define the concept methodology

What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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198 Comments

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection

Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
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• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
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The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE :   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE : If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE :   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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Research Methodology: An Introduction

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Part of the book series: Progress in IS ((PROIS))

Digital age brings the most dramatic changes in this study and research discipline as well as in other fields of human activities. Scientific research is known for a very long time, however in comparison with other research fields the business and management researches are a little bit younger. The information technologies and new research methodologies that have recently emerged, dramatically change the nature of the research. Therefore, researchers should be ready to absorb new possibilities and follow basic roles coming from earlier stages of the discipline. The intention of this chapter is to provide a brief introduction to those aspects of pertinent research to beginner researchers. The chapter presents the nature of scientific research so that it may be clearly understood and uses, as its basic approach, the fundamental principles of problem solving. The scope of the research provides an overviews the entire assumptions about reality, knowledge and human nature, key terms of theory and research presented. Main concepts of the research are discussed and all this is oriented to business, management and economic science specific.

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Davidavičienė, V. (2018). Research Methodology: An Introduction. In: Marx Gómez, J., Mouselli, S. (eds) Modernizing the Academic Teaching and Research Environment. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-74173-4_1

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What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

Introduction to Research Methods

4 defining and measuring concepts.

Back in chapter 2 we discussed one of the largest differences between the natural and social sciences. In the natural sciences concepts are generally universally understood. Ask a scientist in Japan and the United States what an atom is, and they’ll describe the same thing. The same thing with items like acceleration, or a photon, or light, or gravity. All of the important things they need have set definitions. The social sciences have a lot more difficulty defining exactly what we’re studying, and even if we develop a definition measuring it is more complicated. If you ask an American and a Japanese scientist what happiness is, they’ll likely have different definitions. Heck, ask two Americans in the same family and you’ll probably get two different ideas.

What is happiness? The Merriam-Webster dictionary says “a state of well-being and contentment. Wikipedia describes it as “used in the context of mental or emotional states, including positive or pleasant emotions ranging from contentment to intense joy.” Those are both circling the same ideas, but they don’t have the same precision as the definition a scientist could use for acceleration. But we all generally know what we mean by happiness, even if we can’t develop a definition that is universally shared. But let’s say we identify a definition that most people will understand. How should we measure happiness then?

Let’s say I want to do research on whether more equal societies (or countries or states or neighborhoods) are happier. How would I measure the happiness of California vs. Alabama, or in the United States as opposed to Peru? I could do a survey of people, and ask them to rate their own happiness. I could look at twitter by different localities, and rate how happy or unhappy tweets are. I could look at depression rates or how often certain drugs are prescribed in different locations. None of those are directly measuring happiness though. They’re all just approximations that might help me to understand happiness.

Defining your terms and moving from concepts to measures is one of the most dangerous points in the research process, but also in my opinion one of the most fun.

4.1 Concepts and Operationalization

We begin with concepts. We want to study resiliency, or health, or gentrification, or happiness. These are all concepts , abstract ideas or general notions that occur in the mind, in speech, or in thought. The name used to identify a concept is a “term”. For instance, the term “toughness” is a concept. Different people may have a different interpretations of the word “toughness,” but generally speaking people know what the word means when they hear it used in any context.

What we must then do is operationalize the concept. To operationalize is to identify a way we can measure the concept under study. For instance, if we want to measure a community’s health, we might measure it based on the average life span – assuming that individual’s that live longer on average are healthier. There are other operationalizations we could use for community health, such as the percentage of people that are overweight, of the infant mortality rate, or others I’m sure.

But again, these are just operationalizations of the concept, they aren’t themselves actually the concepts we want to study. Acceleration isn’t a concept. It’s mathematically defined, and so there are no questions about what it is in the sciences. We don’t have any definition of happiness, nor a formula to understand it. Thus, every research studying happiness has to define the term and justify the way they’ll operationalize it in their study.

It’s important not to get confused and assume the way we measure a concept is itself the concept. Reification is the assumption that they are the same, but that can often lead people astray. For example, have you ever seen someone on an internet message board that got into an argument that pointed to their education as proof they are smarter: “I have three advanced degrees, I must be smarter than you.” Education is a good measure of intelligence, but they aren’t the same thing. Smarter people are probably typically more educated, but that is often not true. Getting an education takes the means and opportunity to get finish school. In addition, an education generally celebrates a certain type of intelligence, but book smarts aren’t the only way one can understand intelligence.

Let’s step back. Sometimes we aren’t talking about a concept. If I’m a food scientist and I’m curious about the impact of coffee on health, coffee isn’t a concept. It’s just coffee. It’s not measuring an abstract idea, it’s just coffee. If I’m interested in who votes, voting isn’t a concept. A person goes to the ballot box and votes. That’s an act which people won’t disagree about whether happened or not.

Why do we care about voting? It’s unlikely an individual’s vote will have an impact on the election. I’m not trying to devalue voting (please go vote whenever you get the chance!), but why do we care about predictors of who votes, or falling rates of voting in the United States? Why would we care about this headline from the Pew Research Center?

I would say it’s because of the different concepts that voting is being used to operationalize, even if just implicitly. Voting might itself be a clearly defined action, but voting can also be a measurement of different concepts. Voting is an expression of political power in society, and political power is difficult to define or directly measure. Public participation is often operationalized by using the share of individuals that vote in a community because it’s an expression that people care to be engaged in making changes.

4.2 Evaluating Operationalizations

How do we know we’ve done a good job of operationalizing our concept? It isn’t enough to just say a measure approximates something. I could survey people on how many oranges they eat a week and call that health. But even if I say that is health, most people will understand that I’ve done a bad job of measuring my concept.

We have two things to keep in mind in evaluating our measures: validity and reliability. Validity tells you whether what you’re measuring is what you’re supposed to be measuring.

At its core, validity is about shared understanding. Unfortunately, there’s no great test for a measurements validity. The easiest way to make sure your measure is valid is to ask someone else. If you tell someone you’re measuring mental health by how many books they have on their shelf, they’ll probably look at you funny. That’s not a valid measure, and it won’t fit within their shared understanding of what you mean by mental health.

One objection you may get in your research is that your measure is invalid based on how you define it. If I measure intelligence by years of school completed, some reviewers might find that valid, but others might object. Year of schooling isn’t intelligence they may say, it’s a better measure of education. As such, it isn’t the measure itself that is the problem – it’s what I’m purporting that its operationalizing that is.

Reliability is another concern in thinking about the quality of our measures. Reliability is all about consistency – will you get the same response if you measure the same thing multiple times.

For instance, let’s say I survey people about how concerned they are with their health. I’m unlikely to get consistent answers from people, depending on the time of day or year. People are often more concerned about their health around the holidays, after they’ve seen family, been stuck inside because of the weather, and eaten holiday meals. If I ask them in the middle of March though I might get a different answer. With such unreliable answers and responses, I would want to make sure I’m measuring the phenomena multiple ways and multiple times to get a better idea of a person’s opinion.

If I’m concerned with measuring someone’s height, I have fewer concerns. If I ask you how tall you are today and two days later and a year from now, I’ll probably get the same exact response (maybe not over decades, as people do shrink over time)

Weight is an interesting case. Yes, people’s weights change over time. I wouldn’t expect you to weighh the same today and tomorrow, but people will generally give reliable responses, even with a little fib. If I ask people how much they weigh, they’ll probably tell me something roughly correct, just probably 5 pounds lighter. As long as everyone takes the same 5 pounds off their weight before telling, it’s still a reliable question.

I used this example in class once, and you can imagine it for yourself. I had students write down how many pens they thought they had with them in their bags. Everyone did, and then counted the number of pens. Very few people were right, but more concerning was the nature of how they were wrong. Their answers were all over the place, some people over guessed and some people underused. If I was actually concerned with how many pens people had, just asking them wouldn’t provide me any real indication of how many pens they had. It’s an unreliable question.

Those things come up often in recall. How many calories did you eat today? Unless you tracked it, you’ll take a guess and you might be high or low. Humans aren’t great at processing or retaining that information, which makes those questions unreliable.

The image of a target is often used when discussing validity and reliability. A reliable archer puts their arrows all in the same spot, and a valid archer puts them near the target. If your measure is reliable but not valid, you’ll get really consistent measurements (how tall is a person), but it won’t actually be measuring the concept you’re concerned with (near the target). A valid and reliable measure is the goal.

4.3 Summary

In this chapter we’ve described concepts and measures, and particularly the importance in establishing how you’re going to operationalize the terms in your study. You can have the best research question, the coolest data, and a really nice analysis, but if your data isn’t actually measuring what it says it is your study is going to land like a dud.

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Concept – Definition, Types and Examples

Table of Contents

Definition:

Concept is a mental representation or an abstract idea that we use to understand and organize the world around us. It is a general notion that summarizes and simplifies complex information or experiences, making it easier to communicate and process.

For example, the concept of “love” is an abstract idea that represents a range of emotions and behaviors that people experience in their relationships with others. Similarly, the concept of “justice” represents a set of principles and standards that guide our sense of fairness and equality.

Types of Concept

Types of Concepts are as follows:

Concrete Concepts

These are concepts that refer to tangible objects or physical entities that can be perceived through the senses, such as a table, a car, or a flower.

Abstract Concepts

These are concepts that refer to ideas, qualities, or attributes that cannot be perceived through the senses, such as freedom, justice, or happiness.

Formal Concepts

These are concepts that are defined by specific rules or criteria, such as mathematical concepts like a triangle or a circle.

Natural Concepts

These are concepts that are based on our experience and interactions with the world, such as concepts like water, food, or family.

Social Concepts

These are concepts that are based on cultural or social norms, such as concepts like marriage, friendship, or etiquette.

Prototype Concepts

These are concepts that are based on typical or idealized examples of a category, such as a prototype concept of a bird that includes features like wings, feathers, and the ability to fly.

Exemplar Concepts

These are concepts that are based on specific examples or instances of a category, rather than on an idealized prototype.

Examples of Concept

Here are some examples of concepts:

• Love – a feeling of strong attachment or deep affection towards someone or something.
• Democracy – a system of government in which power is vested in the people and exercised through free and fair elections.
• Justice – the quality of being fair and impartial, particularly in the administration of the law.
• Equality – the state of being equal in status, rights, and opportunities.
• Freedom – the state of being free from coercion, constraint, or oppression.
• Creativity – the ability to produce original and imaginative ideas, works, or solutions.
• Sustainability – the ability to maintain ecological balance and meet the needs of the present generation without compromising the ability of future generations to meet their own needs.
• Globalization – the process of integration and interdependence among people, companies, and governments across the world.
• Diversity – the range of different cultures, ethnicities, genders, and other characteristics that exist within a group or society.
• Leadership – the ability to inspire and guide others towards a common goal or vision.

Applications of Concept

Applications of Concept are as follows:

• Education : Concepts play a crucial role in education, where they are used to help students develop a deeper understanding of various subjects. For example, in mathematics, concepts such as fractions, decimals, and geometric shapes are used to solve problems.
• Science : Concepts are used extensively in scientific research to help scientists understand and explain the natural world. For instance, concepts such as energy, matter, and gravity are used to describe and explain various phenomena.
• Business : Concepts such as marketing, branding, and customer service are essential for businesses to succeed. These concepts help businesses develop effective strategies to reach their target audience and improve customer satisfaction.
• Technology : Concepts are the foundation of many technological innovations. For example, the concept of artificial intelligence is used to develop intelligent machines that can perform tasks that would otherwise require human intervention.
• Philosophy : Concepts are a key aspect of philosophical inquiry, where they are used to analyze and understand complex ideas and arguments. For instance, concepts such as justice, ethics, and morality are used to explore ethical dilemmas and the nature of right and wrong.

Purpose of Concept

The purpose of a concept is to provide a mental framework or idea that helps us understand a particular topic or phenomenon. Concepts can range from simple ideas like “honesty” or “loyalty” to more complex ideas like “democracy” or “social justice.”

Concepts allow us to classify, organize, and analyze information, making it easier to understand and communicate. They also help us identify patterns, similarities, and differences between different ideas or things.

Concepts are essential for learning and intellectual development, as they provide a foundation for more advanced understanding and learning. They also allow us to build upon existing knowledge and make connections between different fields or areas of study.

Characteristics of Concept

There are several characteristics of a concept, including:

• Abstractness: A concept is an abstract idea that represents a class of objects, events, or phenomena. It is a mental construct that does not have a physical existence.
• Generalization : A concept represents a general idea that applies to a broad range of situations, objects, or events. It helps to identify commonalities among various things or phenomena.
• Mental Representation : A concept is a mental representation of an idea that we use to understand the world around us.
• Clarity : A concept should be clearly defined and understandable, so that others can comprehend it.
• Universality : A concept is universal and can be applied across different domains or contexts.
• Coherence : A concept should be logically consistent and coherent, so that it can be used to make sense of information and solve problems.
• Relevance : A concept should be relevant to the context in which it is used, and should have practical applications.
• Flexibility : A concept should be flexible enough to accommodate changes in our understanding of the world, and to adapt to new situations and contexts.
• Abstraction : A concept is an abstraction, meaning that it represents a simplified version of reality that is easier to understand and manipulate.

Advantage of Concept

Here are some advantages of concepts:

• Efficient Communication: Concepts provide a way to communicate efficiently by encapsulating complex ideas into simple, easily understandable units. For example, the concept of “love” represents a broad range of emotional experiences and allows us to communicate about this complex subject more easily.
• Problem-Solving: Concepts help us to solve problems by allowing us to identify patterns and similarities between different situations. This enables us to apply solutions that have worked in similar situations to new problems.
• Learning : Concepts provide a way to organize and structure new information, making it easier to learn and remember. By understanding the concept of “gravity,” for example, we can better understand the behavior of objects in the physical world.
• Decision Making: Concepts enable us to make more informed decisions by providing a framework for evaluating options and considering trade-offs. For example, the concept of “opportunity cost” helps us to weigh the benefits and drawbacks of different choices.

Limitations of Concept

Limitations of the Concept are as follows:

• Subjectivity : Concepts are inherently subjective, as they are based on individual experiences, beliefs, and cultural contexts. The meaning and interpretation of a concept may vary from person to person or culture to culture.
• Incompleteness : Concepts are often incomplete, as they represent a simplified version of reality. They may leave out important details or nuances, leading to misunderstandings or misinterpretations.
• Rigidity : Concepts can be rigid and inflexible, as they may not be able to accommodate new information or perspectives. This can lead to resistance to change or an inability to adapt to new situations.
• Overgeneralization : Concepts can also be overgeneralized, as people may apply a concept to situations where it does not apply or make assumptions based on incomplete or inaccurate information.
• Context dependence: The meaning of a concept can depend on the context in which it is used, making it difficult to apply the concept universally. This can lead to confusion or misinterpretation.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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methodology

Definition of methodology

Did you know.

Methodology and Science

The methodology employed in an experiment is essential to its success, and bad methodology has spoiled thousands of research projects. So whenever a piece of research is published in a scientific or medical journal, the researchers always carefully describe their methodology; otherwise, other scientists couldn't possibly judge the quality of what they've done.

Examples of methodology in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'methodology.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

New Latin methodologia , from Latin methodus + -logia -logy

1800, in the meaning defined at sense 1

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Cite this Entry

“Methodology.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/methodology. Accessed 1 Apr. 2024.

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Beyond the classics: A comprehensive look at concept analysis methods in nursing education and research

Joko gunawan.

1 Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand

2 Belitung Raya Foundation, Manggar, East Belitung, Bangka Belitung, Indonesia

Yupin Aungsuroch

Colleen marzilli.

3 University of Maine, School of Nursing, Orono, ME USA

Associated Data

Not applicable.

This editorial presents eight concept analysis methods for use in nursing research and education. In addition to the two classical methods of Walker and Avant’s and Rodgers’ concept analysis approaches that are typically utilized in nursing education and briefly discussed within this editorial, six additional methods are also presented including Schwartz-Barcott and Kim’s Hybrid model, Chinn and Kramer’s approach, Simultaneous Concept Analysis, Pragmatic Utility, Principle-Based Concept Analysis, and Semantic Concept Analysis. By familiarizing nursing educators, researchers, and students with these methods, educators can enhance their critical thinking and understanding of complex nursing concepts, preparing them for enhanced, multi-faceted contributions to nursing science.

Introduction

Concepts serve as abstract mental constructs, mental images of phenomena, units of meaning, or building blocks of theory, intended to summarize specific aspects or elements of the human experience (Chinn & Kramer, 1995 ; Penrod & Hupcey, 2005 ; Smith & Mörelius, 2021 ). However, for theory to be grounded in, and arise from real-world nursing practice, it is essential to bring clarity to the concepts under examination, known as concept analysis (Smith & Mörelius, 2021 ).

The primary aim of a concept analysis is to carefully study, clarify, develop, and critically assess a particular concept (Smith & Mörelius, 2021 ), all to attain a more profound and detailed understanding of the concept. While various methodologies for concept analysis are discussed in the nursing scientific literature, the most prominent approaches used among nursing students include the classical methods of Walker and Avant’s technique (Walker & Avant, 2014 ) and Rodger’s evolutionary approach (Rodgers, 1989 ).

This heavy reliance on these two methodologies raises an important question: are nursing students aware of the spectrum of available concept analysis methodologies rather than just the two common approaches? To our knowledge, teaching focuses mainly on Walker and Avant’s concept analysis and Rodgers’ evolutionary method in doctoral education influencing the analytical patterns of educators, researchers, and students. As a result, many students might not be familiar with other strategies for concept analysis. With this context in mind, this editorial article aims to provide a concise overview of various approaches that can be employed to conduct a thorough concept analysis.

Types of Concept Analysis Methods

1. walker and avant’s concept analysis.

Walker and Avant’s model presents a step-by-step method for analyzing a concept and creating a clear definition of the concept in question (Walker & Avant, 2014 ). It has eight stages based on Wilson’s techniques (Wilson, 1973 ). It starts by choosing a concept related to research goals and outlining the purpose of the analysis. Various uses of the concept in nursing are studied to understand its significance. Identifying defining attributes is crucial, serving as the concept’s core and distinguishing it from related ideas (Walker & Avant, 2014 ). While some researchers spot attributes through repeated terms, others use content analysis, thematic analysis, keyword clustering, or summative content analysis.

To make the concept more transparent, a model case is constructed as a “real-life” example, illustrating all main attributes. Additional cases, including borderline, related, and contrary examples, further explain the concept’s variations, refining its boundaries, and addressing differences from the model case (Walker & Avant, 2014 ). Antecedents and consequences are pinpointed. Empirical referents, or measurable indicators, ensure the concept’s practical applicability and verifiability (Walker & Avant, 2014 ).

It is noted that the method’s strengths lie in its systematic and organized approach, facilitating replication by other researchers. It is also tailored for nursing concepts, ensuring its relevance and practicality. However, the method may potentially oversimplify complex concepts, limit philosophical foundation, overlook contextual considerations and qualitative insights, and overclaim the operational definition of the concept (Weaver & Mitcham, 2008 ). Thus, researchers should consider complementing the method with other approaches to understand the concept under study better.

2. Schwartz-Barcott and Kim’s Hybrid Model

This model aims to refine concepts for theory development, builds upon Wilson’s method, and provides a learning platform for graduate students. As the term “hybrid” suggests, this model connects theoretical analysis and practical observation. It is built upon insights from three knowledge domains: the philosophy of science, the sociology of theory development, and participant observation, or field research. The method comprises three phases: Theoretical, Field Work, and Analytical (Schwartz-Barcott, 2000 ).

The Theoretical Phase establishes a foundation by selecting a loose concept definition, starting a literature review, and outlining essential elements. The Field Work Phase validates and refines through empirical observations and using standard qualitative research steps focusing on definition and measurement. The minimum data collection time is 2.5 to 3 months. The Analytical Phase involves comparing findings, and it also includes addressing the concept’s nursing relevance, justification, support in literature, theory, and data (Schwartz-Barcott, 2000 ).

The Schwartz-Barcott and Kim’s hybrid model provides a comprehensive and structured approach to concept analysis, combining theoretical and empirical aspects. Like any qualitative research approach, this hybrid model has limitations, such as potential bias and limited generalizability to broader groups or settings.

3. Chinn and Kramer’s Method

Chinn and Kramer [Jacobs] introduced their concept analysis methodology in 1983, crediting its origins to Wilson. The steps outlined by Chinn and Kramer in 1991 contrast with the method proposed by Walker and Avant by excluding “identifying antecedents and consequences” and “formulating criteria.” Instead, they formulate criteria after collecting and analyzing data, considering values and social context (Hupcey et al., 1996 ). They also include cases as “data sources” and incorporate various potential data sources for analysis, such as visual images, contemporary and traditional literature, musical expressions, poems, and insights from individuals interacting with the concept.

Chinn and Kramer’s method offers a less linear process that involves more interaction between steps. Their purpose of this technique is to better understand the concept by looking at the term used, what it represents, the linked emotions, principles, and perspectives. Chinn and Jacobs ( 1987 ) also describe the outcomes of a concept analysis as tentative, acknowledging that the concept’s definition and criteria for presence in a specific context may change as new evidence emerges. Chinn and Kramer’s method aligns more closely with Wilson’s approach than Walker and Avant’s interpretation. The method creates cases to find characteristics linked with the concept and distinguish criteria that genuinely belong to it from those that don't. They also explore the social situation and values related to the concept, similar to Wilson. Chinn and Kramer anticipate that criteria should be developed only after examining all these aspects. While Chinn and Kramer consider various factors in concept analysis, they may not stress the same level of intellectual rigor as Wilson (Hupcey et al., 1996 ). Chinn and Kramer’s method of concept analysis consists of choosing, establishing a purpose, investigating data, and developing validation criteria for the concept (Weaver & Mitcham, 2008 ).

4. Rodgers’ Evolutionary Concept Analysis

Rodgers' evolutionary concept analysis is an inductive approach that highlights how concepts evolve over time and are impacted by their context (Rodgers, 1989 ). This approach consistently examines a concept’s context, surrogate and related terms, antecedents, attributes, examples, and consequences. This approach does not offer definitive conclusions but serves as a guide for further research (Rodgers, 1989 ). In essence, Rodgers presents a cyclical model that accommodates the ever-changing nature of concepts.

Rodgers suggests six preliminary activities ( Table 1 ), which can occur simultaneously during the study. Unlike Walker and Avant, the research process is non-linear, rotational, and flexible (Ghadirian et al., 2014 ; Rodgers, 1989 ). The activities involve recognizing the concept of focus and its linked terms, choosing a suitable context, gathering data to determine the traits of the concept and its context, analyzing the collected information, pinpointing a prime example of the concept if applicable, and creating hypotheses and potential outcomes for advancing the concept's understanding. These stages represent the activities that should occur during the study rather than a continuous process. Rogers’ approach emphasizes detailed analysis and focuses on gathering and analyzing raw data, particularly within a profession’s specific social and cultural context (Ghadirian et al., 2014 ; Rodgers, 1989 ).

Summary of the steps/phases/principles of each concept analysis method

Despite its strengths, such as the inductive approach, flexibility and adaptability, and the utilization of comprehensive data sources, the findings derived from Rodger’s concept analysis might not always be readily generalizable to other contexts or populations, as the focus on specific social and cultural contexts restricts the broader applicability of the results. Furthermore, the iterative and flexible nature of the analysis may hinder the study’s reproducibility, making it challenging for other researchers to replicate the exact process and achieve identical results.

5. Simultaneous Concept Analysis

The Simultaneous Concept Analysis method consists of nine executive steps proposed by Haase et al. ( 2000 ), firmly rooted in Rodgers’ evolutionary perspective. The foundational principle of the Simultaneous Concept Analysis model lies in the recognition that numerous concepts have intricate interconnections, rendering isolated analysis impractical. However, these concepts can be effectively comprehended through comparative assessment, as their shared characteristics often warrant examination of closely related counterparts (Tavares et al., 2022 ).

The main goal of the Simultaneous Concept Analysis is not to establish a definitive and ultimate concept definition but to lay the groundwork for future exploration in the field of nursing. The analysis involves carefully examining each article to discover the attributes, antecedents, and outcomes associated with individual concepts (Haase et al., 2000 ; Tavares et al., 2022 ). This first analysis helps make a validity matrix. Thorough analysis ensures the method is systematic, verifiable, and replicable. Attributes, antecedents, and outcomes are gathered from relevant literature and subjected to comprehensive comparison within and across disciplines, thereby setting the stage for constructing a comparative validity matrix. Subsequently, the next step involves independently deriving the concept’s critical attributes, theoretical definitions, antecedents, and consequences (Tavares et al., 2022 ). The Simultaneous Concept Analysis comprises a series of nine stages (see Table 1 ).

6. Pragmatic Utility

Janice M. Morse initially developed the pragmatic utility concept analysis method as an alternative to Wilsonian and Rodgers’ methods. The pragmatic utility method examines the concept maturation level by scrutinizing its internal composition, utility, representational attributes, and interconnections with other concepts (Morse, 2000 ). Contrary to a linear progression, the pragmatic utility embodies a non-linear and iterative approach (Weaver & Mitcham, 2008 ). This method serves various purposes, including refining or elucidating concepts and examining the alignment between a concept’s definition and its operationalization (Zumstein & Riese, 2020 ).

The pragmatic utility aims to develop “partially mature” concepts using literature as data. Instead of synthesis, this meta-analytic approach examines how other researchers use the lay concept in their work. It uncovers definitions, attributes, and uses through systematic analysis, asking analytical questions about their conceptualizations and synthesizing data (Morse, 2016 ). This reveals implied/explicit assumptions, inferred meaning, and components. It identifies the lay concept’s commonalities, differences, perspectives, and operationalization degrees (Morse, 2016 ).

The pragmatic utility is not a literature summary or critique, nor a research synthesis or meta-analysis. It compares more than perspectives; it goes beyond creating new models and insights (Morse, 2016 ). Pragmatic utility stands apart from common literature summaries. It is also noted that this method emphasizes the ‘critical appraisal’ technique (Weaver & Mitcham, 2008 ), comparing attributes from different authors and revealing underlying assumptions and practical applications. Also, Morse et al. ( 1996 ) set a guideline, including the database’s extensiveness, analysis depth, argument logic, abstractness level, validity, and knowledge contribution, to assess rigor. Procedures of pragmatic utility include 1) clarifying the inquiry purpose, 2) pinpointing a partially mature lay concept, 3) determining concept maturity, 4) formulating key analytic questions, and 5) Synthesizing outcomes (Morse, 2016 ).

7. Principle-Based Concept Analysis

Penrod and Hupcey ( 2005 ) developed the Principle-Based Concept Analysis approach based on Morse et al. ( 1996 ) to define concepts based on principles exclusively within scientific use, disregarding creative interpretations found in art or fiction. The intentional and strategic extraction of data forms the foundation of this method. The approach acknowledges the dynamic and evolving nature of concept advancement over time, offering a robust framework for theoretically defining and understanding a concept’s state within the scientific community.

The analysis revolves around four broad principles: epistemological, pragmatic, linguistic, and logical (Penrod & Hupcey, 2005 ). The concept’s alignment with these principles determines its level of maturity and advancement. The epistemological principle is the study of how knowledge plays a role in revealing the scientific knowledge underpinning the concept (Waldon, 2018 ). The epistemological analysis focuses on the concept’s distinctiveness within the discipline’s knowledge base, indicating maturity through differentiation and clear positioning (Penrod & Hupcey, 2005 ). The pragmatic principle assesses a concept’s utility within the discipline and its operationalization, particularly in nursing (Penrod & Hupcey, 2005 ). The principle evaluates whether the concept’s applicability is supported by the literature and recognized by the discipline, profession, and society. Mature concepts are manifested in clinical practice (Penrod & Hupcey, 2005 ). The linguistic principle examines language and human speech, assessing a concept’s contextual flexibility and consistent meaning (Penrod & Hupcey, 2005 ; Waldon, 2018 ). The analysis includes various contexts, ensuring the concept’s relevance across different settings (Penrod & Hupcey, 2005 ). The logical principle involves a concept’s compatibility with related concepts and the clarity of its boundaries. Clearly defined conceptual boundaries prevent ambiguity when the concept is positioned alongside others in a theoretical framework (Penrod & Hupcey, 2005 ).

The outcome of Principle-Based Concept Analysis involves a comprehensive synthesis of the concept using scientific literature, along with identifying gaps and inconsistencies to drive concept development. Subsequently, the results are integrated into a theoretical definition, enhancing the concept’s understanding. In addition, Smith and Mörelius ( 2021 ) also combine Principle-Based Concept Analysis with a phased approach to enhance method clarity.

8. Semantic Concept Analysis

The semantic concept analysis, initially formulated by Koort ( 1975 ) and subsequently refined by Eriksson ( 2010 ), constitutes a prevalent approach in Nordic nursing science research aimed at enhancing comprehension of concepts or phenomena requiring clarification (Almerud Österberg et al., 2023 ). This method transcends a mere combination of words or letters, and it instead intimately intertwines with human existence and lived encounters (Almerud Österberg et al., 2023 ; Eriksson, 2010 ). This method includes an analysis of etymological, semantic, and discrimination (Honkavuo et al., 2018 ). Etymological analysis involves exploring a concept’s origin, transformation, and evolution using etymological dictionaries (Koort, 1975 ). Historical meanings may not persist in current language usage (Honkavuo et al., 2018 ; Koort, 1975 ). The semantic analysis uses dictionaries and synonyms to find linguistic consensus. It is about interpreting linguistic expressions, symbols, words, and terms, and if researchers agree on synonyms, the analysis concludes. If not, a discrimination analysis comes next, exploring closely related concepts to distinguish the concept in question. These related concepts form clusters based on qualitative differences in meanings and degrees of synonymy (Honkavuo et al., 2018 ; Koort, 1975 ).

The eight concept analysis methods discussed above provide various ways to systematically examine and understand complex concepts across different fields, especially in nursing. The first three methods—Walker and Avant’s concept analysis, Schwartz-Barcott and Kim’s hybrid model, and the Chinn and Kramer’s method—have evolved and expanded from Wilson’s foundational approach. Each brings unique contributions, adaptations, and modifications to the concept analysis process.

Rodgers’ cyclical model considers the dynamic nature of concepts, encouraging an iterative analysis and emphasizing the importance of considering the context and cultural factors. The Simultaneous Concept Analysis method incorporates principles from Rodgers’ approach and focuses on how concepts are interconnected. It also highlights the comparison of related concepts, offering a more complete perspective.

Like Rodger’s approach, the pragmatic utility concept analysis method uses a non-linear and iterative approach. It emphasizes the practical usefulness of concepts and how they align with guiding principles. It also strongly emphasizes critical appraisal, resulting in a rigorous evaluation. In addition, the Principle-Based Concept Analysis approach extends Morse’s approach aiming to align concepts with epistemological, pragmatic, linguistic, and logical principles. Lastly, the Semantic Concept Analysis method deeply explores a concept’s linguistic origins, synonyms, and distinctive features. It provides a comprehensive understanding of concepts within their linguistic context.

It is noteworthy that these methods are not mutually exclusive. This editorial aims to spur educators, researchers and students to adopt, adapt, and combine elements from different ways to create a customized approach that suits their research needs as well as to further discuss the concept analysis development. Ultimately, the chosen concept analysis method should align with the research objectives and be accountable conceptually, critically, and philosophically. Additionally, it should contribute to a comprehensive understanding of the concept under study.

Acknowledgment

Declaration of conflicting interest.

The authors declare that they have no conflict of interest in this study.

Second Century Fund (C2F), Chulalongkorn University, Bangkok, Thailand.

Authors’ Contributions

All authors contributed equally in this article.

Authors’ Biographies

Joko Gunawan, S.Kep.Ners, PhD is a Managing Editor of Belitung Nursing Journal and a Research Fellow at the Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand.

Yupin Aungsuroch, PhD, RN is an Associate Professor at the Faculty of Nursing, Chulalongkorn University, Bangkok, Thailand. She is also an Editor-in-Chief of Belitung Nursing Journal.

Colleen Marzilli, PhD, DNP, MBA, RN-BC, CCM, PHNA-BC, CNE, NEA-BC, FNAP is a Professor of Nursing at the University of Maine, School of Nursing, Orono, ME USA. She is also an Editorial Advisory Board Member of Belitung Nursing Journal.

Data Availability

Declaration of use of ai in scientific writing.

Nothing to declare.

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The Basics of Research Methodology: A Key To Quality Research

• November 29, 2021

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How does a researcher decide 

• What data do they want to collect?
• From whom should they collect the data?
• What method should they use for data collection?
• How can they analyze the collected data?

The process of deciding how to design research and conduct it systematically is called Research Methodology. To conduct market or social research, you have to plan out every step, from which questions to ask to what analysis method to use, to ensure that you gather valid and reliable results from the research.

In this article, we have explained how you can design your own research methodology. Dive in to read more. 

What is the research methodology?

A research methodology is an outline of how a given piece of research is carried out. It defines the techniques or procedures that are used to identify and analyse information regarding a specific research topic. The research methodology, therefore, has to do with how a researcher designs their study in a way that allows them to obtain valid and reliable results and meet their research objectives.

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How is the research methodology different from research methods?

The both are related concepts used in the field of research, but they refer to different aspects of the research process.

Research methodology is the overall strategy or approach used by researchers to conduct research. It encompasses the theoretical and philosophical underpinnings of the research, the research design, data collection methods, data analysis techniques, and the overall framework within which the research is conducted. 

Methodology of research provides a framework for how research is planned, conducted, and analyzed, and it guides researchers in making decisions about the most appropriate methods to use in their research.

On the other hand, research methods refer to the specific techniques, procedures, or tools that researchers use to collect, analyze, and interpret data. Research methods can be quantitative, involving numerical data, or qualitative, involving non-numerical data. 

Examples of research methods include surveys, interviews, experiments, observations, content analysis, and statistical analysis, among others. Research methods are selected and employed by researchers based on their research question, research design, and the type of data they want to collect.

In summary, research methodology is the overall strategy or approach used in research, while research methods are the specific techniques or tools used to collect, analyze, and interpret data within that overall strategy.  

It guides the entire research process, while research methods are the practical steps taken by researchers to conduct research.

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What are the major components of typical research methodology?

Here we’ll discuss the key components of a typical research methodology followed up by an example of how a car manufacturing company can use the research methodology. 

1. Research design: 

This involves selecting the most appropriate research design based on the nature of the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.

Let’s say a car manufacturing company decides they will use exploratory and descriptive research to understand consumer preferences and identify relevant driving factors.

2. Sampling: 

This involves selecting a representative subset of the population being studied, known as the sample. Various sampling techniques, such as random sampling, stratified sampling, and purposive sampling, may be used depending on the research design and population characteristics.

In this step, their research team employs stratified random sampling to make sure the sample represents various demographics. The team segments the target audience based on age, income, and geographical location. They randomly select participants from each segment for their research.

3. Data collection:

This involves gathering data using different methods, such as surveys, interviews, observations, experiments, or existing datasets. The data collection process should be carefully planned, documented, and carried out ethically.

For data collection, the team uses online survey tools to design surveys mixed with closed and open-ended questions. The survey includes questions about their current vehicle, the factors behind purchase decisions, their interests in different models, and their perception of certain brands.

4. Data analysis: 

This involves organizing, analyzing, and interpreting the collected data using appropriate statistical or qualitative techniques, depending on the nature of the data and research objectives.

Statistical functionality, such as cross tabs or conjoint analysis, may be used for quantitative data analysis, while qualitative data may be analyzed using techniques like text analysis or sentiment analysis.

Once they gather the survey responses, they use data analysis tools to run statistical analyses on quantitative data. They use cross-tabulations and regression analysis to identify patterns and factors influencing customer preferences. They also use text analysis to analyze qualitative data and gain deeper insights into consumer preferences. 

5. Validity and reliability: 

Ensuring the validity and reliability of research findings is crucial. Validity helps identify the accuracy and truthfulness of the research findings, while reliability refers to the consistency and stability of the results over time and across different conditions.

The car manufacturing company ensures data validity and reliability by using established survey methods, writing clear and unbiased questions, and gathering representative data. Furthermore, they leverage the survey software to conduct pilot tests to ensure survey consistency and effectiveness. 

6. Ethical considerations: 

Research should be conducted ethically, ensuring the protection of human subjects and adherence to ethical guidelines. This involves obtaining consent from participants, maintaining confidentiality, and addressing potential conflicts of interest.

Using an online survey tool ensures that the data the team obtains is from consenting respondents. It also allows the team to inform respondents that they can refuse participation, and if they participate, their data will be treated confidentially and used only for the research. The tool also enables them to comply with ethical considerations such as ensuring data is securely stored and protecting respondent privacy.

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Why do we need a research methodology?

Methodology of research is an essential aspect of researching to ensure that the research process is systematic, rigorous, reliable, and valid. Here are some reasons why methodology in research is important:

1. Clarity and precision: 

It provides a framework and guidelines for researchers to clearly define their research questions, objectives, and hypotheses. It helps researchers identify the most appropriate research design, sampling techniques, data collection methods, and data analysis procedures to ensure that the research is conducted with precision and clarity.

2. Validity and reliability: 

Research methodology helps researchers ensure that their research findings are valid and reliable. Validity helps identify accuracy and truthfulness of research results, while reliability refers to the consistency and stability of research findings over time. Proper methodology helps in minimizing biases, errors, and confounding factors that may affect the validity and reliability of research results.

3. Ethical Considerations: 

It provides ethical guidelines and principles that researchers must follow while conducting research involving human subjects, animals, or sensitive data. It helps researchers ensure that their research is conducted ethically, respecting the rights and dignity of research participants, and following appropriate ethical standards and regulations.

4. Replicability: 

Research methodology emphasizes the importance of replicability of research findings. Replicability means that other researchers should be able to repeat the research study using the same methods and obtain similar results. 

5. Representative: 

This means that research findings can be applied to a broader population or context. Proper research methodology helps in designing research studies that are replicable and generalizable, enhancing the credibility and robustness of research findings.

6. Efficiency and resource utilization: 

It helps researchers in planning and organizing their research study efficiently, optimizing the use of time, resources, and efforts. It ensures that researchers use appropriate data collection methods, sample sizes, and statistical techniques to obtain meaningful results while minimizing unnecessary costs and efforts.

3 types of research methodologies

There are three key types of research methodologies based on the type of data you gather and the method you use. These three types are – 

• Qualitative research. 
• Quantitative research. 
• Mixed-method research. 

Let’s explore these three approaches and understand them with examples. 

01. Qualitative research methodology: 

Qualitative research involves research that is conducted using words and textual data. This method of research is generally used in exploratory research, where a research problem that is not clearly defined is being investigated. It is useful when trying to understand abstract concepts, perceptions, body language, opinions, and even visual data. 

A software company developing a new messaging app uses an online survey with open-ended questions to gather data on users’ needs and preferences. They use the survey to ask users about their current messaging app, experience, likes and dislikes,  preferable features, and pain points. 

The qualitative survey allows users to provide detailed responses and enables the company to identify underlying insights, customer sentiment, and common themes. The meaningful insights inform their app development process. 

02. Quantitative research methodology: 

Quantitative research relies on the measurement and testing of numerical data. Unlike qualitative research, which is more exploratory in nature, quantitative research is typically leveraged when the research objectives are confirmatory in nature. 

An e-commerce company creates an online survey with closed-ended questions like rating scales and MCQs to assess customer satisfaction with the website’s user interface. The survey includes questions about demographics, website, ease of navigation, checkout process, search functionality, and user experience. 

Analyzing the data, the company can quantify the satisfaction level and compare it across different customer segments. They can also identify areas of improvement, enabling the company to make informed decisions to enhance user experience. 

03. Mixed-methods research  methodology: 

As the name suggests, the mixed-method methodology combines qualitative and quantitative methodologies to integrate both their strengths and obtain rich results.

A retail chain uses online survey tools to evaluate the effectiveness of its online advertising campaigns in sales and customer engagement. They leverage survey software that enables them to gather and analyze quantitative and qualitative data. 

The quantitative questions help them gather data on customer awareness and purchase behavior. The qualitative data help them gather data on their perception of the campaign. 

By using mixed-method research, the retail chain gains a holistic understanding of the campaign’s impact and makes informed marketing decisions. 

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What are the main sampling design approaches in research methodology?

Sampling design is an important aspect of methodology of research. It involves selecting a subset of individuals or items from a larger population for study. There are several main sampling designs approaches that researchers commonly use:

1. Random sampling: 

This approach involves selecting individuals or items from a population in a purely random manner, where every member of the population has an equal chance of being selected. This helps to ensure that the sample is representative of the population and reduces the risk of bias.

2. Stratified sampling: 

In this approach, the population is divided into subgroups or strata based on characteristics (such as age, gender, or location), and then random sampling is conducted within each stratum. 

This helps to ensure that each subgroup is adequately represented in the sample, which can be useful when certain subgroups are expected to have different characteristics or when the research questions are specific to certain subgroups.

3. Cluster sampling: 

In this approach, the population is divided into clusters, such as geographic areas or organizations, and then a random sample of clusters is selected. All individuals or items within the selected clusters are then included in the sample. Cluster sampling is often used when it is difficult or expensive to sample individuals directly, and can be efficient in terms of time and resources.

4. Convenience sampling: 

This approach involves selecting individuals or items that are readily available or easy to access. Convenience sampling is often used in situations where it is difficult to randomly sample from the population, such as in exploratory or preliminary research. However, convenience sampling can be prone to selection bias, as it may not result in a representative sample.

5. Purposive sampling: 

Also known as judgmental or selective sampling, this approach involves deliberately selecting individuals or items based on specific criteria or characteristics that are relevant to the research question. You can use purposive sampling in qualitative research or when researchers want to focus on specific subgroups or individuals who are considered to be key informants.

6. Snowball sampling: 

This approach involves initially selecting a few individuals who meet the research criteria, and then asking them to refer to other individuals who also meet the criteria. This method is often used when the target population is difficult to identify or access, such as in studies of hidden or marginalized populations.

These are some of the main sampling design approaches in research methodology. The choice of sampling design depends on the research question, the characteristics of the population, available resources, and the desired level of generalizability or external validity of the research findings. It is important to carefully consider the appropriate sampling design to ensure the validity and reliability of their research results.

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How to write a research methodology?

Now that you have an understanding of what a research methodology is, let’s go over how one can write a research methodology using four constructive steps: 

1. Determine your Methodological Approach: 

In the first step of writing your research methodology, you can provide an explanation of the overall methodological approach of your research. This should include an explanation of your research problem and the type of data (quantitative/qualitative, primary/secondary) you will require to answer it. 

2. Outline the Methods of Data Collection: 

The next step is to describe the methods of data collection that you will be using in your research. There are many different methods that can be used to gather data. Quantitative methods of data collection include experiments, surveys, and existing literature. 

Qualitative data collection methods include participant observation, focus groups, interviews, and existing literature. 

3. Outline the Methods of Analysis: 

This step requires you to outline the methods of analysis that you will be using in your research. If you’re conducting research that is quantitative in nature, you should include any data preparation techniques, statistical tests, or even software used to analyse your data. 

If you’re conducting research that is qualitative in nature, you should include any methods of content analysis, thematic analysis, or discourse analysis used in your research. 

4. Justify your Choices: 

The final step is to evaluate and justify all your methodological choices. You can elaborate on why other methods were not suitable for your research and why the methods you chose were best suited to achieve your research objectives and arrive at an outcome that is reliable and valid.

 You should acknowledge the limitations of the chosen methods while justifying how the strengths outweighed these.

Research methodology is a large topic, and designing a research method properly can be challenging. However, the key tip to take away from this article is that when designing a research method, you should focus on clearly defining the aims and objectives of the research. 

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In conclusion, research methodology is a critical aspect of any research study. It provides the framework and approach used to conduct the research, collect and analyze data, and derive meaningful findings and conclusions.

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5.2 Conceptualization

Learning objectives.

• Define concept
• Identify why defining our concepts is important
• Describe how conceptualization works in quantitative and qualitative research
• Define dimensions in terms of social scientific measurement
• Apply reification to conceptualization

In this section, we’ll take a look at one of the first steps in the measurement process, which is conceptualization. This has to do with defining our terms as clearly as possible and also not taking ourselves too seriously in the process. Our definitions mean only what we say they mean—nothing more and nothing less. Let’s talk first about how to define our terms, and then we’ll examine not taking ourselves (or our terms, rather) too seriously.

Concepts and conceptualization

  So far, the word concept has come up quite a bit, and it would behoove us to make sure we have a shared understanding of that term. A concept is the notion or image that we conjure up when we think of some cluster of related observations or ideas. For example, masculinity is a concept. What do you think of when you hear that word? Presumably, you imagine some set of behaviors and perhaps even a particular style of self-presentation. Of course, we can’t necessarily assume that everyone conjures up the same set of ideas or images when they hear the word masculinity . In fact, there are many possible ways to define the term. And while some definitions may be more common or have more support than others, there isn’t one true, always-correct-in-all-settings definition. What counts as masculine may shift over time, from culture to culture, and even from individual to individual (Kimmel, 2008).  This is why defining our concepts is so important.

You might be asking yourself why you should bother defining a term for which there is no single, correct definition. Believe it or not, this is true for any concept you might measure in a research study—there is never a single, always-correct definition. When we conduct empirical research, our terms mean only what we say they mean. There’s a New Yorker cartoon that aptly represents this idea. It depicts a young George Washington holding an axe and standing near a freshly chopped cherry tree. Young George is looking up at a frowning adult who is standing over him, arms crossed. The caption depicts George explaining, “It all depends on how you define ‘chop.’” Young George Washington gets the idea—whether he actually chopped down the cherry tree depends on whether we have a shared understanding of the term chop .

Without a shared understanding of this term, our understandings of what George has just done may differ. Likewise, without understanding how a researcher has defined her key concepts, it would be nearly impossible to understand the meaning of that researcher’s findings and conclusions. Thus, any decision we make based on findings from empirical research should be made based on full knowledge not only of how the research was designed, but also of how its concepts were defined and measured.

So, how do we define our concepts? This is part of the process of measurement, and this portion of the process is called conceptualization. The answer depends on how we plan to approach our research. We will begin with quantitative conceptualization and then discuss qualitative conceptualization.

In quantitative research, conceptualization involves writing out clear, concise definitions for our key concepts. Sticking with the previously mentioned example of masculinity, think about what comes to mind when you read that term. How do you know masculinity when you see it? Does it have something to do with men? With social norms? If so, perhaps we could define masculinity as the social norms that men are expected to follow. That seems like a reasonable start, and at this early stage of conceptualization, brainstorming about the images conjured up by concepts and playing around with possible definitions is appropriate. However, this is just the first step.

It would make sense as well to consult other previous research and theory to understand if other scholars have already defined the concepts we’re interested in. This doesn’t necessarily mean we must use their definitions, but understanding how concepts have been defined in the past will give us an idea about how our conceptualizations compare with the predominant ones out there. Understanding prior definitions of our key concepts will also help us decide whether we plan to challenge those conceptualizations or rely on them for our own work. Finally, working on conceptualization is likely to help in the process of refining your research question to one that is specific and clear in what it asks.

If we turn to the literature on masculinity, we will surely come across work by Michael Kimmel, one of the preeminent masculinity scholars in the United States. After consulting Kimmel’s prior work (2000; 2008), we might tweak our initial definition of masculinity just a bit. Rather than defining masculinity as “the social norms that men are expected to follow,” perhaps instead we’ll define it as “the social roles, behaviors, and meanings prescribed for men in any given society at any one time” (Kimmel & Aronson, 2004, p. 503).  Our revised definition is both more precise and more complex. Rather than simply addressing one aspect of men’s lives (norms), our new definition addresses three aspects: roles, behaviors, and meanings. It also implies that roles, behaviors, and meanings may vary across societies and over time. To be clear, we’ll also have to specify the particular society and time period we’re investigating as we conceptualize masculinity.

As you can see, conceptualization isn’t quite as simple as merely applying any random definition that we come up with to a term. Sure, it may involve some initial brainstorming, but conceptualization goes beyond that. Once we’ve brainstormed a bit about the images a particular word conjures up for us, we should also consult prior work to understand how others define the term in question. And after we’ve identified a clear definition that we’re happy with, we should make sure that every term used in our definition will make sense to others. Are there terms used within our definition that also need to be defined? If so, our conceptualization is not yet complete. And there is yet another aspect of conceptualization to consider—concept dimensions. We’ll consider that aspect along with an additional word of caution about conceptualization in the next subsection.

Conceptualization in qualitative research

Conceptualization in qualitative research proceeds a bit differently than in quantitative research. Because qualitative researchers are interested in the understandings and experiences of their participants, it is less important for the researcher to find one fixed definition for a concept before starting to interview or interact with participants. The researcher’s job is to accurately and completely represent how their participants understand a concept, not to test their own definition of that concept.

If you were conducting qualitative research on masculinity, you would likely consult previous literature like Kimmel’s work mentioned above. From your literature review, you may come up with a working definition for the terms you plan to use in your study, which can change over the course of the investigation. However, the definition that matters is the definition that your participants share during data collection. A working definition is merely a place to start, and researchers should take care not to think it is the only or best definition out there.

In qualitative inquiry, your participants are the experts (sound familiar, social workers?) on the concepts that arise during the research study. Your job as the researcher is to accurately and reliably collect and interpret their understanding of the concepts they describe while answering your questions. Conceptualization of qualitative concepts is likely to change over the course of qualitative inquiry, as you learn more information from your participants. Indeed, getting participants to comment on, extend, or challenge the definitions and understandings of other participants is a hallmark of qualitative research. This is the opposite of quantitative research, in which definitions must be completely set in stone before the inquiry can begin.

A word of caution about conceptualization

  Whether you have chosen qualitative or quantitative methods, you should have a clear definition for the term masculinity and make sure that the terms we use in our definition are equally clear—and then we’re done, right? Not so fast. If you’ve ever met more than one man in your life, you’ve probably noticed that they are not all exactly the same, even if they live in the same society and at the same historical time period. This could mean there are dimensions of masculinity. In terms of social scientific measurement, concepts can be said to have multiple dimensions when there are multiple elements that make up a single concept. With respect to the term masculinity , dimensions could be regional (is masculinity defined differently in different regions of the same country?), age-based (is masculinity defined differently for men of different ages?), or perhaps power-based (does masculinity differ based on membership to privileged groups?). In any of these cases, the concept of masculinity would be considered to have multiple dimensions. While it isn’t necessarily required to spell out every possible dimension of the concepts you wish to measure, it may be important to do so depending on the goals of your research. The point here is to be aware that some concepts have dimensions and to think about whether and when dimensions may be relevant to the concepts you intend to investigate.

Before we move on to the additional steps involved in the measurement process, it would be wise to remind ourselves not to take our definitions too seriously. Conceptualization must be open to revisions, even radical revisions, as scientific knowledge progresses. Although that we should consult prior scholarly definitions of our concepts, it would be wrong to assume that just because prior definitions exist that they are more real than the definitions we create (or, likewise, that our own made-up definitions are any more real than any other definition). It would also be wrong to assume that just because definitions exist for some concept that the concept itself exists beyond some abstract idea in our heads. This idea, assuming that our abstract concepts exist in some concrete, tangible way, is known as reification .

To better understand reification, take a moment to think about the concept of social structure. This concept is central to critical thinking. When social scientists talk about social structure, they are talking about an abstract concept. Social structures shape our ways of being in the world and of interacting with one another, but they do not exist in any concrete or tangible way. A social structure isn’t the same thing as other sorts of structures, such as buildings or bridges. Sure, both types of structures are important to how we live our everyday lives, but one we can touch, and the other is just an idea that shapes our way of living.

Here’s another way of thinking about reification: Think about the term family . If you were interested in studying this concept, we’ve learned that it would be good to consult prior theory and research to understand how the term has been conceptualized by others. But we should also question past conceptualizations. Think, for example, about how different the definition of family was 50 years ago. Because researchers from that time period conceptualized family using now outdated social norms, social scientists from 50 years ago created research projects based on what we consider now to be a very limited and problematic notion of what family means. Their definitions of family were as real to them as our definitions are to us today. If researchers never challenged the definitions of terms like family, our scientific knowledge would be filled with the prejudices and blind spots from years ago. It makes sense to come to some social agreement about what various concepts mean. Without that agreement, it would be difficult to navigate through everyday living. But at the same time, we should not forget that we have assigned those definitions, they are imperfect and subject to change as a result of critical inquiry.

Key Takeaways

• Conceptualization is a process that involves coming up with clear, concise definitions.
• Conceptualization in quantitative research comes from the researcher’s ideas or the literature.
• Qualitative researchers conceptualize by creating working definitions which will be revised based on what participants say.
• Some concepts have multiple elements or dimensions.
• Researchers should acknowledge the limitations of their definitions for concepts.
• Concept- notion or image that we conjure up when we think of some cluster of related observations or ideas
• Conceptualization- writing out clear, concise definitions for our key concepts, particularly in quantitative research
• Multi-dimensional concepts- concepts that are comprised of multiple elements
• Reification- assuming that abstract concepts exist in some concrete, tangible way

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Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Methodology

• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

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.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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