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Research Methodology – Types, Examples and writing Guide

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Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write Research Methodology

Last Updated: May 27, 2024 Approved

This article was co-authored by Alexander Ruiz, M.Ed. and by wikiHow staff writer, Jennifer Mueller, JD . Alexander Ruiz is an Educational Consultant and the Educational Director of Link Educational Institute, a tutoring business based in Claremont, California that provides customizable educational plans, subject and test prep tutoring, and college application consulting. With over a decade and a half of experience in the education industry, Alexander coaches students to increase their self-awareness and emotional intelligence while achieving skills and the goal of achieving skills and higher education. He holds a BA in Psychology from Florida International University and an MA in Education from Georgia Southern University. wikiHow marks an article as reader-approved once it receives enough positive feedback. In this case, several readers have written to tell us that this article was helpful to them, earning it our reader-approved status. This article has been viewed 522,512 times.

The research methodology section of any academic research paper gives you the opportunity to convince your readers that your research is useful and will contribute to your field of study. An effective research methodology is grounded in your overall approach – whether qualitative or quantitative – and adequately describes the methods you used. Justify why you chose those methods over others, then explain how those methods will provide answers to your research questions. [1] X Research source

Describing Your Methods

• In your restatement, include any underlying assumptions that you're making or conditions that you're taking for granted. These assumptions will also inform the research methods you've chosen.
• Generally, state the variables you'll test and the other conditions you're controlling or assuming are equal.

• If you want to research and document measurable social trends, or evaluate the impact of a particular policy on various variables, use a quantitative approach focused on data collection and statistical analysis.
• If you want to evaluate people's views or understanding of a particular issue, choose a more qualitative approach.
• You can also combine the two. For example, you might look primarily at a measurable social trend, but also interview people and get their opinions on how that trend is affecting their lives.

• For example, if you conducted a survey, you would describe the questions included in the survey, where and how the survey was conducted (such as in person, online, over the phone), how many surveys were distributed, and how long your respondents had to complete the survey.
• Include enough detail that your study can be replicated by others in your field, even if they may not get the same results you did. [4] X Research source

• Qualitative research methods typically require more detailed explanation than quantitative methods.
• Basic investigative procedures don't need to be explained in detail. Generally, you can assume that your readers have a general understanding of common research methods that social scientists use, such as surveys or focus groups.

• For example, suppose you conducted a survey and used a couple of other research papers to help construct the questions on your survey. You would mention those as contributing sources.

Justifying Your Choice of Methods

• Describe study participants specifically, and list any inclusion or exclusion criteria you used when forming your group of participants.
• Justify the size of your sample, if applicable, and describe how this affects whether your study can be generalized to larger populations. For example, if you conducted a survey of 30 percent of the student population of a university, you could potentially apply those results to the student body as a whole, but maybe not to students at other universities.

• Reading other research papers is a good way to identify potential problems that commonly arise with various methods. State whether you actually encountered any of these common problems during your research.

• If you encountered any problems as you collected data, explain clearly the steps you took to minimize the effect that problem would have on your results.

• In some cases, this may be as simple as stating that while there were numerous studies using one method, there weren't any using your method, which caused a gap in understanding of the issue.
• For example, there may be multiple papers providing quantitative analysis of a particular social trend. However, none of these papers looked closely at how this trend was affecting the lives of people.

Connecting Your Methods to Your Research Goals

• Depending on your research questions, you may be mixing quantitative and qualitative analysis – just as you could potentially use both approaches. For example, you might do a statistical analysis, and then interpret those statistics through a particular theoretical lens.

• For example, suppose you're researching the effect of college education on family farms in rural America. While you could do interviews of college-educated people who grew up on a family farm, that would not give you a picture of the overall effect. A quantitative approach and statistical analysis would give you a bigger picture.

• If in answering your research questions, your findings have raised other questions that may require further research, state these briefly.
• You can also include here any limitations to your methods, or questions that weren't answered through your research.

• Generalization is more typically used in quantitative research. If you have a well-designed sample, you can statistically apply your results to the larger population your sample belongs to.

Template to Write Research Methodology

Community Q&A

• Organize your methodology section chronologically, starting with how you prepared to conduct your research methods, how you gathered data, and how you analyzed that data. [13] X Research source Thanks Helpful 0 Not Helpful 0
• Write your research methodology section in past tense, unless you're submitting the methodology section before the research described has been carried out. [14] X Research source Thanks Helpful 2 Not Helpful 0
• Discuss your plans in detail with your advisor or supervisor before committing to a particular methodology. They can help identify possible flaws in your study. [15] X Research source Thanks Helpful 0 Not Helpful 0

You Might Also Like

• ↑ http://expertjournals.com/how-to-write-a-research-methodology-for-your-academic-article/
• ↑ http://libguides.usc.edu/writingguide/methodology
• ↑ https://www.skillsyouneed.com/learn/dissertation-methodology.html
• ↑ https://uir.unisa.ac.za/bitstream/handle/10500/4245/05Chap%204_Research%20methodology%20and%20design.pdf
• ↑ https://elc.polyu.edu.hk/FYP/html/method.htm

About This Article

To write a research methodology, start with a section that outlines the problems or questions you'll be studying, including your hypotheses or whatever it is you're setting out to prove. Then, briefly explain why you chose to use either a qualitative or quantitative approach for your study. Next, go over when and where you conducted your research and what parameters you used to ensure you were objective. Finally, cite any sources you used to decide on the methodology for your research. To learn how to justify your choice of methods in your research methodology, scroll down! Did this summary help you? Yes No

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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
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• Theoretical Framework
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• Evaluating Sources
• Primary Sources
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• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
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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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• How to Write Your Methods

Ensure understanding, reproducibility and replicability

What should you include in your methods section, and how much detail is appropriate?

Why Methods Matter

The methods section was once the most likely part of a paper to be unfairly abbreviated, overly summarized, or even relegated to hard-to-find sections of a publisher’s website. While some journals may responsibly include more detailed elements of methods in supplementary sections, the movement for increased reproducibility and rigor in science has reinstated the importance of the methods section. Methods are now viewed as a key element in establishing the credibility of the research being reported, alongside the open availability of data and results.

A clear methods section impacts editorial evaluation and readers’ understanding, and is also the backbone of transparency and replicability.

For example, the Reproducibility Project: Cancer Biology project set out in 2013 to replicate experiments from 50 high profile cancer papers, but revised their target to 18 papers once they understood how much methodological detail was not contained in the original papers.

What to include in your methods section

What you include in your methods sections depends on what field you are in and what experiments you are performing. However, the general principle in place at the majority of journals is summarized well by the guidelines at PLOS ONE : “The Materials and Methods section should provide enough detail to allow suitably skilled investigators to fully replicate your study. ” The emphases here are deliberate: the methods should enable readers to understand your paper, and replicate your study. However, there is no need to go into the level of detail that a lay-person would require—the focus is on the reader who is also trained in your field, with the suitable skills and knowledge to attempt a replication.

A constant principle of rigorous science

A methods section that enables other researchers to understand and replicate your results is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail . Reproducibility is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information. If a journal still has word-limits—either for the overall article or specific sections—and requires some methodological details to be in a supplemental section, that is OK as long as the extra details are searchable and findable .

Imagine replicating your own work, years in the future

As part of PLOS’ presentation on Reproducibility and Open Publishing (part of UCSF’s Reproducibility Series ) we recommend planning the level of detail in your methods section by imagining you are writing for your future self, replicating your own work. When you consider that you might be at a different institution, with different account logins, applications, resources, and access levels—you can help yourself imagine the level of specificity that you yourself would require to redo the exact experiment. Consider:

• Which details would you need to be reminded of? 
• Which cell line, or antibody, or software, or reagent did you use, and does it have a Research Resource ID (RRID) that you can cite?
• Which version of a questionnaire did you use in your survey? 
• Exactly which visual stimulus did you show participants, and is it publicly available? 
• What participants did you decide to exclude? 
• What process did you adjust, during your work? 

Tip: Be sure to capture any changes to your protocols

You yourself would want to know about any adjustments, if you ever replicate the work, so you can surmise that anyone else would want to as well. Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal methods, or methodological constraints, than to conceal them, which could result in reproducibility or ethical issues downstream.

Visual aids for methods help when reading the whole paper

Consider whether a visual representation of your methods could be appropriate or aid understanding your process. A visual reference readers can easily return to, like a flow-diagram, decision-tree, or checklist, can help readers to better understand the complete article, not just the methods section.

Ethical Considerations

In addition to describing what you did, it is just as important to assure readers that you also followed all relevant ethical guidelines when conducting your research. While ethical standards and reporting guidelines are often presented in a separate section of a paper, ensure that your methods and protocols actually follow these guidelines. Read more about ethics .

Existing standards, checklists, guidelines, partners

While the level of detail contained in a methods section should be guided by the universal principles of rigorous science outlined above, various disciplines, fields, and projects have worked hard to design and develop consistent standards, guidelines, and tools to help with reporting all types of experiment. Below, you’ll find some of the key initiatives. Ensure you read the submission guidelines for the specific journal you are submitting to, in order to discover any further journal- or field-specific policies to follow, or initiatives/tools to utilize.

Tip: Keep your paper moving forward by providing the proper paperwork up front

Be sure to check the journal guidelines and provide the necessary documents with your manuscript submission. Collecting the necessary documentation can greatly slow the first round of peer review, or cause delays when you submit your revision.

Randomized Controlled Trials – CONSORT The Consolidated Standards of Reporting Trials (CONSORT) project covers various initiatives intended to prevent the problems of  inadequate reporting of randomized controlled trials. The primary initiative is an evidence-based minimum set of recommendations for reporting randomized trials known as the CONSORT Statement . 

Systematic Reviews and Meta-Analyses – PRISMA The Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) is an evidence-based minimum set of items focusing  on the reporting of  reviews evaluating randomized trials and other types of research.

Research using Animals – ARRIVE The Animal Research: Reporting of In Vivo Experiments ( ARRIVE ) guidelines encourage maximizing the information reported in research using animals thereby minimizing unnecessary studies. (Original study and proposal , and updated guidelines , in PLOS Biology .) 

Laboratory Protocols Protocols.io has developed a platform specifically for the sharing and updating of laboratory protocols , which are assigned their own DOI and can be linked from methods sections of papers to enhance reproducibility. Contextualize your protocol and improve discovery with an accompanying Lab Protocol article in PLOS ONE .

Consistent reporting of Materials, Design, and Analysis – the MDAR checklist A cross-publisher group of editors and experts have developed, tested, and rolled out a checklist to help establish and harmonize reporting standards in the Life Sciences . The checklist , which is available for use by authors to compile their methods, and editors/reviewers to check methods, establishes a minimum set of requirements in transparent reporting and is adaptable to any discipline within the Life Sciences, by covering a breadth of potentially relevant methodological items and considerations. If you are in the Life Sciences and writing up your methods section, try working through the MDAR checklist and see whether it helps you include all relevant details into your methods, and whether it reminded you of anything you might have missed otherwise.

Summary Writing tips

The main challenge you may find when writing your methods is keeping it readable AND covering all the details needed for reproducibility and replicability. While this is difficult, do not compromise on rigorous standards for credibility!

• Keep in mind future replicability, alongside understanding and readability.
• Follow checklists, and field- and journal-specific guidelines.
• Consider a commitment to rigorous and transparent science a personal responsibility, and not just adhering to journal guidelines.
• Establish whether there are persistent identifiers for any research resources you use that can be specifically cited in your methods section.
• Deposit your laboratory protocols in Protocols.io, establishing a permanent link to them. You can update your protocols later if you improve on them, as can future scientists who follow your protocols.
• Consider visual aids like flow-diagrams, lists, to help with reading other sections of the paper.
• Be specific about all decisions made during the experiments that someone reproducing your work would need to know.

Don’t

• Summarize or abbreviate methods without giving full details in a discoverable supplemental section.
• Presume you will always be able to remember how you performed the experiments, or have access to private or institutional notebooks and resources.
• Attempt to hide constraints or non-optimal decisions you had to make–transparency is the key to ensuring the credibility of your research.
• How to Write a Great Title
• How to Write an Abstract
• How to Report Statistics
• How to Write Discussions and Conclusions
• How to Edit Your Work

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The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

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Published by Nicolas at March 21st, 2024 , Revised On March 12, 2024

The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one. 

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings. 

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount. 

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions. 

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

• Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
• Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

• Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
• Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
• Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

• Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
• Interviews: In-depth conversations with participants, offering qualitative insights.
• Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

• Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
• Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

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Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions. 

Quantitative Research Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

• Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
• Random Assignment: Participants are randomly assigned to different experimental conditions.
• Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

• Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
• Large Sample Size: Surveys often target a large and diverse group of participants.
• Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

• Observation and Data Collection: This involves observing and documenting without manipulating variables.
• Objective Description: Aim to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

• Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
• Contextual Understanding: Aims to understand the context and unique characteristics of the case.
• Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

• Participant Observation: Researchers actively participate in the community or setting.
• Holistic Perspective: Focuses on the interconnectedness of cultural elements.
• Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

• Constant Comparison: Data is continually compared and analyzed during the research process.
• Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
• Iterative Process: The research design evolves as the study progresses.

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

• Flexibility: Allows for flexibility in data collection and analysis.
• Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
• Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

• Structured Data Collection: Involves systematic and structured data collection methods.
• Objective Presentation: Aims to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

• Causal Relationships: Seeks to establish causal relationships between variables.
• Controlled Variables : Often involves controlling certain variables to isolate causal factors.
• Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

• Snapshot View: Provides a snapshot of a population at a specific moment.
• Efficiency: More efficient in terms of time and resources.
• Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

• Temporal Sequence: Allows for the examination of changes over time.
• Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
• Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

• Causality Inference: Enables the inference of cause-and-effect relationships.
• Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

• Natural Settings: Data is often collected in natural settings without intervention.
• Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
• Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour. 

Designing Effective Surveys

Objective Design:

• Clearly define the research objectives to guide the survey design.
• Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

• Use a structured format with standardized questions for consistency.
• Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

• Conduct pilot tests to identify and rectify potential issues with survey design.
• Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

• Develop a robust sampling strategy to ensure a representative participant group.
• Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

• Build rapport with participants to create a comfortable and open environment.
• Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

• Frame open-ended questions to encourage detailed responses.
• Allow participants to express their thoughts and perspectives freely.

Active Listening:

• Practice active listening to understand areas and gather rich data.
• Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

• Obtain informed consent and assure participants of confidentiality.
• Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

• Actively immerse yourself in the setting or group being observed.
• Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

• Maintain detailed and reflective field notes during observations.
• Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

• Be conscious of ethical considerations, ensuring respect for participants.
• Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

• Maintain a more detached and objective stance during non-participant observation.
• Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

• Enhance the reliability of data by reducing observer bias.
• Develop clear observation protocols and guidelines.

Contextual Understanding:

• Strive for a thorough understanding of the observed context.
• Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

• Locate and access archives relevant to the research topic.
• Collaborate with institutions or repositories holding valuable data.

Data Verification:

• Verify the accuracy and reliability of archived data.
• Cross-reference with other sources to ensure data integrity.

Ethical Use:

• Adhere to ethical guidelines when using existing data.
• Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

• Address the possibility of incomplete or inaccurate archival records.
• Acknowledge limitations and uncertainties in the data.

Temporal Bias:

• Recognize potential temporal biases in archived data.
• Consider the historical context and changes that may impact interpretation.

Access Limitations:

• Address potential limitations in accessing certain archives.
• Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

• The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
• Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

• Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
• Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

• Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
• Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

• Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
• Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

• Limited timeframes can compromise the depth and thoroughness of the research process.
• Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

• Inadequate resources, whether financial or human, can impede the execution of research activities.
• Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

• Selecting participants in a way that systematically skews the sample can introduce selection bias.
• Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

• Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
• Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

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Writing The Methodology Chapter

5 Time-Saving Tips & Tools

By: David Phair (PhD) and Amy Murdock (PhD) | July 2022

The methodology chapter is a crucial part of your dissertation or thesis – it’s where you provide context and justification for your study’s design. This in turn demonstrates your understanding of research theory, which is what earns you marks .

Over the years, we’ve helped thousands of students navigate this tricky section of the research process. In this post, we’ll share 5 time-saving tips to help you effectively write up your research methodology chapter .

Overview: Writing The Methodology Chapter

• Develop a (rough) outline before you start writing
• Draw inspiration from similar studies in your topic area
• Justify every research design choice that you make
• Err on the side of too much detail , rather than too little
• Back up every design choice by referencing literature

1. Develop an outline before you start writing 

The first thing to keep in mind when writing your methodology chapter (and the rest of your dissertation) is that it’s always a good idea to sketch out a rough outline of what you are going to write about before you start writing . This will ensure that you stay focused and have a clear structural logic – thereby making the writing process simpler and faster.

An easy method of finding a structure for this chapter is to use frameworks that already exist, such as Saunder’s “ research onion ” as an example. Alternatively, there are many free methodology chapter templates for you to use as a starting point, so don’t feel like you have to create a new one from scratch.

Next, you’ll want to consider what your research approach is , and how you can break it down from a top-down angle, i.e., from the philosophical down to the concrete/tactical level. For example, you’ll need to articulate the following:  

• Are you using a positivist , interpretivist , or pragmatist approach ?
• Are you using inductive or deductive reasoning?
• Are you using a qualitative , quantitative, or mixed methods study?

Keep these questions front of mind to ensure that you have a clear, well-aligned line of argument that will maintain your chapter’s internal and external consistency.

Remember, it’s okay if you feel overwhelmed when you first start the methodology chapter. Nobody is born with an innate knowledge of how to do this, so be prepared for the learning curve associated with new research projects. It’s no small task to write up a dissertation or thesis, so be kind to yourself!

2. Take inspiration from other studies 

Generally, there are plenty of existing journal articles that will share similar methodological approaches to your study. With any luck, there will also be existing dissertations and theses that adopt a similar methodological approach and topic. So, consider taking inspiration from these studies to help curate the contents of your methodology chapter.

Students often find it difficult to choose what content to include in the methodology chapter and what to leave for the appendix. By reviewing other studies with similar approaches, you will get a clearer sense of your discipline’s norms and characteristics . This will help you, especially in terms of deciding on the structure and depth of discussion.  

While you can draw inspiration from other studies, remember that it’s vital to pay close attention to your university’s specific guidelines, so you can anticipate departmental expectations of this section’s layout and content (and make it easier to work with your supervisor). Doing this is also a great way to figure out how in-depth your discussion should be. For example, word-count guidelines can help you decide whether to include or omit certain information.

Need a helping hand?

3. Justify every design choice you make

The golden rule of the methodology chapter is that you need to justify each and every design choice that you make, no matter how small or inconsequential it may seem. We often see that students merely state what they did instead of why they did what they did – and this costs them marks.

Keep in mind that you need to illustrate the strength of your study’s methodological foundation. By discussing the “what”, “why” and “how” of your choices, you demonstrate your understanding of research design and simultaneously justify the relevancy and efficacy of your methodology – both of which will earn you marks.

It’s never an easy task to conduct research. So, it’s seldom the case that you’ll be able to use the very best possible methodology for your research (e.g. due to time or budgetary constraints ). That’s okay – but make sure that you explain and justify your use of an alternate methodology to help justify your approach.

Ultimately, if you don’t justify and explain the logic behind each of your choices, your marker will have to assume that you simply didn’t know any better . So, make sure that you justify every choice, especially when it is a subpar choice (due to a practical constraint, for example). You can see an example of how this is done here.

4. Err on the side of too much detail

We often see a tendency in students to mistakenly give more of an overview of their methodology instead of a step-by-step breakdown . Since the methodology chapter needs to be detailed enough for another researcher to replicate your study, your chapter should be particularly granular in terms of detail. 

Whether you’re doing a qualitative or quantitative study, it’s crucial to convey rigor in your research. You can do this by being especially detailed when you discuss your data, so be absolutely clear about your:  

• Sampling strategy
• Data collection method(s)
• Data preparation
• Analysis technique(s)

As you will likely face an extensive period of editing at your supervisor/reviewer’s direction, you’ll make it much easier for yourself if you have more information than you’d need. Some supervisors expect extensive detail around a certain aspect of your dissertation (like your research philosophy), while others may not expect it at all.

Remember, it’s quicker and easier to remove/ trim down information than it is to add information after the fact, so take the time to show your supervisor that you know what you’re talking about (methodologically) and you’re doing your best to be rigorous in your research.

5. Provide citations to support each design choice

Related to the issue of poor justification (tip #3), it’s important include high-quality academic citations to support the justification of your design choices. In other words, it’s not enough to simply explain why you chose a specific approach – you need to support each justification with reference to academic material.  

Simply put, you should avoid thinking of your methodology chapter as a citation-less section in your dissertation. As with your literature review, your methods section must include citations for every decision you make, since you are building on prior research.  You must show that you are making decisions based on methods that are proven to be effective, and not just because you “feel” that they are effective.

When considering the source of your citations, you should stick to peer-reviewed academic papers and journals and avoid using websites or blog posts (like us, hehe). Doing this will demonstrate that you are familiar with the literature and that you are factoring in what credible academics have to say about your methodology.

As a final tip, it’s always a good idea to cite as you go . If you leave this for the end, then you’ll end up spending a lot of precious time retracing your steps to find your citations and risk losing track of them entirely. So, be proactive and drop in those citations as you write up . You’ll thank yourself later!

Let’s Recap…

In this post, we covered 5 time-saving tips for writing up the methodology chapter:

• Look at similar studies in your topic area
• Justify every design choice that you make
• Back up every design choice by referencing methodology literature

If you’ve got any questions relating to the methodology chapter, feel free to drop a comment below. Alternatively, if you’re interested in getting 1-on-1 help with your thesis or dissertation, be sure to check out our private coaching service .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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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.

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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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The methods section is a critical part of the research papers, allowing researchers to use this to understand your findings and replicate your work when pursuing their own research. However, it is usually also the most difficult section to write. This is where Paperpal can help you overcome the writer’s block and create the first draft in minutes with Paperpal Copilot, its secure generative AI feature suite.  

With Paperpal you can get research advice, write and refine your work, rephrase and verify the writing, and ensure submission readiness, all in one place. Here’s how you can use Paperpal to develop the first draft of your methods section.  

• Generate an outline: Input some details about your research to instantly generate an outline for your methods section 
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• Choose the right words: Enhance text by choosing contextual synonyms based on how the words have been used in previously published work.  
• Check and verify text : Make sure the generated text showcases your methods correctly, has all the right citations, and is original and authentic. .   

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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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• Research Process

Choosing the Right Research Methodology: A Guide for Researchers

• 3 minute read
• 39.7K views

Table of Contents

Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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New crystal production method could enhance quantum computers and electronics

Ultra-thin bismuth material for flexible technologies.

In a study published in Nature Materials , scientists from the University of California, Irvine describe a new method to make very thin crystals of the element bismuth -- a process that may aid the manufacturing of cheap flexible electronics an everyday reality.

"Bismuth has fascinated scientists for over a hundred years due to its low melting point and unique electronic properties," said Javier Sanchez-Yamagishi, assistant professor of physics & astronomy at UC Irvine and a co-author of the study. "We developed a new method to make very thin crystals of materials such as bismuth, and in the process reveal hidden electronic behaviors of the metal's surfaces."

The bismuth sheets the team made are only a few nanometers thick. Sanchez-Yamagishi explained how theorists have predicted that bismuth contains special electronic states allowing it to become magnetic when electricity flows through it -- something essential for quantum electronic devices based on the magnetic spin of electrons.

One of the hidden behaviors observed by the team is so-called quantum oscillations originating from the surfaces of the crystals. "Quantum oscillations arise from the motion of an electron in a magnetic field," said Laisi Chen, a Ph.D. candidate in physics & astronomy at UC Irvine and one of the lead authors of the paper. "If the electron can complete a full orbit around a magnetic field, it can exhibit effects that are important for the performance of electronics. Quantum oscillations were first discovered in bismuth in the 1930s, but have never been seen in nanometer-thin bismuth crystals."

Amy Wu, a Ph.D. candidate in physics in Sanchez-Yamagishi's lab, likened the team's new method to a tortilla press. To make the ultra-thin sheets of bismuth, Wu explained, they had to squish bismuth between two hot plates. To make the sheets as flat as they are, they had to use molding plates that are perfectly smooth at the atomic level, meaning there are no microscopic divots or other imperfections on the surface. "We then made a kind of quesadilla or panini where the bismuth is the cheesy filling and the tortillas are the atomically flat surfaces," said Wu.

"There was this nervous moment where we had spent over a year making these beautiful thin crystals, but we had no idea whether its electrical properties would be something extraordinary," said Sanchez-Yamagishi. "But when we cooled down the device in our lab, we were amazed to observe quantum oscillations, which have not been previously seen in thin bismuth films."

"Compression is a very common manufacturing technique used for making common household materials such as aluminum foil, but is not commonly used for making electronic materials like those in your computers," Sanchez-Yamagishi added. "We believe our method will generalize to other materials, such as tin, selenium, tellurium and related alloys with low melting points, and it could be interesting to explore for future flexible electronic circuits."

Next, the team wants to explore other ways in which compression and injection molding methods can be used to make the next computer chips for phones or tablets.

"Our new team members bring exciting ideas to this project, and we're working on new techniques to gain further control over the shape and thickness of the grown bismuth crystals," said Chen. "This will simplify how we fabricate devices, and take it one step closer for mass production."

The research team included collaborators from UC Irvine, Los Alamos National Laboratory and the National Institute for Materials Science in Japan. The research was primarily funded by the Air Force Office of Scientific Research, with partial support coming from the UC Irvine Center for Complex and Active Materials Seed Program, a Materials Research Science and Engineering Center under the National Science Foundation.

• Spintronics
• Electronics
• Materials Science
• Engineering and Construction
• Spintronics Research
• Quantum Computers
• Computers and Internet
• Electrical engineering
• Circuit design
• Virtual reality
• Random variable

Story Source:

Materials provided by University of California - Irvine . Note: Content may be edited for style and length.

Journal Reference :

• Laisi Chen, Amy X. Wu, Naol Tulu, Joshua Wang, Adrian Juanson, Kenji Watanabe, Takashi Taniguchi, Michael T. Pettes, Marshall A. Campbell, Mingjie Xu, Chaitanya A. Gadre, Yinong Zhou, Hangman Chen, Penghui Cao, Luis A. Jauregui, Ruqian Wu, Xiaoqing Pan, Javier D. Sanchez-Yamagishi. Exceptional electronic transport and quantum oscillations in thin bismuth crystals grown inside van der Waals materials . Nature Materials , 2024; DOI: 10.1038/s41563-024-01894-0

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• Published: 23 May 2024

Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights

• Johayra Prithula 1 ,
• Muhammad E. H. Chowdhury 2 ,
• Muhammad Salman Khan 2 ,
• Khalid Al-Ansari 3 ,
• Susu M. Zughaier 4 ,
• Khandaker Reajul Islam 5 &
• Abdulrahman Alqahtani 6 , 7  

Respiratory Research volume  25 , Article number:  216 ( 2024 ) Cite this article

Metrics details

The growing concern of pediatric mortality demands heightened preparedness in clinical settings, especially within intensive care units (ICUs). As respiratory-related admissions account for a substantial portion of pediatric illnesses, there is a pressing need to predict ICU mortality in these cases. This study based on data from 1188 patients, addresses this imperative using machine learning techniques and investigating different class balancing methods for pediatric ICU mortality prediction. This study employs the publicly accessible “Paediatric Intensive Care database” to train, validate, and test a machine learning model for predicting pediatric patient mortality. Features were ranked using three machine learning feature selection techniques, namely Random Forest, Extra Trees, and XGBoost, resulting in the selection of 16 critical features from a total of 105 features. Ten machine learning models and ensemble techniques are used to make accurate mortality predictions. To tackle the inherent class imbalance in the dataset, we applied a unique data partitioning technique to enhance the model's alignment with the data distribution. The CatBoost machine learning model achieved an area under the curve (AUC) of 72.22%, while the stacking ensemble model yielded an AUC of 60.59% for mortality prediction. The proposed subdivision technique, on the other hand, provides a significant improvement in performance metrics, with an AUC of 85.2% and an accuracy of 89.32%. These findings emphasize the potential of machine learning in enhancing pediatric mortality prediction and inform strategies for improved ICU readiness.

Introduction

Pediatric intensive care unit (PICU) mortality for respiratory diseases significantly impacts children’s lives and the healthcare system [ 1 ]. Such pediatric respiratory diseases as severe pneumonia, acute respiratory distress syndrome (ARDS), and respiratory failure, contribute to accounted for approximately 40% of PICU admissions, with a mortality rate ranging from 7 to 15% [ 2 , 3 ]. Pediatric mortality is steadily deteriorating on a daily basis, accompanied by an alarming decline in the infant survival rate [ 4 ]. Survivors of severe respiratory diseases in the PICU often experience long-term consequences like neurodevelopmental impairments, physical disabilities, and psychological issues. Approximately 25% of survivors of pediatric ARDS experienced new functional limitations six months after discharge [ 2 ]. PICU care for pediatric respiratory diseases incurs substantial healthcare costs [ 5 ]. The mean hospitalization cost for pediatric ARDS was approximately $67,000 [ 6 ], with an average ICU cost of $25,000 per day [ 7 , 8 , 9 ]. By investing in research, healthcare resources, and preventive measures, we can work towards reducing the impact of these diseases on children’s lives and alleviating the burden on the healthcare system [ 7 , 10 ].

Predicting pediatric mortality is of utmost importance in safeguarding young lives, enabling targeted interventions, and allocating resources to mitigate fatal outcomes [ 11 ]. Managing critically ill children with respiratory diseases demands significant medical resources, including ventilators, specialized medications, and skilled healthcare providers, which may strain the healthcare system, leading to potential shortages and increased costs [ 12 , 13 ]. The loss of a child in the PICU due to respiratory diseases has emotional and psychological impacts on families, caregivers, and healthcare providers, leading to long-term grief and mental health challenges. Early detection, effective management, and technological advancements are essential to mitigate these effects.

EHR data analysis and predictions based on machine learning models have gained popularity in recent years due to their ease of implementation and deployment [ 14 , 15 , 16 , 17 , 18 ]. The random forest model with an area under the receiver operating characteristic curve of 0.72 was used in an analysis at the Children's Hospital of Zhejiang University School of Medicine to predict postoperative mortality [ 19 ]. Another study at the University of Twente employed three classification models achieved an acceptable AUROC score of 0.71, underlining the need for additional study on methods for controlling class imbalance and model enhancement [ 20 ]. For newborns having major non-cardiac surgery, several research have developed postoperative mortality prediction models based on logistic regression [ 3 , 21 ]. Another study offers a simple but effective linear machine learning model with 11 key characteristics from a pediatric ICU dataset producing a predictive model with a ROC-AUC score of 0.7531 that beats current techniques like PRISM III (The Pediatric Risk of Mortality is a third-generation, physiology-based predictor for pediatric ICU patients [ 22 ]). The study highlights the improved efficacy and generalizability of their methods for forecasting pediatric ICU mortality.

Biochemical markers have become crucial in machine learning algorithms for accurate predictions of high-risk scenarios in pediatric patients. For instance, Early Plasma Osmolality Levels using locally weighted-regression scatterplot smoothing (LOWESS) to assess its relationship with hospital mortality, plasma osmolality at 290 mmol/L with in-, while levels below 290 mmol/L showed no significant association with mortality [ 23 ]. Serum magnesium levels were also studied, with an optimal range identified for the lowest mortality risk in critically ill children [ 24 ]. Furthermore, a study including albumin, lactate dehydrogenase, lactate, urea, arterial pH, and glucose develops a new scoring system for predicting in-hospital mortality in children outperforming the Pediatric Critical Illness Score (PCIS) showing higher AUC values in both the training and validation sets (0.81 and 0.80, respectively) [ 25 ].

Despite numerous studies on ICU mortality during COVID-19, research on pediatric populations using machine learning is limited, partly due to the scarcity of publicly available datasets. However, recently the PICU dataset [ 26 ] becomes publicly available which has made the possibility of investigating mortality prediction for different disease group. This paper focuses on enhancing mortality prediction accuracy in pediatric patients with respiratory diseases, integrating specific risk factors, biomarkers, and advanced modeling techniques.

Methodology

In this study, the publicly available PICU dataset [ 26 ] was utilized for data collection and to train, validate, and test different machine learning model. The initial dataset consisted of PICU database records and was filtered and preprocessed to remove outliers and repetitions. Three feature ranking approaches were explored to identify the optimal set of data for mortality prediction. To achieve more accurate outcomes in predicting mortality, various machine learning models, including Multilayer Perceptron (MLP) Classifier, Linear Discriminant Analysis, XGBoost Classifier, Random Forest Classifier, Logistic Regression, Support Vector Machine (SVM), Extra Trees Classifier, AdaBoost Classifier, K-Nearest Neighbors (KNN) Classifier, and Gradient Boosting Classifier, along with ensemble models, were applied to the preprocessed data. Given the highly imbalanced dynamics of the dataset (90.49% normal cases to 9.51% mortality cases), a subdivision sampling technique was implemented to obtain the most accurate predictions of mortality in pediatric patients. The prediction models for pediatric respiratory-related mortality were developed using Python software 3.9.13, and the Scikit-learn package was employed for implementing the supervised machine learning algorithms. Figure  1 displays a schematic representation of the methodology:

Step by step flowchart of the methodology

Data description

The PICU database comprises information collected during routine hospital care at The Children’s Hospital, Zhejiang University School of Medicine, from 2010 to 2019. This database follows the main schema of the MIMIC-III database but with localization-specific modifications. Standard codes, such as International Classification of Diseases (ICD-10) [ 27 ] codes for diagnosis, were used for frequently employed terms, and their English equivalents were derived. To ensure patient privacy, all identifiers required by the Health Insurance Portability and Accountability Act (HIPAA) of the United States were removed, resulting in completely de-identified patient data. The database contains a total of 13,944 ICU admissions and is structured into 16 tables [ 28 ].

Data preprocessing

The PICU database follows the framework of the MIMIC database, organized into tables for various information groupings. Before inputting this data into our machine learning model, preprocessing steps are necessary to format the database appropriately for training.

Data structuring

The database consists of 17 tables, with three dictionaries helping to interpret certain data fields, and two surgical data tables, which are not relevant to our research. Our dataset is derived from the information in the remaining 12 tables. For each patient admission case, diagnostic information is available, documented using ICD_10 codes. A mapping of ICD_10 codes to diagnose is provided in one of the dictionaries mentioned earlier. The diagnoses are categorized into admission, discharge, and clinical diagnostic categories. Additionally, the dataset includes information about the length of stay (LOS) in the ICU for each admission case, as well as physiological excretion and lab reports, which are mapped using the provided itemid (documentation of lab items mapped from the D_ITEMS table to numeric format) dictionary. The final dataset, constructed using these tables, comprises 13,941 instances and 592 columns.

Missing value removal

Out of the 592 columns used to construct the dataset, not all of them are relevant. Columns with a majority of missing data may introduce bias if imputed, so an iterative process is performed to discard columns lacking more than 70% of data. As a result, the dataset is reduced to 109 columns after discarding 483 columns.

After this reduction, each admission instance is evaluated within these 109 columns to check if the majority of column values are absent. Consequently, the initial 13,941 instances are further reduced to 12,841 instances (Fig.  2 ).

Proposed stacking ensemble technique with base models and meta-model

Filtering and outlier removal

In this study, we focused on respiratory system diseases in the diagnostic column, specifically using ICD-10 index J00-J99. Given the focus on pediatric patients, we also included congenital malformations of the respiratory system (ICD-10 index Q30–Q34). Additionally, four identifier columns were removed in this stage (Additional file 1 : Figure S1). As a result, the filtered dataset comprises a total of 1188 instances and 105 columns [ 29 ].

After filtering the data for our investigation, we conducted a detailed examination of the dataset to identify outliers. Outliers are values that do not align with medical norms as per published laboratory guidelines (Additional file 1 : Figure S2). Through a comprehensive iteration of the 105 columns in the filtered dataset, we removed values that exceeded the thresholds specified in Additional file 1 : Table S1.

Missing data imputation

Ensuring data completeness in the dataset is crucial for the success of this study. The dataset includes multiple demographic and medical biomarker data for each patient admission. However, some parameters may be missing for certain patients. Simply disregarding the available data can lead to the loss of valuable contextual information. To address this issue, data imputation is employed as an alternative to retain and fill in these missing values. Machine learning-based data imputation has been shown to be effective, and for this investigation, we utilized the MICE imputation technique [ 30 ]. Additional file 1 : Figure S3 illustrates the missing values for various characteristics in the dataset, with the spark lines on the figure’s right indicating data completeness.

Data splitting and normalization

To ensure unbiased model performance during training, the training dataset is divided into test sets using cross-validation, a well-established procedure. The entire dataset is split into 5 sets, each containing 80% training data and 20% test data [ 31 ].

For effective training of the machine learning model on the dataset, data normalization is essential to achieve generalized performance [ 32 ]. Normalization ensures that each feature contributes equally to the training process by transforming or scaling the entire dataset to a standardized range. Studies have shown improved performance when using normalized data for training instead of unprocessed data. In our study, we employed standard scalar to normalize the training data, and the scaling parameters were applied to the test set as well [ 32 ].

Data balancing

The dataset poses a fundamental challenge due to the class imbalance. While there are records for 1,075 cases (90.49%) that are alive, only 113 cases (9.51%) are deceased. This imbalance during training can introduce bias, leading the model to primarily recognize healthy cases. To mitigate this issue, a data augmentation method is proposed.

Data augmentation techniques are employed to provide synthetic data for minority classes. One such technique is Synthetic Minority Over-sampling Technique (SMOTE), a well-known method that generates synthetic data using the nearest kNN data point [ 33 ]. In our study, for both machine learning and ensemble techniques, the minority classes in the training sets are oversampled during augmentation to match the majority class.

Additionally, for the subdivision technique, each division is proportionally oversampled to achieve a balanced dataset. This approach helps address the class imbalance, enhancing the performance of the machine learning models and resulting in more accurate predictions.

Statistical analysis

The Chi-square univariate test and rank-sum test were employed to identify statistically significant characteristics between the two groups. The detailed description of this study is explained in Additional file 1 : S1. This analysis calculates the difference between the observed frequency (O) and the expected frequency (E) for each cell. It then squares the difference, divides it by the expected frequency, and sums the results for all cells in the contingency table [ 34 , 35 ].

Feature ranking

In the preprocessed dataset containing 105 features and a column with target variables, using all features may lead to overfitting and impractical deployment for real-time prediction. To select the most relevant features, three machine learning feature selection models are employed: XGboost, RandomForest and Extratrees. Descriptions of these feature ranking techniques are given in Additional file 1 : S2.

Using these feature selection models, we can identify the most relevant features to enhance prediction accuracy while avoiding overfitting and ensuring practical deployment in real-time scenarios.

Machine learning model development

This study explores several machine learning models from the Sci-kit learn library. We trained our data on MLP Classifier, Linear Discriminant Analysis, XGBoost Classifier, Random Forest Classifier, Logistic Regression, SVM, Extra Trees Classifier, Ada Boost Classifier, KNN Classifier, and Gradient Boosting Classifier [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. Notably, Extra Trees, Random Forest and Catboost classifier demonstrated the most promising performance. In the subsequent section, a comprehensive overview of these top-performing models is provided:

Extra trees classifier

Extremely Randomized Trees, or ExtraTrees (ET) Classifier, is a tree-based ensemble technique used in supervised learning. This model introduces extreme randomness in attribute values and tree node cutoff points. It is a subset of the RandomForest classifier, offering computational efficiency through more extensive randomization. The classification score measurement for ExtraTrees is a specific normalization of information gain. For a sample S and a division s, the measure is given by:

where \({H}_{s}(S)\) is the (log) entropy of the classification in S, \({H}_{s}(S)\) is the split entropy (also called split information by Quinlan (1986)), and \({I}_{c}^{s}\left(S\right)\) is the mutual information of the split outcome and the classification [ 42 , 46 , 47 ].

Random forest classifier

The Random Forest (RF) Classifier is a classification-focused machine learning algorithm that uses an ensemble approach by combining multiple decision trees. The term “random forest” comes from the fact that the algorithm creates a forest of decision trees with arbitrary constructions. Important division points in the data, like Gini impurity or information gain, are used to build decision trees based on different criteria. However, in Random Forest, the selection of split points is limited to a random subset of features at each node, rather than considering all features [ 39 , 48 , 49 ]. Additional file 1 : Figure S4 depicts the framework for the RandomForest Classifier.

Catboost classifier

CatBoost (CB) Classifier is a gradient boosting algorithm tailored for efficient handling of categorical features. By constructing decision trees and combining their predictions, it achieves accurate classifications. This specialized algorithm efficiently manages categorical features, feature scaling, and missing values, optimizing training performance. Compared to conventional gradient boosting algorithms, CatBoost offers a more streamlined and automated approach [ 50 , 51 ].

Stacking based machine learning model

Ensemble models are employed when individual models fall short of achieving desired outcomes [ 52 , 53 ]. This method has found extensive application, including in medical applications, where it proves effective in improving the accuracy of predictions by leveraging insights from various models [ 16 , 54 , 55 ]. Stacking ensemble technique is used in this study, combining the predictions of our top three models. Stacking ensemble, also known as stacked generalization, involves training a meta-model to optimally combine base models' predictions, resulting in improved overall performance. By utilizing input x and the predictions of the base-level classifier set M, a probability distribution is created, leading to a final prediction:

where ( \({{\text{c}}}_{1}\) , \({{\text{c}}}_{2}\) … \({{\text{c}}}_{{\text{m}}}\) ) represents the set of potential class values and \({{\text{P}}}^{{\text{M}}}\left({{\text{c}}}_{{\text{i}}}|{\text{x}}\right)\) represents the probability that example x belongs to class \({{\text{c}}}_{{\text{i}}}\) , as calculated (and predicted) by classifier M [ 52 , 53 ]. This investigation employs the classifiers Extra-trees, RandomForest, and CatBoost. The Gradient boosting classifier was used for the meta-model. Our proposed architecture for the stacking ensemble method is depicted in Fig.  2 below:

Data subdivision: an approach for highly imbalances datasets

The main challenge in our study is the significant class disparity, with a distribution of 90.49% to 9.51%, which can lead to biased predictions and an inability to accurately predict the minority class. To address this issue, we explore different techniques to mitigate data imbalance, namely undersampling and oversampling. Undersampling involves reducing the number of samples from the majority class to equalize class distribution. However, this approach results in the loss of valuable information, as a considerable percentage of data is discarded. On the other hand, oversampling aims to increase the number of samples in the minority class by duplicating data points, but applying this method to highly imbalanced datasets can lead to overfitting. The model becomes too reliant on the specific minority data points, leading to inaccuracies in predicting new data.

To overcome these challenges, we propose a subset method for handling imbalanced data in our study. We divide the majority class into three subsets and then create three Subdivisions by combining each subset with an oversampled version of the entire minority class. This division of the dataset into smaller Subdivisions helps reduce class disparity compared to the complete dataset. As a result, when oversampling is applied, it encounters a much lower discrepancy and generates fewer duplications of the minority data points, reducing the risk of overfitting. During the training process, we apply fivefold Cross-Validation for each Subdivision and use SMOTE to achieve class balance in the training set of each fold. The results of each Subdivision are later averaged to obtain the final prediction. This approach ensures that each Subdivision is given equal importance, and the ensemble of results helps improve overall performance. Figure  3 illustrates the data subdivision technique used in our study, depicting how the dataset is divided into Subdivisions, oversampled, and finally combined to achieve more balanced training data.

Data subdivision technique

By adopting the data subdivision technique, we aim to enhance the accuracy and reliability of our machine learning models in predicting the minority class while avoiding the pitfalls of traditional undersampling and oversampling methods. This innovative approach contributes to more robust and effective predictions in our study, paving the way for improved results in handling imbalanced data sets in various domains.

To balance the dataset, we divided the majority class into three subsets (359, 359, and 357 cases) and merged them with the minority class (113 instances). SMOTE was then used to achieve class balance.

Performance metrics

The receiver operating characteristic (ROC) curves and area under the curve (AUC), along with Precision, Sensitivity, Specificity, Accuracy, and F1-Score, were used to evaluate the performance of the classifiers. In addition, we utilized five-fold cross-validation, which results in a division of 80% and 20% for the train and test sets, respectively, and according to the fold number, this procedure is repeated five times to validate the entire dataset.

We utilized per-class weighted metrics and overall precision because the number of instances varied between classes. In addition, the AUC value was utilized as an evaluation metric. Five evaluation metrics (weighted sensitivity or recall, specificity, precision, overall accuracy, and F1 score) are represented mathematically in Eqs.  3 through 7 .

here true positive, true negative, false positive, and false negative are represented as TP, TN, FP, and FN, respectively.

Experimental setup

This study was carried out with the sklearn package and Python 3.9.13. All the models were trained with the specifications: Nvidia GForce 1050ti GPU, AMD Ryzen 7 5800X 8-Core Processor and 32 GB High RAM.

The statistical analysis was conducted using the scipy library and the chi-square test on our dataset. Demographic variables were excluded from the analysis, leaving continuous numeric columns. The chi-square rank-sum test was used to assess the statistical significance of individual characteristics for each group, with a significance threshold of P < 0.05. The dataset consisted of 1075 (90.49%) living cases and 113 (9.51%) deceased cases. The mean (SD) value of lactate for deceased cases was 9.99 (7.42), while for living cases, it was 3.63 (2.92). ALB/GLB and Chloride_Whole_Blood had P-values greater than 0.8, indicating no significant difference between the groups. The P-values for Creatine_Kinase (CK), Mean_Platelet_Volume (MPV), thrombin_time, Hematocrit, WBC_Urine, WBC/pus_cell, and Monocyte_Count ranged from 0.79 to 0.50. Additional file 1 : Table S2 presents the class-wise mean, standard deviation, and P-values for all biochemical markers and continuous variables.

In this study, three machine learning feature selection models were employed: XGBoost, RandomForest, and Extra trees. In the initial analysis, RandomForest yielded the most favorable rankings, resulting in higher accuracy scores for predictions compared to the other two methods. Out of the 106 features, the top 16 features were identified as the most effective for achieving optimal results with a minimal number of features. Figure  4 illustrates the F1-Scores for class 1 corresponding to the top features in our three best models.

F1-Scores for Class 1 across the top features

In Fig.  5 , the top 20 characteristics assessed by RandomForest are presented, and out of these, 16 were utilized. Among them, lactate was identified as the most significant characteristic.

Features ranked according to Random Forest feature selection algorithm

Machine learning model performances

The top 16 features, as ranked by Random Forest's feature importance attribute, along with the ‘HOSPITAL_EXPIRE_FLAG’ as the target variable, were used to train the algorithms. The models were then tested using fivefold cross-validation on the entire dataset. The performance of the top three machine learning models was investigated and evaluated. In the following section, we present and discuss the results of each experiment.

The ET classifier achieved an AUC score of 72.22% and an accuracy of 89.14%. However, its class-wise precision for the deceased class (class 1) was only 43.94%, indicating poor performance in accurately detecting the deceased cases. The RF classifier obtained an AUC score of 70.91% and an accuracy of 88.22%. However, when analyzing individual classes, the precision for class 1 was found to be 40.28%. The CB classifier demonstrates the highest AUC (77.11%) and accuracy (87.96%) among the three classifiers. However, it exhibits lower precision (41%) in predicting the deceased class compared to other classifiers. The stacking technique was employed to create an ensemble model by combining the top three performing models. The layered models were trained using gradient boosting classifier. As a result, the AUC score decreased to 60.59%, while the accuracy increased to 88.89%. Table 1 provides a summary of the results for the ET, RF, CB and stacking ML classifiers.

Figure  6 shows the confusion matrix for Extra Tree, Random Forest, CatBoost and stacking ML model. It is apparent that among these models CatBoost is performing the best in terms of sensitivity and AUC. However, none of the models are showing acceptable performance in this highly imbalance dataset (d). The ROC curves for ET, RF, CB and stacking ML model can be seen in Fig.  7 .

Confusion matrix for Extra Tree ( a ), Random Forest ( b ), CatBoost ( c ) and stacking ensemble method ( d )

ROC curves for Extra Tree ( a ), Random Forest ( b ), CatBoost ( c ) and stacking ensemble method ( d )

Data subset performances

Utilizing the top 16 features, we employ the CB classifier for the subdivision method. Dividing the dataset into three subdivisions, we independently train each subset on the CB model and then aggregate the results by averaging them. The subdivision method exhibits a noteworthy average subset accuracy of 89.32% with an AUC of 85.20%. The precision and sensitivity for this model are 77.98% and 77.29%, respectively, while the specificity and F1-score stand at 93.11% and 89.30%. For a visual representation of the model’s performance, refer to Fig.  8 , which illustrates the ROC curve for the subdivision method. The summary of the average result of the subdivision method and results for each subdivision is stated in Table  2 and 3 .

Confusion matrix for the subsets for the best performing model—CB Classifier and average ROC curve for the subdivision technique

The confusion matrix for each subset and average ROC curve are depicted in Fig.  8 .

The findings of this study showcase the significant potential of biomarkers in predicting mortality, offering valuable insights that can aid clinicians in making well-informed decisions. In our exploration of feature selection models for machine learning, namely XGBoost, RandomForest, and Extra tree, we discovered that the top 16 features selected by RandomForest yielded the most optimal results with minimal feature utilization during the initial investigations. This indicated that RandomForest outperformed its competitors in terms of predictive performance.

However, upon conducting further analysis, we unveiled certain limitations of the classifiers, particularly their inability to accurately predict the deceased class. Despite the promising results and efficiency of RandomForest in feature selection, it became evident that more advanced techniques were necessary to tackle the challenge of effectively predicting mortality in the dataset. This highlighted the importance of continually exploring and refining machine learning methodologies to enhance their predictive capabilities and address specific complexities in clinical scenarios. As such, our study not only underscores the significance of biomarkers in mortality prediction but also emphasizes the ongoing need for sophisticated algorithms to achieve more accurate and comprehensive predictions in critical healthcare settings.

We focused on the subdivision technique using the top 16 features for the CB classifier. Dividing the dataset into three distinct subsets, we proceeded to train each of these subsets independently on the CB model. Subsequently, the results were skillfully combined by averaging them, yielding a highly commendable average subset accuracy of 89.32%. Moreover, the AUC for this method achieved an impressive 85.2%, indicative of its robustness in discrimination capability. As a result of this approach, not only did we achieve superior accuracy, but we also observed significant improvements in precision, sensitivity, specificity, and F1-score, all of which are crucial performance metrics in medical predictive modeling. These outcomes underscore the effectiveness of the subdivision technique and its potential to further enhance the reliability and precision of our predictive model.

However, while the CB classifier excelled in predicting the living cases, it exhibited limitations when it came to accurately predicting the deceased class. The model struggled to achieve satisfactory performance in detecting the minority class of deceased cases, resulting in lower sensitivity and F1-score values. This indicates that additional research and further refinement are essential to enhance the model's ability to accurately predict the deceased class. To address these identified limitations, future investigations could focus on improving the handling of imbalanced data and exploring more advanced ensemble techniques or hybrid models that may provide a better balance between the two classes. Moreover, fine-tuning the feature selection process and incorporating domain-specific knowledge may also contribute to enhancing the model's predictive capabilities for the deceased class. A quantitative comparison among relevant studies is provided in Table  4 .

The data size in our study, encompassing 13,944 pediatric ICU cases, is comparable to that in Hong et al.’s study and larger than the datasets used in other referenced studies. This extensive data size provides a robust basis for our analysis and enhances the generalizability of our results. Our approach, focusing on feature engineering and data subdivision, yielded an accuracy of 0.8932 and an AUC of 0.8520. These results are notably higher than those achieved in the studies by Hu et al., Wang et al., and Zhang et al., indicating a strong predictive capability of our model. It is noteworthy that our study’s AUC is comparable to that achieved by Li et al., who employed advanced fusion models.

The variance in approaches and outcomes across these studies underscores the diverse methodologies in mortality prediction research. Our study contributes to this growing body of work by demonstrating the efficacy of feature engineering combined with data subdivision techniques in a pediatric ICU setting. This approach shows promise in enhancing predictive accuracy and could be a valuable addition to the clinician’s toolkit for mortality prediction, emphasizing the need for personalized and data-driven patient care. This comparative analysis not only positions our study within the existing research landscape but also highlights its potential clinical utility and relevance. By benchmarking our findings against these studies, we gain valuable insights into the evolving nature of machine learning applications in healthcare and identify avenues for future research and development in predictive modeling for pediatric respiratory diseases. The findings of this study need to be approached with caution due to the limitations posed by the relatively small dataset size and the class imbalance between deceased and living cases. The restricted sample size may impact the generalizability and robustness of the results. Furthermore, the class imbalance can introduce biases and hinder the accurate prediction of the minority class. To enhance the credibility and efficacy of mortality prediction models for pediatric patients with respiratory diseases, future research endeavors should focus on gathering larger and more balanced datasets. By increasing the sample size, the models can be trained on a more diverse and representative set of instances, leading to improved performance and better generalization to real-world scenarios. In addition to dataset size and class balance, researchers should also explore the incorporation of additional relevant features and biomarkers to refine the predictive models further. Integrating comprehensive and diverse patient data can enable the development of more comprehensive and accurate mortality prediction systems. Moreover, it is essential to conduct external validation of the developed models on independent datasets to verify their reliability and effectiveness in different healthcare settings. This validation process will provide crucial insights into the model’s robustness and its potential to be applied in diverse clinical environments.

Monitoring ICU patients’ parameters (lactate, pCO2, LDH, anion gap, electrolytes, INR, potassium, creatinine, bicarbonate and WBC) provide valuable insights into their pathophysiology i.e. medical progress and severity of critical illness, which help in guiding treatment or decision-making. The following explains the significance of the top parameters: elevated lactate levels indicate tissue hypoxia and anaerobic metabolism, often seen in shock or hypo perfusion states of ICU patients. Monitoring lactate helps assess tissue perfusion and response to treatment. Carbon dioxide (pCO2) is a byproduct of metabolism and is eliminated through respiration. Changes in pCO2 can indicate respiratory status and acid–base balance, especially in patients with respiratory failure or ventilation issues. Lactate Dehydrogenase (LDH) is an enzyme found in various tissues, including the heart, liver, and muscles. Elevated LDH levels can indicate tissue damage or breakdown, as seen in conditions like myocardial infarction, liver disease, or muscle injury. The elevated levels of LDH reflect the severity of critical illness. Whereas the anion gap is a calculated parameter that helps assess metabolic acidosis. An increased anion gap may indicate the presence of unmeasured anions, such as lactate, ketones, or toxins, which can be seen in conditions like diabetic ketoacidosis or lactic acidosis conditions that require extensive monitoring in ICU. Therefore, monitoring electrolytes like sodium, potassium, and chloride helps assess fluid and electrolyte balance, which is crucial in critically ill patients to prevent complications like arrhythmias or neurologic abnormalities. Potassium in particular is essential for proper cardiac and neuromuscular function. Abnormal potassium levels can lead to life-threatening arrhythmias and are often seen in conditions like renal failure or metabolic disorders. Bicarbonate is a buffer that helps maintain acid–base balance in the body. Changes in bicarbonate levels can indicate metabolic acidosis or alkalosis, which can occur in various critical illnesses. Creatinine is a waste product of muscle metabolism and is excreted by the kidneys. Elevated creatinine levels indicate impaired renal function, which is common in critically ill patients and can impact drug dosing and fluid management. Monitoring WBC (White Blood Cell Count helps assess the inflammatory response and immune function in critically ill patients. Elevated WBC counts may indicate infection or inflammatory processes. Similarly, monitoring PCT (procalcitonin) as biomarker of bacterial infections. Additionally, INR (International Normalized Ratio) is a measure of blood coagulation and is used to monitor patients on anticoagulant therapy. Changes in INR can indicate alterations in the coagulation cascade and may require adjustments in medication [ 58 , 59 , 60 , 61 ].

In summary, addressing the limitations of dataset size and class imbalance and incorporating advanced feature selection techniques and external validation can advance the accuracy and dependability of mortality prediction models for pediatric patients with respiratory diseases. These efforts will ultimately contribute to more effective and personalized patient care, leading to improved clinical outcomes for this vulnerable patient population.

In conclusion, this study sheds light on the promising potential of biomarkers in predicting mortality among pediatric patients with respiratory diseases, empowering clinicians to make well-informed admission decisions. Through meticulous evaluation of diverse classifiers, the CatBoost (CB) classifier emerged as the standout performer, exhibiting the highest AUC score and accuracy. However, the challenge lies in improving precision for the deceased class. By employing the stacking ensemble method, we were able to enhance overall accuracy, albeit at the expense of a slightly lower AUC score. Subsequently, the subdivision technique applied to the CB classifier using the top 16 features led to remarkable improvements in precision (89.32%), AUC (85.20%), and other essential predictive metrics. Overall, the CB classifier with the subdivision algorithm proved to be the most effective approach for mortality prediction. Looking ahead, our future objectives for this mortality prediction model in pediatrics encompass its seamless integration into clinical settings, especially in resource-constrained environments, and customization to suit the needs of specific populations. Additionally, we aim to incorporate real-time data streams to ensure up-to-date and accurate predictions. Collaborative efforts to enhance the dataset’s size and diversity are paramount to ensure the model’s robustness and generalizability. By diligently pursuing these avenues, we envision a significant impact on pediatric healthcare, as our model’s enhanced accuracy will bolster preparedness and improve patient outcomes, ultimately saving lives and benefiting young patients and their families.

Availability of data and materials

The preprocessed version of the dataset used in this study is available upon reasonable request to the corresponding author.

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This work was made possible by High Impact grant# QUHI-CENG-23/24-216 from Qatar University and is also supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1445). The statements made herein are solely the responsibility of the authors.

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Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh

Johayra Prithula

Department of Electrical Engineering, Qatar University, 2713, Doha, Qatar

Muhammad E. H. Chowdhury & Muhammad Salman Khan

Emergency Medicine Department, Sidra Medicine, Doha, Qatar

Khalid Al-Ansari

Department of Basic Medical Sciences, College of Medicine, Qatar University, 2713, Doha, Qatar

Susu M. Zughaier

Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, 56000, Kuala Lumpur, Malaysia

Khandaker Reajul Islam

Department of Biomedical Technology, College of Applied Medical Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia

Abdulrahman Alqahtani

Department of Medical Equipment Technology, College of Applied, Medical Science, Majmaah University, 11952, Majmaah, Saudi Arabia

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Conceptualization: JP, MEHC; Data curation: JP, KRI; Formal analysis: JP; Funding acquisition: MEHC, MSK, KA, SMZ, AA; Investigation: JP, MEHC; Project administration: MEHC, MSK, AA; Software: JP, KRI; Supervision: MEHC, MSK, AA; Validation: MEHC, KA, SMZ; Visualization: JP; writing—original draft: JP, MEHC, AA; Writing—review & editing: JP, MEHC, MSK, KA, SMZ, KRI, AA.

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Correspondence to Muhammad E. H. Chowdhury .

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Prithula, J., Chowdhury, M.E.H., Khan, M.S. et al. Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights. Respir Res 25 , 216 (2024). https://doi.org/10.1186/s12931-024-02753-x

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Received : 22 September 2023

Accepted : 29 February 2024

Published : 23 May 2024

DOI : https://doi.org/10.1186/s12931-024-02753-x

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