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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
  • Get Data, Get Help!

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

Grad Coach

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

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

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

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

Research Methodology 101

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

Free Webinar: Research Methodology 101

What is research methodology?

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

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

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

So, it’s the same as research design?

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

Need a helping hand?

method used in this research

What are qualitative, quantitative and mixed-methods?

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

Let’s take a closer look.

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

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

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

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

What is sampling strategy?

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

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

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

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

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

What are data collection methods?

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

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

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

What are data analysis methods?

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

Popular data analysis methods in qualitative research include:

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

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

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

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

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

How do I choose a research methodology?

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

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

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

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

Example of a research methodology chapter

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

method used in this research

Psst... there’s more!

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

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Inferential stats 101

199 Comments

Leo Balanlay

Thank you for this simple yet comprehensive and easy to digest presentation. God Bless!

Derek Jansen

You’re most welcome, Leo. Best of luck with your research!

Asaf

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Hafiz Tahir

Thankyou So much Sir Derek…

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ATUL KUMAR

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MAHAMUDUL HASSAN

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Good morning thanks so much for the on line lectures am a student of university of Makeni.select a research topic and deliberate on is so that we will continue to understand more.sorry that’s a suggestion.

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Odirile

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Visor Likali

Well written

Pondris Patrick

I am writing a APA Format paper . I using questionnaire with 120 STDs teacher for my participant. Can you write me mthology for this research. Send it through email sent. Just need a sample as an example please. My topic is ” impacts of overcrowding on students learning

Thanks for your comment.

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

All the best with your research.

Anon

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Keke

Thank you. Explicit explanation

Sophy

Thank you, Derek and Kerryn, for making this simple to understand. I’m currently at the inception stage of my research.

Luyanda

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Beulah Emmanuel

excellent explanation

Gino Raz

I’m currently working on my master’s thesis, thanks for this! I’m certain that I will use Qualitative methodology.

Abigail

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Yonas Tesheme

I am currently doing my dissertation proposal and I am sure that I will do quantitative research. Thank you very much it was extremely helpful.

zahid t ahmad

Very interesting and informative yet I would like to know about examples of Research Questions as well, if possible.

Maisnam loyalakla

I’m about to submit a research presentation, I have come to understand from your simplification on understanding research methodology. My research will be mixed methodology, qualitative as well as quantitative. So aim and objective of mixed method would be both exploratory and confirmatory. Thanks you very much for your guidance.

Mila Milano

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Christabel

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Lika

I’m going to write synopsis which will be quantitative research method and I don’t know how to frame my topic, can I kindly get some ideas..

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Modie Maria Neswiswi

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Sarah

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Sikandar Ali Shah

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Debbie

Thanks dude

Deborah

Thank you Doctor Derek for this wonderful piece, please help to provide your details for reference purpose. God bless.

Michael

Many compliments to you

Dana

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Aryan

Thank you. I had to give a presentation on this topic. I have looked everywhere on the internet but this is the best and simple explanation.

omodara beatrice

thank you, its very informative.

WALLACE

Well explained. Now I know my research methodology will be qualitative and exploratory. Thank you so much, keep up the good work

GEORGE REUBEN MSHEGAME

Well explained, thank you very much.

Ainembabazi Rose

This is good explanation, I have understood the different methods of research. Thanks a lot.

Kamran Saeed

Great work…very well explanation

Hyacinth Chebe Ukwuani

Thanks Derek. Kerryn was just fantastic!

Great to hear that, Hyacinth. Best of luck with your research!

Matobela Joel Marabi

Its a good templates very attractive and important to PhD students and lectuter

Thanks for the feedback, Matobela. Good luck with your research methodology.

Elie

Thank you. This is really helpful.

You’re very welcome, Elie. Good luck with your research methodology.

Sakina Dalal

Well explained thanks

Edward

This is a very helpful site especially for young researchers at college. It provides sufficient information to guide students and equip them with the necessary foundation to ask any other questions aimed at deepening their understanding.

Thanks for the kind words, Edward. Good luck with your research!

Ngwisa Marie-claire NJOTU

Thank you. I have learned a lot.

Great to hear that, Ngwisa. Good luck with your research methodology!

Claudine

Thank you for keeping your presentation simples and short and covering key information for research methodology. My key takeaway: Start with defining your research objective the other will depend on the aims of your research question.

Zanele

My name is Zanele I would like to be assisted with my research , and the topic is shortage of nursing staff globally want are the causes , effects on health, patients and community and also globally

Oluwafemi Taiwo

Thanks for making it simple and clear. It greatly helped in understanding research methodology. Regards.

Francis

This is well simplified and straight to the point

Gabriel mugangavari

Thank you Dr

Dina Haj Ibrahim

I was given an assignment to research 2 publications and describe their research methodology? I don’t know how to start this task can someone help me?

Sure. You’re welcome to book an initial consultation with one of our Research Coaches to discuss how we can assist – https://gradcoach.com/book/new/ .

BENSON ROSEMARY

Thanks a lot I am relieved of a heavy burden.keep up with the good work

Ngaka Mokoena

I’m very much grateful Dr Derek. I’m planning to pursue one of the careers that really needs one to be very much eager to know. There’s a lot of research to do and everything, but since I’ve gotten this information I will use it to the best of my potential.

Pritam Pal

Thank you so much, words are not enough to explain how helpful this session has been for me!

faith

Thanks this has thought me alot.

kenechukwu ambrose

Very concise and helpful. Thanks a lot

Eunice Shatila Sinyemu 32070

Thank Derek. This is very helpful. Your step by step explanation has made it easier for me to understand different concepts. Now i can get on with my research.

Michelle

I wish i had come across this sooner. So simple but yet insightful

yugine the

really nice explanation thank you so much

Goodness

I’m so grateful finding this site, it’s really helpful…….every term well explained and provide accurate understanding especially to student going into an in-depth research for the very first time, even though my lecturer already explained this topic to the class, I think I got the clear and efficient explanation here, much thanks to the author.

lavenda

It is very helpful material

Lubabalo Ntshebe

I would like to be assisted with my research topic : Literature Review and research methodologies. My topic is : what is the relationship between unemployment and economic growth?

Buddhi

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Ekokobe Aloysius

THANKS SO MUCH FOR EXPLANATION, ITS VERY CLEAR TO ME WHAT I WILL BE DOING FROM NOW .GREAT READS.

Asanka

Short but sweet.Thank you

Shishir Pokharel

Informative article. Thanks for your detailed information.

Badr Alharbi

I’m currently working on my Ph.D. thesis. Thanks a lot, Derek and Kerryn, Well-organized sequences, facilitate the readers’ following.

Tejal

great article for someone who does not have any background can even understand

Hasan Chowdhury

I am a bit confused about research design and methodology. Are they the same? If not, what are the differences and how are they related?

Thanks in advance.

Ndileka Myoli

concise and informative.

Sureka Batagoda

Thank you very much

More Smith

How can we site this article is Harvard style?

Anne

Very well written piece that afforded better understanding of the concept. Thank you!

Denis Eken Lomoro

Am a new researcher trying to learn how best to write a research proposal. I find your article spot on and want to download the free template but finding difficulties. Can u kindly send it to my email, the free download entitled, “Free Download: Research Proposal Template (with Examples)”.

fatima sani

Thank too much

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Thank you very much for your comprehensive explanation about research methodology so I like to thank you again for giving us such great things.

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Good very well explained.Thanks for sharing it.

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Thank u sir, it is really a good guideline.

Vimbainashe

so helpful thank you very much.

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Thanks for the video it was very explanatory and detailed, easy to comprehend and follow up. please, keep it up the good work

AVINASH KUMAR NIRALA

It was very helpful, a well-written document with precise information.

orebotswe morokane

how do i reference this?

Roy

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

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

sheryl

Your explanation is easily understood. Thank you

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research methodologies

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memory

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Rabilu yau

Comment * thanks very much

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You’re most welcome 🙂

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I am nkasa lizwi doing my research proposal on honors with the university of Walter Sisulu Komani I m on part 3 now can you assist me.my topic is: transitional challenges faced by educators in intermediate phase in the Alfred Nzo District.

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Titilayo

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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.

Table of contents

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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Research Methods: What are research methods?

  • What are research methods?
  • Searching specific databases

What are research methods

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

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

Types of research

  • Qualitative Research
  • Quantitative Research
  • Mixed Methods Research

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

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

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

Finding Mixed Methods research in the Databases 

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

Data collection tools

Sage research methods.

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

Help and Information

Help and information

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  • URL: https://libguides.newcastle.edu.au/researchmethods
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  • Research Design & Method

Research Methods Guide: Research Design & Method

  • Introduction
  • Survey Research
  • Interview Research
  • Data Analysis
  • Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

method used in this research

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

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

Which research method should I choose ?

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

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

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

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

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

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What is research methodology?

method used in this research

The basics of research methodology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Generally, they can be grouped as follows:

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

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

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

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

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

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

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

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

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

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

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

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

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

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

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

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

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

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

Rhetorical analysis illustration

Pfeiffer Library

Research Methodologies

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

What are research methods?

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

  • Additional Sources

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

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

Constructing Questionnaires

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

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

Quantitative Research Measures

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

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

Focus Groups

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

Advantages of Focus Groups

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

Disadvantages of Focus Groups

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

Observation

There are two ways to conduct research observations:

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

Open-Ended Questionnaires

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

Semi-structured Interviews

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

Theoretical Analysis

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

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

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

Unstructured Interviews

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

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

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

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

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

2.2 Research Methods

Learning objectives.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Field Research

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

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

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

Sociology in the Real World

Beyoncé and lady gaga as sociological subjects.

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

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

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

Participant Observation

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

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

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

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

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

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

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

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

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

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

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

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

Ethnography

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

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

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

Institutional Ethnography

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

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

Sociological Research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Experiments

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

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

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

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

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

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

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

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

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

Secondary Data Analysis

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

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

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

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

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

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

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

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

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  • Authors: Tonja R. Conerly, Kathleen Holmes, Asha Lal Tamang
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Research Methodology

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your research objectives. Methodology is the first step in planning a research project.

Qualitative Data Coding

qualitative coding

What Is a Focus Group?

Reviewed by Olivia Guy-Evans, MSc

Cross-Cultural Research Methodology In Psychology

What is internal validity in research.

Reviewed by Saul Mcleod, PhD

Scientific Method

Qualitative research, experiments.

The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.

Learn More: Steps of the Scientific Method

Variables apply to experimental investigations. The independent variable is the variable the experimenter manipulates or changes. The dependent variable is the variable being tested and measured in an experiment, and is 'dependent' on the independent variable.

Learn More: Independent and Dependent Variables

When you perform a statistical test a p-value helps you determine the significance of your results in relation to the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.

Learn More: P-Value and Statistical Significance

Qualitative research is a process used for the systematic collection, analysis, and interpretation of non-numerical data. Qualitative research can be used to gain a deep contextual understanding of the subjective social reality of individuals.

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups.

Learn More: How the Experimental Method Works in Psychology

Frequent Asked Questions

What does p-value of 0.05 mean?

A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the results have occurred by random chance rather than a real effect. Therefore, we reject the null hypothesis and accept the alternative hypothesis.

However, it is important to note that the p-value is not the only factor that should be considered when interpreting the results of a hypothesis test. Other factors, such as effect size, should also be considered.

Learn More: What A p-Value Tells You About Statistical Significance

What does z-score tell you?

A  z-score  describes the position of a raw score in terms of its distance from the mean when measured in standard deviation units. It is also known as a standard score because it allows the comparison of scores on different variables by standardizing the distribution. The z-score is positive if the value lies above the mean and negative if it lies below the mean.

Learn More: Z-Score: Definition, Calculation, Formula, & Interpretation

What is an independent vs dependent variable?

The independent variable is the variable the experimenter manipulates or changes and is assumed to have a direct effect on the dependent variable. For example, allocating participants to either drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

Learn More : What are Independent and Dependent Variables?

What is the difference between qualitative and quantitative?

Quantitative data is numerical information about quantities and qualitative data is descriptive and regards phenomena that can be observed but not measured, such as language.

Learn More: What’s the difference between qualitative and quantitative research?

Explore Research Methodology

Businessman holding pencil at big complete checklist with tick marks

What Is Face Validity In Research? Importance & How To Measure

criterion validity

Criterion Validity: Definition & Examples

convergent validity

Convergent Validity: Definition and Examples

content validity

Content Validity in Research: Definition & Examples

construct validity

Construct Validity In Psychology Research

concurrent validity

Concurrent Validity In Psychology

Internal and external validity 1

Internal vs. External Validity In Psychology

Qualitative

Qualitative Research: Characteristics, Design, Methods & Examples

Demand Characteristics 1 3

Demand Characteristics In Psychology: Definition, Examples & Control

experimental design

Between-Subjects vs. Within-Subjects Study Design

random assignment 1

Random Assignment in Psychology: Definition & Examples

RCT

Double-Blind Experimental Study And Procedure Explained

Observer Bias

Observer Bias: Definition, Examples & Prevention

Sample Target Population

Sampling Bias: Types, Examples & How to Avoid It

Probability and statistical significance in ab testing. Statistical significance in a b experiments

What is The Null Hypothesis & When Do You Reject The Null Hypothesis

Independent Measures Design 2

Between-Subjects Design: Overview & Examples

case control study

What Is A Case Control Study?

case study

Case Study Research Method in Psychology

prospective Cohort study

Cohort Study: Definition, Designs & Examples

cluster sampling

Cluster Sampling: Definition, Method and Examples

Convenience sample

Convenience Sampling: Definition, Method and Examples

variables

Confounding Variables in Psychology: Definition & Examples

In experiments, scientists compare a control group and an experimental group that is identical in all respects. Unlike the experimental group, the control group is not exposed to the variable under investigation. It provides a baseline against which any changes in the experimental group can be compared.

Control Group vs Experimental Group

controlled experiment

Controlled Experiment

types of correlation. Scatter plot. Positive negative and no correlation

Correlation in Psychology: Meaning, Types, Examples & coefficient

variables

Extraneous Variables In Research: Types & Examples

ethnocentric

Ethnocentrism In Psychology: Examples, Disadvantages, & Cultural Relativism

psychology research ethics 1

Ethical Considerations In Psychology Research

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Types of Research Methods: Examples and Tips

method used in this research

What are research methods?

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

Common Types of Research Methods

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

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

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

Data Collection Methods

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

Quantitative vs qualitative data

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

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

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

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

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

Primary vs secondary data

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

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

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

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

Experimental vs descriptive data collection

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

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

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

Descriptive data examples:

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

Experimental data examples:

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

Examples of Difference Data Collection Methods

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15 Types of Research Methods

types of research methods, explained below

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

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

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

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

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

Types of Research Methods

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

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

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

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

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

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

Qualitative Research Methods

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

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

1. Ethnographic Research

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

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

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

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

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

Example of Ethnography

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

2. Phenomenological Research

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

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

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

Example of Phenomenological Research

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

3. Historical Research

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

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

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

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

Example of Historical Research

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

4. Content Analysis

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

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

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

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

Example of Content Analysis

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

5. Grounded Theory Research

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

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

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

Grounded Theory Example

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

6. Action Research

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

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

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

Action Research Example

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

7. Natural Observational Research

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

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

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

Observational Research Example

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

8. Case Study Research

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

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

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

See More: Case Study Advantages and Disadvantages

Example of a Case Study

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

Quantitative Research Methods

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

9. Experimental Research

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

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

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

Example of Experimental Research

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

10. Surveys and Questionnaires

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

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

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

Example of a Survey Study

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

11. Longitudinal Studies

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

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

a visual representation of a longitudinal study demonstrating that data is collected over time on one sample so researchers can examine how variables change over time

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

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

Example of a Longitudinal Study

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

12. Cross-Sectional Studies

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

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

A visual representation of a cross-sectional group of people, demonstrating that the data is collected at a single point in time and you can compare groups within the sample

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

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

Example of a Cross-Sectional Study

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

13. Correlational Research

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

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

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

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

Example of Correlational Research

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

14. Quasi-Experimental Design Research

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

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

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

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

Example of Quasi-Experimental Design

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

Related: Examples and Types of Random Assignment in Research

15. Meta-Analysis Research

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

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

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

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

Example of a Meta-Analysis

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

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

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

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

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

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

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

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

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

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

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Different Types of Research Methods

  • Mallika Rangaiah
  • Dec 22, 2021
  • Updated on: Nov 21, 2023

Different Types of Research Methods title banner

Unlike what a layman generally presumes, Research is not just about determining a hypothesis and unraveling a conclusion for that hypothesis. Every research approach that we take up falls under the category of a type of methodology and every methodology is exclusive and intricate in its depth. 

So what are these research methodologies and how do the researchers make use of them? This is what we are going to explore through this blog. Before we attempt to understand these methods, let us understand what research methodology actually means. 

What are Research Methods ?

Firstly, let's understand why we undertake research? What exactly is the point of it? 

Research is mainly done to gain knowledge to support a survey or quest regarding a particular conception or theory and to reach a resolute conclusion regarding the same.  Research is generally an approach for gaining knowledge which is required to interpret, write, delve further and to distribute data. 

For ensuring that a fulfilling experience is delivered, it is essential that the Research is premium in its quality and that’s where Research Methods come to the rescue. 

(Recommended blog - Research Market Analysis )

Types of Research Methods

An area is selected, a specific hypothesis is determined and a defined conclusion is required to be achieved. But how is this conclusion reached? What is the approach that can be taken up? As per CR Kothari’s book “Research Methodology Methods and Techniques” (The Second Revised Edition),  the basic types of Research Methods are the following : 

The image depicts the Types of Research Methods and has the following points :1. Descriptive Research2. Analytical Research3. Applied Research4. Fundamental Research5. Quantitative Research6. Qualitative Research7. Conceptual Research8. Empirical Research

Descriptive Research

Descriptive Research is a form of research that incorporates surveys as well as different varieties of fact-finding investigations. This form of research is focused on describing the prevailing state of affairs as they are. Descriptive Research is also termed as Ex post facto research. 

This research form emphasises on factual reporting, the researcher cannot control the involved variables and can only report the details as they took place or as they are taking place. 

Researchers mainly make use of a descriptive research approach for purposes such as when the research is aimed at deciphering characteristics, frequencies or trends. 

Ex post facto studies also include attempts by researchers to discover causes even when they cannot control the variables. The descriptive research methods are mainly, observations, surveys as well as case studies. 

(Speaking of variables, have you ever wondered - What are confounding variables? )

Analytical Research

Analytical Research is a form of research where the researcher has to make do with the data and factual information available at their behest and interpret this information to undertake an acute evaluation of the data. 

This form of research is often undertaken by researchers to uncover some evidence that supports their present research and which makes it more authentic. It is also undertaken for concocting fresh ideas relating to the topic on which the research is based. 

From conducting meta analysis, literary research or scientific trials and learning public opinion, there are many methods through which this research is done. 

Applied Research

When a business or say, the society is faced with an issue that needs an immediate solution or resolution, Applied Research is the research type that comes to the rescue. 

We primarily make use of Applied Research when it comes to resolving the issues plaguing our daily lives, impacting our work, health or welfare. This research type is undertaken to uncover solutions for issues relating to varying sectors like education, engineering, psychology or business. 

For instance, a company might employ an applied researcher for concluding the best possible approach of selecting employees that would be the best fit for specific positions in the company. 

The crux of Applied Research is to figure out the solution to a certain growing practical issue. 

The 3 Types of Applied Research are mainly 

Evaluation Research - Research where prevailing data regarding the topic is interpreted to arrive at proper decisions

Research and Development - Where the focus is on setting up fresh products or services which focus on the target market requirements

Action Research - Which aims at offering practical solutions for certain business issues by giving them proper direction, are the 3 types of Applied Research. 

(Related blog - Target Marketing using AI )

Fundamental Research

This is a Research type that is primarily concerned with formulating a theory or understanding a particular natural phenomenon. Fundamental Research aims to discover information with an extensive application base, supplementing the existing concepts in a certain field or industry. 

Research on pure mathematics or research regarding generalisation of the behavior of humans are also examples of Fundamental Research. This form of research is mainly carried out in sectors like Education, Psychology and Science. 

For instance, in Psychology fundamental research assists the individual or the company in gaining better insights regarding certain behaviors such as deciphering how consumption of caffeine can possibly impact the attention span of a student or how culture stereotypes can possibly trigger depression. 

Quantitative Research

Quantitative Research, as the name suggests, is based on the measurement of a particular amount or quantity of a particular phenomenon. It focuses on gathering and interpreting numerical data and can be adopted for discovering any averages or patterns or for making predictions.

This form of Research is number based and it lies under the two main Research Types. It makes use of tables, data and graphs to reach a conclusion. The outcomes generated from this research are measurable and can be repeated unlike the outcomes of qualitative research. This research type is mainly adopted for scientific and field based research.

Quantitative research generally involves a large number of people and a huge section of data and has a lot of scope for accuracy in it. 

These research methods can be adopted for approaches like descriptive, correlational or experimental research.

Descriptive research - The study variables are analyzed and a summary of the same is seeked.

Correlational Research - The relationship between the study variables is analyzed. 

Experimental Research - It is deciphered to analyse whether a cause and effect relationship between the variables exists. 

Quantitative research methods

  • Experiment Research - This method controls or manages independent variables for calculating the effect it has on dependent variables. 
  • Survey - Surveys involve inquiring questions from a certain specified number or set of people either online, face to face or over the phone. 
  • (Systematic) observation - This method involves detecting any occurrence and monitoring it in a natural setting. 
  • Secondary research : This research focuses on making use of data which has been previously collected for other purposes such as for say, a national survey. 

(Related blog - Hypothesis Testing )

Qualitative Research

As the name suggests, this form of Research is more considered with the quality of a certain phenomenon, it dives into the “why” alongside the “what”. For instance, let’s consider a gender neutral clothing store which has more women visiting it than men. 

Qualitative research would be determining why men are not visiting the store by carrying out an in-depth interview of some potential customers in this category.

This form of research is interested in getting to the bottom of the reasons for human behaviour, i.e understanding why certain actions are taken by people or why they think certain thoughts. 

Through this research the factors influencing people into behaving in a certain way or which control their preferences towards a certain thing can be interpreted.

An example of Qualitative Research would be Motivation Research . This research focuses on deciphering the rooted motives or desires through intricate methods like in depth interviews. It involves several tests like story completion or word association. 

Another example would be Opinion Research . This type of research is carried out to discover the opinion and perspective of people regarding a certain subject or phenomenon.

This is a theory based form of research and it works by describing an issue by taking into account the prior concepts, ideas and studies. The experience of the researcher plays an integral role here.

The Types of Qualitative Research includes the following methods :

Qualitative research methods

  • Observations: In this method what the researcher sees, hears of or encounters is recorded in detail.
  • Interviews: Personally asking people questions in one-on-one conversations.
  • Focus groups: This involves asking questions and discussions among a group of people to generate conclusions from the same. 
  • Surveys: In these surveys unlike the quantitative research surveys, the questionnaires involve extensive open ended questions that require elaborate answers. 
  • Secondary research: Gathering the existing data such as images, texts or audio or video recordings. This can involve a text analysis, a research of a case study, or an In-depth interview.

Conceptual Research

This research is related to an abstract idea or a theory. It is adopted by thinkers and philosophers with the aim of developing a new concept or to re-examine the existing concepts. 

Conceptual Research is mainly defined as a methodology in which the research is conducted by observing and interpreting the already present information on a present topic. It does not include carrying out any practical experiments. 

This methodology has often been adopted by famous Philosophers like Aristotle, Copernicus, Einstein and Newton for developing fresh theories and insights regarding the working of the world and for examining the existing ones from a different perspective. 

The concepts were set up by philosophers to observe their environment and to sort, study, and summarise the information available. 

Empirical Research

This is a research method that focuses solely on aspects like observation and experience, without focusing on the theory or system. It is based on data and it can churn conclusions that can be confirmed or verified through observation and experiment. Empirical Research is mainly undertaken to determine proof that certain variables are affecting the others in a particular way.   

This kind of research can also be termed as Experimental Research. In this research it is essential that all the facts are received firsthand, directly from the source so that the researcher can actively go and carry out the actions and manipulate the concerned materials to gain the information he requires.

In this research a hypothesis is generated and then a path is undertaken to confirm or invalidate this hypothesis. The control that the researcher holds over the involved variables defines this research. The researcher can manipulate one of these variables to examine its effect.

(Recommended blog - Data Analysis )

Other Types of Research

All research types apart from the ones stated above are mainly variations of them, either in terms of research purpose or in the terms of the time that is required for accomplishing the research, or say, the research environment. 

If we take the perspective of time, research can be considered as either One-time research or Longitudinal Research. 

One time Research : The research is restricted to a single time period. 

Longitudinal Research : The research is executed over multiple time periods. 

A research can also be set in a field or a laboratory or be a simulation, it depends on the environment that the research is based on. 

We’ve also got Historical Research which makes use of historical sources such as documents and remains for examining past events and ideas. This also includes the philosophy of an individual and groups at a particular time. 

Research may be clinical or diagnostic . These kinds of research generally carry out case study or in-depth interview approaches to determine basic causal relationships. 

Research can also be Exploratory or Formalized. 

Exploratory Research: This is a research that is more focused on establishing hypotheses than on deriving the result. This form of Research focuses on understanding the prevailing issue but it doesn’t really offer defining results. 

Formalized research: This is a research that has a solid structure and which also has specific hypotheses for testing. 

We can also classify Research as conclusion-oriented and decision-oriented. 

Conclusion Oriented Research: In this form of research, the researcher can select an issue, revamp the enquiry as he continues and visualize it as per his requirements. 

Decision-oriented research: This research depends on the requirement of the decision maker and offers less freedom to the research to conduct it as he pleases. 

The common and well known research methods have been listed in this blog. Hopefully this blog will give the readers and present and future researchers proper knowledge regarding important methods they can adopt to conduct their Research.

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Proof-of-concept study pioneers new brain imaging technique through a transparent skull implant

by Keck School of Medicine of USC

brain

In the first study of its kind, researchers from the Keck School of Medicine of USC and the California Institute of Technology (Caltech) designed and implanted a transparent window in the skull of a patient, then used functional ultrasound imaging (fUSI) to collect high-resolution brain imaging data through the window.

Their preliminary findings , published in Science Translational Medicine , suggest that this sensitive, non-invasive approach could open new avenues for patient monitoring and clinical research , as well as broader studies of how the brain functions.

"This is the first time anyone had applied functional ultrasound imaging through a skull replacement in an awake, behaving human performing a task," said Charles Liu, MD, Ph.D., a professor of clinical neurological surgery, urology and surgery at the Keck School of Medicine and director of the USC Neurorestoration Center.

"The ability to extract this type of information noninvasively through a window is pretty significant, particularly since many of the patients who require skull repair have or will develop neurological disabilities. In addition, 'windows' can be surgically implanted in patients with intact skulls if functional information can help with diagnosis and treatment."

The research participant, 39-year-old Jared Hager, sustained a traumatic brain injury (TBI) from a skateboarding accident in 2019. During emergency surgery , half of Hager's skull was removed to relieve pressure on his brain, leaving part of his brain covered only with skin and connective tissue. Because of the pandemic, he had to wait more than two years to have his skull restored with a prosthesis.

During that time, Hager volunteered for earlier research conducted by Liu, Jonathan Russin, MD, associate surgical director of the USC Neurorestoration Center, and another Caltech team on a new type of brain imaging called fPACT.

The experimental technique had been done on soft tissue, but could only be tested on the brain in patients like Hager who were missing a part of their skull. When the time came for implanting the prosthesis, Hager again volunteered to team up with Liu and his colleagues, who designed a custom skull implant to study the utility of fUSI—which cannot be done through the skull or a traditional implant—while repairing Hager's injury.

Before the reconstructive surgery, the research team tested and optimized fUSI parameters for brain imaging, using both a phantom (a scientific device designed to test medical imaging equipment) and animal models. They then collected fUSI data from Hager while he completed several tasks, both before his surgery and after the clear implant was installed, finding that the window offered an effective way to measure brain activity.

Functional brain imaging, which collects data on brain activity by measuring changes in blood flow or electrical impulses, can offer key insights about how the brain works, both in healthy people and those with neurological conditions.

But current methods, such as functional magnetic resonance imaging (fMRI) and intracranial electroencephalography (EEG) leave many questions unanswered. Challenges include low resolution, a lack of portability or the need for invasive brain surgery. fUSI may eventually offer a sensitive and precise alternative.

"If we can extract functional information through a patient's skull implant, that could allow us to provide treatment more safely and proactively," including to TBI patients who suffer from epilepsy, dementia, or psychiatric problems, Liu said.

A new frontier for brain imaging

As a foundation for the present study, Liu has collaborated for years with Mikhail Shapiro, Ph.D. and Richard Andersen, Ph.D., of Caltech, to develop specialized ultrasound sequences that can measure brain function, as well as to optimize brain-computer interface technology, which transcribes signals from the brain to operate an external device.

With these pieces in place, Liu and his colleagues tested several transparent skull implants on rats, finding that a thin window made from polymethyl methacrylate (PMMA)—which resembles plexiglass—yielded the clearest imaging results. They then collaborated with a neurotechnology company, Longeviti Neuro Solutions, to build a custom implant for Hager.

Before surgery, the researchers collected fUSI data while Hager did two activities: solving a "connect-the-dots" puzzle on a computer monitor and playing melodies on his guitar. After the implant was installed, they collected data on the same tasks, then compared the results to determine whether fUSI could provide accurate and useful imaging data.

"The fidelity of course decreased, but importantly, our research showed that it's still high enough to be useful," Liu said. "And unlike other brain-computer interface platforms, which require electrodes to be implanted in the brain, this has far less barriers to adoption."

fUSI may offer finer resolution than fMRI and unlike intracranial EEG, it does not require electrodes to be implanted inside the brain. It is also less expensive than those methods and could provide some clinical advantages for patients over non-transparent skull implants, said Russin, who is also an associate professor of neurological surgery at the Keck School of Medicine and director of cerebrovascular surgery at Keck Hospital of USC.

"One of the big problems when we do these surgeries is that a blood clot can form underneath the implant, but having a clear window gives us an easy way to monitor that," he said.

Refining functional ultrasound technology

In addition to better monitoring of patients, the new technique could offer population-level insights about TBI and other neurological conditions. It could also allow scientists to collect data on the healthy brain and learn more about how it controls cognitive, sensory, motor and autonomic functions.

"What our findings show is that we can extract useful functional information with this method," Liu said. "The next step is: What specific functional information do we want, and what can we use it for?"

Until the new technologies undergo clinical trials, fUSI and the clear implant are experimental. In the meantime, the research team is working to improve their fUSI protocols to further enhance image resolution . Future research should also build on this early proof-of-concept study by testing more participants to better establish the link between fUSI data and specific brain functions, the researchers said.

"Jared is an amazing guy," said Liu, who is continuing to collaborate with the study participant on refining new technologies, including laser spectroscopy, which measures blood flow in the brain. "His contributions have really helped us explore new frontiers that we hope can ultimately help many other patients."

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  • Open access
  • 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
  • Computer science
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  • Data integration
  • Data mining
  • Data processing
  • Human behaviour
  • Information technology
  • Literature mining
  • Machine learning
  • Scientific data

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 .

figure 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

figure 2

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.

figure 3

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 :

figure 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 .

figure 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.

figure 6

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.

figure 7

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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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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Zhao, J., Liu, H., Zhang, W. et al. Research on domain ontology construction based on the content features of online rumors. Sci Rep 14 , 12134 (2024). https://doi.org/10.1038/s41598-024-62459-4

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method used in this research

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Published on 31.5.2024 in Vol 26 (2024)

Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review

Authors of this article:

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  • Sharon Guardado 1 , MSc   ; 
  • Maria Karampela 1 , PhD   ; 
  • Minna Isomursu 1 , PhD   ; 
  • Casandra Grundstrom 2 , PhD  

1 Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland

2 Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway

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Sharon Guardado, MSc

Faculty of Information Technology and Electrical Engineering

University of Oulu

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Email: [email protected]

Background: Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients’ behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context.

Objective: This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them.

Methods: A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses.

Results: The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients’ devices. PGHD about patients’ behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies.

Conclusions: PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce.

International Registered Report Identifier (IRRID): RR2-10.2196/39389

Introduction

The term “mobile health” (mHealth) has been in use for nearly 2 decades to refer to the application of mobile technologies in delivering health services and collecting data pertinent to disease diagnosis, prevention, and management [ 1 , 2 ]. In the last decade, the scope of mHealth has expanded to include consumer-grade devices, such as smartphones, wearable, sensors, and other quasi-medical devices, while it increasingly targets specific health conditions, in addition to wellness [ 2 , 3 ]. Whereas medical-grade mobile devices require clinical evidence for certification, often requiring years to bring a device to the market [ 4 ], consumer-grade mobile devices evolving at a rapid pace, and open numerous possibilities through their capacity for ubiquitous data collection [ 5 ]. mHealth solutions have become integral to many people’s lives, serving as tools for tracking health and well-being. Research has found that mHealth solutions can benefit individuals in general by fostering moderate increases in physical activity [ 6 ] or by being a convenient tool for self-management of health issues [ 7 ]. For individuals with chronic diseases, mHealth solutions have been particularly effective in offering support for condition management, goal setting, and enhancing overall satisfaction [ 7 , 8 ]. In addition to supporting people’s efforts to manage their health, mHealth solutions also enable the collection of electronic patient-generated health data (PGHD), which can be used in the clinical context. PGHD refer to health-related data created, recorded, and gathered by and from patients outside of the clinical settings [ 9 , 10 ]. PGHD encompasses a broad range of data types from both passive and active sensing [ 1 , 11 ]. Passive data collection usually involves sensors that are connected to a mobile device that may be worn or embedded, limiting the patient’s participation to wearing, carrying, or activating the device [ 12 ]. Active data collection requires patients to manually enter information or interact with an external device such as a peak flowmeter, glucometer, or thermometer to generate information. These data are “patient-generated” since the patient has actively participated in collecting and recording [ 12 ]. It has been hypothesized that through both passively and actively collected PGHD, health care professionals (HCPs) could gain insights into patients’ activities, lifestyle, and physical condition to inform care decisions and personalize care approaches [ 13 ].

In countries with medium or high levels of digitalization, more than 56% of people appear willing to share their personal health data, even if the purpose of sharing them is not directly related to the improvement of their health [ 14 ]. Similarly, 46.3% of individuals who owned a wearable medical device indicated having shared data with a health provider in 2019 [ 15 ]. With mHealth solutions becoming increasingly accessible, it can be expected that more people may be interested in sharing their health data with HCPs if they believe that it could help them improve health care. However, a recent study found that although providers of mHealth solutions for chronic condition self-management encourage data sharing with HCPs, few solutions are designed to facilitate HCPs’ review of these data [ 4 ]. This issue, in combination with already known challenges such as interoperability, data privacy issues, data validity, and the added burden of reviewing [ 9 , 16 ], makes the use of PGHD in the clinic an unrealistic possibility for many HCPs.

HCPs might have different approaches and goals when deciding to ponder PGHD collected through nonmedical mobile devices. According to Nittas et al [ 17 ], when integrating PGHD into the care process, HCPs can take the supporter or the reviewer role. In the supporter role, they limit themselves to motivating patients to use mHealth, whereas in the reviewer role, HCPs assess PGHD to complement medical data. Taking the reviewer role implies an active stance, and though some might value PGHD’s contribution to care, this type of data may still be a new and unfamiliar source of information for some HCPs [ 18 ]. For PGHD for mobile devices to be feasible as a complementary tool in the clinical setting, their use should benefit both patients and HCPs. Though the adoption of mHealth solutions by patients supports their well-being and enables the availability of PGHD, such availability does not automatically equate to usefulness for HCPs. Despite the acceptance and adoption of mHealth solutions by HCPs being one of the most influential factors regarding the success of those solutions [ 19 , 20 ], there has not been significant research on the role HCPs are expected to take in the use of mHealth solutions [ 4 , 17 ] or on the concrete experiences and motivators of those willing to review PGHD.

The main objective of our review is to systematically analyze existing scientific literature to identify what types of PGHD and in what health conditions HCPs have been using PGHD from consumer-grade mobile devices, as well as further context information for their motivations to use these types of data as a complementary tool in the clinic.

To attain these objectives the proposed research questions for our review are as follows: (1) In what health conditions have PGHD from consumer-grade mobile been a suitable tool for HCP? (2) What types of PGHD have HCPs found useful in the care of chronic conditions? (3) What are the main motivations behind HCPs’ decision to review PGHD from consumer-grade devices?

Study Design

A systematic literature review (SLR) was selected as our main research method to comprehensively synthesize evidence and prior research on HCPs’ experiences reviewing PGHD from consumer-grade mobile devices to address our research questions. We wanted to follow a transparent and systematic method to inform further research on this topic. We adopted methodologies from the Guidelines for Performing Systematic Literature Reviews in Software Engineering [ 21 ] and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement) [ 22 , 23 ], both of which provide reliable methodologies to perform SLRs in the fields of computer science and medicine, respectively ( Multimedia Appendix 1 [ 24 ]). We deemed it pertinent to combine methodological traditions from both computer science and medicine, as our research topic combines technical and care viewpoints and is interdisciplinary by nature [ 24 ].

To perform this SLR, we adhered to a systematic review protocol that was prepared before starting the searches and screening process. The protocol has been published elsewhere [ 25 ] and provides an ample description of the methods used in the search strategy and the inclusion and exclusion criteria used. The review adhered closely to the original protocol, with no significant deviations.

Search Strategy

Eligible studies were identified through comprehensive literature searches we conducted in bibliographical databases on health and biomedicine and information technology domains. The searched databases included PubMed, ACM Digital Library (including the ACM Guide to Computing Literature), IEEE Xplore, and Scopus. The searches were carried out in May 2022.

To ensure the identification of relevant papers, we constructed the search strategy in an iterative way [ 25 ]. The search string used for each database is available in Multimedia Appendix 2 . Based on the specific objectives of our review, and after conducting a pilot search in PubMed, we determined that the literature search should be constructed around 3 specific key topics: “patient-generated health data,” “health personnel,” and “mobile technologies.” We used the corresponding Medical Subject Headings (MeSH) and their possible variants to construct the final search query. Once the query had been tested, it was validated by a research librarian from the University of Oulu. After completing the electronic searches, we performed a supplementary hand search of the citations found within other SLRs and scoping reviews that were retrieved during the literature searches.

Eligibility Criteria

The defined eligibility criteria ( Table 1 ) aimed to include original papers that reported on the use of PGHD created via consumer-grade mHealth solutions by HCPs. PGHD reported in the studies should have been collected outside of the clinical setting, through either the patients’ use of mobile health apps or the wearable devices such as smartwatches, smart rings, fitness trackers, and similar wearable trackers; studies reporting on PGHD collected by HCPs during appointments or inside the clinical settings were excluded. Studies were limited to those involving consumer-grade devices to focus on, excluding those solely focusing on PGHD from medical-grade devices. The included papers report on the experiences of HCPs who have experience using PGHD in their clinical practice, as part of a stand-alone mHealth solution, by personal initiative, or for any other reasons. We excluded papers that focus solely on the perceptions or perspectives of HCPs as potential users of PGHD. Eligible publications were restricted to those accepted in peer-reviewed journals and conference proceedings written in the English language.

a PGHD: patient-generated health data.

b HCP: health care professional.

Though current consumer-grade mobile and wearable technologies started to become more accessible in the first half of the last decade, their impact on the health care scene started to become evident only years later. In 2013, it was acknowledged that only a few studies had assessed the impact of mobile apps in the health context, and all those studies referred to apps that had been created only for research purposes and were not available to the public at that time [ 26 ]. Therefore, we limited our search to papers with publication dates starting in 2013. Our search criteria did not delimit aspects such as the medical profile or specialties of the HCPs participating in the studies, or the health conditions treated, as we aimed to ascertain whether PGHD usage by HCPs would be more prevalent in the treatment of certain medical conditions or certain medical fields.

Selection Process

After the electronic search, the resulting papers were imported into Covidence (Veritas Health Innovation) for screening. The screening process was divided into 2 stages carried out independently by 2 researchers (SG and MK) with computer science backgrounds and previous research experience with mHealth and PGHD. Initially, the screening was limited to titles and abstracts. Before starting this stage, the reviewers completed a joint exercise to validate the review methodology and ensure that the inclusion and exclusion criteria were correctly understood. The disagreements that arose during the initial stage were all discussed and resolved between the 2 reviewers before starting the second screening stage. The second screening round included the review of the full text of all the preliminarily included papers.

Data Extraction

The relevant information of the included papers was collected using a structured data extraction form constructed in Covidence ( Multimedia Appendix 3 ). The most relevant data extracted for each paper included the professions of the participants; health conditions treated; mobile technologies used; the type of PGHD collected; and the channels used for visualization. In addition, to understand what motivated HCPs to review PGHD quotes related to their motivations and conclusions related to the topic were extracted from each study.

The data extraction task was completed by the 2 original reviewers (SG and MK) and 2 additional reviewers (CG and MI), all of whom have previous research experience with the topic of this review. Each paper was randomly assigned to be examined by 2 of the reviewers. Each reviewer performed the data extraction independently. Upon completion, the data extracted by both reviewers were compared. Discrepancies were resolved through discussion between the reviewers and a final consensus was reached in all cases.

Data Analysis and Synthesis

Quantitative and qualitative studies were included in this review. Due to the significant heterogeneity observed in the studies’ design, types of health conditions, types of PGHD, and types of mHealth solutions, methods such as meta-analysis or meta-synthesis were not deemed the most appropriate approach for the data synthesis. The extracted data were summarized using a combination of descriptive and narrative syntheses [ 27 ]. The descriptive analysis was conducted to summarize data from the different studies. This involved classifying the studies based on the type of mobile technologies used, health conditions treated, and the types of PGHD reviewed. This approach arranged the studies into more homogenous subgroups, which aided in synthesizing different types of data. The data related to the motivations of HCPs were examined using a thematic analysis, from which different categories were derived. For the narrative synthesis, similarities, and differences between the findings of different studies were identified. The analysis and synthesis comprised three major steps: (1) organization of the included studies, (2) descriptive analysis of the findings within studies, and (3) a narrative synthesis aiming at exploring interconnections between the studies.

Quality Assessment

The quality of the included studies was assessed in parallel to the data extraction process. From the checklists, the quality of studies proposed by Kitchenham and Charters [ 21 ] were assessed. As the included studies were both qualitative and quantitative, we selected the questions that were most appropriate for our specific research questions that were present in both the qualitative and quantitative checklists.

Upon assessment, all reviewers agreed that the included studies had credible findings; proper data collection methods; clear and coherent reporting; and clear links between data, interpretation, and conclusions ( Figure 1 ).

method used in this research

The single topic that produced some uncertainty during the quality assessment was the lack of clarity on whether some of the selected studies had explored enough diversity of perspective and context. This can likely be attributed to the fact that almost all included studies were performed in developed countries, predominantly in the United States or Europe, which is a typical setting for digital health studies. Hence, the findings of these investigations will provide the most accurate depiction of the state of health care systems in developed countries.

Ethical Considerations

The Ethics Committee of Human Sciences of the University of Oulu guidelines state that as no human or animal subjects were involved in the study, no separate ethics statement is required. However, the general ethical guidelines from the Finnish National Board on Research Integrity [ 28 ] guided the ethics of the study.

Our search across electronic databases and supplementary hand searches identified 1696 papers. Covidence automatically removed 374 duplicates. We screened 1322 titles and abstracts, resulting in 86 papers for full-text screening. Following the completion of this second screening stage, 18 papers met all the inclusion criteria. However, upon closer examination, it was observed that 2 pairs of papers ([ 29 , 30 ] and [ 31 , 32 ]) had similar authors and identical samples and methodologies. Each pair was merged into a single study for analysis, resulting in the final inclusion of 16 studies for our SLR ( Figure 2 ).

During full-text screening, papers were primarily excluded for focusing exclusively on PGHD from medical-grade devices (31/68, 46%); evaluating the usability of specific mHealth solutions, rather than PGHD use (27/68, 39%); lacking data collection from HCPs (9/68, 13%); and discussing potential rather than actual use of PGHD (1/68, 1%).

method used in this research

Characteristics of the Included Studies

We included studies spanning 2015-2021. Notably, more than two-thirds of the papers (11/16, 69%) were published in 2019 or later, indicating a growing interest in the topic both before and during the COVID-19 pandemic. The predominant location was North America (11/16, 69%), specifically the United States and Canada; within Europe (3/16, 19%), Sweden and the United Kingdom were the primary locations; and 1 study was conducted in Asia and 1 in a multicountry setting. The authors used diverse methodologies for data collection, with interviews (8/16, 50%) and mixed methods (4/16, 25%) being the most common. More comprehensive insights into the specific study designs and data collection methods are available in Table 2 . A complete summary of the included studies can be found in Multimedia Appendix 4 [ 29 , 30 , 32 - 46 ].

a HCP: health care professional.

b PGHD: patient-generated health data.

Medical Profiles and Specialties

Although some of the studies examined data collected from various stakeholders such as patients, researchers, hospital managers, or solution providers, our focus centered on data collected from HCPs. Collectively, the studies in our review had 355 HCPs as participants. Among the represented professions, physicians accounted for the largest number of participants, present in 81% (13/16) of the studies. While approximately half of those studies referred to physicians using a general term, the other half provided clear information about the medical specialties of the physicians. Nurses were the second most represented profession, participating in 62% (10/16) of the studies. Physiotherapists were the third most represented, participating in 38% (6/16) of the studies. Other health professions present were psychologists (3/16, 19%) and surgeons, dietitians, health coaches, and assistant practitioners, each mentioned in 12% (2/16) of the studies ( Table 2 ).

All the studies reported the medical specialties where PGHD was being used. Those specialties included geriatrics, anesthesiology, orthopedic surgery, gastroenterology, dietetics and nutrition, behavioral and clinical psychology, psychiatry, obstetrics and gynecology, infertility, endocrinology, internal medicine, family medicine, rehabilitation, pediatric nephrology, otorhinolaryngology, and audiology.

Health Conditions Treated

The studies examined a wide range of health conditions, classified according to the WHO International Classification of Diseases , Eleventh Revision ( ICD-11 ), into categories such as endocrine, nutritional, or metabolic diseases; mental, behavioral, or neurodevelopmental disorders; diseases of the nervous, circulatory respiratory, and digestive systems, and diseases of the musculoskeletal system or connective tissue. In addition, some studies reported the use of PGHD for other types of medical tasks including perioperative care and care of older adults.

The most cited health conditions for which PGHD from mobile devices were reviewed by HCPs were diabetes and obesity, each mentioned in at least 3 studies. A quarter of the studies did not address a specific health condition. In those cases, the contextual information provided was limited to medical specialties or professions ( Table 2 ).

Types of mHealth Solutions

Among the 16 included studies, 5 mentioned specific mHealth solutions patients had been using to self-manage their health condition. The remaining studies mentioned commercial mHealth solutions in general. In half of the studies, HCPs reported using PGHD derived from a combination of diverse mHealth solutions, which included 1 or multiple mobile health apps and wearable devices. The remaining half of the studies addressed the experience of HCPs using PGHD exclusively generated through mobile health apps installed in patients’ smartphones (4/16, 25%) or captured from wearable devices (4/16, 25%).

Types of PGHD

Various classifications of PGHD have been proposed in terms of purpose (self-use, behavior change, clinical use, and research), management of a condition (eg, diabetes, hypertension), data type (physiological, behavioral, or environmental), mode of data capture (using sensors, external devices, implanted devices, patient portals, web-based surveys, and manual entry), and whether the process is active, passive, or mixed [ 12 ]. In this study, we focused on classifying PGHD based on data types.

Physiological data were reviewed in all studies. In 7 of 16 studies, at least 3 different types of physiological data were collected. Weight was the most frequently mentioned physiological data, reported in 44% (7/16) of the studies, followed by mood (6/16, 38%) and vital signs (5/16, 31%). Other less commonly reviewed types of data were pain, blood glucose level, and other symptoms ( Table 3 ).

Behavioral data constituted the most used category of PGHD. More than 80% (13/16) of the studies indicated that HCPs had reviewed some form of behavioral data, although always in combination with physiological data. Physical activity seems to be the most reviewed type of PGHD produced by consumer-grade devices, with 75% of the studies reporting its use, followed by food intake (9/16, 56%), sleep quality or quantity (8/16, 50%), and medication adherence (6/16, 38%).

Only 12% (2/16) of the studies reported the use of environmental data, which were primarily collected through passive sensing, using wearables, whereas physiological and behavioral types of data were reported to be collected through either passive or active sensing or by a combination of both. For instance, certain types of PGHD, such as sleep, physical activity, or sedentariness, were collected through active sensing in some studies and through passive sensing in others.

Access to PGHD

Diverse channels for PGHD access were presented. Notably, 19% (3/16) of the papers did not describe the precise channels HCPs used to access PGHD. Dashboards or solution portals were used in 56% (9/16) of the studies. The second most common channel was the patient’s mobile device (5/16, 31%). In a few studies, HCPs accessed PGHD through integration with the electronic health record (EHR; 2/16, 12%), by email (2/16, 12%), or from patients’ verbal summaries of data from their mobile devices (1/16, 6%).

Motivation for Reviewing PGHD

Although not all studies cited the reasons behind HCPs’ willingness to review PGHD from consumer-grade devices, motivation for reviewing them centered into 3 main categories: benefits for the patient, supporting their clinical roles, and strengthening the patient-HCP relationship ( Figure 3 ). Key motivations that showed how PGHD supported HCPs included topics such as accessing additional data types, identifying health patterns, and reducing data collection workload.

method used in this research

Principal Findings

Our review underlines a growing interest in understanding the experiences of HCPs who are using PGHD in the clinic. We aimed to identify how PGHD from consumer-grade mobile devices have been used to assist them in clinical practice. HCPs, who were primarily physicians and nurses, shared their experience on the topic. The health conditions for which HCPs most resorted to PGHD were diabetes and obesity. We found that physiological data, such as weight, mood, and vital signs, and behavioral data, such as physical activity, food intake, and sleep quality, have been frequently used. HCPs had access to PGHD through different channels, such as web portals provided by the mHealth solutions or through integration with the EHR.

Previous reviews have explored the role of PGHD in facilitating prevention and health promotion [ 17 ], their use in clinical practice [ 18 ], and their effect on patient-clinician relationships [ 47 ]. However, those studies have concentrated on PGHD from medical-grade devices, which tend to be more accurate and more accepted in the medical community. PGHD created through consumer-grade mHealth solutions, although praised for their potential to transform health care, have typically not been deemed reliable or accurate enough for the clinical context [ 3 , 48 , 49 ]. Despite concerns over PGHD accuracy and reliability, HCPs recognized that their clinical value outweighs the absence of data [ 40 ]. This value comes with a caveat, as recent studies indicate that PGHD must be curated by HCPs to ascribe actionable clinical value, but even then, they can be treated as supplementary to data collected through clinically recognized standards such as through laboratory tests [ 50 , 51 ]. PGHD from consumer-grade solutions have been used by HCPs in the treatment of a wide variety of health conditions, although it seems common only in the care of diabetes, cardiovascular diseases, and obesity ( Table 2 ).

The most frequently used types of data (physical activity, food intake, sleep quantity, and weight) are highly associated with lifestyle health risks, implying that access to lifestyle-related data can provide valuable insights into the control of lifestyle-related diseases. Furthermore, patients having these conditions are more willing to share PGHD, therefore, fostering HCPs’ familiarity with those types of data [ 15 ]. Our findings reveal that PGHD’s use in clinical practice remains relatively scarce [ 29 , 36 , 42 ], pointing out a gap between their potential and their current use. This finding is in line with a recent study suggesting that in comparison with the expectations of policies related to the European Health Data Space, the prompting and reviewing of PGHD from consumer-grade devices seem still relatively rare [ 50 ]. It is plausible that these types of PGHD have been used by HCPs in practice, but research on the practicalities of this phenomenon has only increased in the last 5 years.

HCPs indicated that PGHD are useful in the identification of patterns, to support certain diagnoses, and for certain types of monitoring. For example, lifestyle diseases [ 36 ], irritable bowel syndrome [ 42 ], or infertility [ 37 ] requires long-term management or presents a high level of uncertainty. In these cases, PGHD can provide longitudinal insights into patients’ health between clinic visits or even before they start treatment, saving time in identifying patterns. It is worth noting that, although HCPs in those studies acknowledged the value of PGHD, they also indicated engaging with PGHD infrequently and only with a few specific data types, in comparison with the substantial amount of data some patients want to share. For patients with chronic diseases, knowing that HCPs are reviewing their PGHD can be a comfortable way to know that they are being monitored and can provide data at the right time to facilitate decision-making and early intervention [ 41 , 52 ].

Multiple types of data were collected in all the studies, which signifies that as more data are collected, the need for analytical strategies that can support HCPs in reviewing and analyzing the potential relationships between different categories of data will be higher. Most existing mHealth solutions for self-monitoring lack standardized formats and mechanisms for patients to control and share PGHD [ 40 , 45 ]. Support for HCPs’ data access and use requires standardization and, in some cases, EHR integration [ 44 , 45 ].

Limitations

We limited our inclusion to papers written in English. However, this approach may have excluded relevant papers from developing regions where English is not the primary language for scientific dissemination but where the interest and potential for mHealth solutions and PGHD are growing. Similarly, a gap in the current body of research regarding these topics in developing regions is highlighted, since all studies came from countries with high economic and digitalization levels.

PGHD is a relatively recent definition, and some relevant papers published prior to its official designation as a MeSH term may have employed alternative terminology to describe the same concept of PGHD used in our study.

The shift toward digital health solutions the COVID-19 pandemic potentiated may have modified HCPs’ perceptions of PGHD use. However, no studies explicitly examining this relationship were identified in our prior searches or a later search. Therefore, future research could explore whether the shift toward digital health has catalyzed the adoption of consumer-grade technologies and PGHD in clinical settings.

Conclusions

Despite skepticism regarding the reliability and accuracy of PGHD and the multiple challenges that they convey, our study highlights a noticeable shift toward recognizing their practical value in health care, particularly in managing chronic conditions such as diabetes, obesity, and cardiovascular diseases. Yet, their impact in supporting the clinical practice is not clear from the literature. Many HCPs in the study, predominantly physicians and nurses, showed interest in using PGHD in the clinical workflows, albeit with a cautious approach that considers them as supplementary to traditional clinical data only. While they acknowledged the benefit of reviewing PGHD for the patient-HCP relationship, it was also noted that only certain types of PGHD are truly deemed useful and even then, they are not regularly used by HCPs. The findings call for continued research and innovation in mHealth, with a focus on enhancing the reliability, usability, and clinical relevance of PGHD, which in return can foster a culture of trust and collaboration between patients and HCPs.

Acknowledgments

We would like to acknowledge the More Stamina Project research group for supporting the development of this work. We acknowledge the use of ChatGPT version 4 and Grammarly to identify improvements in the organization of our text and to improve the writing style in the Introduction and Discussion sections.

Data Availability

This literature review synthesizes findings from peer-reviewed journal papers and conference papers. Given the nature of this review, it does not generate new primary data; instead, it compiled and analyzed existing publications on the use of PGHD from mobile technologies by HCPs. The reviewed papers are all available in public scientific databases. The data extraction form and the extracted data are available in the Multimedia Appendix section. These resources aim to ensure the reproducibility of our methods and facilitate future research in this area.

Conflicts of Interest

None declared.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

Search strategies for all searched databases.

Data extraction form.

Summary of the included studies.

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Abbreviations

Edited by A Mavragani; submitted 26.05.23; peer-reviewed by CM Chu, P Dunn, A Brigden, C Baxter; comments to author 08.02.24; revised version received 05.04.24; accepted 11.04.24; published 31.05.24.

©Sharon Guardado, Maria Karampela, Minna Isomursu, Casandra Grundstrom. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Novel MS hyphenated separation methods for complex mixtures of peptides and proteins

29 May 2024

One paper, authored by PhD candidate Rick van den Hurk, describes an advanced two dimensional LC method. The other, by PhD candidate Ziran Zhai, reports on a nanoflow native ion exchange method. Both represent significant advancements to the toolbox for protein analysis.

Parallel gradients in 2DLC-HRMS analysis

Investigating the proteins in biological samples can help understand and identify diseases and improve the effectiveness of medication. To study proteins in these samples, they are typically digested into peptides and subsequently analyzed. The paper of Van den Hurk and coworkers describes the use of parallel gradients in two-dimensional liquid chromatography (LC×LC) hyphenated with high-resolution mass spectrometry (HRMS). The parallel-gradient concept omits the need for column re-equilibration and enables the use of the entire separation space. To assess its applicability to real complex samples, it was used in the analysis of a human IMR90 lung fibroblast cell line digest. There, it was able to identify significantly more proteins than the current state-of-the-art methods while using the same analysis time and at a lower solvent consumption. In general, the use of parallel-gradients achieves higher surface coverages and sensitivity at lower effective peak capacities. It allows for lower flow rates and maintains the sensitivity for low-abundant analytes.

method used in this research

Paper details:  Rick S. van den Hurk, Bart Lagerwaard, Nathan J. Terlouw, Mingzhe Sun, Job J. Tieleman, Anniek X. Verstegen, Saer Samanipour, Bob W.J. Pirok, and Andrea F.G. Gargano : Comprehensive Two-Dimensional Liquid Chromatography–High-Resolution Mass Spectrometry for Complex Protein Digest Analysis Using Parallel Gradients. Anal. Chem. 2024, Publication date May 17, 2024. DOI: 10.1021/acs.analchem.4c02172

Read more:  Parallel Gradients 2DLC-HRMS of complex protein digest

Nanoflow native ion exchange HRMS

Proteoforms are protein products arising from homologous genes as a result of sequence variations, alternative splicing, and post-translational modifications. They play a crucial role in a wide range of critical biological functions. The standard approach for their characterization is ‘bottom-up proteomics’ where their presence is inferred from peptides. The alternative approaches of ‘top-down’ proteomics and intact protein mass spectrometry often involve denaturing, which unfolds proteins and leads to the loss of non-covalent protein complexes. The paper of Ziran Zhai and coworkers presents a novel approach where nanoflow (250 or 500 nL min -1 ) strong cation exchange chromatography (SCX) is directly coupled to nano-electrospray-ionization (nESI) under native MS (nMS) conditions. Proteins were separated on packed capillary SCX columns and eluted according to their pI values by a salt-mediated pH gradient method. The low flow promoted desolvation/ionization efficiency allows for sensitive detection of low-abundant proteins and complexes. The method was successfully applied in the analysis of an  E. coli  cell lysate, revealing hundreds of proteins with masses up to 150 kDa. It provides a universal strategy to overcome detection limitations in native top-down proteomics.

method used in this research

Paper details: Ziran Zhai, Despoina Mavridou, Matteo Damian, Francesco G. Mutti, Peter J. Schoenmakers, and Andrea F. G. Gargano: Characterization of Complex Proteoform Mixtures by Online Nanoflow Ion-Exchange Chromatography-Native Mass Spectrometry . Anal. Chem. 2024, publication date May 21, 2024. DOI: 10.1021/acs.analchem.4c01760

Read more:  Nanoflow IEC-HRMS to study complex proteoform mixtures  

The SEparation and Mass spectrometry (SEMpre) group forms part of the Chemometrics & Advanced Separations Team (CAST), joining Analytical Chemistry researchers Andrea Gargano, Bob Pirok and researchers from the University of Amsterdam and VU University Amsterdam. They are also part of the Centre for Analytical Sciences Amsterdam (CASA).

  • Chemometrics and Advanced Separations Team (CAST)
  • Centre for Analytical Sciences Amsterdam (CASA)

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Research Method

Home » Research Approach – Types Methods and Examples

Research Approach – Types Methods and Examples

Table of Contents

Research Approach

Research Approach

Definition:

Research approaches refer to the systematic and structured ways that researchers use to conduct research, and they differ in terms of their underlying logic and methods of inquiry.

Types of Research Approach

The Three main research approaches are deductive, inductive, and abductive.

Deductive Approach

The deductive approach starts with a theory or a hypothesis, and the researcher tests the hypothesis through the collection and analysis of data. The researcher develops a research design and data collection methods based on the theory or hypothesis. The goal of this approach is to confirm or reject the hypothesis.

Inductive Approach

The inductive approach starts with the collection and analysis of data. The researcher develops a theory or an explanation based on the patterns and themes that emerge from the data. The goal of this approach is to generate a new theory or to refine an existing one.

Abductive Approach

The abductive approach is a combination of deductive and inductive approaches. It starts with a problem or a phenomenon that is not fully understood, and the researcher develops a theory or an explanation that can account for the data. The researcher then tests the theory through the collection and analysis of more data. The goal of this approach is to generate a plausible explanation or theory that can be further refined or tested.

Research Approach Methods

Research approach methods are the specific techniques or tools that are used to conduct research within a particular research approach. Below are some examples of methods that are commonly used in each research approach:

Deductive approach methods:

  • Surveys and questionnaires: to collect data from a large sample of participants
  • Experiments: to manipulate variables and test hypotheses under controlled conditions
  • Statistical analysis: to test the significance of relationships between variables
  • Content analysis: to analyze and interpret text-based data

Inductive approach methods:

  • Interviews: to collect in-depth data and explore individual experiences and perspectives
  • Focus groups: to collect data from a group of participants who share common characteristics or experiences
  • Observations: to gather data on naturalistic settings and behaviors
  • Grounded theory: to develop theories or concepts from data through iterative cycles of analysis and interpretation

Abductive approach methods:

  • Case studies: to examine a phenomenon in its real-life context and generate new insights or explanations
  • Triangulation: to combine multiple data sources or methods to enhance the validity and reliability of findings
  • Exploratory research: to gather preliminary data and generate new research questions
  • Concept mapping: to visually represent relationships and patterns in data and develop new theoretical frameworks.

Applications of Research Approach

Here are some common applications of research approach:

  • Academic Research : Researchers in various academic fields, such as sociology, psychology, economics, and education, use research approaches to study a wide range of topics.
  • Business Research : Organizations use research approaches to gather information on customer preferences, market trends, and competitor behavior to make informed business decisions.
  • Medical Research : Researchers use research approaches to study various diseases and medical conditions, develop new treatments and drugs, and improve public health.
  • Social Research: Researchers use research approaches to study social issues, such as poverty, crime, discrimination, and inequality, and to develop policies and programs to address these issues.
  • Environmental Research: Researchers use research approaches to study environmental problems, such as climate change, pollution, and biodiversity loss, and to develop strategies to mitigate these problems.
  • Marketing Research : Companies use research approaches to study consumer behavior, preferences, and needs in order to develop effective marketing strategies.
  • Educational Research: Researchers use research approaches to study teaching and learning processes, develop new teaching methods and materials, and improve educational outcomes.
  • Legal Research : Lawyers and legal scholars use research approaches to study legal precedents, statutes, and regulations in order to make legal arguments and develop new laws and policies.

Examples of Research Approach

Examples Deductive approach:

  • A researcher starts with a theory or hypothesis and then develops a research design to test it. For example, a researcher might hypothesize that students who receive positive feedback from their teachers are more likely to perform well academically. The researcher would then design a study to test this hypothesis, such as surveying students to assess their feedback from teachers and comparing their academic performance.
  • Another example of a deductive approach is a clinical trial to test the effectiveness of a new medication. The researchers start with a theory that the medication will be effective and then design the study to test this theory by comparing the outcomes of patients who receive the medication with those who receive a placebo.

Examples Inductive approach:

  • A researcher begins with data and then develops a theory or explanation to account for it. For example, a researcher might collect data on the experiences of immigrants in a particular city and then use that data to develop a theory about the factors that contribute to their success or challenges.
  • Another example of an inductive approach is ethnographic research, where the researcher immerses themselves in a cultural context to observe and document the practices, beliefs, and values of the community. The researcher might then develop a theory or explanation for these practices based on the observed patterns and themes.

Examples Abductive approach:

  • A researcher starts with a puzzle or a phenomenon that is not easily explained by existing theories and uses a combination of deductive and inductive reasoning to generate a new explanation or theory. For example, a researcher might notice a pattern of behavior in a particular group of people that is not easily explained by existing theories and then use both deductive and inductive reasoning to develop a new theory to explain the behavior.
  • Another example of an abductive approach is diagnosis in medicine. A physician starts with a set of symptoms and uses deductive reasoning to generate a list of possible diagnoses. The physician then uses inductive reasoning to gather more information about the patient and the symptoms to narrow down the list of possible diagnoses and arrive at a final diagnosis.

Purpose of Research Approach

The purpose of a research approach is to provide a systematic and logical way of conducting research to achieve the research goals and objectives. It helps the researcher to plan, design, and conduct research effectively and efficiently, ensuring that the research is reliable, valid, and useful. Different research approaches have different purposes and are suited for different types of research questions and contexts.

Here are some specific purposes of different research approaches:

Deductive approach:

  • To test hypotheses or theories
  • To confirm or refute existing knowledge
  • To generalize findings to broader populations or contexts

Inductive approach:

  • To generate new theories or hypotheses
  • To identify patterns, themes, or relationships in data
  • To develop an understanding of social or natural phenomena

Abductive approach:

  • To develop new explanations or theories when existing ones are inadequate
  • To identify new patterns or phenomena that may be overlooked by existing theories
  • To propose new research questions or directions

About the author

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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  28. Research Approach

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