that female undergraduate students are not substantially more talkative than male undergraduate students. But while quantitative research is good at providing precise answers to specific research questions, it is not nearly as good at generating novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And quantitative research is not very good at communicating what it is actually like to be a member of a particular group in a particular situation.

But the relative weaknesses of quantitative research are the relative strengths of qualitative research. Qualitative research can help researchers to generate new and interesting research questions and hypotheses. The research of Lindqvist and colleagues, for example, suggests that there may be a general relationship between how unexpected a suicide is and how consumed the family is with trying to understand why the teen committed suicide. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Among qualitative researchers, this depth is often referred to as “thick description” (Geertz, 1973) .

Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the “lived experience” of the research participants. Lindqvist and colleagues, for example, describe how all the families spontaneously offered to show the interviewer the victim’s bedroom or the place where the suicide occurred—revealing the importance of these physical locations to the families. It seems unlikely that a quantitative study would have discovered this detail. The table below lists some contrasts between qualitative and quantitative research

Table listing major differences between qualitative and quantitative approaches to research. Highlights of qualitative research include deep exploration of a very small sample, conclusions based on interpretation drawn by the investigator and that the focus is both global and exploratory.

Data Collection and Analysis in Qualitative Research

Data collection approaches in qualitative research are quite varied and can involve naturalistic observation, participant observation, archival data, artwork, and many other things. But one of the most common approaches, especially for psychological research, is to conduct interviews. Interviews in qualitative research can be unstructured—consisting of a small number of general questions or prompts that allow participants to talk about what is of interest to them—or structured, where there is a strict script that the interviewer does not deviate from. Most interviews are in between the two and are called semi-structured interviews, where the researcher has a few consistent questions and can follow up by asking more detailed questions about the topics that come up. Such interviews can be lengthy and detailed, but they are usually conducted with a relatively small sample. The unstructured interview was the approach used by Lindqvist and colleagues in their research on the families of suicide victims because the researchers were aware that how much was disclosed about such a sensitive topic should be led by the families, not by the researchers.

Another approach used in qualitative research involves small groups of people who participate together in interviews focused on a particular topic or issue, known as focus groups. The interaction among participants in a focus group can sometimes bring out more information than can be learned in a one- on-one interview. The use of focus groups has become a standard technique in business and industry among those who want to understand consumer tastes and preferences. The content of all focus group interviews is usually recorded and transcribed to facilitate later analyses. However, we know from social psychology that group dynamics are often at play in any group, including focus groups, and it is useful to be aware of those possibilities. For example, the desire to be liked by others can lead participants to provide inaccurate answers that they believe will be perceived favorably by the other participants. The same may be said for personality characteristics. For example, highly extraverted participants can sometimes dominate discussions within focus groups.

Data Analysis in Qualitative Research

Although quantitative and qualitative research generally differ along several important dimensions (e.g., the specificity of the research question, the type of data collected), it is the method of data analysis that distinguishes them more clearly than anything else. To illustrate this idea, imagine a team of researchers that conducts a series of unstructured interviews with people recovering from alcohol use disorder to learn about the role of their religious faith in their recovery. Although this project sounds like qualitative research, imagine further that once they collect the data, they code the data in terms of how often each participant mentions God (or a “higher power”), and they then use descriptive and inferential statistics to find out whether those who mention God more often are more successful in abstaining from alcohol. Now it sounds like quantitative research. In other words, the quantitative-qualitative distinction depends more on what researchers do with the data they have collected than with why or how they collected the data.

But what does qualitative data analysis look like? Just as there are many ways to collect data in qualitative research, there are many ways to analyze data. Here we focus on one general approach called grounded theory (Glaser & Strauss, 1967) . This approach was developed within the field of sociology in the 1960s and has gradually gained popularity in psychology. Remember that in quantitative research, it is typical for the researcher to start with a theory, derive a hypothesis from that theory, and then collect data to test that specific hypothesis. In qualitative research using grounded theory, researchers start with the data and develop a theory or an interpretation that is “grounded in” those data. They do this analysis in stages. First, they identify ideas that are repeated throughout the data. Then they organize these ideas into a smaller number of broader themes. Finally, they write a theoretical narrative—an interpretation of the data in terms of the themes that they have identified. This theoretical narrative focuses on the subjective experience of the participants and is usually supported by many direct quotations from the participants themselves.

As an example, consider a study by researchers Laura Abrams and Laura Curran, who used the grounded theory approach to study the experience of postpartum depression symptoms among low-income mothers (Abrams & Curran, 2009) . Their data were the result of unstructured interviews with 19 participants. The table below hows the five broad themes the researchers identified and the more specific repeating ideas that made up each of those themes. In their research report, they provide numerous quotations from their participants, such as this one from “Destiny:”

“Well, just recently my apartment was broken into and the fact that his Medicaid for some reason was cancelled so a lot of things was happening within the last two weeks all at one time. So that in itself I don’t want to say almost drove me mad but it put me in a funk….Like I really was depressed. (p. 357)”

Their theoretical narrative focused on the participants’ experience of their symptoms, not as an abstract “affective disorder” but as closely tied to the daily struggle of raising children alone under often difficult circumstances. The table below illustrates the process of creating themes from repeating ideas in the qualitative research gathering and analysis process.

Table illustrates the process of grouping repeating ideas to identify recurring themes in the qualitative research gathering process. This requires a degree of interpretation of the data unique to the qualitative approach.

Given their differences, it may come as no surprise that quantitative and qualitative research do not coexist in complete harmony. Some quantitative researchers criticize qualitative methods on the grounds that they lack objectivity, are difficult to evaluate in terms of reliability and validity, and do not allow generalization to people or situations other than those actually studied. At the same time, some qualitative researchers criticize quantitative methods on the grounds that they overlook the richness of human behavior and experience and instead answer simple questions about easily quantifiable variables.

In general, however, qualitative researchers are well aware of the issues of objectivity, reliability, validity, and generalizability. In fact, they have developed a number of frameworks for addressing these issues (which are beyond the scope of our discussion). And in general, quantitative researchers are well aware of the issue of oversimplification. They do not believe that all human behavior and experience can be adequately described in terms of a small number of variables and the statistical relationships among them. Instead, they use simplification as a strategy for uncovering general principles of human behavior.

Many researchers from both the quantitative and qualitative camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). In fact, the studies by Lindqvist and colleagues and by Abrams and Curran both combined quantitative and qualitative approaches. One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. Again, while a qualitative study might suggest that families who experience an unexpected suicide have more difficulty resolving the question of why, a well-designed quantitative study could test a hypothesis by measuring these specific variables in a large sample. A second approach to combining quantitative and qualitative research is referred to as triangulation. The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge and how can they be reconciled?

Using qualitative research can often help clarify quantitative results via triangulation. Trenor, Yu, Waight, Zerda, and Sha (2008) investigated the experience of female engineering students at a university. In the first phase, female engineering students were asked to complete a survey, where they rated a number of their perceptions, including their sense of belonging. Their results were compared across the student ethnicities, and statistically, the various ethnic groups showed no differences in their ratings of their sense of belonging.

One might look at that result and conclude that ethnicity does not have anything to do with one’s sense of belonging. However, in the second phase, the authors also conducted interviews with the students, and in those interviews, many minority students reported how the diversity of cultures at the university enhanced their sense of belonging. Without the qualitative component, we might have drawn the wrong conclusion about the quantitative results.

This example shows how qualitative and quantitative research work together to help us understand human behavior. Some researchers have characterized qualitative research as best for identifying behaviors or the phenomenon whereas quantitative research is best for understanding meaning or identifying the mechanism. However, Bryman (2012) argues for breaking down the divide between these arbitrarily different ways of investigating the same questions.

Key Takeaways

  • The qualitative approach is centered on an inductive method of reasoning
  • The qualitative approach focuses on understanding phenomenon through the perspective of those experiencing it
  • Researchers search for recurring topics and group themes to build upon theory to explain findings
  • A mixed methods approach uses both quantitative and qualitative methods to explain different aspects of a phenomenon, processes, or practice
  • This chapter can be attributed to Research Methods in Psychology by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted. This adaptation constitutes the fourth edition of this textbook, and builds upon the second Canadian edition by Rajiv S. Jhangiani (Kwantlen Polytechnic University) and I-Chant A. Chiang (Quest University Canada), the second American edition by Dana C. Leighton (Texas A&M University-Texarkana), and the third American edition by Carrie Cuttler (Washington State University) and feedback from several peer reviewers coordinated by the Rebus Community. This edition is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. ↵

Gathering and Analyzing Qualitative Data Copyright © by megankoster is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Data Collection: Best Methods + Practical Examples

data collection featured image

Data is an extremely important factor when it comes to gaining insights about a specific topic, study, research, or even people. This is why it is regarded as a vital component of all of the systems that make up our world today. 

In fact, data offers a broad range of applications and uses in the modern age. So whether or not you’re considering digital transformation, data collection is an aspect that you should never brush off, especially if you want to get insights, make forecasts, and manage your operations in a way that creates significant value. 

However, many people still gravitate towards confusion when they come to terms with the idea of data collection. 

In this article, we will help you understand:

  • What is data collection 
  • Why collecting and acquiring data can be beneficial for your business
  • What are the different methods of collecting data
  • Modern tools for data collection

Need help collecting data for your business? We can help! At Iterators, we design, build and maintain custom software solutions that will help you achieve desired results.

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What is Data Collection?

Data collection is defined as a systematic method of obtaining, observing, measuring, and analyzing accurate information to support research conducted by groups of professionals regardless of the field where they belong. 

While techniques and goals may vary per field, the general data collection methods used in the process are essentially the same. In other words, there are specific standards that need to be strictly followed and implemented to make sure that data is collected accurately.

Not to mention, if the appropriate procedures are not given importance, a variety of problems might arise and impact the study or research being conducted.

The most common risk is the inability to identify answers and draw correct conclusions for the study, as well as failure to validate if the results are correct. These risks may also result in questionable research, which can greatly affect your credibility.

So before you start collecting data, you have to rethink and review all of your research goals. Start by creating a checklist of your objectives. Here are some important questions to take into account:

  • What is the goal of your research?
  • What type of data are you collecting?
  • What data collection methods and procedures will you utilize to acquire, store, and process the data you’ve gathered?

Take note that bad data can never be useful. This is why you have to ensure that you only collect high-quality ones. But to help you gain more confidence when it comes to collecting the data you need for your research, let’s go through each question presented above.

What is the Goal of your Research?

Identifying exactly what you want to achieve in your research can significantly help you collect the most relevant data you need. Besides, clear goals always provide clarity to what you are trying to accomplish. With clear objectives, you can easily identify what you need and determine what’s most useful to your research.

What Type of Data are you Collecting?

Data can be divided into two major categories: qualitative data and quantitative data. Qualitative data is the classification given to a set of data that refers to immeasurable attributes. Quantitative data, on the other hand, can be measured using numbers. Based on the goal of your research, you can either collect qualitative data or quantitative data; or a combination of both.

What Data Collection Methods will you use?

There are specific types of data collection methods that can be used to acquire, store, and process the data. If you’re not familiar with any of these methods, keep reading as we will tackle each of them in the latter part of this article. But to give you a quick overview, here are some of the most common data collection methods that you can utilize:

  • Observation
  • Ethnography
  • Secondary data collection
  • Archival research
  • Interview/focus group

Note : We will discuss these methods more in the Data Collection Methods + Examples section of this article.

Benefits of Collecting Data

Regardless of the field, data collection offers heaps of benefits. To help you become attuned to these advantages, we’ve listed some of the most notable ones below:

  • Collecting good data is extremely helpful when it comes to identifying and verifying various problems, perceptions, theories, and other factors that can impact your business.
  • It allows you to focus your time and attention on the most important aspects of your business.
  • It helps you understand your customers better. Collecting data allows your company to truly understand what your consumers expect from you, the unique products or services they desire, and how they want to connect with your brand as a whole.
  • Collecting data allows you to study and analyze trends better.
  • Data collection enables you to make more effective decisions and come up with solutions to common industry problems.
  • It allows you to resolve problems and improve your products or services based on data collected.
  • Accurate data collection can help build trust, establish productive and professional discussions, and win the support of important decision-makers and investors.
  • When engaging with key decision-makers, collecting, monitoring, and assessing data on a regular basis may offer businesses reliable, relevant information.
  • Collecting relevant data can positively influence your marketing campaigns, which can help you develop new strategies in the future.
  • Data collection enables you to satisfy customer expectations for personalized messages and recommendations.

These are just a few of the many benefits of data collection in general. In fact, there are still a lot of advantages when it comes to collecting consumer data that you can benefit from.

Data Collection Methods + Examples

As mentioned earlier, there are specific types of data collection methods that you can utilize when gathering data for your research. These data collection methods involve conventional, straightforward, and more advanced data gathering and analysis techniques.

Furthermore, it is important to remember that the data collection method being used will depend on the type of business you’re running. Therefore, not all types of data collection methods are appropriate for the study or research that you are conducting for your business. That is why being mindful of these methods can definitely help you find the best one for your needs.

Here are the top 5 data collection methods and examples that we’ve summarized for you:

1. Surveys and Questionnaires

Surveys and questionnaires, in their most foundational sense, are a means of obtaining data from targeted respondents with the goal of generalizing the results to a broader public. Almost everyone involved in data collection, especially in the business and academic sector relies on surveys and questionnaires to obtain credible data and insights from their target audience.

Here are several key points to remember when utilizing this data collection method:

  • Surveys can be easily done online and with ease. Fact that the digital landscape is constantly evolving, online surveys are becoming more and more prevalent every day.
  • Online surveys can be accessed anytime and anywhere. The accessibility that online surveys and questionnaires provide is one of the most significant advantages that you can utilize to collect data from your target audience with ease.
  • Low price method. Compared to the other data collection methods, creating surveys and questionnaires don’t require large spends.
  • Offers a wide range of methods of data collection. When utilizing surveys and questionnaires, you will have the power to collect different data types such as opinions, values, preferences, etc.
  • Flexibility when it comes to analyzing data. Surveys and questionnaires are easier to analyze compared to other methods.

Here is an example of an online survey/questionnaire:

data-collection-survey

2. Interviews

An interview is accurately defined as a formal meeting between two individuals in which the interviewer asks the interviewee questions in order to gather information. An interview not only collects personal information from the interviewees, but it is also a way to acquire insights into people’s other skills.

Here is the summary of advantages you can gain from this data collection method:

  • Conducting interviews can help reveal more data about the subject. Interviews can assist you in explaining, understanding, and exploring the perspectives, behavior, and experiences of participants.
  • Interviews are more accurate. Since it is an interview, subjects won’t be able to falsify their identities such as lying about their age, gender, or race.
  • An interview is a flowing and open-ended conversation. Unlike other methods, interviews enable interviewers to ask follow-up questions in order to better understand the subject.

Should you want to take advantage of this data collection method, you can refer to the table below for guidance:

Types of Interviews

example of data gathering procedure in research paper

3. Observations

The observation method of data collection involves seeing people in a certain setting or place at a specific time and day. Essentially, researchers study the behavior of the individuals or surroundings in which they are analyzing. This can be controlled, spontaneous, or participant-based research.

Here are the advantages of Observation as a data collection method:

  • Ease of data collection. This data collection method does not require researchers’ technical skills when it comes to data gathering.
  • Offers detailed data collection. Observations give researchers the ability and freedom to be as detail-oriented as possible when it comes to describing or analyzing their subjects’ behaviors and actions.
  • Not dependent on people’s proactive participation. The Observation method doesn’t require people to actively share about themselves, given the fact that some may not be comfortable with doing that.

When a researcher utilizes a defined procedure for observing individuals or the environment, this is known as structured observation . When individuals are observed in their natural environment, this is known as naturalistic observation .  In participant observation , the researcher immerses himself or herself in the environment and becomes a member of the group being observed.

Here are relevant case studies and citations from PRESSBOOKS that provide in-depth examples of Observational research.

Structured Observation

“Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999). One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider.“

Naturalistic Observation

“Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr. Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied.ng that.” 

Participant Observation

“Another example of participant observation comes from a study by sociologist Amy Wilkins (published in Social Psychology Quarterly) on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008). Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.”

4. Records and Documents

This data collection method involves analyzing an organization’s existing records and documents to track or project substantial changes over a specific time period. The data may include the following:

  • Staff reports
  • Information logs
  • Minutes of meetings

Here are the significant advantages of using records and documents as a data collection method for your business:

  • The data is already available. There is no need for you to conduct any active research because the information you need is already made available.
  • Easy tracking of collected data. Records and documents will allow you to recheck the history of a specific event that can help you find answers to questions, such as why your supplies ran out way outside your projected schedule for example.

Examples of Records and Documents:

Customer Database

example of data gathering procedure in research paper

5. Focus Groups

A focus group is a group interview of six to twelve persons with comparable qualities or shared interests. A moderator leads the group through a series of planned topics. The moderator creates an atmosphere that encourages people to discuss their thoughts and opinions. Focus groups are a type of qualitative data collection in which the information is descriptive and cannot be quantified statistically.

Here are the advantages of Focus Groups as a data collection method:

  • Easy collection of qualitative data. Focus groups can easily collect qualitative data since the moderator can ask questions to determine the respondents’ reactions.
  • Non-verbal cues can be easily observed. The presence of the moderator is an essential part of the data collection. With the moderator around, it will be easier to obtain data from non-verbal responses from the participants. 

Since Focus Groups are commonly carried out in person, there are no tangible examples to refer to. Moreover, here’s a diagram from QuestionPro to show how it works:

example of data gathering procedure in research paper

Quantitative Data vs. Qualitative Data

Data collection is comprehensive, analytical, and in some cases, extremely difficult. But when you categorize the data into the two categories we’ve mentioned earlier in this article, it becomes easy to deal with. To provide you with a brief understanding of qualitative data collection methods and quantitative data collection methods, we’ve outlined each of them below:

Quantitative Data

Quantitative data is numerical and is generally organized which means that it is more precise and definite. And because this method of data collection is measured in terms of numbers and values, it is a better choice for statistical analysis.

Here are some of the most popular quantitative data collection methods you can use to obtain concrete results:

  • Experiments
  • Market reports

Quantitative data examples:

example of data gathering procedure in research paper

Qualitative Data

Unlike quantitative data, qualitative data is composed of non-statistical information that is commonly structured or unstructured. Qualitative data isn’t also measured based on concrete statistics that are used to create graphs and charts. They are classified according to characteristics, features, identities, and other categorizations.

Qualitative data is also exploratory in nature and is frequently left wide open until more study has been completed. Theorizations, assessments, hypotheses, and presumptions are all based on qualitative research data.

Here are some of the most commonly known qualitative data collection methods you can use to generate non-statistical results:

  • Records and documents
  • Interview transcripts
  • Focus groups
  • Observation research

Qualitative data examples:

example of data gathering procedure in research paper

Operationalization

Operationalization is the process of turning theoretical data into measurable observations. With the help of operationalization, you can effectively gather data on concepts that can’t be easily measured. This method converts a hypothetical, abstract variable into a collection of specific processes or procedures that determine the variable’s meaning in a given research. In a nutshell, operationalization serves as a link between hypothetically grounded ideas and the procedures employed to validate them.

Operationalization is a crucial element of empirically grounded research because it allows researchers to describe how a notion is analyzed or generated in a given study. There are three key phases in the operationalization process:

  • Determine which of the major ideas or concepts you want to learn more about.
  • Each idea should be represented by a different variable.
  • For each of your variables, choose indicators.

To provide you with a clear guide on how operationalization works, let’s illustrate how the process is carried out based on the three key phases.

Please refer to the following:

1. Determine which of the major ideas or concepts you want to learn more about.  

For example, the two main ideas you want to learn more about are the following:

  • Business Performance

From the chosen concepts, formulate a question that will lead you to realize your research goal. Is there are correlation between marketing and business performance?

2. Each idea should be represented by a different variable.

Here is an illustration of the second phase of the operationalization process:

example of data gathering procedure in research paper

Take note that in order to find the alternate and null hypothesis of the following variables, utilizing the right data collection method is extremely important.

3. For each of your variables, choose indicators.

Your indicators will help you collect the necessary data that you need in order to arrive at the most credible conclusions.

example of data gathering procedure in research paper

Data Collection Tools

There are heaps of data collection tools that you can utilize to gather good data online. Some of these tools have already been discussed above such as interviews, surveys, focus groups, etc.

While most of the aforementioned methods of data collection are effective, there are other data collection tools that offer convenience to business researchers. Here are some of them:

Data Scraping

Data scraping is the process of collecting data from a website and saving it as a local file on a computer. It’s among the most effective data collection tools that you can use to gather information from the web.

Some of the most popular data scraping utilization includes the following:

  • Locating sales leads
  • Conducting market research
  • Finding business intelligence
  • Sending product data

You may customize your scraping criteria or parameters to selectively target a specific attribute, especially with the proper data scraping tool. You can easily collect qualitative and quantitative data in a manner that can be readily implemented into your study or business procedures.

Information Management Systems

Although these management systems are generally meant to manage and monitor your database, they may also assist you in collecting data, particularly internal data generated by your business. Some of the information management systems used by various businesses that you can collect data from can be found in the following areas or categories:

  • Sales and Marketing
  • Research & Development
  • Human Resources
  • Productivity
  • Artificial Intelligence and Cognitive Computing
  • Business Intelligence

Data Collection Software

There is plenty of data collection software that can be used to acquire information from the internet. One of the best examples is Google Forms. It allows you to develop specific forms like job application forms, making it simple to collect information from applicants.

Here is some data collection software you can use:

  • KoboToolbox

Data collection has become a crucial strategy for many professionals and businesses. While it might be a difficult task for tenderfoot researchers or business owners, understanding its methods can be contributory to collecting data in the most accurate way.

Well illustrated, clear and understandable. Thank you so much.

Thank you now have a picture of what i want to do

Good introduction Very useful.

thank you very much for the explanations

thanks alot. you have really simplified my work

Great information. It is always a good reminder to ask ourselves what is our purpose of our intended data collection and how can we design a meaningful action plan to collect and utilize it.

I’m doing my bachelors level research in which I have one independent and two dependent variables. I’m seeing the impact of the independent variable on the two dependent variables separately. The two dependent variables have no connection with each other. The participants for the study are 60 and are not grouped. Which test would be recommended for it?

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  • Data Collection Methods | Step-by-Step Guide & Examples

Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

  • The  aim of the research
  • The type of data that you will collect
  • The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address, and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

  • Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
  • Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

  • Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
  • Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

  • Experimental research is primarily a quantitative method.
  • Interviews , focus groups , and ethnographies are qualitative methods.
  • Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalisation means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

  • You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
  • You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organise and store your data.

  • If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
  • If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
  • You can prevent loss of data by having an organisation system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

To ensure that high-quality data is recorded in a systematic way, here are some best practices:

  • Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
  • Double-check manual data entry for errors.
  • If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

When conducting research, collecting original data has significant advantages:

  • You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
  • You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

Reliability and validity are both about how well a method measures something:

  • Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
  • Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

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.

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Why You Should Read a Data Gathering Procedure Example

Patricia Stones

The data gathering procedure you employ in your paper determines if you receive a piece that is trustworthy or not. Therefore, it is crucial to employ the best procedure to get the perfect results. It improves the quality of the paper and makes you sound scholarly.

Most people struggle when need to gather data. While some do not know the data collection methodologies to follow, the majority do not have the experience in data handling. Eventually, they prepare papers that only earn them low grades. What is the remedy in such cases? Have a look at a perfect data gathering procedure example to be well-versed with the procedure that can work for your situation. In the process, you can make your work easier and improve the general quality of the papers you can prepare.

The best remedy for those without the sills in data gathering is to hire experts who are proficient in this field. Fortunately, we stand out as the company that can assist you with such issues. We have worked on a variety of papers that require verifiable data and understand what can work perfectly for you. With our assistance, you do not strain with data collection and handling. You follow every stage to ensure what you receive is perfect.

Table of Contents

What Is the Definition of Data Gathering Procedure?

Dissertation writing involves the handling of statistical data. Therefore, you need to know the best data to use in your paper. The definition of data gathering procedure is that it is the technique used to obtain the information used in a dissertation to substantiate the claims made by a writer. To get the perfect outcome, you should use the best procedure. If you are unsure of how to obtain your data, it is advisable to hire experts in this field to offer assistance. We have data experts who can help with these tasks.

What are the data collection methods that you can use? They are explained below:

  • Use of surveys

The method is mainly effective for those who need qualitative data to use in their academic documents. In surveys, open-ended questions are used. What kind of information can be collected using this method? They include the perception people have on a product, attitudes towards government policy, the beliefs people hold, or the knowledge people have on a given issue, among other information types. For the exact information needed, the questions should not be leading and should cover the exact areas needed by the researchers. The data is later analyzed to obtain the conclusions needed.

  • Conducting interviews

This quantitative research data gathering procedure is used to obtain from people on a one-on-one basis. In this case, the researcher should have several predetermined questions. The interview questions can be close-ended, like in the case where the interviewees are expected to provide the ‘YES’ or ‘NO’ type of responses. It can also have open-ended questions in which the respondent has the freedom to provide a response they are comfortable with. To ensure the data collected is rich in the content required, the interviewer should ensure there are follow-up questions for areas where the respondent may provide ambiguous information.

There are different ways the interviews can be conducted. The first way is to do it face-to-face. As the respondent provides the answers, the interviewee can record them by writing or tape-recording. The data collected is later sorted and written in the paper. The other method is through phone conversations. Your respondent should provide the answers required as you keep a clean record that you can use later to write the paper needed.

  • Taking a focus group

In this case, the interviewee can take a group and get the information from them. There is a set of predetermined questions that are inquired from the respondents in turns. The method is effective when different people hold varied opinions on the same issue. Focus groups differ depending on the type of responses required in the probe. To get the most reliable results from this method, the number of people in the group should be between 5 and 10 people.

  • Direct observation

The data gathering procedure for qualitative research applies the sensory organs such as the eyes to see what is going on, ears to hear the things going on, and the ears to smell. The method helps the researcher to avoid bias in what people say.

  • Content Analysis

The researcher uses data that is already available and supports their point of view. Different documents can be used in this case, including newspapers with reputation, research articles from known experts, approved government reports, and other online data sources that can be of help in this case. For the reliability of the data, different sources should be used for research.

It is you to determine the methodology that can work for your case when it comes to data collection. Choosing a wrong procedure may mean that you obtain unreliable or irrelevant data. You do not want to face the frustrations of presenting data that is unrelated to your topic. Therefore, it is advisable to hire an expert who understands how things work as far as data is concerned. We come in handy in such situations. Do not use faulty data gathering procedures when we can assist you in collecting the best data using our proven collection techniques.

What Determines the Sample of Data Gathering Procedure

Not all the procedures are effective for your paper. What applies to one paper may not be recommended for another. What are the factors in assessing to settle on the best procedure? Get answers:

The Course and Topic of Study Handled

Different courses require varying procedures when it comes to the collection and handling of data. While there are those courses where secondary information sources can work, others need data that one obtains first first-hand. For example, the type of data that is acceptable for those handling engineering courses is not the same as what works for those pursuing psychology. The same applies to the topic. The data needs for different subjects vary. Therefore, you must analyze the needs of your course and topic before selecting a procedure for data gathering.

The Specific Faculty Guidelines on Data Gathering

Your department has its instructions when it comes to the sample of data gathering procedure. Failure to adhere to what is specified may mean you miss important marks because your paper may not be as good as what is expected from you. Therefore, it is crucial to be well-versed with your faculty guidelines. Where the rules seem too strict for you, it is advisable to get experts who are comfortable with the specifications. We are the best company when it comes to adherence to the rules. The professionals assess all the guidelines you submit to ensure the data obtained meet the specifications you submit.

Personal Preferences in Data Gathering

The convenience encountered in data gathering varies from one person to the next. What one person considers to be hard may be easy for another. On a personal level, you should opt for a procedure that you are comfortable with. It is you who decide on the topic, settle on the data, analyze and come up with the conclusion. Therefore, selecting a procedure you are sure can work for you is fundamental. A convenient information gathering procedure saves you from stress.

What Should You Do Before Data Gathering?

You should not embark on the data gathering if you are unsure of what is required. The first step is to analyze and understand the topic you have. The keywords encountered determine whether you need a quantitative or qualitative type of data. Where you are expected to settle on your own topic, take something you are sure you can easily obtain data to defend.

The next procedure is to study the guidelines that are provided for doing the paper and collection of the data. For example, some professors insist that a student should use a given method of data collection. Your grade depends on whether you adhere to that specification or not.

Prepare adequately before you begin the gathering. For instance, you have to settle on a given method and determine the tools you need for data gathering. You can read an approved data gathering procedure pdf to understand what to do.

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Data Gathering Procedure

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Related Papers

Sachin Kumar

example of data gathering procedure in research paper

Md Raihan Ubaidullah

Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. The data collection component of research is common to all fields of study including physical and social sciences, humanities, business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture quality evidence that then translates to rich data analysis and allows the building of a convincing and credible answer to questions that have been posed (www.en.wikipedia.org). It is an endeavor to discuss about some techniques of data collection.

Metin Kozak

Sapthami KKM

Jessa Arcina

Research design is the blue print of the procedures that enable the research to test hypothesis by reaching valid conclusions about the relationship between dependent and in depend variables. It is a plan structure and strategy of research prepared to obtain answer to research questions and to control variances. Before doing the various studies on the present thesis the researcher has fixed the topic and area because it provide the entire draft of the scheme of the research staring from writing the hypothesis their operational implications to the final analysis of the data. The structural of the research is more specific as it provides the outline, the scheme the paradigm o f the operation of the variables. It presents a series of guide posts to enable the researcher to progress in the right direction it gives an outline of the

Maira Muchlis

Q ualitative researchers typically rely on four methods for gathering information: (a) participating in the setting, (b) observing directly, (c) interviewing in depth, and (d) analyzing documents and material culture. These form the core of their inquiry—the staples of the diet. Several secondary and specialized methods of data collection supplement them. This chapter provides a brief discussion of the primary and the secondary methods to be considered in designing a qualitative study. This discussion does not replace the many excellent, detailed references on data collection (we refer to several at the end of this chapter). Its purpose is to guide the proposal writer in stipulating the methods of choice for his study and in describing for the reader how the data will inform his research questions. How the researcher plans to use these methods, however, depends on several considerations. Chapter 1 presents an introductory discussion of qualitative method-ological assumptions. As the grounding for a selection of methods, we extend that discussion here, using Brantlinger's (1997) useful summary of seven categories of crucial assumptions for qualitative inquiry. The first concerns the researcher's views of the nature of the research: Is the inquiry technical and neutral, intending to conform to traditional research within her discipline, or is it controversial and critical, with an ❖ ❖ ❖

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