research methods in a research paper

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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

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

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

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

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

Quantitative Research
Qualitative Research
Data Science Methods (Machine Learning, AI, Big Data)
Text Mining and Computational Text Analysis
Evidence Synthesis/Systematic Reviews
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About Research Methods

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

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

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

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

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

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

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

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

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

Library GIS Services

Other Data Services at Berkeley

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

General Research Methods Resources

Here are some general resources for assistance:

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

Consultants

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

Related Resourcex

IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
Sponsored Projects Sponsored projects works with researchers applying for major external grants.
Next: Quantitative Research >>
Last Updated: Apr 25, 2024 11:09 AM
URL: https://guides.lib.berkeley.edu/researchmethods

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Knowledge Base
Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

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

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

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

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

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

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

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

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Advanced Research Methods

Writing the research paper.

What Is Research?
Library Research
Writing a Research Proposal

Before Writing the Paper

Methods, thesis and hypothesis, clarity, precision and academic expression, format your paper, typical problems, a few suggestions, avoid plagiarism.

Presenting the Research Paper

Find a topic.

Try to find a subject that really interests you.
While you explore the topic, narrow or broaden your target and focus on something that gives the most promising results.
Don't choose a huge subject if you have to write a 3 page long paper, and broaden your topic sufficiently if you have to submit at least 25 pages.
Consult your class instructor (and your classmates) about the topic.

Explore the topic.

Find primary and secondary sources in the library.
Read and critically analyse them.
Take notes.
Compile surveys, collect data, gather materials for quantitative analysis (if these are good methods to investigate the topic more deeply).
Come up with new ideas about the topic. Try to formulate your ideas in a few sentences.
Review your notes and other materials and enrich the outline.
Try to estimate how long the individual parts will be.
Do others understand what you want to say?
Do they accept it as new knowledge or relevant and important for a paper?
Do they agree that your thoughts will result in a successful paper?
Qualitative: gives answers on questions (how, why, when, who, what, etc.) by investigating an issue
Quantitative:requires data and the analysis of data as well
the essence, the point of the research paper in one or two sentences.
a statement that can be proved or disproved.
Be specific.
Avoid ambiguity.
Use predominantly the active voice, not the passive.
Deal with one issue in one paragraph.
Be accurate.
Double-check your data, references, citations and statements.

Academic Expression

Don't use familiar style or colloquial/slang expressions.
Write in full sentences.
Check the meaning of the words if you don't know exactly what they mean.
Avoid metaphors.
Almost the rough content of every paragraph.
The order of the various topics in your paper.
On the basis of the outline, start writing a part by planning the content, and then write it down.
Put a visible mark (which you will later delete) where you need to quote a source, and write in the citation when you finish writing that part or a bigger part.
Does the text make sense?
Could you explain what you wanted?
Did you write good sentences?
Is there something missing?
Check the spelling.
Complete the citations, bring them in standard format.

Use the guidelines that your instructor requires (MLA, Chicago, APA, Turabian, etc.).

Adjust margins, spacing, paragraph indentation, place of page numbers, etc.
Standardize the bibliography or footnotes according to the guidelines.

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(Based on English Composition 2 from Illinois Valley Community College):

Weak organization
Poor support and development of ideas
Weak use of secondary sources
Excessive errors
Stylistic weakness

When collecting materials, selecting research topic, and writing the paper:

Be systematic and organized (e.g. keep your bibliography neat and organized; write your notes in a neat way, so that you can find them later on.
Use your critical thinking ability when you read.
Write down your thoughts (so that you can reconstruct them later).
Stop when you have a really good idea and think about whether you could enlarge it to a whole research paper. If yes, take much longer notes.
When you write down a quotation or summarize somebody else's thoughts in your notes or in the paper, cite the source (i.e. write down the author, title, publication place, year, page number).
If you quote or summarize a thought from the internet, cite the internet source.
Write an outline that is detailed enough to remind you about the content.
Read your paper for yourself or, preferably, somebody else.
When you finish writing, check the spelling;
Use the citation form (MLA, Chicago, or other) that your instructor requires and use it everywhere.

Plagiarism : somebody else's words or ideas presented without citation by an author

Cite your source every time when you quote a part of somebody's work.
Cite your source every time when you summarize a thought from somebody's work.
Cite your source every time when you use a source (quote or summarize) from the Internet.

Consult the Citing Sources research guide for further details.

<< Previous: Writing a Research Proposal
Next: Presenting the Research Paper >>
Last Updated: May 16, 2024 10:20 AM
URL: https://guides.library.ucla.edu/research-methods

Organizing Academic Research Papers: 6. The Methodology

Purpose of Guide
Design Flaws to Avoid
Glossary of Research Terms
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Choosing a Title
Making an Outline
Paragraph Development
Executive Summary
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tertiary Sources
What Is Scholarly vs. Popular?
Qualitative Methods
Quantitative Methods
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Annotated Bibliography
Dealing with Nervousness
Using Visual Aids
Grading Someone Else's Paper
How to Manage Group Projects
Multiple Book Review Essay
Reviewing Collected Essays
About Informed Consent
Writing Field Notes
Writing a Policy Memo
Writing a Research Proposal
Acknowledgements

The methods section of a research paper provides the information by which a study’s validity is judged. The method section answers two main questions: 1) How was the data collected or generated? 2) How was it analyzed? The writing should be direct and precise and written in the past tense.

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 choose affects the results and, by extension, how you likely interpreted those results.
Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and it misappropriates interpretations of findings .
In most cases, there are a variety of different methods you can choose to investigate a research problem. Your methodology section of your paper should make clear the reasons why you chose a particular method or procedure .
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 questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
The research method must be appropriate to the objectives of the study . For example, be sure 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 did arise, you must describe the ways in which their impact was minimized or why these problems do not affect the findings in any way that impacts your interpretation of the data.
Often in social science research, it is useful for other researchers to adapt or replicate your methodology. Therefore, it is important to always provide sufficient information to allow others to use or replicate the study . This information is particularly important when a new method had been developed or an innovative use of an existing method has been utilized.

Bem, Daryl J. Writing the Empirical Journal Article . Psychology Writing Center. University of Washington; 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 empirical-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 hypotheses that need to be tested. This approach is focused on explanation .
The interpretative group is focused on understanding phenomenon in a comprehensive, holistic way . This research method allows you to recognize your connection to the subject under study. Because the interpretative group focuses more on subjective knowledge, it requires careful interpretation of variables.

II. Content

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 should have a clear connection with 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 unsuited 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.
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?
Provide background and 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 rationale 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 statisics being used? If other data sources exist, explain why the data you chose is most appropriate.
Address 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.

III. Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but to the point. Don’t provide any background information that doesn’t directly help the reader to understand why a particular method was chosen, how the data was gathered or obtained, and how it was analyzed. 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. NOTE: An exception to this rule is if you select an unconventional approach to doing the method; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall research process. Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data. 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 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; 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; 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; 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.

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 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.

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Next: Qualitative Methods >>
Last Updated: Jul 18, 2023 11:58 AM
URL: https://library.sacredheart.edu/c.php?g=29803
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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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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

How often do you use social media per day?
Less than 30 minutes
30 minutes to 1 hour
1 to 2 hours
2 to 4 hours
More than 4 hours
Which social media platforms do you use?
Others (Please specify)
How often do you experience the following on social media?
Social comparison (comparing yourself to others)
Cyberbullying
Fear of Missing Out (FOMO)
Have you ever experienced any of the following mental health problems in the past month?
Do you think social media use has a positive or negative impact on your mental health?
Very positive
Somewhat positive
Somewhat negative
Very negative
In your opinion, which factors contribute to the negative impact of social media on mental health?
Social comparison
In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
Education on healthy social media use
Counseling for mental health problems caused by social media
Social media detox programs
Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to write the methods section of a research paper

Affiliation.

1 Respiratory Care Services, San Francisco General Hospital, NH:GA-2, 1001 Potrero Avenue, San Francisco, CA 94110, USA. [email protected]
PMID: 15447808

The methods section of a research paper provides the information by which a study's validity is judged. Therefore, it requires a clear and precise description of how an experiment was done, and the rationale for why specific experimental procedures were chosen. The methods section should describe what was done to answer the research question, describe how it was done, justify the experimental design, and explain how the results were analyzed. Scientific writing is direct and orderly. Therefore, the methods section structure should: describe the materials used in the study, explain how the materials were prepared for the study, describe the research protocol, explain how measurements were made and what calculations were performed, and state which statistical tests were done to analyze the data. Once all elements of the methods section are written, subsequent drafts should focus on how to present those elements as clearly and logically as possibly. The description of preparations, measurements, and the protocol 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. Material in each section should be organized by topic from most to least important.

Biomedical Research*
Research Design
Writing* / standards

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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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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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Neurol Res Pract

How to use and assess qualitative research methods

Loraine busetto.

1 Department of Neurology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany

Wolfgang Wick

2 Clinical Cooperation Unit Neuro-Oncology, German Cancer Research Center, Heidelberg, Germany

Christoph Gumbinger

Associated data.

Not applicable.

This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 – 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 – 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig. 1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

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Object name is 42466_2020_59_Fig1_HTML.jpg

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig. 2 .

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Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig. 3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

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From data collection to data analysis

Attributions for icons: see Fig. Fig.2, 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 – 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig. 4 .

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Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 – 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 – 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table Table1. 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Take-away-points

Acknowledgements

Abbreviations, authors’ contributions.

LB drafted the manuscript; WW and CG revised the manuscript; all authors approved the final versions.

no external funding.

Availability of data and materials

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

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Glossary of research terms.

Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Reading Research Effectively
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Applying Critical Thinking
Choosing a Title
Making an Outline
Paragraph Development
Research Process Video Series
Executive Summary
The C.A.R.S. Model
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tiertiary Sources
Scholarly vs. Popular Publications
Qualitative Methods
Quantitative Methods
Insiderness
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Writing Concisely
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This glossary is intended to assist you in understanding commonly used terms and concepts when reading, interpreting, and evaluating scholarly research. Also included are common words and phrases defined within the context of how they apply to research in the social and behavioral sciences.

Acculturation -- refers to the process of adapting to another culture, particularly in reference to blending in with the majority population [e.g., an immigrant adopting American customs]. However, acculturation also implies that both cultures add something to one another, but still remain distinct groups unto themselves.
Accuracy -- a term used in survey research to refer to the match between the target population and the sample.
Affective Measures -- procedures or devices used to obtain quantified descriptions of an individual's feelings, emotional states, or dispositions.
Aggregate -- a total created from smaller units. For instance, the population of a county is an aggregate of the populations of the cities, rural areas, etc. that comprise the county. As a verb, it refers to total data from smaller units into a large unit.
Anonymity -- a research condition in which no one, including the researcher, knows the identities of research participants.
Baseline -- a control measurement carried out before an experimental treatment.
Behaviorism -- school of psychological thought concerned with the observable, tangible, objective facts of behavior, rather than with subjective phenomena such as thoughts, emotions, or impulses. Contemporary behaviorism also emphasizes the study of mental states such as feelings and fantasies to the extent that they can be directly observed and measured.
Beliefs -- ideas, doctrines, tenets, etc. that are accepted as true on grounds which are not immediately susceptible to rigorous proof.
Benchmarking -- systematically measuring and comparing the operations and outcomes of organizations, systems, processes, etc., against agreed upon "best-in-class" frames of reference.
Bias -- a loss of balance and accuracy in the use of research methods. It can appear in research via the sampling frame, random sampling, or non-response. It can also occur at other stages in research, such as while interviewing, in the design of questions, or in the way data are analyzed and presented. Bias means that the research findings will not be representative of, or generalizable to, a wider population.
Case Study -- the collection and presentation of detailed information about a particular participant or small group, frequently including data derived from the subjects themselves.
Causal Hypothesis -- a statement hypothesizing that the independent variable affects the dependent variable in some way.
Causal Relationship -- the relationship established that shows that an independent variable, and nothing else, causes a change in a dependent variable. It also establishes how much of a change is shown in the dependent variable.
Causality -- the relation between cause and effect.
Central Tendency -- any way of describing or characterizing typical, average, or common values in some distribution.
Chi-square Analysis -- a common non-parametric statistical test which compares an expected proportion or ratio to an actual proportion or ratio.
Claim -- a statement, similar to a hypothesis, which is made in response to the research question and that is affirmed with evidence based on research.
Classification -- ordering of related phenomena into categories, groups, or systems according to characteristics or attributes.
Cluster Analysis -- a method of statistical analysis where data that share a common trait are grouped together. The data is collected in a way that allows the data collector to group data according to certain characteristics.
Cohort Analysis -- group by group analytic treatment of individuals having a statistical factor in common to each group. Group members share a particular characteristic [e.g., born in a given year] or a common experience [e.g., entering a college at a given time].
Confidentiality -- a research condition in which no one except the researcher(s) knows the identities of the participants in a study. It refers to the treatment of information that a participant has disclosed to the researcher in a relationship of trust and with the expectation that it will not be revealed to others in ways that violate the original consent agreement, unless permission is granted by the participant.
Confirmability Objectivity -- the findings of the study could be confirmed by another person conducting the same study.
Construct -- refers to any of the following: something that exists theoretically but is not directly observable; a concept developed [constructed] for describing relations among phenomena or for other research purposes; or, a theoretical definition in which concepts are defined in terms of other concepts. For example, intelligence cannot be directly observed or measured; it is a construct.
Construct Validity -- seeks an agreement between a theoretical concept and a specific measuring device, such as observation.
Constructivism -- the idea that reality is socially constructed. It is the view that reality cannot be understood outside of the way humans interact and that the idea that knowledge is constructed, not discovered. Constructivists believe that learning is more active and self-directed than either behaviorism or cognitive theory would postulate.
Content Analysis -- the systematic, objective, and quantitative description of the manifest or latent content of print or nonprint communications.
Context Sensitivity -- awareness by a qualitative researcher of factors such as values and beliefs that influence cultural behaviors.
Control Group -- the group in an experimental design that receives either no treatment or a different treatment from the experimental group. This group can thus be compared to the experimental group.
Controlled Experiment -- an experimental design with two or more randomly selected groups [an experimental group and control group] in which the researcher controls or introduces the independent variable and measures the dependent variable at least two times [pre- and post-test measurements].
Correlation -- a common statistical analysis, usually abbreviated as r, that measures the degree of relationship between pairs of interval variables in a sample. The range of correlation is from -1.00 to zero to +1.00. Also, a non-cause and effect relationship between two variables.
Covariate -- a product of the correlation of two related variables times their standard deviations. Used in true experiments to measure the difference of treatment between them.
Credibility -- a researcher's ability to demonstrate that the object of a study is accurately identified and described based on the way in which the study was conducted.
Critical Theory -- an evaluative approach to social science research, associated with Germany's neo-Marxist “Frankfurt School,” that aims to criticize as well as analyze society, opposing the political orthodoxy of modern communism. Its goal is to promote human emancipatory forces and to expose ideas and systems that impede them.
Data -- factual information [as measurements or statistics] used as a basis for reasoning, discussion, or calculation.
Data Mining -- the process of analyzing data from different perspectives and summarizing it into useful information, often to discover patterns and/or systematic relationships among variables.
Data Quality -- this is the degree to which the collected data [results of measurement or observation] meet the standards of quality to be considered valid [trustworthy] and reliable [dependable].
Deductive -- a form of reasoning in which conclusions are formulated about particulars from general or universal premises.
Dependability -- being able to account for changes in the design of the study and the changing conditions surrounding what was studied.
Dependent Variable -- a variable that varies due, at least in part, to the impact of the independent variable. In other words, its value “depends” on the value of the independent variable. For example, in the variables “gender” and “academic major,” academic major is the dependent variable, meaning that your major cannot determine whether you are male or female, but your gender might indirectly lead you to favor one major over another.
Deviation -- the distance between the mean and a particular data point in a given distribution.
Discourse Community -- a community of scholars and researchers in a given field who respond to and communicate to each other through published articles in the community's journals and presentations at conventions. All members of the discourse community adhere to certain conventions for the presentation of their theories and research.
Discrete Variable -- a variable that is measured solely in whole units, such as, gender and number of siblings.
Distribution -- the range of values of a particular variable.
Effect Size -- the amount of change in a dependent variable that can be attributed to manipulations of the independent variable. A large effect size exists when the value of the dependent variable is strongly influenced by the independent variable. It is the mean difference on a variable between experimental and control groups divided by the standard deviation on that variable of the pooled groups or of the control group alone.
Emancipatory Research -- research is conducted on and with people from marginalized groups or communities. It is led by a researcher or research team who is either an indigenous or external insider; is interpreted within intellectual frameworks of that group; and, is conducted largely for the purpose of empowering members of that community and improving services for them. It also engages members of the community as co-constructors or validators of knowledge.
Empirical Research -- the process of developing systematized knowledge gained from observations that are formulated to support insights and generalizations about the phenomena being researched.
Epistemology -- concerns knowledge construction; asks what constitutes knowledge and how knowledge is validated.
Ethnography -- method to study groups and/or cultures over a period of time. The goal of this type of research is to comprehend the particular group/culture through immersion into the culture or group. Research is completed through various methods but, since the researcher is immersed within the group for an extended period of time, more detailed information is usually collected during the research.
Expectancy Effect -- any unconscious or conscious cues that convey to the participant in a study how the researcher wants them to respond. Expecting someone to behave in a particular way has been shown to promote the expected behavior. Expectancy effects can be minimized by using standardized interactions with subjects, automated data-gathering methods, and double blind protocols.
External Validity -- the extent to which the results of a study are generalizable or transferable.
Factor Analysis -- a statistical test that explores relationships among data. The test explores which variables in a data set are most related to each other. In a carefully constructed survey, for example, factor analysis can yield information on patterns of responses, not simply data on a single response. Larger tendencies may then be interpreted, indicating behavior trends rather than simply responses to specific questions.
Field Studies -- academic or other investigative studies undertaken in a natural setting, rather than in laboratories, classrooms, or other structured environments.
Focus Groups -- small, roundtable discussion groups charged with examining specific topics or problems, including possible options or solutions. Focus groups usually consist of 4-12 participants, guided by moderators to keep the discussion flowing and to collect and report the results.
Framework -- the structure and support that may be used as both the launching point and the on-going guidelines for investigating a research problem.
Generalizability -- the extent to which research findings and conclusions conducted on a specific study to groups or situations can be applied to the population at large.
Grey Literature -- research produced by organizations outside of commercial and academic publishing that publish materials, such as, working papers, research reports, and briefing papers.
Grounded Theory -- practice of developing other theories that emerge from observing a group. Theories are grounded in the group's observable experiences, but researchers add their own insight into why those experiences exist.
Group Behavior -- behaviors of a group as a whole, as well as the behavior of an individual as influenced by his or her membership in a group.
Hypothesis -- a tentative explanation based on theory to predict a causal relationship between variables.
Independent Variable -- the conditions of an experiment that are systematically manipulated by the researcher. A variable that is not impacted by the dependent variable, and that itself impacts the dependent variable. In the earlier example of "gender" and "academic major," (see Dependent Variable) gender is the independent variable.
Individualism -- a theory or policy having primary regard for the liberty, rights, or independent actions of individuals.
Inductive -- a form of reasoning in which a generalized conclusion is formulated from particular instances.
Inductive Analysis -- a form of analysis based on inductive reasoning; a researcher using inductive analysis starts with answers, but formulates questions throughout the research process.
Insiderness -- a concept in qualitative research that refers to the degree to which a researcher has access to and an understanding of persons, places, or things within a group or community based on being a member of that group or community.
Internal Consistency -- the extent to which all questions or items assess the same characteristic, skill, or quality.
Internal Validity -- the rigor with which the study was conducted [e.g., the study's design, the care taken to conduct measurements, and decisions concerning what was and was not measured]. It is also the extent to which the designers of a study have taken into account alternative explanations for any causal relationships they explore. In studies that do not explore causal relationships, only the first of these definitions should be considered when assessing internal validity.
Life History -- a record of an event/events in a respondent's life told [written down, but increasingly audio or video recorded] by the respondent from his/her own perspective in his/her own words. A life history is different from a "research story" in that it covers a longer time span, perhaps a complete life, or a significant period in a life.
Margin of Error -- the permittable or acceptable deviation from the target or a specific value. The allowance for slight error or miscalculation or changing circumstances in a study.
Measurement -- process of obtaining a numerical description of the extent to which persons, organizations, or things possess specified characteristics.
Meta-Analysis -- an analysis combining the results of several studies that address a set of related hypotheses.
Methodology -- a theory or analysis of how research does and should proceed.
Methods -- systematic approaches to the conduct of an operation or process. It includes steps of procedure, application of techniques, systems of reasoning or analysis, and the modes of inquiry employed by a discipline.
Mixed-Methods -- a research approach that uses two or more methods from both the quantitative and qualitative research categories. It is also referred to as blended methods, combined methods, or methodological triangulation.
Modeling -- the creation of a physical or computer analogy to understand a particular phenomenon. Modeling helps in estimating the relative magnitude of various factors involved in a phenomenon. A successful model can be shown to account for unexpected behavior that has been observed, to predict certain behaviors, which can then be tested experimentally, and to demonstrate that a given theory cannot account for certain phenomenon.
Models -- representations of objects, principles, processes, or ideas often used for imitation or emulation.
Naturalistic Observation -- observation of behaviors and events in natural settings without experimental manipulation or other forms of interference.
Norm -- the norm in statistics is the average or usual performance. For example, students usually complete their high school graduation requirements when they are 18 years old. Even though some students graduate when they are younger or older, the norm is that any given student will graduate when he or she is 18 years old.
Null Hypothesis -- the proposition, to be tested statistically, that the experimental intervention has "no effect," meaning that the treatment and control groups will not differ as a result of the intervention. Investigators usually hope that the data will demonstrate some effect from the intervention, thus allowing the investigator to reject the null hypothesis.
Ontology -- a discipline of philosophy that explores the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality.
Panel Study -- a longitudinal study in which a group of individuals is interviewed at intervals over a period of time.
Participant -- individuals whose physiological and/or behavioral characteristics and responses are the object of study in a research project.
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Statistical Bias -- characteristics of an experimental or sampling design, or the mathematical treatment of data, that systematically affects the results of a study so as to produce incorrect, unjustified, or inappropriate inferences or conclusions.
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Subcultures -- ethnic, regional, economic, or social groups exhibiting characteristic patterns of behavior sufficient to distinguish them from the larger society to which they belong.
Testing -- the act of gathering and processing information about individuals' ability, skill, understanding, or knowledge under controlled conditions.
Theory -- a general explanation about a specific behavior or set of events that is based on known principles and serves to organize related events in a meaningful way. A theory is not as specific as a hypothesis.
Treatment -- the stimulus given to a dependent variable.
Trend Samples -- method of sampling different groups of people at different points in time from the same population.
Triangulation -- a multi-method or pluralistic approach, using different methods in order to focus on the research topic from different viewpoints and to produce a multi-faceted set of data. Also used to check the validity of findings from any one method.
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Variable -- any characteristic or trait that can vary from one person to another [race, gender, academic major] or for one person over time [age, political beliefs].
Weighted Scores -- scores in which the components are modified by different multipliers to reflect their relative importance.
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Elliot, Mark, Fairweather, Ian, Olsen, Wendy Kay, and Pampaka, Maria. A Dictionary of Social Research Methods. Oxford, UK: Oxford University Press, 2016; Free Social Science Dictionary. Socialsciencedictionary.com [2008]. Glossary. Institutional Review Board. Colorado College; Glossary of Key Terms. Writing@CSU. Colorado State University; Glossary A-Z. Education.com; Glossary of Research Terms. Research Mindedness Virtual Learning Resource. Centre for Human Servive Technology. University of Southampton; Miller, Robert L. and Brewer, John D. The A-Z of Social Research: A Dictionary of Key Social Science Research Concepts London: SAGE, 2003; Jupp, Victor. The SAGE Dictionary of Social and Cultural Research Methods . London: Sage, 2006.

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Published: 27 May 2024

Research on domain ontology construction based on the content features of online rumors

Jianbo Zhao 1 ,
Huailiang Liu 1 ,
Weili Zhang 1 ,
Tong Sun 1 ,
Qiuyi Chen 1 ,
Yuehai Wang 2 ,
Jiale Cheng 2 ,
Yan Zhuang 1 ,
Xiaojin Zhang 1 ,
Shanzhuang Zhang 1 ,
Bowei Li 3 &
Ruiyu Ding 2

Scientific Reports volume 14 , Article number: 12134 ( 2024 ) Cite this article

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Computational neuroscience
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Human behaviour
Information technology
Literature mining
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Online rumors are widespread and difficult to identify, which bring serious harm to society and individuals. To effectively detect and govern online rumors, it is necessary to conduct in-depth semantic analysis and understand the content features of rumors. This paper proposes a TFI domain ontology construction method, which aims to achieve semantic parsing and reasoning of the rumor text content. This paper starts from the term layer, the frame layer, and the instance layer, and based on the reuse of the top-level ontology, the extraction of core literature content features, and the discovery of new concepts in the real corpus, obtains the core classes (five parent classes and 88 subclasses) of the rumor domain ontology and defines their concept hierarchy. Object properties and data properties are designed to describe relationships between entities or their features, and the instance layer is created according to the real rumor datasets. OWL language is used to encode the ontology, Protégé is used to visualize it, and SWRL rules and pellet reasoner are used to mine and verify implicit knowledge of the ontology, and judge the category of rumor text. This paper constructs a rumor domain ontology with high consistency and reliability.

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Introduction.

Online rumors are false information spread through online media, which have the characteristics of wide content 1 , hard to identify 2 , 3 . Online rumors can mislead the public, disrupt social order, damage personal and collective reputations, and pose a great challenge to the governance of internet information content. Therefore, in order to effectively detect and govern online rumors, it is necessary to conduct an in-depth semantic analysis and understanding of the rumor text content features.

The research on the content features of online rumors focuses on the lexical, syntactic and semantic features of the rumor text, including lexical, syntactic and semantic features 4 , syntactic structure and functional features 5 , source features 5 , 6 , rhetorical methods 7 , narrative structure 6 , 7 , 8 , language style 6 , 9 , 10 , corroborative means 10 , 11 and emotional features 10 , 12 , 13 , 14 , 15 , 16 , 17 , 18 . Most of the existing researches on rumor content features are feature mining under a single domain topic type, and lack of mining the influence relationship between multiple features. Therefore, this paper proposes to build an online rumor domain ontology to realize fine-grained hierarchical modeling of the relationship between rumor content features and credible verification of its effectiveness. Domain ontology is a systematic description of the objective existence in a specific discipline 19 . The construction methods mainly include TOVE method 20 , skeleton method 21 , IDEF-5 method 22 , 23 , methontology method 24 , 25 and seven-step method 26 , 27 , among which seven-step method is the most mature and widely used method at present 28 , which has strong systematicness and applicability 29 , but it does not provide quantitative indicators and methods about the quality and effect of ontology. The construction technology can be divided into the construction technology based on thesaurus conversion, the construction technology based on existing ontology reuse and the semi-automatic and automatic construction technology based on ontology engineering method 30 . The construction technology based on thesaurus conversion and the construction technology based on existing ontology reuse can save construction time and cost, and improve ontology reusability and interoperability, but there are often differences in structure, semantics and scene. Semi-automatic and automatic construction technology based on ontology engineering method The application of artificial intelligence technology can automatically extract ontology elements and structures from data sources with high efficiency and low cost, but the quality and accuracy are difficult to guarantee. Traditional domain ontology construction methods lack effective quality evaluation support, and construction technology lacks effective integration application. Therefore, this paper proposes an improved TFI network rumor domain ontology construction method based on the seven-step method. Starting from the terminology layer, the framework layer and the instance layer, it integrates the top-level ontology and core document content feature reuse technology, the bottom-up semi-automatic construction technology based on N-gram new word discovery algorithm and RoBERTa-Kmeans clustering algorithm, defines the fine-grained features of network rumor content and carries out hierarchical modeling. Using SWRL rules and pellet inference machine, the tacit knowledge of ontology is mined, and the quality of ontology validity and consistency is evaluated and verified.

The structure of this paper is as follows: Sect “ Related work ” introduces the characteristics of rumor content and the related work of domain ontology construction.; Sect “ Research method ” constructs the term layer, the frame layer and the instance layer of the domain ontology; Sect “ Domain ontology construction ” mines and verifies the implicit knowledge of the ontology based on SWRL rules and Pellet reasoner; Sect “ Ontology reasoning and validation ” points out the research limitations and future research directions; Sect “ Discussion ” summarizes the research content and contribution; Sect “ Conclusion ” summarizes the research content and contribution of this paper.

Related Work

Content features of online rumors.

The content features of online rumors refer to the adaptive description of vocabulary, syntax and semantics in rumor texts. Fu et al. 5 have made a linguistic analysis of COVID-19’s online rumors from the perspectives of pragmatics, discourse analysis and syntax, and concluded that the source of information, the specific place and time of the event, the length of the title and statement, and the emotions aroused are the important characteristics to judge the authenticity of the rumors; Zhang et al. 6 summarized the narrative theme, narrative characteristics, topic characteristics, language style and source characteristics of new media rumors; Li et al. 7 found that rumors have authoritative blessing and fear appeal in headline rhetoric, and they use news and digital headlines extensively, and the topic construction mostly uses programmed fixed structure; Yu et al. 8 analyzed and summarized the content distribution, narrative structure, topic scene construction and title characteristics of rumors in detail; Mourao et al. 9 found that the language style of rumors is significantly different from that of real texts, and rumors tend to use simpler, more emotional and more radical discourse strategies; Zhou et al. 10 analyzed the rumor text based on six analysis categories, such as content type, focus object and corroboration means, and found that the epidemic rumors were mostly “infectious” topics, with narrative expression being the most common, strong fear, and preference for exaggerated and polarized discourse style. Huang et al. 11 conducted an empirical study based on WeChat rumors, and found that the “confirmation” means of rumors include data corroboration and specific information, hot events and authoritative release; Butt et al. 12 analyzed the psycholinguistic features of rumors, and extracted four features from the rumor data set: LIWC, readability, senticnet and emotions. Zhou et al. 13 analyzed the semantic features of fake news content in theme and emotion, and found that the distribution of fake news and real news is different in theme features, and the overall mood, negative mood and anger of fake news are higher; Tan et al. 14 divided the content characteristics of rumors into content characteristics with certain emotional tendency and social characteristics that affect credibility; Damstra et al. 15 identified the elements as a consistent indicator of intentionally deceptive news content, including negative emotions causing anger or fear, lengthy sensational headlines, using informal language or swearing, etc. Lai et al. 16 put forward that emotional rumors can make the rumor audience have similar positive and negative emotions through emotional contagion; Yuan et al. 17 found that multimedia evidence form and topic shaping are important means to create rumors, which mostly convey negative emotions of fear and anger, and the provision of information sources is related to the popularity and duration of rumors; Ruan et al. 18 analyzed the content types, emotional types and discourse focus of Weibo’s rumor samples, and found that the proportion of social life rumors was the highest, and the emotional types were mainly hostile and fearful, with the focus on the general public and the personnel of the party, government and military institutions.

The forms and contents of online rumors tend to be diversified and complicated. The existing research on the content features of rumors is mostly aimed at the mining of content characteristics under specific topics, which cannot cover various types of rumor topics, and lacks fine-grained hierarchical modeling of the relationship between features and credible verification of their effectiveness.

Domain ontology construction

Domain ontology is a unified definition, standardized organization and visual representation of the concepts of knowledge in a specific domain 31 , 32 , and it is an important source of information for knowledge-based systems 19 , 33 . Theoretical methods include TOVE method 20 , skeleton method 21 , IDEF-5 method 22 , 23 , methontology method 24 , 25 and seven-step method 26 , 27 . TOVE method transforms informal description into formal ontology, which is suitable for fields that need accurate knowledge, but it is complex and time-consuming, requires high-level domain knowledge and is not easy to expand and maintain. Skeleton method forms an ontology skeleton by defining the concepts and relationships of goals, activities, resources, organizations and environment, which can be adjusted according to needs and is suitable for fields that need multi-perspective and multi-level knowledge, but it lacks formal semantics and reasoning ability. Based on this method, Ran et al. 34 constructed the ontology of idioms and allusions. IDEF5 method uses chart language and detailed description language to construct ontology, formalizes and visualizes objective knowledge, and is suitable for fields that need multi-source data and multi-participation, but it lacks a unified ontology representation language. Based on this method, Li et al. 35 constructed the business process activity ontology of military equipment maintenance support, and Song et al. 36 established the air defense and anti-missile operation process ontology. Methontology is a method close to software engineering. It systematically develops ontologies through the processes of specification, knowledge acquisition, conceptualization, integration, implementation, evaluation and document arrangement, which is suitable for fields that need multi-technology and multi-ontology integration, but it is too complicated and tedious, and requires a lot of resources and time 37 . Based on this method, Yang et al. 38 completed the ontology of emergency plan, Duan et al. 39 established the ontology of high-resolution images of rural residents, and Chen et al. 40 constructed the corpus ontology of Jiangui. Seven-step method is the most mature and widely used method at present 28 . It is systematic and applicable to construct ontology by determining its purpose, scope, terms, structure, attributes, limitations and examples 29 , but it does not provide quantitative indicators and methods about the quality and effect of ontology. Based on this method, Zhu et al. 41 constructed the disease ontology of asthma, Li et al. 42 constructed the ontology of military events, the ontology of weapons and equipment and the ontology model of battlefield environment, and Zhang et al. 43 constructed the ontology of stroke nursing field, and verified the construction results by expert consultation.

Domain ontology construction technology includes thesaurus conversion, existing ontology reuse and semi-automatic and automatic construction technology based on ontology engineering method 30 . The construction technology based on thesaurus transformation takes the existing thesaurus as the knowledge source, and transforms the concepts, terms and relationships in the thesaurus into the entities and relationships of domain ontology through certain rules and methods, which saves the time and cost of ontology construction and improves the quality and reusability of ontology. However, it is necessary to solve the structural and semantic differences between thesaurus and ontology and adjust and optimize them according to the characteristics of different fields and application scenarios. Wu et al. 44 constructed the ontology of the natural gas market according to the thesaurus of the natural gas market and the mapping of subject words to ontology, and Li et al. 45 constructed the ontology of the medical field according to the Chinese medical thesaurus. The construction technology based on existing ontology reuse uses existing ontologies or knowledge resources to generate new domain ontologies through modification, expansion, merger and mapping, which saves time and cost and improves the consistency and interoperability of ontologies, but it also needs to solve semantic differences and conflicts between ontologies. Chen et al. 46 reuse the top-level framework of scientific evidence source information ontology (SEPIO) and traditional Chinese medicine language system (TCMLS) to construct the ontology of clinical trials of traditional Chinese medicine, and Xiao et al. 47 construct the domain ontology of COVID-19 by extracting the existing ontology and the knowledge related to COVID-19 in the diagnosis and treatment guide. Semi-automatic and automatic construction technology based on ontology engineering method semi-automatically or automatically extracts the elements and structures of ontology from data sources by using natural language processing, machine learning and other technologies to realize large-scale, fast and low-cost domain ontology construction 48 , but there are technical difficulties, the quality and accuracy of knowledge extraction can not be well guaranteed, and the quality and consistency of different knowledge sources need to be considered. Suet al. 48 used regular templates and clustering algorithm to construct the ontology of port machinery, Zheng et al. 49 realized the automatic construction of mobile phone ontology through LDA and other models, Dong et al. 50 realized the automatic construction of ontology for human–machine ternary data fusion in manufacturing field, Linli et al. 51 proposed an ontology learning algorithm based on hypergraph, and Zhai et al. 52 learned from it through part-of-speech tagging, dependency syntax analysis and pattern matching.

At present, domain ontology construction methods are not easy to expand, lack of effective quality evaluation support, lack of effective integration and application of construction technology, construction divorced from reality can not guide subsequent practice, subjective ontology verification and so on. Aiming at the problems existing in the research of content characteristics and domain ontology construction of online rumors, this paper proposes an improved TFI network rumor domain ontology construction method based on seven-step method, which combines top-down existing ontology reuse technology with bottom-up semi-automatic construction technology, and establishes rumor domain ontology based on top-level ontology reuse, core document content feature extraction and new concept discovery in the real corpus from the terminology layer, framework layer and instance layer. Using Protégé as a visualization tool, the implicit knowledge mining of ontology is carried out by constructing SWRL rules to verify the semantic parsing ability and consistency of domain ontology.

Research method

This paper proposes a TFI online rumor domain ontology construction method based on the improvement of the seven-step method, which includes the term layer, the frame layer and the instance layer construction.

Term layer construction

Determine the domain and scope: the purpose of constructing the rumor domain ontology is to support the credible detection and governance of online rumors, and the domain and scope of the ontology are determined by answering questions.

Three-dimensional term set construction: investigate the top-level ontology and related core literature, complete the mapping of reusable top-level ontology and rumor content feature concept extraction semi-automatically from top to bottom; establish authoritative real rumor datasets, and complete the domain new concept discovery automatically from bottom to top; based on this, determine the term set of the domain ontology.

Frame layer construction

Define core classes and hierarchical relationships: combine the concepts of the three-dimensional rumor term set, based on the data distribution of the rumor dataset, define the parent class, summarize the subclasses, design hierarchical relationships and explain the content of each class.

Define core properties and facets of properties: in order to achieve deep semantic parsing of rumor text contents, define object properties, data properties and property facets for each category in the ontology.

Instance layer construction

Create instances: analyze the real rumor dataset, extract instance data, and add them to the corresponding concepts in the ontology.

Encode and visualize ontology: use OWL language to encode ontology, and use Protégé to visualize ontology, so that ontology can be understood and operated by computer.

Ontology verification: use SWRL rules and pellet reasoner to mine implicit knowledge of ontology, and verify its semantic parsing ability and consistency.

Ethical statements

This article does not contain any studies with human participants performed by any of the authors.

Determine the professional domain and scope of the ontology description

This paper determines the domain and scope of the online rumor domain ontology by answering the following four questions:

(1) What is the domain covered by the ontology?

The “Rumor Domain Ontology” constructed in this paper only considers content features, not user features and propagation features; the data covers six rumor types of politics and military, disease prevention and treatment, social life, science and technology, nutrition and health, and others involved in China’s mainstream internet rumor-refuting websites.

(2) What is the purpose of the ontology?

To perform fine-grained hierarchical modeling of the relationships among the features of multi-domain online rumor contents, realize semantic parsing and credibility reasoning verification of rumor texts, and guide fine-grained rumor detection and governance. It can also be used as a guiding framework and constraint condition for online rumor knowledge graph construction.

(3) What kind of questions should the information in the ontology provide answers for?

To provide answers for questions such as the fine-grained rumor types of rumor instances, the valid features of rumor types, etc.

(4) Who will use the ontology in the future?

Users of online rumor detection and governance, users of online rumor knowledge graphs construction.

Three-dimensional term set construction

Domain concepts reused by top-level ontology.

As a mature and authoritative common ontology, top-level ontology can be shared and reused in a large range, providing reference and support for the construction of domain ontology. The domain ontology of online rumors established in this paper focuses on the content characteristics, mainly including the content theme, events and emotions of rumor texts. By reusing the terminology concepts in the existing top-level ontology, the terminology in the terminology set can be unified and standardized. At the same time, the top-level concept and its subclass structure can guide the framework construction of domain ontology and reduce the difficulty and cost of ontology construction. Reusable top-level ontologies include: SUMO, senticnet and ERE after screening.

SUMO ontology: a public upper-level knowledge ontology containing some general concepts and relations for describing knowledge in different domains. The partial reusable SUMO top-level concepts and subclasses selected in this paper are shown in Table 1 , which provides support for the sub-concept design of text topics in rumor domain ontology.

Senticnet: a knowledge base for concept-based sentiment analysis, which contains semantic, emotional, and polarity information related to natural language concepts. The partial reusable SenticNet top-level concepts and subclasses selected in this paper are shown in Table 2 , which provides support for the sub-concept design of text topics in rumor domain ontology.

Entities, relations, and events (ERE): a knowledge base of events and entity relations. The partial reusable ERE top-level concepts and subclasses selected in this paper are shown in Table 3 , which provides support for the sub-concept design of text elements in the rumor domain ontology.

Extracting domain concepts based on core literature content features

Domain core literature is an important source for extracting feature concepts. This paper uses ‘rumor detection’ as the search term to retrieve 274 WOS papers and 257 CNKI papers from the WOS and CNKI core literature databases. The content features of rumor texts involved in the literature samples are extracted, the repetition content features are eliminated, the core content features are screened, and the canonical naming of synonymous concepts from different literatures yields the domain concepts as shown in Table 4 . Among them, text theme, text element, text style, text feature and text rhetoric are classified as text features; emotional category, emotional appeal and rumor motive are classified as emotional characteristics; source credibility, evidence credibility and testimony method are classified as information credibility characteristics; social context is implicit.

Extracting domain concepts based on new concept discovery

This paper builds a general rumor dataset based on China’s mainstream rumor-refuting websites as data sources, and proposes a domain new concept discovery algorithm to discover domain new words in the dataset, add them to the word segmentation dictionary to improve the accuracy of word segmentation, and cluster them according to rumor type, resulting in a concept subclass dictionary based on the real rumor dataset, which provided realistic basis and data support for the conceptual design of each subclass in domain ontology.

Building a general rumor dataset

The rumor dataset constructed in this paper contains 12,472 texts, with 6236 rumors and 6236 non-rumors; the data sources are China’s mainstream internet rumor-refuting websites: 1032 from the internet rumor exposure platform of China internet joint rumor-refuting platform, 270 from today’s rumor-refuting of China internet joint rumor-refuting platform, 1852 from Tencent news Jiaozhen platform, 1744 from Baidu rumor-refuting platform, 7036 from science rumor-refuting platform, and 538 from Weibo community management center. This paper invited eight researchers to annotate the labels (rumor, non-rumor), categories (politics and military, disease prevention and treatment, social life, science and technology, nutrition and health, others) of the rumor dataset. Because data annotation is artificial and subjective, in order to ensure the effectiveness and consistency of annotation, before inviting researchers to annotate, this paper formulates annotation standards, including the screening method, trigger words and sentence break identification of rumor information and corresponding rumor information, and clearly explains and exemplifies the screening method and trigger words of rumor categories, so as to reduce the understanding differences among researchers; in view of this standard, researchers are trained in labeling to familiarize them with labeling specifications, so as to improve their labeling ability and efficiency. The method of multi-person cross-labeling is adopted when labeling, and each piece of data is independently labeled by at least two researchers. In case of conflicting labeling results, the labeling results are jointly decided by the data annotators to increase the reliability and accuracy of labeling. After labeling, multi-person cross-validation method is used to evaluate the labeling results. Each piece of data is independently verified by at least two researchers who did not participate in labeling, and conflicting labeling results are jointly decided by at least five researchers to ensure the consistency of evaluation results. Examples of the results are shown in Table 5 .

N-gram word granularity rumor text new word discovery algorithm

Existing neologism discovery algorithms are mostly based on the granularity of Chinese characters, and the time complexity of long word discovery is high and the accuracy rate is low. The algorithm’s usefulness is low, and the newly discovered words are mostly already found in general domain dictionaries. To solve these problems, this paper proposes an online rumor new word discovery algorithm based on N-gram word granularity, as shown in Fig. 1 .

Flowchart of domain new word discovery algorithm.

First, obtain the corpus to be processed ${\varvec{c}}=\{{{\varvec{s}}}_{1},{{\varvec{s}}}_{2},...,{{\varvec{s}}}_{{{\varvec{n}}}_{{\varvec{c}}}}\}$ , and perform the first preprocessing on the corpus to be processed, which includes: sentence segmentation, Chinese word segmentation and punctuation removal for the corpus to be processed. Obtain the first corpus ${{\varvec{c}}}^{{\varvec{p}}}=\{{{\varvec{s}}}_{1}^{{\varvec{p}}},{{\varvec{s}}}_{2}^{{\varvec{p}}},...,{{\varvec{s}}}_{{{\varvec{n}}}_{{\varvec{c}}}}^{{\varvec{p}}}\}$ ; where ${s}_{i}$ represents the $i$ -th sentence in the corpus to be processed, ${n}_{c}$ represents the number of sentences in the corpus to be processed, and ${s}_{i}^{p}$ is the i-th sentence in the first corpus; perform N-gram operation on each sentence in the first corpus separately, and obtain multiple candidate words $n=2\sim 5$ ; count the word frequency of each candidate word in the first corpus, and remove the candidate words with word frequency less than the first threshold, and obtain the first class of candidate word set;calculate the cohesion of each candidate word in the first class of candidate word set according to the following formula:

In the formula, $P(\cdot )$ represents word frequency.Then filter according to the second threshold corresponding to N-gram operation, and obtain the second class of candidate word set; after loading the new words in the second class of candidate word set into LTP dictionary, perform the second preprocessing on the corpus to be processed ${\varvec{c}}=\{{{\varvec{s}}}_{1},{{\varvec{s}}}_{2},...,{{\varvec{s}}}_{{{\varvec{n}}}_{{\varvec{c}}}}\}$ ; and obtain the second corpus ${{\varvec{c}}}^{{\varvec{p}}\boldsymbol{^{\prime}}}=\{{{\varvec{s}}}_{1}^{{\varvec{p}}\boldsymbol{^{\prime}}},{{\varvec{s}}}_{2}^{{\varvec{p}}\boldsymbol{^{\prime}}},...,{{\varvec{s}}}_{{{\varvec{n}}}_{{\varvec{c}}}}^{{\varvec{p}}\boldsymbol{^{\prime}}}\}$ ; where the second preprocessing includes: sentence segmentation, Chinese word segmentation and stop word removal for the corpus to be processed; after obtaining the vector representation of each word in the second corpus, determine the vector representation of each new word in the second class of candidate word set; according to the vector representation of each new word, use K-means algorithm for clustering; according to the clustering results and preset classification rules, classify each new word to the corresponding domain. The examples of new words discovered are shown in Table 6 :

RoBERTa-Kmeans rumor text concepts extraction algorithm

After adding the new words obtained by the new word discovery to the LTP dictionary, the accuracy of LTP word segmentation is improved. The five types of rumor texts established in this paper are segmented by using the new LTP dictionary, and the word vectors are obtained by inputting them into the RoBERTa word embedding layer after removing the stop words. The word vectors are clustered by k-means according to rumor type to obtain the concept subclass dictionary. The main process is as follows:

(1) Word embedding layer

The RoBERTa model uses Transformer-Encode for computation, and each module contains multi-head attention mechanism, residual connection and layer normalization, feed-forward neural network. The word vectors are obtained by representing the rumor texts after accurate word segmentation through one-hot encoding, and the position encoding represents the relative or absolute position of the word in the sequence. The word embedding vectors generated by superimposing the two are used as input X. The multi-head attention mechanism uses multiple independent Attention modules to perform parallel operations on the input information, as shown in formula ( 2 ):

where $\left\{{\varvec{Q}},{\varvec{K}},{\varvec{V}}\right\}$ is the input matrix, ${{\varvec{d}}}_{{\varvec{k}}}$ is the dimension of the input matrix. After calculation, the hidden vectors obtained after computation are residual concatenated with layer normalization, and then calculated by two fully connected layers of feed-forward neural network for input, as shown in formula ( 3 ):

where $\left\{{{\varvec{W}}}_{{\varvec{e}}},{{\varvec{W}}}_{0}\boldsymbol{^{\prime}}\right\}$ are the weight matrices of two connected layers, $\left\{{{\varvec{b}}}_{{\varvec{e}}},{{\varvec{b}}}_{0}\boldsymbol{^{\prime}}\right\}$ are the bias terms of two connected layers.

After calculation, a bidirectional association between word embedding vectors is established, which enables the model to learn the semantic features contained in each word embedding vector in different contexts. Through fine-tuning, the learned knowledge is transferred to the downstream clustering task.

(2) K-means clustering

Randomly select k initial points to obtain k classes, and iterate until the loss function of the clustering result is minimized. The loss function can be defined as the sum of squared errors of each sample point from its cluster center point, as shown in formula ( 4 ).

where ${x}_{i}$ represents the $i$ sample, ${a}_{i}$ is the cluster that ${x}_{i}$ belongs to, ${u}_{{a}_{i}}$ represents the corresponding center point, $N$ is the total number of samples.

After RoBERTa-kmeans calculation, the concept subclasses obtained are manually screened, merged repetition items, deleted invalid items, and finally obtained 79 rumor concept subclasses, including 14 politics and military subclasses, 23 disease prevention and treatment subclasses, 15 social life subclasses, 13 science and technology subclasses, and 14 nutrition and health subclasses. Some statistics are shown in Table 7 .

Each concept subclass is obtained by clustering several topic words. For example, the topic words that constitute the subclasses of body part, epidemic prevention and control, chemical drugs, etc. under the disease prevention and treatment topic are shown in Table 8 .

(3) Determining the terminology set

This paper constructs a three-dimensional rumor domain ontology terminology set based on the above three methods, and unifies the naming of the terms. Some of the terms are shown in Table 9 .

Framework layer construction

Define core classes and hierarchy, define parent classes.

This paper aims at fine-grained hierarchical modeling of the relationship between the content characteristics of multi-domain network rumors. Therefore, the top-level parent class needs to include the rumor category and the main content characteristics of a sub-category rumor design. The main content characteristics are the clustering results of domain concepts extracted based on the content characteristics of core documents, that is, rumor text feature, rumor emotional characteristic, rumor credibility and social context. The specific contents of the five top parent classes are as follows:

Rumor type: the specific classification of rumors under different subject categories; Rumor text feature, the common features of rumor texts in terms of theme, style, rhetoric, etc. Rumor emotional characteristic: the emotional elements of rumor texts, the Rumor motive of the publisher, and the emotional changes they hope to trigger in the receiver. Rumor credibility: the authority of the information source, the credibility of the evidence material provided by the publisher, and the effectiveness of the testimony method. Social context: the relevant issues and events in the society when the rumor is published.

Induce subclasses and design hierarchical relationships

In this paper, under the top-level parent class, according to the top-level concepts of top-level ontologies such as SUMO, senticnet and ERE and their subclass structures, and the rumor text features of each category extracted from the real rumor text dataset, we summarize its 88 subclasses and design the hierarchical relationships, as shown in Fig. 2 , which include:

(1) Rumor text feature

Diagram of the core classes and hierarchy of the rumor domain ontology.

① Text theme 6 , 8 , 13 , 18 , 53 : the theme or topic that the rumor text content involves. Based on the self-built rumor dataset, it is divided into politics and military 54 , involving information such as political figures, political policies, political relations, political activities, military actions, military events, strategic objectives, politics and military reviews, etc.; nutrition and health 55 , involving information such as the relationship between human health and nutrition, the nutritional components and value of food, the plan and advice for healthy eating, health problems and habits, etc.; disease prevention and treatment 10 , involving information such as the definition of disease, vaccine, treatment, prevention, data, etc.; social life 56 , involving information such as social issues, social environment, social values, cultural activities, social media, education system, etc.; science and technology 57 , involving information such as scientific research, scientific discovery, technological innovation, technological application, technological enterprise, etc.; other categories.

② Text element 15 : the structured information of the rumor text contents. It is divided into character, political character, public character, etc.; geographical position, city, region, area, etc.; event, historical event, current event, crisis event, policy event, etc.; action, protection, prevention and control, exercise, fighting, crime, eating, breeding, health preservation, rest, exercise, education, sports, social, cultural, ideological, business, economic, transportation, etc.; material, food, products (food, medicine, health products, cosmetics, etc.) and the materials they contain and their relationship with human health. effect, nutrition, health, harm, natural disaster, man-made disaster, guarantee, prevention, treatment, etc.; institution, government, enterprise, school, hospital, army, police, social group, etc.; nature, weather, astronomy, environment, agriculture, disease, etc.

③ Text style 7 , 10 : the discourse style of the rumor text contents, preferring exaggerated and emotional expression. It is divided into gossip style, creating conflict or entertainment effect; curious style, satisfying people’s curiosity and stimulation; critical style, using receivers’ stereotypes or preconceptions; lyrical style, creating resonance and influencing emotion; didactic style influencing receivers’ thought and behavior from an authoritative perspective; plain style concise objective arousing resonance etc.

④ Text feature 7 , 58 : special language means in the rumor text contents that can increase the transmission and influence of the rumor. It is divided into extensive punctuation reminding or attracting receivers’ attention; many mood words enhancing emotional color and persuasiveness; many emoji conveying attitude; induce forwarding using @ symbol etc. to induce receivers to forward etc.

⑤ Text rhetoric 15 : common rhetorical devices in rumor contents. It is divided into metaphor hyperbole repetition personification etc.

(2) Rumor emotional characteristic

① Emotion category 17 , 59 , 60 : the emotional tendency and intensity expressed in the rumor texts. It is divided into positive emotion happy praise etc.; negative emotion fear 10 anger sadness anxiety 61 dissatisfaction depression etc.; neutral emotion no preference plain objective etc.

② Emotional appeal 16 , 62 , 63 : the online rumor disseminator hopes that the rumor they disseminate can trigger some emotional changes in the receiver. It is divided into “joy” happy pleasant satisfied emotions that prompt receivers to spread or believe some rumors that are conducive to social harmony; “love” love appreciation admiration emotions that prompt receivers to spread or believe some rumors that are conducive to some people or group interests; “anger” angry annoyed dissatisfied emotions that prompt receivers to spread or believe some rumors that are anti-social or intensify conflicts; “fear” fearful afraid nervous emotions that prompt receivers to spread or believe some rumors that have bad effects deliberately exaggerated; “repugnance” disgusted nauseous emotions that prompt receivers to spread or believe some rumors that are detrimental to social harmony; “surprise” surprised shocked amazed emotions that prompt receivers to spread or believe some rumors that deliberately attract traffic exaggerated fabricated etc.

③ Rumor motive 17 , 64 , 65 , 66 : the purpose and need of the rumor publisher to publish rumors and the receiver to forward rumors. Such as profit-driven seeking fame and fortune deceiving receivers; emotional catharsis relieving dissatisfaction emotions by venting; creating panic creating social unrest and riots disrupting social order; entertainment fooling receivers seeking stimulation; information verification digging out the truth of events etc.

(3) Rumor credibility

① source credibility 7 , 17 : the degree of trustworthiness that the information source has. Such as official institutions and authoritative experts and scholars in the field with high credibility; well-known encyclopedias and large-scale civil organizations with medium credibility; small-scale civil organizations and personal hearsay personal experience with low credibility etc.

② evidence credibility 61 : the credibility of the information proof material provided by the publisher. Data support such as scientific basis based on scientific theory or method; related feature with definite research or investigation result in data support; temporal background with clear time place character event and other elements which related to the information content; the common sense of life in line with the facts and scientific common sense that are widely recognized.

③ testimony method 10 , 11 , 17 : the method to support or refute a certain point of view. Such as multimedia material expressing or fabricating content details through pictures videos audio; authority endorsement policy documents research papers etc. of authorized institutions or persons; social identity identity of social relation groups.

(4) Social context

① social issue 67 : some bad phenomena or difficulties in society such as poverty pollution corruption crime government credibility decline 68 etc.

② public attention 63 : events or topics that arouse widespread attention or discussion in the society such as sports events technological innovation food safety religious beliefs Myanmar fraud nuclear wastewater discharge etc.

③ emergency(public sentiment) 69 : some major or urgent events that suddenly occur in society such as earthquake flood public safety malignant infectious disease outbreaks etc.

(5) Rumor type

① Political and military rumor:

Political image rumor: rumors related to images closely connected to politics and military, such as countries, political figures, institutions, symbols, etc. These include positive political image smear rumor, negative political image whitewash rumor, political image fabrication and distortion rumor, etc.

Political event rumor: rumors about military and political events, such as international relations, security cooperation, military strategy, judicial trial, etc. These include positive political event smear rumor, negative political event whitewash rumor, political event fabrication and distortion rumor, etc.

② Nutrition and health rumor:

Food product rumor: rumors related to food, products (food, medicine, health products, cosmetics, etc.), the materials they contain and their association with human health. These include positive effect of food product rumor, negative effect of food product rumor, food product knowledge rumor, etc.

Living habit rumor: rumors related to habitual actions in life and their association with human health. These include positive effect of living habit rumor, negative effect of living habit rumor, living habit knowledge rumor, etc.

③ Disease prevention and treatment rumor:

Disease management rumor: rumors related to disease management and control methods that maintain and promote individual and group health. These include positive prevention and treatment rumor, negative aggravating disease rumor, disease management knowledge rumor, etc.

Disease confirmed transmission rumor: rumors about the confirmation, transmission, and immunity of epidemic diseases at the social level in terms of causes, processes, results, etc. These include local confirmed cases rumor, celebrity confirmed cases rumor, transmission mechanism rumor, etc.

Disease notification and advice rumor: rumors that fabricate or distort the statements of authorized institutions or experts in the field, and provide false policies or suggestions related to diseases. These include institutional notification rumor, expert advice rumor, etc.

④ Social life rumor:

Public figure public opinion rumor: rumors related to public figures’ opinions, actions, private lives, etc. These include positive public figure smear rumor, negative public figure whitewash rumor, public figure life exposure rumor, etc.

Social life event rumor: rumors related to events, actions, and impacts on people's social life. These include positive event sharing rumor, negative event exposure rumor, neutral event knowledge rumor, etc.

Disaster occurrence rumor: rumors related to natural disasters or man-made disasters and their subsequent developments. These include natural disaster occurrence rumor, man-made disaster occurrence rumor, etc.

⑤ Science and technology rumor:

Scientific knowledge rumor: rumors related to natural science or social science theories and knowledge. These include scientific theory rumor, scientific concept rumor, etc.

Science and technology application rumor: rumors related to the research and development and practical application of science and technology and related products. These include scientific and technological product rumor, scientific and technological information rumor, etc.

⑥ Other rumor: rumors that do not contain elements from the above categories.

Definition of core properties and facets of properties

Properties in the ontology are used to describe the relationships between entities or the characteristics of entities. Object properties are relationships that connect two entities, describing the interactions between entities; data properties represent the characteristics of entities, usually in the form of some data type. Based on the self-built rumor dataset, this paper designs object properties, data properties and facets of properties for the parent classes and subclasses of the rumor domain ontology.

Object properties

A partial set of object properties is shown in Table 10 .

Data attributes

The partial data attribute set is shown in Table 11 .

Creating instances

Based on the defined core classes and properties, this paper creates instances according to the real rumor dataset. An example is shown in Table 12 .

This paper selects the online rumor that “Lin Chi-ling was abused by her husband Kuroki Meisa, the tears of betrayal, the shadow of gambling, all shrouded her head. Even if she tried to divorce, she could not get a solution…..” as an example, and draws a structure diagram of the rumor domain ontology instance, as shown in Fig. 3 . This instance shows the seven major text features of the rumor text: text theme, text element, text style, emotion category, emotional appeal, rumor motivation, and rumor credibility, as well as the related subclass instances, laying a foundation for building a multi-source rumor domain knowledge graph.

Schematic example of the rumor domain ontology.

Encoding ontology and visualization

Encoding ontology.

This paper uses OWL language to encode the rumor domain ontology, to accurately describe the entities, concepts and their relationships, and to facilitate knowledge reasoning and semantic understanding. Classes in the rumor domain ontology are represented by the class “Class” in OWL and the hierarchical relationship is represented by subclassof. For example, in the creation of the rumor emotional characteristic class and its subclasses, the OWL code is shown in Fig. 4 :

Partial OWL codes of the rumor domain ontology.

The ontology is formalized and stored as a code file using the above OWL language, providing support for reasoning.

Ontology visualization

This paper uses protégé5.5 to visualize the rumor domain ontology, showing the hierarchical structure and relationship of the ontology parent class and its subclasses. Due to space limitations, this paper only shows the ontology parent class “RumorEmotionalFeatures” and its subclasses, as shown in Fig. 5 .

Ontology parent class “RumorEmotionalFeatures” and its subclasses.

Ontology reasoning and validation

Swrl reasoning rule construction.

SWRL reasoning rule is an ontology-based rule language that can be used to define Horn-like rules to enhance the reasoning and expressive ability of the ontology. This paper uses SWRL reasoning rules to deal with the conflict relationships between classes and between classes and instances in the rumor domain ontology, and uses pellet reasoner to deeply mine the implicit semantic relationships between classes and instances, to verify the semantic parsing ability and consistency of the rumor domain ontology.

This paper summarizes the object property features of various types of online rumors based on the self-built rumor dataset, maps the real rumor texts with the rumor domain ontology, constructs typical SWRL reasoning rules for judging 32 typical rumor types, as shown in Table 13 , and imports them into the protégé rule library, as shown in Fig. 6 . In which x, n, e, z, i, t, v, l, etc. are instances of rumor types, text theme, emotion category, effect, institution, event, action, geographical position, etc. in the ontology. HasTheme, HasEmotion, HasElement, HasSource, HasMood and HasSupport are object property relationships. Polarity value is a data property relationship.

Partial SWRL rules for the rumor domain ontology.

Implicit knowledge mining and verification based on pellet reasoner

This paper extracts corresponding instances from the rumor dataset, imports the rumor domain ontology and SWRL rule description into the pellet reasoner in the protégé software, performs implicit knowledge mining of the rumor domain ontology, judges the rumor type of the instance, and verifies the semantic parsing ability and consistency of the ontology.

Positive prevention and treatment of disease rumors are mainly based on the theme of disease prevention and treatment, usually containing products to be sold (including drugs, vaccines, equipment, etc.) and effect of disease names, claiming to have positive effects (such as prevention, cure, relief, etc.) on certain diseases or symptoms, causing positive emotions such as surprise and happiness among patients and their families, thereby achieving the purpose of selling products. The text features and emotional features of this kind of rumors are relatively clear, so this paper takes the rumor text “Hong Kong MDX Medical Group released the ‘DCV Cancer Vaccine’, which can prevent more than 12 kinds of cancers, including prostate cancer, breast cancer and lung cancer.” as an example to verify the semantic parsing ability of the rumor domain ontology. The analysis result of this instance is shown in Fig. 7 . The text theme is cancer prevention in disease prevention and treatment, the text style is plain narrative style, and the text element includes product-DCV cancer vaccine, positive effect-prevention, disease name-prostate cancer, disease name-breast cancer, disease name-lung cancer; the emotion category of this instance is a positive emotion, emotional appeal is joy, love, surprise; The motive for releasing rumors is profit-driven in selling products, the information source is Hong Kong MDX medical group, and pictures and celebrity endorsements are used as testimony method. This paper uses a pellet reasoner to reason on the parsed instance based on SWRL rules, and mines out the specific rumor type of this instance as positive prevention and treatment of disease rumor. This paper also conducted similar instance analysis and reasoning verification for other types of rumor texts, and the results show that the ontology has high consistency and reliability.

Implicit relationship between rumor instance parsing results and pellet reasoner mining.

Comparison and evaluation of ontology performance

In this paper, the constructed ontology is compared with the representative rumor index system in the field. By inviting four experts to make a comprehensive evaluation based on the self-built index system 70 , 71 , 72 , their performance in the indicators of reliability, coverage and operability is evaluated. According to the ranking order given by experts, they are given 1–4 points, and the first place in each indicator item gets four points. The average value given by three experts is taken as the single indicator score of each subject, and the total score of each indicator item is taken as the final score of the subject.

As can be seen from Table 14 , the rumor domain ontology constructed in this paper constructs a term set through three ways: reusing the existing ontology, extracting the content features of core documents and discovering new concepts based on real rumor data sets, and the ontology structure has been verified by SWRL rule reasoning of pellet inference machine, which has high reliability; ontology covers six kinds of Chinese online rumors, including the grammatical, semantic, pragmatic and social characteristics of rumor text characteristics, emotional characteristics, rumor credibility and social background, which has a high coverage; ontology is coded by OWL language specification and displayed visually on protege, which is convenient for further expansion and reuse of scholars and has high operability.

The construction method of TFI domain ontology proposed in this paper includes terminology layer, framework layer and instance layer. Compared with the traditional methods, this paper adopts three-dimensional data set construction method in terminology layer construction, investigates top-level ontology and related core documents, and completes the mapping of reusable top-level ontology from top to bottom and the concept extraction of rumor content features in existing literature research. Based on the mainstream internet rumor websites in China, the authoritative real rumor data set is established, and the new word discovery algorithm of N-gram combined with RoBERTa-Kmeans clustering algorithm is used to automatically discover new concepts in the field from bottom to top; determine the terminology set of domain ontology more comprehensively and efficiently. This paper extracts the clustering results of domain concepts based on the content characteristics of core documents in the selection of parent rumors content characteristics in the framework layer construction, that is, rumors text characteristics, rumors emotional characteristics, rumors credibility characteristics and social background characteristics; based on the emotional characteristics and the entity categories of real rumor data sets, the characteristics of rumor categories are defined. Sub-category rumor content features combine the concept of three-dimensional rumor term set and the concept distribution based on real rumor data set, define the sub-category concept and hierarchical relationship close to the real needs, and realize the fine-grained hierarchical modeling of the relationship between multi-domain network rumor content features. In this paper, OWL language is used to encode the rumor domain ontology in the instance layer construction, and SWRL rule language and Pellet inference machine are used to deal with the conflict and mine tacit knowledge, judge the fine-grained categories of rumor texts, and realize the effective quality evaluation of rumor ontology. This makes the rumor domain ontology constructed in this paper have high consistency and reliability, and can effectively analyze and reason different types of rumor texts, which enriches the knowledge system in this field and provides a solid foundation for subsequent credible rumor detection and governance.

However, the study of the text has the following limitations and deficiencies:

(1) The rumor domain ontology constructed in this paper only considers the content characteristics, but does not consider the user characteristics and communication characteristics. User characteristics and communication characteristics are important factors affecting the emergence and spread of online rumors, and the motivation and influence of rumors can be analyzed. In this paper, these factors are not included in the rumor feature system, which may limit the expressive ability and reasoning ability of the rumor ontology and fail to fully reflect the complexity and multidimensional nature of online rumors.

(2) In this paper, the mainstream Internet rumor-dispelling websites in China are taken as the data source of ontology instantiation. The data covers five rumor categories: political and military, disease prevention, social life, science and technology, and nutrition and health, and the data range is limited. And these data sources are mainly official or authoritative rumor websites, and their data volume and update frequency may not be enough to reflect the diversity and variability of online rumors, and can not fully guarantee the timeliness and comprehensiveness of rumor data.

(3) The SWRL reasoning rules used in this paper are based on manual writing, which may not cover all reasoning scenarios, and the degree of automation needs to be improved. The pellet inference engine used in this paper is an ontology inference engine based on OWL-DL, which may have some computational complexity problems and lack of advanced reasoning ability.

The following aspects can be considered for optimization and improvement in the future:

(1) This paper will introduce user characteristics into the rumor ontology, and analyze the factors that cause and accept rumors, such as social attributes, psychological state, knowledge level, beliefs and attitudes, behavioral intentions and so on. This paper will introduce the characteristics of communication, and analyze the propagation dynamic factors of various types of rumors, such as propagation path, propagation speed, propagation range, propagation period, propagation effect, etc. This paper hopes to introduce these factors into the rumor feature system, increase the breadth and depth of the rumor domain ontology, and provide more credible clues and basis for the detection, intervention and prevention of rumors.

(2) This paper will expand the data sources, collect the original rumor data directly from social media, news media, authoritative rumor dispelling institutions and other channels, and build a rumor data set with comprehensive types, diverse expressions and rich characteristics; regularly grab the latest rumor data from these data sources and update and improve the rumor data set in time; strengthen the expressive ability of rumor ontology instance layer, and provide full data support and verification for the effective application of ontology.

(3) The text will introduce GPT, LLaMA, ChantGLM and other language models, and explore the automatic generation algorithm and technology of ontology inference rules based on rumor ontology and dynamic Prompt, so as to realize more effective and intelligent rumor ontology evaluation and complex reasoning.

This paper proposed a method of constructing TFI network rumor domain ontology. Based on the concept distribution of three-dimensional term set and real rumor data set, the main features of network rumors are defined, including text features, emotional features, credibility features, social background features and category features, and the relationships among these multi-domain features are modeled in a fine-grained hierarchy, including five parent classes and 88 subcategories. At the instance level, 32 types of typical rumor category judgment and reasoning rules are constructed, and the ontology is processed by using SWRL rule language and pellet inference machine for conflict processing and tacit knowledge mining, so that the semantic analysis and reasoning of rumor text content are realized, which proves its effectiveness in dealing with complex, fuzzy and uncertain information in online rumors and provides a new perspective and tool for the interpretable analysis and processing of online rumors.

Data availability

The datasets generated during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This study was financially supported by Xi'an Major Scientific and Technological Achievements Transformation and Industrialization Project (20KYPT0003-10).

This work was supported by Xi’an Municipal Bureau of Science and Technology, 20KYPT0003-10.

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Jianbo Zhao, Huailiang Liu, Weili Zhang, Tong Sun, Qiuyi Chen, Yan Zhuang, Xiaojin Zhang & Shanzhuang Zhang

School of Artificial Intelligence, Xidian University, 266 Xifeng Road, Xi’an, 710071, China

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H.L. formulated the overall research strategy and guided the work. J.Z kept the original data on which the paper was based and verified whether the charts and conclusions accurately reflected the collected data. J.Z. W.Z. and T.S. wrote the main manuscript text. W.Z. Y.W. and Q.C. finished collecting and sorting out the data. J.C. Y.Z. and X.Z. prepared Figs. 1 – 7 , S.Z. B.L. and R.D. prepared Tables 1 – 14 . All authors reviewed the manuscript.

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A new Hungarian method may aid protein research

I n a paper recently published in Nature Communications , the HUN-REN-ELTE Protein Modeling Research Group (Institute of Chemistry) has laid the foundations for a mathematical method, allowing the computer-assisted comparison of the three-dimensional structures of proteins. The method is unique in that while the alternatives available so far only took into account the position of the atoms, the new technique, called LoCoHD (Local Composition Hellinger Distance), also includes the chemical information of the atoms.

Proteins are molecular machines that carry out processes necessary for cells to function, acting as molecular switches, transcribing information from DNA, transporting small and large molecules and regulating metabolism related chemical reactions. However, for all this to succeed, the protein in question must have the right spatial conformation, i.e. its own, correct 3D arrangement.

Several experimental methods (X-ray crystallography, nuclear magnetic resonance spectroscopy, cryo-electron microscopy) are available to determine the arrangement of atoms in a protein, and over the last few decades, protein researchers have discovered the shape of nearly 220,000 proteins. These results increasingly demand the development of computational methods capable of analyzing these arrangements.

One such method is the algorithm called LoCoHD, developed by Zsolt Fazekas, a Ph.D. candidate at the ELTE Hevesy György School of Chemistry and a researcher in Dr. András Perczel's research group. The algorithm compares local environments around amino acids in proteins based on their chemical nature (e.g., elemental composition, charge, hydrophobicity, etc.).

The method decides on a simple scale of 0 to 1 how different the structures in question are from each other. Values close to 0 suggest a high similarity between atomic arrangements and chemical properties, while values close to 1 indicate that the proteins being compared may have very different properties. The resulting numerical value (a so-called metric) can thus be used to obtain new information about the system under study.

The algorithm uses a multi-step protocol to generate the number representing the structural differences. In the first step, it converts real atoms in the protein into so-called primitive atoms. These can be represented as virtually labeled positions whose labels tell the chemical nature of the original atom.

So, for example, a primitive atom can be a "positively charged nitrogen," a "negatively charged oxygen," a "neutrally charged oxygen," an "aromatic carbon," etc. The labels are generated according to a so-called primitive typing scheme, which tells us in a tabulated manner how to convert real atoms into primitive atoms. The user can freely specify this table, fixing the chemical resolution of the method.

The second step is to determine the reference points of the comparison by selecting a subset of primitive atoms. These selected special primitive atoms are called the anchor atoms. For each selected anchor atom pair, the algorithm performs a comparison step, the result of which gives the dissimilarity measure we want. These numbers can be used at a local level, or they can be averaged into a single descriptor characterizing the whole protein.

In the study, the researchers highlighted that the method can also be used in the biannual CASP (Critical Assessment of Protein Structure Prediction) competitions, which is a well known competition in the field of protein research. During this event, competitors use different algorithms to model the shape of proteins having yet unpublished structures. CASP judges use a number of structure comparison methods to evaluate the contenders, but none of these take into account the chemistry of the local amino acid environments.

Using data from the 2020 CASP14 competition, the researchers have now performed comparative analysis of several modeled proteins, including the structures predicted by the artificial-intelligence-based AlphaFold2 method. Among these, they highlighted the analysis of a protein from the SARS-CoV-2 virus called ORF8. In the modeled structures of this protein, amino acid environments were identified that differ significantly in their interaction patterns from the environments found in the experimental structure.

In addition to studying static structures, the researchers also tested whether the method is suitable for analyzing the internal motion of proteins. They used simulations capable of reproducing molecular motions and data extracted from structural ensembles. One of the systems under study was the podocin protein, which performs vital functions in the kidney and whose mutations can cause severe, often fatal conditions.

The LoCoHD method was used to identify amino acids in the protein that undergo major chemical-environmental changes during the movement of podocin, which can affect both its structure and function. Similarly, the LoCoHD method has been applied successfully in the study of the HIV-1 capsid protein, in which an amino acid critical for the formation of the viral envelope has been identified.

These results are not only research curiosities, but by studying protein structures more effectively, we can get closer to better understanding the pathogens causing severe diseases and to developing effective drugs and therapeutics.

More information: Zsolt Fazekas et al, LoCoHD: a metric for comparing local environments of proteins, Nature Communications (2024). DOI: 10.1038/s41467-024-48225-0

Provided by Eötvös Loránd University

The figure shows the LoCoHD curves (top graphs) and structures (bottom panels) of His276 and Met197 in podocin, two strongly bimodal amino acids, measured from a molecular dynamics simulation. In the simulation, amino acid His276 plays a role in shortening a helix, while amino acid Met197 is responsible for filling a hydrophobic cavity. Credit: Eötvös Loránd University

Research on anti swing control method of four-rotor hanging load system under variable load conditions

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Published: 29 May 2024

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Huaitao Shi 1 ,
Bowen Xia 1 ,
Yunjian Hu ORCID: orcid.org/0000-0003-0405-7111 1 ,
Jinbao Zhao 1 &
Guowei Li 2

This paper designs a controller based on the energy coupling combined with the adaptive principle to address the issues of swing and inaccurate positioning caused by load changes in a four-rotor hanging load system. Considering load variability during modeling, it is divided into two parts: a fixed part and a changing part. The changing part involves online estimation using the adaptive principle. This approach effectively resolves the variable load problem while addressing the initial value problem associated with the load mass estimation. The controller accounts for the coupling between the load’s swing angle and the displacement in the four-rotor system. It exploits the underactuated characteristics of the system to suppress the load swing angle. Additionally, a potential function is incorporated into the controller to compensate for the positioning error resulting from estimation errors. The simulation results show that the designed controller can adapt to various flight transportation situations. The experimental results show that the designed controller can accurately locate and effectively suppress the swing caused by variable load compared with the conventional controller.

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This research has been supported in part by the Central Leading Local Science and Technology Development Fund (NO:2023JH6/100100040), Xingliao Yingcai Innovation Team of Liaoning (No:XLYC2008016), “Open bidding for selecting the best candidates” Industrial Generic Technology Project of Shenyang (NO:22-316-1-17), Key Science and Technology Research of Shenyang (NO:20-202-4-40), “Open bidding for selecting the best candidates” Key Scientific And Technological in Liaoning (2022JH1/10400028).

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Title: rank-refining seed selection methods for budget constrained influence maximisation in multilayer networks under linear threshold model.

Abstract: The problem of selecting an optimal seed set to maximise influence in networks has been a subject of intense research in recent years. However, despite numerous works addressing this area, it remains a topic that requires further elaboration. Most often, it is considered within the scope of classically defined graphs with a spreading model in the form of Independent Cascades. In this work, we focus on the problem of budget-constrained influence maximisation in multilayer networks using a Linear Threshold Model. Both the graph model and the spreading process we employ are less prevalent in the literature, even though their application allows for a more precise representation of the opinion dynamics in social networks. This paper aims to answer which of the sixteen evaluated seed selection methods is the most effective and how similar they are. Additionally, we focus our analysis on the impact of spreading model parameters, network characteristics, a budget, and the seed selection methods on the diffusion effectiveness in multilayer networks. Our contribution also includes extending several centrality measures and heuristics to the case of such graphs. The results indicate that all the factors mentioned above collectively contribute to the effectiveness of influence maximisation. Moreover, there is no seed selection method which always provides the best results. However, the seeds chosen with VoteRank-based methods (especially with the $v-rnk-m$ variant we propose) usually provide the most extensive diffusion.

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The clear failure of the classical method to obtain high-quality solutions even at small sizes makes LABS an appealing candidate problem for quantum optimization heuristics. ... Technology Applied Research center of JPMorgan Chase & Co., Argonne National Laboratory, and Quantinuum LLC. This paper is not a product of the Research Department of ...
Research on anti swing control method of four-rotor hanging ...
This paper designs a controller based on the energy coupling combined with the adaptive principle to address the issues of swing and inaccurate positioning caused by load changes in a four-rotor hanging load system. Considering load variability during modeling, it is divided into two parts: a fixed part and a changing part. The changing part involves online estimation using the adaptive ...
[2405.18059] Rank-Refining Seed Selection Methods for Budget
The problem of selecting an optimal seed set to maximise influence in networks has been a subject of intense research in recent years. However, despite numerous works addressing this area, it remains a topic that requires further elaboration. Most often, it is considered within the scope of classically defined graphs with a spreading model in the form of Independent Cascades. In this work, we ...

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Why Methods Matter

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

What to include in your methods section

A constant principle of rigorous science

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Ethical Considerations

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Research Methods | Definition, Types, Examples

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Qualitative vs quantitative data

Primary vs secondary data

Descriptive vs experimental data

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Qualitative analysis methods

Quantitative analysis methods

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Before Writing the Paper

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Organizing Academic Research Papers: 6. The Methodology

Importance of a Good Methodology Section

Structure and Writing Style

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Research Support

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The Ultimate Guide To Research Methodology

What Is Research Methodology?

Facilitating Rigorous Inquiry

Ensuring Reproducibility And Reliability

Guiding Decision-Making Processes

Contributing To Academic Excellence

Enhancing Problem-Solving Abilities

Understanding Research Methodology

Basics Of Research

Types of Research

Components Of Research Methodology

Research Design

Data Collection Methods

Data Analysis Techniques

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Choosing a Research Method

Quantitative Research Methods

Experimental Research

Survey Research

Descriptive Research

Qualitative Research Methods

Ethnography

Grounded Theory

Exploratory, Descriptive, And Explanatory Designs

Descriptive Design

Explanatory Design

Cross-Sectional Vs. Longitudinal Designs

Longitudinal Design

Experimental Vs Non-experimental Designs

Non-Experimental Design

Designing Effective Surveys

Conducting Interviews

Observation

2. Non-participant Observation

Archival Research

2. Challenges and Considerations