what is a data in research methodology

Data Module #1: What is Research Data?

Defining research data.

• Qualitative vs. Quantitative
• Types of Research Data
• Data and Statistics
• Let's Review...

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Data Modules Table of Contents

#1 - What is Research Data? #2 - Planning for Your Data Use #3 - Finding & Collecting Data #4 -  Keeping Your Data Organized #5 -  Intellectual Property & Ethics #6 -  Storage, Backup, & Security #7 - Documentation

Library Resources

Module created by Aaron Albertson, Beth Hillemann, & Ron Joslin.

Many people think of data-driven research as something that primarily happens in the sciences. It is often thought of as involving a spreadsheet filled with numbers. Both of these beliefs are incorrect. Research data are collected and used in scholarship across all academic disciplines and, while it can consist of numbers in a spreadsheet, it also takes many different formats, including videos, images, artifacts, and diaries. Whether a psychologist collecting survey data to better understand human behavior, an artist using data to generate images and sounds, or an anthropologist using audio files to document observations about different cultures, scholarly research across all academic fields is increasingly data-driven.

In our Data Literacy Modules, we will demonstrate the ways in which research data are gathered and used across various academic disciplines by discussing it in a very broad sense. We define research data as: any information collected, stored, and processed to produce and validate original research results. Data might be used to prove or disprove a theory, bolster claims made in research, or to further the knowledge around a specific topic or problem.

Other Definitions of Research Data

There are many different definitions of research data available. Here are just a few examples of other definitions. We share these examples to illustrate there is not universal consensus on a definition, although many similarities are apparent.

• U.S. Office of Management & Budget

“research data, unlike other types of information, is collected, observed, or created, for purposes of analysis to produce original research results”  

• University of Edinburgh

"...recorded factual material commonly accepted in the scientific community as necessary to validate research findings..."  

• National Endowment for the Humanities

"...materials generated or collected during the course of conducting research..."

Research Data Formats

Research data takes many different forms.  Data may be intangible as in measured numerical values found in a spreadsheet or an object as in physical research materials such samples of rocks, plants, or insects. Here are some examples of the formats that data can take:

• Next: Qualitative vs. Quantitative >>
• Last Updated: Feb 2, 2024 1:41 PM
• URL: https://libguides.macalester.edu/data1

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

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

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

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

Research Methodology 101

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

What is research methodology?

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

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

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

So, it’s the same as research design?

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

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

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

Let’s take a closer look.

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

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

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

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

What is sampling strategy?

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

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

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

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

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

What are data collection methods?

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

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

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

What are data analysis methods?

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

Popular data analysis methods in qualitative research include:

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

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

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

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

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

How do I choose a research methodology?

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

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

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

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

Example of a research methodology chapter

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

Psst... there’s more!

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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199 Comments

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Thanks for your comment.

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

All the best with your research.

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

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

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What is Research Methodology? Definition, Types, and Examples

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

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

What is research methodology ?

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

Why is research methodology important?

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

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

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

Types of research methodology.

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

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

What are the types of sampling designs in research methodology?

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

• Probability sampling

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

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

What are data collection methods?

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

Qualitative research 5

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

Quantitative research 6

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

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What are data analysis methods.

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

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

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

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

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

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

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

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

How to choose a research methodology?

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

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

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How to write a research methodology .

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

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

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Frequently Asked Questions

Q1. What are the key components of research methodology?

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

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

Q2. Why is ethical consideration important in research methodology?

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

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

Q3. What is the difference between methodology and method?

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

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

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

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• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.

It should include:

• The type of research you conducted
• How you collected and analysed your data
• Any tools or materials you used in the research
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyse?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.

Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analysing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorising and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviours, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalised beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalisable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives  and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

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 objectives.

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

In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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.

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.

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

The Ultimate Guide To Research Methodology

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

Table of Contents

What Is Research Methodology?

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

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

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

Facilitating Rigorous Inquiry

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

Ensuring Reproducibility And Reliability

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

Guiding Decision-Making Processes

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

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

Contributing To Academic Excellence

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

Enhancing Problem-Solving Abilities

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

Understanding Research Methodology

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

Basics Of Research

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

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

Types of Research

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

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

Components Of Research Methodology

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

Research Design

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

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

Data Collection Methods

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

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

Data Analysis Techniques

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

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

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Choosing a Research Method

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

Quantitative Research Methods

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

Experimental Research

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

Key Characteristics:

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

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

Survey Research

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

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

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

Descriptive Research

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

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

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

Qualitative Research Methods

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

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

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

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

Ethnography

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

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

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

Grounded Theory

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

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

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

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

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

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

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

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

Descriptive Design

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

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

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

Explanatory Design

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

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

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

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

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

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

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

Longitudinal Design

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

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

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

Experimental Vs Non-experimental Designs

Experimental design.

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

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

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

Non-Experimental Design

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

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

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

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

Designing Effective Surveys

Objective Design:

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

Structured Format:

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

Pilot Testing:

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

Sampling Strategy:

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

Conducting Interviews

Establishing Rapport:

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

Open-Ended Questions:

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

Active Listening:

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

Ethical Considerations:

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

Observation

1. participant observation.

Immersive Participation:

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

Field Notes:

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

Ethical Awareness:

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

2. Non-participant Observation

Objective Observation:

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

Data Reliability:

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

Contextual Understanding:

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

Archival Research

1. using existing data.

Identifying Relevant Archives:

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

Data Verification:

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

Ethical Use:

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

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

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

Temporal Bias:

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

Access Limitations:

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

Common Challenges in Research Methodology

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

Sampling Issues

Sampling bias:.

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

Sample Size Determination:

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

Data Quality And Validity

Measurement error:.

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

Construct Validity:

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

Time And Resource Constraints

Timeline pressures:.

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

Resource Availability:

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

Managing Bias in Research

Selection bias:.

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

Confirmation Bias:

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

Tips On How To Write A Research Methodology

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

Tip 1. Clear Research Objectives

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

Tip 2. Comprehensive Literature Review

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

Tip 3. Detailed Research Plan

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

Tip 4. Ethical Considerations

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

Tip 5. Stay Updated On Methodologies

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

Tip 6. Adaptability In Methods

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

Tip 7. Iterative Approach

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

Frequently Asked Questions

What is the research methodology.

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

What are the methodologies in research?

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

How to write research methodology?

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

How to write the methodology section of a research paper?

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

What is mixed research methodology?

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

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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

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Research Methodology: An Introduction

• First Online: 31 March 2018

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• Vida Davidavičienė 3  

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

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

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Data Analysis in Research: Types & Methods

Data analysis is a crucial step in the research process, transforming raw data into meaningful insights that drive informed decisions and advance knowledge. This article explores the various types and methods of data analysis in research, providing a comprehensive guide for researchers across disciplines.

Data Analysis in Research

Overview of Data analysis in research

Data analysis in research is the systematic use of statistical and analytical tools to describe, summarize, and draw conclusions from datasets. This process involves organizing, analyzing, modeling, and transforming data to identify trends, establish connections, and inform decision-making. The main goals include describing data through visualization and statistics, making inferences about a broader population, predicting future events using historical data, and providing data-driven recommendations. The stages of data analysis involve collecting relevant data, preprocessing to clean and format it, conducting exploratory data analysis to identify patterns, building and testing models, interpreting results, and effectively reporting findings.

• Main Goals : Describe data, make inferences, predict future events, and provide data-driven recommendations.
• Stages of Data Analysis : Data collection, preprocessing, exploratory data analysis, model building and testing, interpretation, and reporting.

Types of Data Analysis

1. descriptive analysis.

Descriptive analysis focuses on summarizing and describing the features of a dataset. It provides a snapshot of the data, highlighting central tendencies, dispersion, and overall patterns.

• Central Tendency Measures : Mean, median, and mode are used to identify the central point of the dataset.
• Dispersion Measures : Range, variance, and standard deviation help in understanding the spread of the data.
• Frequency Distribution : This shows how often each value in a dataset occurs.

2. Inferential Analysis

Inferential analysis allows researchers to make predictions or inferences about a population based on a sample of data. It is used to test hypotheses and determine the relationships between variables.

• Hypothesis Testing : Techniques like t-tests, chi-square tests, and ANOVA are used to test assumptions about a population.
• Regression Analysis : This method examines the relationship between dependent and independent variables.
• Confidence Intervals : These provide a range of values within which the true population parameter is expected to lie.

3. Exploratory Data Analysis (EDA)

EDA is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It helps in discovering patterns, spotting anomalies, and checking assumptions with the help of graphical representations.

• Visual Techniques : Histograms, box plots, scatter plots, and bar charts are commonly used in EDA.
• Summary Statistics : Basic statistical measures are used to describe the dataset.

4. Predictive Analysis

Predictive analysis uses statistical techniques and machine learning algorithms to predict future outcomes based on historical data.

• Machine Learning Models : Algorithms like linear regression, decision trees, and neural networks are employed to make predictions.
• Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to forecast future trends.

5. Causal Analysis

Causal analysis aims to identify cause-and-effect relationships between variables. It helps in understanding the impact of one variable on another.

• Experiments : Controlled experiments are designed to test the causality.
• Quasi-Experimental Designs : These are used when controlled experiments are not feasible.

6. Mechanistic Analysis

Mechanistic analysis seeks to understand the underlying mechanisms or processes that drive observed phenomena. It is common in fields like biology and engineering.

Methods of Data Analysis

1. quantitative methods.

Quantitative methods involve numerical data and statistical analysis to uncover patterns, relationships, and trends.

• Statistical Analysis : Includes various statistical tests and measures.
• Mathematical Modeling : Uses mathematical equations to represent relationships among variables.
• Simulation : Computer-based models simulate real-world processes to predict outcomes.

2. Qualitative Methods

Qualitative methods focus on non-numerical data, such as text, images, and audio, to understand concepts, opinions, or experiences.

• Content Analysis : Systematic coding and categorizing of textual information.
• Thematic Analysis : Identifying themes and patterns within qualitative data.
• Narrative Analysis : Examining the stories or accounts shared by participants.

3. Mixed Methods

Mixed methods combine both quantitative and qualitative approaches to provide a more comprehensive analysis.

• Sequential Explanatory Design : Quantitative data is collected and analyzed first, followed by qualitative data to explain the quantitative results.
• Concurrent Triangulation Design : Both qualitative and quantitative data are collected simultaneously but analyzed separately to compare results.

4. Data Mining

Data mining involves exploring large datasets to discover patterns and relationships.

• Clustering : Grouping data points with similar characteristics.
• Association Rule Learning : Identifying interesting relations between variables in large databases.
• Classification : Assigning items to predefined categories based on their attributes.

5. Big Data Analytics

Big data analytics involves analyzing vast amounts of data to uncover hidden patterns, correlations, and other insights.

• Hadoop and Spark : Frameworks for processing and analyzing large datasets.
• NoSQL Databases : Designed to handle unstructured data.
• Machine Learning Algorithms : Used to analyze and predict complex patterns in big data.

Applications and Case Studies

Numerous fields and industries use data analysis methods, which provide insightful information and facilitate data-driven decision-making. The following case studies demonstrate the effectiveness of data analysis in research:

Medical Care:

• Predicting Patient Readmissions: By using data analysis to create predictive models, healthcare facilities may better identify patients who are at high risk of readmission and implement focused interventions to enhance patient care.
• Disease Outbreak Analysis: Researchers can monitor and forecast disease outbreaks by examining both historical and current data. This information aids public health authorities in putting preventative and control measures in place.
• Fraud Detection: To safeguard clients and lessen financial losses, financial institutions use data analysis tools to identify fraudulent transactions and activities.
• investing Strategies: By using data analysis, quantitative investing models that detect trends in stock prices may be created, assisting investors in optimizing their portfolios and making well-informed choices.
• Customer Segmentation: Businesses may divide up their client base into discrete groups using data analysis, which makes it possible to launch focused marketing efforts and provide individualized services.
• Social Media Analytics: By tracking brand sentiment, identifying influencers, and understanding consumer preferences, marketers may develop more successful marketing strategies by analyzing social media data.
• Predicting Student Performance: By using data analysis tools, educators may identify at-risk children and forecast their performance. This allows them to give individualized learning plans and timely interventions.
• Education Policy Analysis: Data may be used by researchers to assess the efficacy of policies, initiatives, and programs in education, offering insights for evidence-based decision-making.

Social Science Fields:

• Opinion mining in politics: By examining public opinion data from news stories and social media platforms, academics and policymakers may get insight into prevailing political opinions and better understand how the public feels about certain topics or candidates.
• Crime Analysis: Researchers may spot trends, anticipate high-risk locations, and help law enforcement use resources wisely in order to deter and lessen crime by studying crime data.

Data analysis is a crucial step in the research process because it enables companies and researchers to glean insightful information from data. By using diverse analytical methodologies and approaches, scholars may reveal latent patterns, arrive at well-informed conclusions, and tackle intricate research inquiries. Numerous statistical, machine learning, and visualization approaches are among the many data analysis tools available, offering a comprehensive toolbox for addressing a broad variety of research problems.

Data Analysis in Research FAQs:

What are the main phases in the process of analyzing data.

In general, the steps involved in data analysis include gathering data, preparing it, doing exploratory data analysis, constructing and testing models, interpreting the results, and reporting the results. Every stage is essential to guaranteeing the analysis’s efficacy and correctness.

What are the differences between the examination of qualitative and quantitative data?

In order to comprehend and analyze non-numerical data, such text, pictures, or observations, qualitative data analysis often employs content analysis, grounded theory, or ethnography. Comparatively, quantitative data analysis works with numerical data and makes use of statistical methods to identify, deduce, and forecast trends in the data.

What are a few popular statistical methods for analyzing data?

In data analysis, predictive modeling, inferential statistics, and descriptive statistics are often used. While inferential statistics establish assumptions and draw inferences about a wider population, descriptive statistics highlight the fundamental characteristics of the data. To predict unknown values or future events, predictive modeling is used.

In what ways might data analysis methods be used in the healthcare industry?

In the healthcare industry, data analysis may be used to optimize treatment regimens, monitor disease outbreaks, forecast patient readmissions, and enhance patient care. It is also essential for medication development, clinical research, and the creation of healthcare policies.

What difficulties may one encounter while analyzing data?

Answer: Typical problems with data quality include missing values, outliers, and biased samples, all of which may affect how accurate the analysis is. Furthermore, it might be computationally demanding to analyze big and complicated datasets, necessitating certain tools and knowledge. It’s also critical to handle ethical issues, such as data security and privacy.

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• Published: 07 September 2020

A tutorial on methodological studies: the what, when, how and why

• Lawrence Mbuagbaw   ORCID: orcid.org/0000-0001-5855-5461 1 , 2 , 3 ,
• Daeria O. Lawson 1 ,
• Livia Puljak 4 ,
• David B. Allison 5 &
• Lehana Thabane 1 , 2 , 6 , 7 , 8  

BMC Medical Research Methodology volume  20 , Article number:  226 ( 2020 ) Cite this article

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Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.

We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?

Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.

Peer Review reports

The field of meta-research (or research-on-research) has proliferated in recent years in response to issues with research quality and conduct [ 1 , 2 , 3 ]. As the name suggests, this field targets issues with research design, conduct, analysis and reporting. Various types of research reports are often examined as the unit of analysis in these studies (e.g. abstracts, full manuscripts, trial registry entries). Like many other novel fields of research, meta-research has seen a proliferation of use before the development of reporting guidance. For example, this was the case with randomized trials for which risk of bias tools and reporting guidelines were only developed much later – after many trials had been published and noted to have limitations [ 4 , 5 ]; and for systematic reviews as well [ 6 , 7 , 8 ]. However, in the absence of formal guidance, studies that report on research differ substantially in how they are named, conducted and reported [ 9 , 10 ]. This creates challenges in identifying, summarizing and comparing them. In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts).

In the past 10 years, there has been an increase in the use of terms related to methodological studies (based on records retrieved with a keyword search [in the title and abstract] for “methodological review” and “meta-epidemiological study” in PubMed up to December 2019), suggesting that these studies may be appearing more frequently in the literature. See Fig.  1 .

Trends in the number studies that mention “methodological review” or “meta-

epidemiological study” in PubMed.

The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies. However, the research questions posed in methodological studies do not always require the approaches listed above, and guidance is needed on when and how to apply these methods to a methodological study. Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research.

The objectives of this paper are to provide some insights on how to conduct methodological studies so that there is greater consistency between the research questions posed, and the design, analysis and reporting of findings. We provide multiple examples to illustrate concepts and a proposed framework for categorizing methodological studies in quantitative research.

What is a methodological study?

Any study that describes or analyzes methods (design, conduct, analysis or reporting) in published (or unpublished) literature is a methodological study. Consequently, the scope of methodological studies is quite extensive and includes, but is not limited to, topics as diverse as: research question formulation [ 11 ]; adherence to reporting guidelines [ 12 , 13 , 14 ] and consistency in reporting [ 15 ]; approaches to study analysis [ 16 ]; investigating the credibility of analyses [ 17 ]; and studies that synthesize these methodological studies [ 18 ]. While the nomenclature of methodological studies is not uniform, the intents and purposes of these studies remain fairly consistent – to describe or analyze methods in primary or secondary studies. As such, methodological studies may also be classified as a subtype of observational studies.

Parallel to this are experimental studies that compare different methods. Even though they play an important role in informing optimal research methods, experimental methodological studies are beyond the scope of this paper. Examples of such studies include the randomized trials by Buscemi et al., comparing single data extraction to double data extraction [ 19 ], and Carrasco-Labra et al., comparing approaches to presenting findings in Grading of Recommendations, Assessment, Development and Evaluations (GRADE) summary of findings tables [ 20 ]. In these studies, the unit of analysis is the person or groups of individuals applying the methods. We also direct readers to the Studies Within a Trial (SWAT) and Studies Within a Review (SWAR) programme operated through the Hub for Trials Methodology Research, for further reading as a potential useful resource for these types of experimental studies [ 21 ]. Lastly, this paper is not meant to inform the conduct of research using computational simulation and mathematical modeling for which some guidance already exists [ 22 ], or studies on the development of methods using consensus-based approaches.

When should we conduct a methodological study?

Methodological studies occupy a unique niche in health research that allows them to inform methodological advances. Methodological studies should also be conducted as pre-cursors to reporting guideline development, as they provide an opportunity to understand current practices, and help to identify the need for guidance and gaps in methodological or reporting quality. For example, the development of the popular Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guidelines were preceded by methodological studies identifying poor reporting practices [ 23 , 24 ]. In these instances, after the reporting guidelines are published, methodological studies can also be used to monitor uptake of the guidelines.

These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste. For example, Samaan et al. conducted a scoping review of adherence to different reporting guidelines in health care literature [ 18 ]. Methodological studies can also be used to determine the factors associated with reporting practices. For example, Abbade et al. investigated journal characteristics associated with the use of the Participants, Intervention, Comparison, Outcome, Timeframe (PICOT) format in framing research questions in trials of venous ulcer disease [ 11 ].

How often are methodological studies conducted?

There is no clear answer to this question. Based on a search of PubMed, the use of related terms (“methodological review” and “meta-epidemiological study”) – and therefore, the number of methodological studies – is on the rise. However, many other terms are used to describe methodological studies. There are also many studies that explore design, conduct, analysis or reporting of research reports, but that do not use any specific terms to describe or label their study design in terms of “methodology”. This diversity in nomenclature makes a census of methodological studies elusive. Appropriate terminology and key words for methodological studies are needed to facilitate improved accessibility for end-users.

Why do we conduct methodological studies?

Methodological studies provide information on the design, conduct, analysis or reporting of primary and secondary research and can be used to appraise quality, quantity, completeness, accuracy and consistency of health research. These issues can be explored in specific fields, journals, databases, geographical regions and time periods. For example, Areia et al. explored the quality of reporting of endoscopic diagnostic studies in gastroenterology [ 25 ]; Knol et al. investigated the reporting of p -values in baseline tables in randomized trial published in high impact journals [ 26 ]; Chen et al. describe adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement in Chinese Journals [ 27 ]; and Hopewell et al. describe the effect of editors’ implementation of CONSORT guidelines on reporting of abstracts over time [ 28 ]. Methodological studies provide useful information to researchers, clinicians, editors, publishers and users of health literature. As a result, these studies have been at the cornerstone of important methodological developments in the past two decades and have informed the development of many health research guidelines including the highly cited CONSORT statement [ 5 ].

Where can we find methodological studies?

Methodological studies can be found in most common biomedical bibliographic databases (e.g. Embase, MEDLINE, PubMed, Web of Science). However, the biggest caveat is that methodological studies are hard to identify in the literature due to the wide variety of names used and the lack of comprehensive databases dedicated to them. A handful can be found in the Cochrane Library as “Cochrane Methodology Reviews”, but these studies only cover methodological issues related to systematic reviews. Previous attempts to catalogue all empirical studies of methods used in reviews were abandoned 10 years ago [ 29 ]. In other databases, a variety of search terms may be applied with different levels of sensitivity and specificity.

Some frequently asked questions about methodological studies

In this section, we have outlined responses to questions that might help inform the conduct of methodological studies.

Q: How should I select research reports for my methodological study?

A: Selection of research reports for a methodological study depends on the research question and eligibility criteria. Once a clear research question is set and the nature of literature one desires to review is known, one can then begin the selection process. Selection may begin with a broad search, especially if the eligibility criteria are not apparent. For example, a methodological study of Cochrane Reviews of HIV would not require a complex search as all eligible studies can easily be retrieved from the Cochrane Library after checking a few boxes [ 30 ]. On the other hand, a methodological study of subgroup analyses in trials of gastrointestinal oncology would require a search to find such trials, and further screening to identify trials that conducted a subgroup analysis [ 31 ].

The strategies used for identifying participants in observational studies can apply here. One may use a systematic search to identify all eligible studies. If the number of eligible studies is unmanageable, a random sample of articles can be expected to provide comparable results if it is sufficiently large [ 32 ]. For example, Wilson et al. used a random sample of trials from the Cochrane Stroke Group’s Trial Register to investigate completeness of reporting [ 33 ]. It is possible that a simple random sample would lead to underrepresentation of units (i.e. research reports) that are smaller in number. This is relevant if the investigators wish to compare multiple groups but have too few units in one group. In this case a stratified sample would help to create equal groups. For example, in a methodological study comparing Cochrane and non-Cochrane reviews, Kahale et al. drew random samples from both groups [ 34 ]. Alternatively, systematic or purposeful sampling strategies can be used and we encourage researchers to justify their selected approaches based on the study objective.

Q: How many databases should I search?

A: The number of databases one should search would depend on the approach to sampling, which can include targeting the entire “population” of interest or a sample of that population. If you are interested in including the entire target population for your research question, or drawing a random or systematic sample from it, then a comprehensive and exhaustive search for relevant articles is required. In this case, we recommend using systematic approaches for searching electronic databases (i.e. at least 2 databases with a replicable and time stamped search strategy). The results of your search will constitute a sampling frame from which eligible studies can be drawn.

Alternatively, if your approach to sampling is purposeful, then we recommend targeting the database(s) or data sources (e.g. journals, registries) that include the information you need. For example, if you are conducting a methodological study of high impact journals in plastic surgery and they are all indexed in PubMed, you likely do not need to search any other databases. You may also have a comprehensive list of all journals of interest and can approach your search using the journal names in your database search (or by accessing the journal archives directly from the journal’s website). Even though one could also search journals’ web pages directly, using a database such as PubMed has multiple advantages, such as the use of filters, so the search can be narrowed down to a certain period, or study types of interest. Furthermore, individual journals’ web sites may have different search functionalities, which do not necessarily yield a consistent output.

Q: Should I publish a protocol for my methodological study?

A: A protocol is a description of intended research methods. Currently, only protocols for clinical trials require registration [ 35 ]. Protocols for systematic reviews are encouraged but no formal recommendation exists. The scientific community welcomes the publication of protocols because they help protect against selective outcome reporting, the use of post hoc methodologies to embellish results, and to help avoid duplication of efforts [ 36 ]. While the latter two risks exist in methodological research, the negative consequences may be substantially less than for clinical outcomes. In a sample of 31 methodological studies, 7 (22.6%) referenced a published protocol [ 9 ]. In the Cochrane Library, there are 15 protocols for methodological reviews (21 July 2020). This suggests that publishing protocols for methodological studies is not uncommon.

Authors can consider publishing their study protocol in a scholarly journal as a manuscript. Advantages of such publication include obtaining peer-review feedback about the planned study, and easy retrieval by searching databases such as PubMed. The disadvantages in trying to publish protocols includes delays associated with manuscript handling and peer review, as well as costs, as few journals publish study protocols, and those journals mostly charge article-processing fees [ 37 ]. Authors who would like to make their protocol publicly available without publishing it in scholarly journals, could deposit their study protocols in publicly available repositories, such as the Open Science Framework ( https://osf.io/ ).

Q: How to appraise the quality of a methodological study?

A: To date, there is no published tool for appraising the risk of bias in a methodological study, but in principle, a methodological study could be considered as a type of observational study. Therefore, during conduct or appraisal, care should be taken to avoid the biases common in observational studies [ 38 ]. These biases include selection bias, comparability of groups, and ascertainment of exposure or outcome. In other words, to generate a representative sample, a comprehensive reproducible search may be necessary to build a sampling frame. Additionally, random sampling may be necessary to ensure that all the included research reports have the same probability of being selected, and the screening and selection processes should be transparent and reproducible. To ensure that the groups compared are similar in all characteristics, matching, random sampling or stratified sampling can be used. Statistical adjustments for between-group differences can also be applied at the analysis stage. Finally, duplicate data extraction can reduce errors in assessment of exposures or outcomes.

Q: Should I justify a sample size?

A: In all instances where one is not using the target population (i.e. the group to which inferences from the research report are directed) [ 39 ], a sample size justification is good practice. The sample size justification may take the form of a description of what is expected to be achieved with the number of articles selected, or a formal sample size estimation that outlines the number of articles required to answer the research question with a certain precision and power. Sample size justifications in methodological studies are reasonable in the following instances:

Comparing two groups

Determining a proportion, mean or another quantifier

Determining factors associated with an outcome using regression-based analyses

For example, El Dib et al. computed a sample size requirement for a methodological study of diagnostic strategies in randomized trials, based on a confidence interval approach [ 40 ].

Q: What should I call my study?

A: Other terms which have been used to describe/label methodological studies include “ methodological review ”, “methodological survey” , “meta-epidemiological study” , “systematic review” , “systematic survey”, “meta-research”, “research-on-research” and many others. We recommend that the study nomenclature be clear, unambiguous, informative and allow for appropriate indexing. Methodological study nomenclature that should be avoided includes “ systematic review” – as this will likely be confused with a systematic review of a clinical question. “ Systematic survey” may also lead to confusion about whether the survey was systematic (i.e. using a preplanned methodology) or a survey using “ systematic” sampling (i.e. a sampling approach using specific intervals to determine who is selected) [ 32 ]. Any of the above meanings of the words “ systematic” may be true for methodological studies and could be potentially misleading. “ Meta-epidemiological study” is ideal for indexing, but not very informative as it describes an entire field. The term “ review ” may point towards an appraisal or “review” of the design, conduct, analysis or reporting (or methodological components) of the targeted research reports, yet it has also been used to describe narrative reviews [ 41 , 42 ]. The term “ survey ” is also in line with the approaches used in many methodological studies [ 9 ], and would be indicative of the sampling procedures of this study design. However, in the absence of guidelines on nomenclature, the term “ methodological study ” is broad enough to capture most of the scenarios of such studies.

Q: Should I account for clustering in my methodological study?

A: Data from methodological studies are often clustered. For example, articles coming from a specific source may have different reporting standards (e.g. the Cochrane Library). Articles within the same journal may be similar due to editorial practices and policies, reporting requirements and endorsement of guidelines. There is emerging evidence that these are real concerns that should be accounted for in analyses [ 43 ]. Some cluster variables are described in the section: “ What variables are relevant to methodological studies?”

A variety of modelling approaches can be used to account for correlated data, including the use of marginal, fixed or mixed effects regression models with appropriate computation of standard errors [ 44 ]. For example, Kosa et al. used generalized estimation equations to account for correlation of articles within journals [ 15 ]. Not accounting for clustering could lead to incorrect p -values, unduly narrow confidence intervals, and biased estimates [ 45 ].

Q: Should I extract data in duplicate?

A: Yes. Duplicate data extraction takes more time but results in less errors [ 19 ]. Data extraction errors in turn affect the effect estimate [ 46 ], and therefore should be mitigated. Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [ 47 , 48 ]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [ 46 , 49 ].

Q: Should I assess the risk of bias of research reports included in my methodological study?

A : Risk of bias is most useful in determining the certainty that can be placed in the effect measure from a study. In methodological studies, risk of bias may not serve the purpose of determining the trustworthiness of results, as effect measures are often not the primary goal of methodological studies. Determining risk of bias in methodological studies is likely a practice borrowed from systematic review methodology, but whose intrinsic value is not obvious in methodological studies. When it is part of the research question, investigators often focus on one aspect of risk of bias. For example, Speich investigated how blinding was reported in surgical trials [ 50 ], and Abraha et al., investigated the application of intention-to-treat analyses in systematic reviews and trials [ 51 ].

Q: What variables are relevant to methodological studies?

A: There is empirical evidence that certain variables may inform the findings in a methodological study. We outline some of these and provide a brief overview below:

Country: Countries and regions differ in their research cultures, and the resources available to conduct research. Therefore, it is reasonable to believe that there may be differences in methodological features across countries. Methodological studies have reported loco-regional differences in reporting quality [ 52 , 53 ]. This may also be related to challenges non-English speakers face in publishing papers in English.

Authors’ expertise: The inclusion of authors with expertise in research methodology, biostatistics, and scientific writing is likely to influence the end-product. Oltean et al. found that among randomized trials in orthopaedic surgery, the use of analyses that accounted for clustering was more likely when specialists (e.g. statistician, epidemiologist or clinical trials methodologist) were included on the study team [ 54 ]. Fleming et al. found that including methodologists in the review team was associated with appropriate use of reporting guidelines [ 55 ].

Source of funding and conflicts of interest: Some studies have found that funded studies report better [ 56 , 57 ], while others do not [ 53 , 58 ]. The presence of funding would indicate the availability of resources deployed to ensure optimal design, conduct, analysis and reporting. However, the source of funding may introduce conflicts of interest and warrant assessment. For example, Kaiser et al. investigated the effect of industry funding on obesity or nutrition randomized trials and found that reporting quality was similar [ 59 ]. Thomas et al. looked at reporting quality of long-term weight loss trials and found that industry funded studies were better [ 60 ]. Kan et al. examined the association between industry funding and “positive trials” (trials reporting a significant intervention effect) and found that industry funding was highly predictive of a positive trial [ 61 ]. This finding is similar to that of a recent Cochrane Methodology Review by Hansen et al. [ 62 ]

Journal characteristics: Certain journals’ characteristics may influence the study design, analysis or reporting. Characteristics such as journal endorsement of guidelines [ 63 , 64 ], and Journal Impact Factor (JIF) have been shown to be associated with reporting [ 63 , 65 , 66 , 67 ].

Study size (sample size/number of sites): Some studies have shown that reporting is better in larger studies [ 53 , 56 , 58 ].

Year of publication: It is reasonable to assume that design, conduct, analysis and reporting of research will change over time. Many studies have demonstrated improvements in reporting over time or after the publication of reporting guidelines [ 68 , 69 ].

Type of intervention: In a methodological study of reporting quality of weight loss intervention studies, Thabane et al. found that trials of pharmacologic interventions were reported better than trials of non-pharmacologic interventions [ 70 ].

Interactions between variables: Complex interactions between the previously listed variables are possible. High income countries with more resources may be more likely to conduct larger studies and incorporate a variety of experts. Authors in certain countries may prefer certain journals, and journal endorsement of guidelines and editorial policies may change over time.

Q: Should I focus only on high impact journals?

A: Investigators may choose to investigate only high impact journals because they are more likely to influence practice and policy, or because they assume that methodological standards would be higher. However, the JIF may severely limit the scope of articles included and may skew the sample towards articles with positive findings. The generalizability and applicability of findings from a handful of journals must be examined carefully, especially since the JIF varies over time. Even among journals that are all “high impact”, variations exist in methodological standards.

Q: Can I conduct a methodological study of qualitative research?

A: Yes. Even though a lot of methodological research has been conducted in the quantitative research field, methodological studies of qualitative studies are feasible. Certain databases that catalogue qualitative research including the Cumulative Index to Nursing & Allied Health Literature (CINAHL) have defined subject headings that are specific to methodological research (e.g. “research methodology”). Alternatively, one could also conduct a qualitative methodological review; that is, use qualitative approaches to synthesize methodological issues in qualitative studies.

Q: What reporting guidelines should I use for my methodological study?

A: There is no guideline that covers the entire scope of methodological studies. One adaptation of the PRISMA guidelines has been published, which works well for studies that aim to use the entire target population of research reports [ 71 ]. However, it is not widely used (40 citations in 2 years as of 09 December 2019), and methodological studies that are designed as cross-sectional or before-after studies require a more fit-for purpose guideline. A more encompassing reporting guideline for a broad range of methodological studies is currently under development [ 72 ]. However, in the absence of formal guidance, the requirements for scientific reporting should be respected, and authors of methodological studies should focus on transparency and reproducibility.

Q: What are the potential threats to validity and how can I avoid them?

A: Methodological studies may be compromised by a lack of internal or external validity. The main threats to internal validity in methodological studies are selection and confounding bias. Investigators must ensure that the methods used to select articles does not make them differ systematically from the set of articles to which they would like to make inferences. For example, attempting to make extrapolations to all journals after analyzing high-impact journals would be misleading.

Many factors (confounders) may distort the association between the exposure and outcome if the included research reports differ with respect to these factors [ 73 ]. For example, when examining the association between source of funding and completeness of reporting, it may be necessary to account for journals that endorse the guidelines. Confounding bias can be addressed by restriction, matching and statistical adjustment [ 73 ]. Restriction appears to be the method of choice for many investigators who choose to include only high impact journals or articles in a specific field. For example, Knol et al. examined the reporting of p -values in baseline tables of high impact journals [ 26 ]. Matching is also sometimes used. In the methodological study of non-randomized interventional studies of elective ventral hernia repair, Parker et al. matched prospective studies with retrospective studies and compared reporting standards [ 74 ]. Some other methodological studies use statistical adjustments. For example, Zhang et al. used regression techniques to determine the factors associated with missing participant data in trials [ 16 ].

With regard to external validity, researchers interested in conducting methodological studies must consider how generalizable or applicable their findings are. This should tie in closely with the research question and should be explicit. For example. Findings from methodological studies on trials published in high impact cardiology journals cannot be assumed to be applicable to trials in other fields. However, investigators must ensure that their sample truly represents the target sample either by a) conducting a comprehensive and exhaustive search, or b) using an appropriate and justified, randomly selected sample of research reports.

Even applicability to high impact journals may vary based on the investigators’ definition, and over time. For example, for high impact journals in the field of general medicine, Bouwmeester et al. included the Annals of Internal Medicine (AIM), BMJ, the Journal of the American Medical Association (JAMA), Lancet, the New England Journal of Medicine (NEJM), and PLoS Medicine ( n  = 6) [ 75 ]. In contrast, the high impact journals selected in the methodological study by Schiller et al. were BMJ, JAMA, Lancet, and NEJM ( n  = 4) [ 76 ]. Another methodological study by Kosa et al. included AIM, BMJ, JAMA, Lancet and NEJM ( n  = 5). In the methodological study by Thabut et al., journals with a JIF greater than 5 were considered to be high impact. Riado Minguez et al. used first quartile journals in the Journal Citation Reports (JCR) for a specific year to determine “high impact” [ 77 ]. Ultimately, the definition of high impact will be based on the number of journals the investigators are willing to include, the year of impact and the JIF cut-off [ 78 ]. We acknowledge that the term “generalizability” may apply differently for methodological studies, especially when in many instances it is possible to include the entire target population in the sample studied.

Finally, methodological studies are not exempt from information bias which may stem from discrepancies in the included research reports [ 79 ], errors in data extraction, or inappropriate interpretation of the information extracted. Likewise, publication bias may also be a concern in methodological studies, but such concepts have not yet been explored.

A proposed framework

In order to inform discussions about methodological studies, the development of guidance for what should be reported, we have outlined some key features of methodological studies that can be used to classify them. For each of the categories outlined below, we provide an example. In our experience, the choice of approach to completing a methodological study can be informed by asking the following four questions:

What is the aim?

Methodological studies that investigate bias

A methodological study may be focused on exploring sources of bias in primary or secondary studies (meta-bias), or how bias is analyzed. We have taken care to distinguish bias (i.e. systematic deviations from the truth irrespective of the source) from reporting quality or completeness (i.e. not adhering to a specific reporting guideline or norm). An example of where this distinction would be important is in the case of a randomized trial with no blinding. This study (depending on the nature of the intervention) would be at risk of performance bias. However, if the authors report that their study was not blinded, they would have reported adequately. In fact, some methodological studies attempt to capture both “quality of conduct” and “quality of reporting”, such as Richie et al., who reported on the risk of bias in randomized trials of pharmacy practice interventions [ 80 ]. Babic et al. investigated how risk of bias was used to inform sensitivity analyses in Cochrane reviews [ 81 ]. Further, biases related to choice of outcomes can also be explored. For example, Tan et al investigated differences in treatment effect size based on the outcome reported [ 82 ].

Methodological studies that investigate quality (or completeness) of reporting

Methodological studies may report quality of reporting against a reporting checklist (i.e. adherence to guidelines) or against expected norms. For example, Croituro et al. report on the quality of reporting in systematic reviews published in dermatology journals based on their adherence to the PRISMA statement [ 83 ], and Khan et al. described the quality of reporting of harms in randomized controlled trials published in high impact cardiovascular journals based on the CONSORT extension for harms [ 84 ]. Other methodological studies investigate reporting of certain features of interest that may not be part of formally published checklists or guidelines. For example, Mbuagbaw et al. described how often the implications for research are elaborated using the Evidence, Participants, Intervention, Comparison, Outcome, Timeframe (EPICOT) format [ 30 ].

Methodological studies that investigate the consistency of reporting

Sometimes investigators may be interested in how consistent reports of the same research are, as it is expected that there should be consistency between: conference abstracts and published manuscripts; manuscript abstracts and manuscript main text; and trial registration and published manuscript. For example, Rosmarakis et al. investigated consistency between conference abstracts and full text manuscripts [ 85 ].

Methodological studies that investigate factors associated with reporting

In addition to identifying issues with reporting in primary and secondary studies, authors of methodological studies may be interested in determining the factors that are associated with certain reporting practices. Many methodological studies incorporate this, albeit as a secondary outcome. For example, Farrokhyar et al. investigated the factors associated with reporting quality in randomized trials of coronary artery bypass grafting surgery [ 53 ].

Methodological studies that investigate methods

Methodological studies may also be used to describe methods or compare methods, and the factors associated with methods. Muller et al. described the methods used for systematic reviews and meta-analyses of observational studies [ 86 ].

Methodological studies that summarize other methodological studies

Some methodological studies synthesize results from other methodological studies. For example, Li et al. conducted a scoping review of methodological reviews that investigated consistency between full text and abstracts in primary biomedical research [ 87 ].

Methodological studies that investigate nomenclature and terminology

Some methodological studies may investigate the use of names and terms in health research. For example, Martinic et al. investigated the definitions of systematic reviews used in overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks [ 88 ].

Other types of methodological studies

In addition to the previously mentioned experimental methodological studies, there may exist other types of methodological studies not captured here.

What is the design?

Methodological studies that are descriptive

Most methodological studies are purely descriptive and report their findings as counts (percent) and means (standard deviation) or medians (interquartile range). For example, Mbuagbaw et al. described the reporting of research recommendations in Cochrane HIV systematic reviews [ 30 ]. Gohari et al. described the quality of reporting of randomized trials in diabetes in Iran [ 12 ].

Methodological studies that are analytical

Some methodological studies are analytical wherein “analytical studies identify and quantify associations, test hypotheses, identify causes and determine whether an association exists between variables, such as between an exposure and a disease.” [ 89 ] In the case of methodological studies all these investigations are possible. For example, Kosa et al. investigated the association between agreement in primary outcome from trial registry to published manuscript and study covariates. They found that larger and more recent studies were more likely to have agreement [ 15 ]. Tricco et al. compared the conclusion statements from Cochrane and non-Cochrane systematic reviews with a meta-analysis of the primary outcome and found that non-Cochrane reviews were more likely to report positive findings. These results are a test of the null hypothesis that the proportions of Cochrane and non-Cochrane reviews that report positive results are equal [ 90 ].

What is the sampling strategy?

Methodological studies that include the target population

Methodological reviews with narrow research questions may be able to include the entire target population. For example, in the methodological study of Cochrane HIV systematic reviews, Mbuagbaw et al. included all of the available studies ( n  = 103) [ 30 ].

Methodological studies that include a sample of the target population

Many methodological studies use random samples of the target population [ 33 , 91 , 92 ]. Alternatively, purposeful sampling may be used, limiting the sample to a subset of research-related reports published within a certain time period, or in journals with a certain ranking or on a topic. Systematic sampling can also be used when random sampling may be challenging to implement.

What is the unit of analysis?

Methodological studies with a research report as the unit of analysis

Many methodological studies use a research report (e.g. full manuscript of study, abstract portion of the study) as the unit of analysis, and inferences can be made at the study-level. However, both published and unpublished research-related reports can be studied. These may include articles, conference abstracts, registry entries etc.

Methodological studies with a design, analysis or reporting item as the unit of analysis

Some methodological studies report on items which may occur more than once per article. For example, Paquette et al. report on subgroup analyses in Cochrane reviews of atrial fibrillation in which 17 systematic reviews planned 56 subgroup analyses [ 93 ].

This framework is outlined in Fig.  2 .

A proposed framework for methodological studies

Conclusions

Methodological studies have examined different aspects of reporting such as quality, completeness, consistency and adherence to reporting guidelines. As such, many of the methodological study examples cited in this tutorial are related to reporting. However, as an evolving field, the scope of research questions that can be addressed by methodological studies is expected to increase.

In this paper we have outlined the scope and purpose of methodological studies, along with examples of instances in which various approaches have been used. In the absence of formal guidance on the design, conduct, analysis and reporting of methodological studies, we have provided some advice to help make methodological studies consistent. This advice is grounded in good contemporary scientific practice. Generally, the research question should tie in with the sampling approach and planned analysis. We have also highlighted the variables that may inform findings from methodological studies. Lastly, we have provided suggestions for ways in which authors can categorize their methodological studies to inform their design and analysis.

Availability of data and materials

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Abbreviations

Consolidated Standards of Reporting Trials

Evidence, Participants, Intervention, Comparison, Outcome, Timeframe

Grading of Recommendations, Assessment, Development and Evaluations

Participants, Intervention, Comparison, Outcome, Timeframe

Preferred Reporting Items of Systematic reviews and Meta-Analyses

Studies Within a Review

Studies Within a Trial

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LM conceived the idea and drafted the outline and paper. DOL and LT commented on the idea and draft outline. LM, LP and DOL performed literature searches and data extraction. All authors (LM, DOL, LT, LP, DBA) reviewed several draft versions of the manuscript and approved the final manuscript.

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Mbuagbaw, L., Lawson, D.O., Puljak, L. et al. A tutorial on methodological studies: the what, when, how and why. BMC Med Res Methodol 20 , 226 (2020). https://doi.org/10.1186/s12874-020-01107-7

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• Acknowledgments

The data for the historical analysis in this report is derived from the Annual Social and Economic Supplements (ASEC) of the Current Population Survey (CPS), which are conducted in March of every year. The specific files used in this report are from March 1971 to March 2023 and contain data on the annual income of households from 1970 to 2022. Conducted jointly by the U.S. Census Bureau and the Bureau of Labor Statistics, the CPS is a monthly survey of about 60,000 households and is the source of the nation’s official statistics on unemployment . The ASEC survey in March typically features a larger sample size (about 75,000 in recent years) and is the source of the annual income and poverty estimates reported by the Census Bureau.

The historical analysis for Asian Americans is limited to CPS data for 2010 and 2023. That is because Asian Americans alone were not identified in the CPS until 2003. Prior to that year, and starting only in 1988, the CPS reported data for Asians and Native Hawaiians and Pacific Islanders (NHPI) combined. But the sample size for the NHPI population is only about 300 households in 2010 and 2023. For that reason, we do not report historical trends for the NHPI population. We also do not report historical trends for American Indian and Alaska Native (AIAN) or multiracial populations. These two populations are not uniquely identified in the CPS.

Methodological and other revisions to the CPS may have an impact on estimated trends. For example, the 2015 ASEC introduced a redesigned set of income questions , and definitions of key socioeconomic categories, such as race and educational attainment , have changed over time. 

The demographic analysis is principally based on the data from the 2022 American Community Survey (ACS). The public-use version of the ACS is a 1% sample of the U.S. population, or more than 3 million people. It is designed to collect the detailed information previously collected in the long form of the decennial census. But the ACS data are available only from 2005 onwards and are less suitable for long-run historical analyses.

The ACS is conducted in every month of the year, with data collected from about one-twelfth of the total sample in each month. The monthly responses are combined to form an annual portrait of the nation and of smaller geographic units. Because of its large sample size, the ACS is a better source than the CPS for analyses of subnational or subgroup demographic and income data. Nonetheless, we do not report the distribution of NHPI workers across income tiers by occupation and industry because of small sample sizes.

In some instances, the demographic analysis uses data from the 5-year ACS for the period 2018-2022. This file contains data from the five ACS surveys conducted over this period and represents a 5% sample of the U.S. population, or more than 15 million people. We exclude the data for 2020 because the COVID-19 pandemic had a significant impact on data collection in that year. The resulting file with four years of data – about 12 million people – was used to study smaller demographic groups, such as Korean Americans or Pakistani Americans. But we are still limited in the extent to which we can analyze the distribution of the AIAN and NHPI populations across income tiers in U.S. metropolitan areas.

The CPS and ACS microdata used are the Integrated Public Use Microdata Series ( IPUMS ) provided by the University of Minnesota. The IPUMS assigns uniform codes, to the extent possible, to data collected in the CPS and ACS over the years. More information about the IPUMS, including variable definition and sampling error, is available at IPUMS CPS and IPUMS USA .

Household income is the sum of incomes earned by all members of the household ages 15 and older. Income is defined as money income received (exclusive of certain money receipts, such as capital gains) before payments for such things as personal income taxes, Social Security, union dues and Medicare deductions. Noncash transfers such as SNAP benefits (food stamps), health benefits, subsidized housing and energy assistance are not included. More detail on the measurement and collection of income in government data can be found in the documentation of the CPS or in the documentation of the ACS .

The CPS collects data on income received by the household in the calendar year preceding the date of the survey . The ACS is a rolling monthly survey, and the household income data refer to income received during the 12 months preceding the survey month . In other words, a household surveyed in January 2022 is expected to report income received from January 2021 to December 2021, a household surveyed in February 2022 is expected to report income received from February 2021 to January 2022, and so on. Households surveyed in December 2022 report income received from December 2021 to November 2022. Thus, in the 2022 ACS, the income data refer to the period from January 2021 to November 2022, a span of 23 months.

Because the income data collected in the ACS does not refer to a calendar year, the Census Bureau provides an adjustment factor that converts reported incomes to the levels they would have been had they been earned during a calendar year. Although this adjustment factor has its limits , we apply it to the income data in the 2022 ACS to convert reported incomes to their projected levels in the 2022 calendar year. In the 5-year ACS files, the reported incomes have already been standardized by the Census Bureau to dollars as valued in the final year of data included in the file.

The data on income are adjusted for inflation and reported in 2023 dollars in this report. We use the price index series published in the Census Bureau’s 2022 income report . From 2000 to 2022, this series is the same as the Chained Consumer Price Index for all Urban Consumers (C-CPI-U) published by the Bureau of Labor Statistics (BLS). From 1978 to 1999, this series is the BLS’s Consumer Price Index for all Urban Consumers Retroactive Series (R-CPI-U-RS). For years prior to 1978, the Census Bureau uses an experimental price index series from the BLS known as the CPI-U-X1. We apply the C-CPI-U from the BLS to extend the Census Bureau’s price index series from 2022 to 2023.

The choice of a price index does not affect the allocation of households into lower-, middle- or upper-income categories at a point in time. That is because the same price index applies to the incomes of all households and does not affect their income-based rank. However, the choice of a price index does affect measures of absolute progress over time. For example, from 2000 to 2023, the price level rose either 78% (R-CPI-U-RS) or 67% (C-CPI-U). This means that someone who earned $10,000 per year in 2000 would need to earn either $17,800 (using the R-CPI-U-RS) or $16,700 (using the C-CPI-U) in 2023 to be just as well off as in 2000.

Households in census data

The Census Bureau defines a household as the entire group of persons who live in a single dwelling unit. A household may consist of several persons living together or one person living alone. It includes the household head and all of their relatives living in the dwelling unit and also any lodgers, live-in housekeepers, nannies and other residents not related to the head of the household.

Adjusting income for household size

Household income data reported in this study are adjusted for the number of people in a household. That is done because a four-person household with an income of, say, $50,000 faces a tighter budget constraint than a two-person household with the same income. In addition to comparisons across households at a given point in time, this adjustment is useful for measuring changes in the income of households over time. That is because average household size in the U.S. decreased from 3.1 persons in 1970 to 2.5 persons in 2023, a drop of about 20%. Ignoring this demographic change would mean ignoring a commensurate loosening of the household budget constraint.

At its simplest, adjusting for household size could mean converting household income into per capita income. Thus, a two-person household with an income of $50,000 would have a per capita income of $25,000, double the per capita income of a four-person household with the same total income.

A more sophisticated framework for household size adjustment recognizes that there are economies of scale in consumer expenditures. For example, a two-bedroom apartment may not cost twice as much to rent as a one-bedroom apartment. Two household members could carpool to work for the same cost as a single household member, and so on. For that reason, many researchers make adjustments for household size using the method of “ equivalence scales .”

A common equivalence-scale adjustment is defined as follows:

Adjusted household income = Household income / (Household size) N

By this method, household income is divided by household size exponentiated by “N,” where N is a number between 0 and 1.

Note that if N=0, the denominator equals 1. In that case, no adjustment is made for household size. If N=1, the denominator equals household size, and that is the same as converting household income into per capita income. The usual approach is to let N be some number between 0 and 1. Following other researchers , this study uses N=0.5. In practical terms, this means that household income is divided by the square root of household size – 1.41 for a two-person household, 1.73 for a three-person household, 2.00 for a four-person household and so on.

One issue with adjusting for household size is that while demographic data on household composition pertain to the survey date, income data typically pertain to the preceding calendar year or the preceding 12-month period. Because household composition can change over time, for example, through marriage, divorce or death, the household size that is measured at the survey date may not be the same as that at the time the income was earned and spent.

Once household incomes have been converted to a “uniform” household size, they can be scaled to reflect any household size. The income data reported in this study are computed for three-person households, the closest whole number to the average size of a U.S. household since 1970. That is done as follows:

Three-person household income = Adjusted household income * [(3) 0.5 ]

Adjusting for household size does have an effect on trends in income since 1970. However, it is important to note that once the adjustment has been made, it is immaterial whether one scales incomes to one-, two-, three- or four-person households. Regardless of the choice of household size, the same results would emerge with respect to the trends in the well-being of lower-, middle- and upper-income groups.

Adjusting income for the cost of living in metropolitan and other areas

In our analysis of the ACS data, “middle-income” Americans live in households with incomes that are two-thirds to double the national median, after incomes have been adjusted for household size and the cost of living in their area. Their area of residence may be in a known metropolitan area, an unidentified metro area or outside of a metro area.

A metropolitan area consists of at least one urbanized area with a population of 50,000 or more people, plus neighboring areas that are socially and economically integrated with the core. Metropolitan areas may cross state boundaries, such as the Washington-Arlington-Alexandria, DC-VA-MD-WV area.

Our analysis of the state of the middle class for all Americans encompasses 222 of 381 metropolitan areas in the United States, as defined by the Office of Management and Budget in its 2013 classification of metro areas. The 222 metropolitan areas included are the maximum number of areas that could be identified in the ACS (IPUMS) data that also had a sample of at least 500 households. Together, these areas accounted for 72% of the nation’s population in 2022. Overall, the 2022 ACS identifies 248 metro areas.

The cost-of-living adjustment for all areas is based on price indexes published by the U.S. Bureau of Economic Analysis. These indexes, known as Regional Price Parities (RPP), compare the prices of goods and services in an area with the national average prices for the same goods and services. The latest available estimates are for 2022.

RPPs are available for three types of areas. The RPPs for metropolitan areas were used to adjust the incomes of households in metro areas identified in the ACS data. RPPs for the metropolitan portions of a state were used to adjust the incomes of households known to live in a metro area in that state but for whom the identity of the metro area was not known. Finally, RPPs for all nonmetro areas in a state were used to adjust the incomes of households located in nonmetro areas in that state.

Race, ethnicity, nativity, educational attainment and marital status

In our analyses, White, Black and Asian Americans, American Indians or Alaska Natives, and Native Hawaiians or Pacific Islanders are people who identified with a single major racial group and who are not Hispanic. Asian includes people who identified with more than one Asian race or origin, e.g., Japanese and Filipino. Native Hawaiians or Pacific Islanders include people identifying with more than one Micronesian race. Multiracial includes people who identified with more than one major racial group, e.g., White and Black, Black and Korean, or Chinese and Hawaiian, and are not Hispanic. Hispanics are of any race.

U.S. born refers to individuals who are U.S. citizens at birth, including people born in the 50 U.S. states, the District of Columbia, Puerto Rico or other U.S. territories, as well as those born elsewhere to at least one parent who is a U.S. citizen. The terms foreign born and immigrant are used interchangeably in this report. They refer to people who are not U.S. citizens at birth.

High school graduate refers to those who have a high school diploma or its equivalent, such as a General Education Development (GED) certificate, and those who had completed 12th grade, but their diploma status was unclear (those who had finished 12th grade but not received a diploma are excluded). Adults with some college include those with an associate degree and those who attended college but did not obtain a degree. In the estimates for 1971, adults with a bachelor’s degree or higher level of education are those who completed at least four years of college.

Unmarried includes never married, divorced, separated and widowed. Married includes those whose spouse may be absent.

Region of birth and ancestry

Region of birth is defined using traditional geographic boundaries as identified in the IPUMS USA variable BPL . North America and U.S. territories includes the U.S., American Samoa, Guam, Puerto Rico and the U.S. Virgin Islands, Canada, and Atlantic Islands. Central America and the Caribbean includes Mexico, the rest of Central America and Caribbean islands. South America is all countries in South America, and Europe is all countries in Europe, including Russia. Asia is geographic Asia, including the Middle East, and Africa refers to all countries on that continent. Oceania encompasses Australia, New Zealand and various Pacific Islands.

Ancestry refers to a person’s self-reported ancestry or ethnic origin and is based on the IPUMS USA variable ANCESTR1 . Respondents may provide more than one ancestry. We use the first-reported ancestry in our analysis. North American refers to U.S. or Canadian origins and American Indian and Alaska Native tribes. Central and South American and other Hispanic ancestry refers not only to places of origin, such as Mexico, Puerto Rico and Peru, but also to ethnic origins, such as Mexican American, Spaniard and Hispanic. Caribbean includes the West Indies and non-Hispanic Central and South America.

South Asian includes origin groups from Afghanistan, India and neighboring countries, except Burma. Other Asian encompasses groups from Burma to the countries on the Pacific rim of Asia. Middle Eastern & North African origin groups are from countries ranging from Morocco to Iran.  Sub-Saharan African groups are from Africa, excluding countries in North Africa. Pacific Islanders trace their origin to Australia, New Zealand and Pacific Islands, including Hawaii. Western European and Soviet or Eastern European ancestries are defined along familiar geographic classifications of these regions, except that the latter category also includes Europeans not elsewhere classified in the source data.

Statistical significance

Comparisons between most estimates are tested for statistical significance using the replicate weights in the source data. These tests for statistical significance are conducted using 95% confidence intervals. However, replicate weights are not available in the CPS for years prior to 2005. Thus, when comparisons are made between CPS estimates from 1971 and 2023, or in other instances where replicate weights are not available, the standard errors are determined using the “pweight” option in Stata, and tests of significance are conducted using 99% confidence intervals.

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Home » Research Methods – Types, Examples and Guide

Research Methods – Types, Examples and Guide

Table of Contents

Research Methods

Definition:

Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.

Types of Research Methods

Types of Research Methods are as follows:

Qualitative research Method

Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.

Quantitative Research Method

Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.

Mixed Method Research

Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.

Key Differences Between Research Methods

The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods

Examples of Research Methods

Examples of Research Methods are as follows:

Qualitative Research Example:

A researcher wants to study the experience of cancer patients during their treatment. They conduct in-depth interviews with patients to gather data on their emotional state, coping mechanisms, and support systems.

Quantitative Research Example:

A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.

Mixed Research Example:

A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.

Applications of Research Methods

Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields:

• Psychology : Research methods are widely used in psychology to study human behavior, emotions, and mental processes. For example, researchers may use experiments, surveys, and observational studies to understand how people behave in different situations, how they respond to different stimuli, and how their brains process information.
• Sociology : Sociologists use research methods to study social phenomena, such as social inequality, social change, and social relationships. Researchers may use surveys, interviews, and observational studies to collect data on social attitudes, beliefs, and behaviors.
• Medicine : Research methods are essential in medical research to study diseases, test new treatments, and evaluate their effectiveness. Researchers may use clinical trials, case studies, and laboratory experiments to collect data on the efficacy and safety of different medical treatments.
• Education : Research methods are used in education to understand how students learn, how teachers teach, and how educational policies affect student outcomes. Researchers may use surveys, experiments, and observational studies to collect data on student performance, teacher effectiveness, and educational programs.
• Business : Research methods are used in business to understand consumer behavior, market trends, and business strategies. Researchers may use surveys, focus groups, and observational studies to collect data on consumer preferences, market trends, and industry competition.
• Environmental science : Research methods are used in environmental science to study the natural world and its ecosystems. Researchers may use field studies, laboratory experiments, and observational studies to collect data on environmental factors, such as air and water quality, and the impact of human activities on the environment.
• Political science : Research methods are used in political science to study political systems, institutions, and behavior. Researchers may use surveys, experiments, and observational studies to collect data on political attitudes, voting behavior, and the impact of policies on society.

Purpose of Research Methods

Research methods serve several purposes, including:

• Identify research problems: Research methods are used to identify research problems or questions that need to be addressed through empirical investigation.
• Develop hypotheses: Research methods help researchers develop hypotheses, which are tentative explanations for the observed phenomenon or relationship.
• Collect data: Research methods enable researchers to collect data in a systematic and objective way, which is necessary to test hypotheses and draw meaningful conclusions.
• Analyze data: Research methods provide tools and techniques for analyzing data, such as statistical analysis, content analysis, and discourse analysis.
• Test hypotheses: Research methods allow researchers to test hypotheses by examining the relationships between variables in a systematic and controlled manner.
• Draw conclusions : Research methods facilitate the drawing of conclusions based on empirical evidence and help researchers make generalizations about a population based on their sample data.
• Enhance understanding: Research methods contribute to the development of knowledge and enhance our understanding of various phenomena and relationships, which can inform policy, practice, and theory.

When to Use Research Methods

Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.

Here are some situations when research methods may be appropriate:

• To investigate a problem : Research methods can be used to investigate a problem or a research question in a particular field. This can help in identifying the root cause of the problem and developing solutions.
• To gather data: Research methods can be used to collect data on a particular subject. This can be done through surveys, interviews, observations, experiments, and more.
• To evaluate programs : Research methods can be used to evaluate the effectiveness of a program, intervention, or policy. This can help in determining whether the program is meeting its goals and objectives.
• To explore new areas : Research methods can be used to explore new areas of inquiry or to test new hypotheses. This can help in advancing knowledge in a particular field.
• To make informed decisions : Research methods can be used to gather information and data to support informed decision-making. This can be useful in various fields such as healthcare, business, and education.

Advantages of Research Methods

Research methods provide several advantages, including:

• Objectivity : Research methods enable researchers to gather data in a systematic and objective manner, minimizing personal biases and subjectivity. This leads to more reliable and valid results.
• Replicability : A key advantage of research methods is that they allow for replication of studies by other researchers. This helps to confirm the validity of the findings and ensures that the results are not specific to the particular research team.
• Generalizability : Research methods enable researchers to gather data from a representative sample of the population, allowing for generalizability of the findings to a larger population. This increases the external validity of the research.
• Precision : Research methods enable researchers to gather data using standardized procedures, ensuring that the data is accurate and precise. This allows researchers to make accurate predictions and draw meaningful conclusions.
• Efficiency : Research methods enable researchers to gather data efficiently, saving time and resources. This is especially important when studying large populations or complex phenomena.
• Innovation : Research methods enable researchers to develop new techniques and tools for data collection and analysis, leading to innovation and advancement in the field.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Open access
• Published: 29 May 2024

Exploring the use of body worn cameras in acute mental health wards: a mixed-method evaluation of a pilot intervention

• Una Foye 1 , 2 ,
• Keiran Wilson 1 , 2 ,
• Jessica Jepps 1 , 2 ,
• James Blease 1 ,
• Ellen Thomas 3 ,
• Leroy McAnuff 3 ,
• Sharon McKenzie 3 ,
• Katherine Barrett 3 ,
• Lilli Underwood 3 ,
• Geoff Brennan 1 , 2 &
• Alan Simpson 1 , 2  

BMC Health Services Research volume  24 , Article number:  681 ( 2024 ) Cite this article

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Metrics details

Body worn cameras (BWC) are mobile audio and video capture devices that can be secured to clothing allowing the wearer to record some of what they see and hear. This technology is being introduced in a range of healthcare settings as part of larger violence reduction strategies aimed at reducing incidents of aggression and violence on inpatient wards, however limited evidence exists to understand if this technology achieves such goals.

This study aimed to evaluate the implementation of BWCs on two inpatient mental health wards, including the impact on incidents, the acceptability to staff and patients, the sustainability of the resource use and ability to manage the use of BWCs on these wards.

The study used a mixed-methods design comparing quantitative measures including ward activity and routinely collected incident data at three time-points before during and after the pilot implementation of BWCs on one acute ward and one psychiatric intensive care unit, alongside pre and post pilot qualitative interviews with patients and staff, analysed using a framework based on the Consolidated Framework for Implementation Research.

Results showed no clear relationship between the use of BWCs and rates or severity of incidents on either ward, with limited impact of using BWCs on levels of incidents. Qualitative findings noted mixed perceptions about the use of BWCs and highlighted the complexity of implementing such technology as a violence reduction method within a busy healthcare setting Furthermore, the qualitative data collected during this pilot period highlighted the potential systemic and contextual factors such as low staffing that may impact on the incident data presented.

This study sheds light on the complexities of using such BWCs as a tool for ‘maximising safety’ on mental health settings. The findings suggest that BWCs have a limited impact on levels of incidents on wards, something that is likely to be largely influenced by the process of implementation as well as a range of contextual factors. As a result, it is likely that while BWCs may see successes in one hospital site this is not guaranteed for another site as such factors will have a considerable impact on efficacy, acceptability, and feasibility.

Peer Review reports

Body worn cameras (BWC) are mobile audio and video capture devices that can be secured to clothing allowing the wearer to record some of what they see and hear. In England, these have been introduced in the National Health Service (NHS) as part of a violence reduction strategy [ 1 ] which emphasises the reduction of aggression and violence against staff. The NHS Staff Survey 2022 found that 14.7% of NHS staff had experienced at least one incident of physical violence from patients, relatives or other members of the public in the previous 12 months. Violent attacks on staff were found to contribute to almost half of staff illness [ 2 ]. Levels of violence against staff working in mental health trusts remain much higher than other types of healthcare providers [ 3 ]. Numerous reports internationally highlight the increased risks faced by staff working in psychiatric care [ 4 ], though studies have reported that both ward staff and mental health patients experience violence and feeling unsafe on inpatient wards [ 5 , 6 ].

Body worn cameras have been in use for over a decade within law enforcement, where they hoped to provide transparency and accountability within use-of-force incidents and in the event of citizen complaints against police [ 7 ]. It was believed that video surveillance would help identify integral problems within the organisation, improve documentation of evidence, reduce use-of-force incidents, improve police-community relations, and provide training opportunities for officers [ 8 ]. However, a recent extensive international systematic review by Lum et al. [ 9 ], found that despite the successes noted in early evaluations, the way BWCs are currently used by police may not substantially affect most officer or citizen behaviours. Irrespective of these findings, other public services such as train operators have been implementing BWCs for security purposes, with reductions reported in the number of assaults on railway staff [ 10 ].

A recent systematic review of BWC use in public sector services established that there is a poor evidence base supporting the use of BWCs in the reduction of violence and aggression [ 11 ]. Yet, we are seeing a swift increase in the use of BWCs in mental health settings with that aim, with few studies conducted on the use of BWC technology in inpatient mental health wards, and even fewer studies exploring staff or patients’ views. Two evaluations conducted in England reported mixed results with both increases and decreases in violence and aggression found, and variation between types of wards. There is some suggestion of a reduction in more serious incidents and the use of restraint, but quality of evidence is low [ 12 , 13 ].

The use of BWCs in mental healthcare settings for safety and security remains a contentious topic due to the lack of evidence regarding the influence that such technology has on preventing violence and aggression and the complex philosophical and ethical issues raised, particularly where many patients may lack capacity and/or are detained under mental health legislation [ 14 ]. Additionally, there are concerns that BWCs may be used as a ‘quick fix’ for staff shortages rather than addressing the wider systemic and resourcing issues facing services [ 15 ]. With little independent evaluation of body-worn cameras in mental health settings, many of these concerns remain unanswered. There is also limited understanding of this technology from an implementation perspective. Therefore, in this study we aimed to conduct an independent evaluation of the introduction of BWCs as a violence reduction intervention on two inpatient mental health wards during a six-month pilot period to explore the impact of using the technology, alongside an exploration of the facilitators and barriers to implementation.

Research aim(s)

To evaluate the implementation of BWCs on two inpatient mental health wards, including the impact on incidents, the acceptability to staff and patients, the sustainability of the resource use and ability to manage the use of BWCs on these wards.

Patient and public involvement

The research team included a researcher and independent consultant, each with lived experience of mental health inpatient care. In addition, we recruited and facilitated a six member Lived Experience Advisory Panel (LEAP). This group was made up of patients and carers, some of whom had experienced the use of BWCs. Members were of diverse ethnic backgrounds and included four women and two men. The LEAP provided guidance and support for the research team in developing an understanding of the various potential impacts of the use of BWCs on inpatient mental health wards. Members contributed to the design of the study, development of the interview schedule, practice interviews prior to data collection on the wards, and supported the analysis and interpretation of the data, taking part in coding sessions to identify themes in the interview transcripts. The LEAP met once a month for two hours and was chaired by the Lived Experience Research Assistant and Lived Experience Consultant. Participants in the LEAP were provided with training and paid for their time.

The pilot introduction of the body worn cameras was conducted within a London mental health Trust consisting of four hospital sites with 17 acute wards. The research team were made aware of extensive preparatory work and planning that was conducted at a directorate and senior management level prior to camera implementation, including lived experience involvement and consultation, and the development of relevant policies and protocols inclusive of a human rights assessment and legal consultation.

The pilot period ran from 25th April to 25th October 2022. Reveal (a company who supply BWCs nationally across the UK) provided the Trust with 12 Calla BWCs for a flat fee that covered use of the cameras, cloud-based storage of footage, management software, and any support/maintenance required during the pilot period. Cameras were introduced to two wards based on two hospital sites, with six cameras provided to each of the wards on the same date. Training on using the BWCs was provided by the BWC company to staff working on both wards prior to starting the pilot period. Ward one was a 20-bed male acute inpatient ward, representing the most common ward setting where cameras have been introduced. Ward two was a ten-bed male Psychiatric Intensive Care Unit (PICU), representing smaller and more secure wards in which patients are likely to present as more unwell and where there are higher staff to patient ratios.

To answer our research questions, we used a mixed-methods design [ 16 ]. Using this design allowed us to investigate the impact of implementing BWCs in mental health settings on a range of quantitative and qualitative outcomes. This mixed methods design allows the study to statistically evaluate the effectiveness of using BWCs in these settings on key dependent variables (i.e., rates of violence and aggression, and incidents of conflict and containment) alongside qualitatively exploring the impact that the implementation of such technology has on patients and staff.

To ensure that the study was able to capture the impact and effect of implementation of the cameras, a repeated measures design was utilised to capture data at three phases on these wards:

Pre-pilot data: data prior of the implementation of the BWCs (quantitative and qualitative data).

Pilot period data: data collected during the six-month pilot period when BWCs were implemented on the wards (quantitative and qualitative data).

Post-pilot: data collected after the pilot period ended and cameras had been removed from the wards (quantitative data only).

Quantitative methods

Quantitative data was collected at all three data collection periods:

Pre-period: Data spanning six months prior to the implementation of BWCs (Nov 21 to May 22).

Pilot period: Data spanning the six months of the Trusts pilot period of using BWCs on the wards (June 22 to Nov 22).

Post-pilot: Data spanning the six months following the pilot period, when BWCs had been removed (Dec 22 to May 23).

Quantitative measures

To analyse the impact of BWC implementation, we collected two types of incident data related to violence and aggression and use of containment measures, including BWCs. Combined, these data provide a view of a wide range of incidents and events happening across the wards prior to, during, and after the implementation and removal of the BWCs.

The patient-staff conflict checklist

The Patient-staff Conflict Checklist (PCC-SR) [ 17 ] is an end of shift report that is completed by nurses to collate the frequency of conflict and containment events. This measure has been used successfully in several studies on inpatient wards [ 18 , 19 , 20 ].The checklist consists of 21 conflict behaviour items, including physical and verbal aggression, general rule breaking (e.g., smoking, refusing to attend to personal hygiene), eight containment measures (e.g., special observation, seclusion, physical restraint, time out), and staffing levels. In tests based on use with case note material, the PCC-SR has demonstrated an interrater reliability of 0.69 [ 21 ] and has shown a significant association with rates of officially reported incidents [ 22 ].

The checklist was revised for this study to include questions related to the use of BWCs ( e.g., how many uses of BWCs happened during the shift when a warning was given and the BWC was not used; when a warning was given and the BWC was used; when the BWC was switched on with no warning given ) in order to provide insight into how the cameras were being used on each ward (see appendix 1). Ward staff were asked to complete the checklist online at the end of each shift.

Routinely collected incident data (via datix system)

To supplement the PCC-SR-R, we also used routinely collected incident data from both wards for all three data collection phases. This data is gathered as part of routine practice by ward staff members via the Datix system Datix [ 23 ] is a risk management system used widely across mental health wards and Trusts in the UK to gather information on processes and errors. Previous studies have utilised routinely collect data via this system [ 24 , 25 ]. Incidents recorded in various Datix categories were included in this study (see Table  1 ). Incidents were anonymised before being provided to the research team to ensure confidentiality.

Routinely collected data included:

Recorded incidents of violence and aggression.

Recorded use of restrictive practices including seclusion, restraint, and intra-muscular medication/rapid tranquilisations.

Patient numbers.

Staffing levels.

Numbers of staff attending BWC training.

Quantitative data analysis

Incident reports.

Incident reports retrieved from Datix were binary coded into aggregate variables to examine violence and aggression, self-harm, and other conflict as outlined in Table  1 . Multivariate analyses of variance (MANOVA) were used to identify differences in type of incident (violence against person, violence against object, verbal aggression, self-harm, conflict) for each ward. MANOVA was also used to examine differences in incident outcomes (severity, use of restrictive practice, police involvement) across pre-trial, trial, and post-trial periods for each ward. Incident severity was scored by ward staff on a four-point scale (1 = No adverse outcome, 2 = Low severity, 3 = Moderate severity, 4 = Severe). Use of restrictive practice and police involvement were binary coded for presence or absence. Analyses were conducted using SPSS [ 26 ].

Patient-staff conflict checklist shift-report – revised (PCC-SR-R; )

Data were condensed into weeks for analysis rather than shifts to account for variability in PCC-SR-R submission by shift. Linear regressions assessed the relationship between BWC use and incident outcome (severity, use of restrictive practice, police involvement).

Qualitative methods

We used semi-structured qualitative interviews to explore participants’ experiences of BWCs on the ward to understand the impact of their use as well as to identify any salient issues for patients, staff and visitors that align with the measures utilised within the quantitative aspect of this study. These interviews were conducted at two time points: pre-pilot and at the end of the six-month pilot period.

Sample selection, eligibility, and recruitment

Convenience sampling was used to recruit staff and patients on wards. Researchers approached ward managers to distribute information sheets to staff, who shared that information with patients. Staff self-selected to participate in the study by liaising directly with the research team. Patients that were identified as close to discharge and having capacity to consent were approached by a clinical member of the team who was briefed on the study inclusion criteria (see Table  2 ). The staff member spoke with the patient about the study and provided them with a copy of the information sheet to consider. If patients consented, a member of the research team approached the participant to provide more information on the study and answer questions. After initial contact with the research team, participants were given a 24-hour period to consider whether they wanted to participate before being invited for an interview.

Participants were invited to take part in an interview within a private space on the ward. Interviews were scheduled for one hour with an additional 15 min before and after to obtain informed consent and answer any questions. Participation was voluntary and participants were free to withdraw at any time. To thank patients for their time, we offered a £10 voucher following the interview. Interviews were audio-recorded and saved to an encrypted server. Interview recordings were transcribed by an external company, and the research team checked the transcripts for accuracy and pseudonymised all participants. All transcripts were allocated a unique ID number and imported to MicroSoft Excel [ 27 ] for analysis.

Qualitative data analysis

Qualitative data were analysed using a framework analysis [ 28 ] informed by implementation science frameworks. Our coding framework used the Consolidated Framework for Implementation Research (CFIR) [ 29 ], which is comprised of five major domains including: Intervention Characteristics, Implementation Processes, Outer Setting, Inner Setting, and Characteristics of the Individual. Each domain consists of several constructs that reflect the evidence base of the types of factors that are most likely to influence implementation of interventions. The CFIR is frequently used to design and conduct implementation evaluations and is commonly used for complex health care delivery interventions to understand barriers and facilitators to implementation. Based on its description, the CFIR is an effective model to address our research question, particularly given the complexity of the implementation of surveillance technology such as BWCs in this acute care setting.

The initial analytic stage was undertaken by eight members of the study team with each researcher charting data summaries onto the framework for each of the interviews they had conducted on MicroSoft Excel [ 27 ]. Sub-themes within each broad deductive theme from our initial framework were then derived inductively through further coding and collaborative discussion within the research team, inclusive of Lived Experience Researcher colleagues. Pseudonyms were assigned to each participant during the anonymisation of transcripts along with key identifiers to provide context for illustrative quotes (e.g., P = patient, S = staff, A = acute ward, I = Intensive Care, Pre = pre-BWC implementation interview, Post = Post BWC implementation interview).

All participants gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Health Research Authority: London - Camden & Kings Cross Research Ethics Committee (IRAS Project ID 322,268, REC Reference 23/LO/0337).

Quantitative results

Exploring how body worn cameras were used during the pilot period.

Analysis of the PCC-SR-R provides information about how the BWCs were used on a day-to-day basis during the pilot period. Out of 543 total shift reports completed, BWC use was reported 50 times, indicating that BWCs were used on less than 10% of shifts overall; 78% of those deployments were on the Acute ward (see Figure 1 ). Overall, the majority of deployments happened as activations without a warning being given ( n  = 30, 60% of activations), 19 times the BWC was deployed with a warning but the camera was not activated (38%), and only one was the camera activated without a warning being given (2%).

BWC use by ward per week of pilot (no data available before week 6 on Ward 1)

According to the PCC-SR-R, a total of 227 incidents of aggression occurred during the pilot period across both wards (see Table  3 ). Overall, there were small statistically significant correlations between BWC usage and certain types of conflict, aggression, and restrictive practice. Results found that BWC use was positively correlated with verbal aggression and use of physical restraint. BWC use was moderately positively correlated with verbal aggression ( r  = .37, p  < .001). This indicates that BWCs were more likely to be used in incidents involving verbal aggression, which do not tend to be documented in Datix. Similarly, BWC use was moderately positively correlated with physical restraint ( r  = .31, p  < .001) indicating that they were also more likely to be used alongside physical restraint.

Exploring the impact of BWCs utilising routinely collected ward data

Acute ward results.

Routine data collected via Datix records were used to examine differences in frequency of conflict and aggression, incident severity, and use of containment measures before, during, and after introduction of BWCs on each trial ward (see Table  4 ).

There was no effect of trial period on incident type ( F (10, 592) = 1.703, p  = .077, Wilk’s Λ = 0.945), meaning there was no discernible difference in the type of incidents that occurred (E.g., verbal aggression, physical aggression) before, during, and after the pilot phase.

Incident outcomes

There was an effect of trial period on incident outcomes ( F (6, 596) = 10.900, p  < .001, Wilk’s Λ = 0.812). Incident severity was statistically significantly higher in the trial and post-trial periods compared to the pre-trial period. Use of restrictive practice was significantly lower in the post-trial period compared to the pre-trial and trial period. Police involvement was also lower in the post-trial period compared to the pre-trial and trial periods (see Table  5 ).

Results for the psychiatric intensive care unit

There was an effect of trial period on incident type ( F (10, 490) = 4.252, p  < .001, Wilk’s Λ = 0.847). Verbal aggression was statistically significantly higher in the post-trial period compared to the pre and trial periods. Self-harm was statistically significantly higher in the trial period compared to the pre-trial and post-trial periods. There were no differences in violence against a person ( p  = .162), violence against an object or conflict behaviour (see Table  4 ).

There was a statistically significant difference in incident outcome across the trial periods ( F (6, 494) = 12.907, p  < .001, Wilk’s Λ = 0.747). There was no difference in incident severity or police involvement. However, use of restrictive practice was statistically significantly higher in the pre-trial period, reducing in the test period, and reducing further in the post-trial period (see Table  5 ).

Qualitative findings

A total of 22 participants took part in interviews: five patients and 16 staff members. During the pre-pilot interviews a total of nine staff took part (five in the acute ward, four in the PICU ward) and two patients (both from the acute ward). After the pilot period, a total of eight staff took part (four from each ward) and three patients (all from the acute ward). Table  6 includes a full description of participants.

Below we have presented the key themes aligning to the five core CFIR categories of Intervention Characteristics, Characteristics of Individuals, The Process of Implementation, the Inner Setting, and The Outer Setting (see Table  7 ).

Intervention characteristics

Design and usability of wearing a bwc on the ward.

When discussing the use of the BWCs, staff noted a range of design issues related to the cameras that they said impacted on their use and acceptance of the cameras. This included the nature of the camera pulling on clothing necklines (a particular issue for female staff working on male wards), and overheating causing discomfort and irritation to skin, challenges with infection control, as well as the issue of cameras in a mental health setting where they can be easily grabbed, thrown and broken during an incident. Staff often cited these design issues as related to the lack of proactive use of the cameras on the wards.

There were issues around the devices getting overheated or about it going on your clothing, it pulls down the top… we had one person who was leading on it, whenever he was around, of course, the camera was being used, but if he wasn’t there, people weren’t as proactive in using the camera. Petra (f), Staff, A, Post.

There were also issues with staff forgetting to wear the cameras, forgetting to switch them on during incidents, and forgetting to charge them at the end of the shift, reducing the potential use of the cameras by other staff. These were perceived as key logistical issues prior to the pilot and were reported as issues at the end of the pilot by several staff on the wards.

The practicalities of will they actually turn it on in those sorts of incidents, I don’t know. Just little stuff as well, like if they don’t put it back on the docking station, so you think you’re charging it for next shift but then it’s not charged and the battery is dead, that’s one less camera to use, so little stuff. Jamal (m), Staff, A, Pre.

In relation to usability, staff noted that the cameras were small and easy to use given their simple single switch interface. It was felt that not having to upload and manage the data themselves made cameras more user friendly and usable by staff members. Protocols put into place such as signing the cameras in and out, and allocation for use during shifts were likened to procedures in place for other security measures therefore the implementation of this for the BWCs was viewed as easy for many staff.

It’s just like the ASCOM alarms that we wear. There’s a system to sign in and sign out, and that’s it. Alice (f), Staff, A, Pre.

While staff were generally positive about the usability of the cameras, some were cautious of with concerns for those less confident with technology.

… you have to be conscious that there’s some people – it’s quite easy to use, but I can say that because I’m alright using devices and all that but there’s some that are older age or not that familiar with using devices that may struggle with using it… they’re feeling a bit anxious and a bit scared, if they’re not familiar with it then they won’t use it. Jamal (m), Staff, A, Pre.

Evidence strength and quality: do BWCs change anything?

There were conflicting reports regarding the potential benefits of using BWCs on the wards, with both staff and patients reporting mixed perceptions as to whether the cameras might reduce violence and aggression. In the pre-pilot interviews, some staff reported feeling that the BWCs may have a positive impact on reducing physical violence.

I think it’s going to reduce violence and aggression on the ward…I don’t think they’ll want to punch you…they might be verbally abusive but in terms of physical that might reduce. Sarah (f), Staff, I, Pre.

Patients however noted that the cameras might hold staff to account of their own behaviours and therefore may improve care, however they felt that this impact would wear off after the first few months after which people might forget about the cameras being there.

Now they’ve got the body cams, it’s going to be a lot of changes. They’ll think, ‘Ooh well he’s on tape’. So, it might do something to their conscience, they actually start to listen to patients… until the novelty wears off and it might go back to square one again. Ian (m), Patient, A, Pre.

One staff member suggested that incident rates had reduced following introduction of the BWCs, but they remained unsure as to whether this was due to the cameras, reflecting that violence and aggression on wards can be related to many factors.

I know our violence and aggression has reduced significantly since the start of the cameras pilot… I don’t know, because obviously wearing the camera’s one thing, but if they weren’t in use, I don’t know maybe just the presence of the camera made a difference. But yeah, it’s hard to tell. Petra (f), Staff, A, Post.

In contrast, several staff reported that they had seen limited evidence for such changes.

I used it yesterday. He was aggressive and I used it, but he even when I was using [it] he doesn’t care about the camera… it didn’t make any difference… It doesn’t stop them to do anything, this camera does not stop them to do anything. Abraham (m), Staff, I, Post.

Some staff suggested that in some circumstances the cameras increased patient agitation and created incidents, so there was a need to consider whether the BWCs were going to instigate aggression in some circumstances.

There has been with a few patients because they will threaten you. They will tell you, ‘if you turn it on, I’m gonna smash your head in’. So incidents like that, I will not turn it on… Yeah, or some of them will just tell you, ‘if you come close by, I’m going to pull that off your chest’. So things like that, I just stay back. Ada (f), Staff, A, Post.

One rationale for a potential lack of effectiveness was noted by both staff and patients and was related to the levels of acute illness being experienced by patients which meant that for many they were too unwell to have insight into their own actions or those of staff switching on the cameras.

We’ve had instances where patients are so unwell that they just don’t care. You switch on the camera, whether you switch it on or not, it doesn’t really change the behaviour. ‘All right, okay, whatever switch it on’. They’re so unwell, they’re not really understanding. Petra (f), Staff, A, Post. It might make [staff] feel safer as a placebo effect, but I don’t think it would necessarily make them safer… I think the people that are likely to attack a member of staff are crazy enough that they’re not gonna even consider the camera as a factor. Harry (m), Patient, A, Pre.

This lack of evidence that the cameras were necessarily effective in reducing incident rates or severity of incidents may have had an impact on staff buy-in and the use of the cameras as a result. One staff member reflected that having feedback from senior management about the impact and evidence would have been useful during the pilot period to inform ward staff whether the cameras were influencing things or not.

Staff want feedback. I don’t think we’ve had any since we’ve had the cameras… it would be nice to get feedback from, I don’t know, whoever is watching it, and stuff like that. Ada (f), Staff, A, Post.

Relative advantage: are BWCs effective and efficient for the ward?

Due to a combination of personal beliefs related to BWCs, the lack of evidence of their impact on violence and aggression, and other elements of care and culture on the wards, a number of staff and patients explored alternative interventions and approaches that may be more beneficial than BWCs. Both staff and patients suggested that Closed Circuit Television (CCTV) as an intervention that provided the transparency of using cameras and video footage but with an independent perspective. This was felt by many to remove the bias that could be introduced in BWC use as the video capture didn’t require staff control of the filming.

I feel like [BWCs] puts all the power and trust into the hands of the staff and I feel that it would be better to have CCTV on the ward because CCTV is neutral. Harry (m), Patient, A, Pre. I have control over that [BWC recording] … It kind of gives that split as well between staff and patients. You can tell me or I can tell you when to switch it on. Whereas I feel like a CCTV camera is there all the time. Nobody’s asking to switch it on. It’s there. If you wanted to review the footage you can request it, anyone can request to view the footage for a legitimate reason. Whereas the camera can come across as if you’re threatening. Petra (f), Staff, A, Post.

In addition, some participants reflected that the nature and design of BWCs meant that unless staff were present for an incident it wouldn’t be captured, whereas CCTV has the advantage of being always present.

If there’s CCTV, then it’s the same thing, you get me. Like, if its body worn cameras that people can always do things away from staff. They can always go down to that corridor to have their fight or go to the side where staff ain’t gonna see them to have their fight, but with CCTV you can’t do that. Elijah (m), Patient, A, Post.

In addition to exploring technological and video-based interventions, many staff noted that the key tool to violence reduction had to be the use of de-escalation skills, noting that the use of communication and positive relationships had to be the primary tool before other interventions such as BWCs or CCTV.

We do a lot of verbal de-escalation. So we got our destress room now still open. That has a punch bag, and it’s got sensory tiles, and the aim and hope is that when people do get frustrated, because we’re all human. We all get annoyed at anything or many little things in life. There is the aim that they go into that room and start punching the bag instead of property and damaging furniture. But we also are working really hard on verbal de-escalation and actually trying to listen to patients and talk to them before anything else. And that’s helped a lot. And between this kind of shared, or role modelling, where while we’re showing staff, actually even spending an extra 20 min is okay. If it means you’re not going to end up having to restrain a patient. Petra (f), Staff, A, Post.

By using communication skills and de-escalation techniques skilfully, some staff felt there was no need to utilise the BWCs. One concern with the introduction of the BWCs for staff was that the use of this technology may negatively impact on trust and relationships and the use of de-escalation.

Some situations I feel like it can make a situation worse sometimes… I think a lot of situations can be avoided if you just talk with people…. Trying to find out why they’re angry, trying to just kind of see it from their point of view, understand them… I think maybe additional training for verbal de-escalation is needed first. Patrick (m), Staff, A, Post.

Characteristics of individuals

Staff and patients’ knowledge and beliefs about the intervention.

Overall, there were mixed views among both staff and patients as to whether cameras would reduce incidents, prior to and after the pilot period. When considering the possible impact on violence and aggressive incidents there was a view among staff that there was the need for a nuanced and person-centred view.

All the patients that come in, they’re different you know. They have different perceptions; they like different things… everyone is different. So, it just depends. We might go live, and then we have good feedback because the patients they are open and the understand why we have it, and then as they get discharged and new patients come in it might not go as well. It just depends. Serene (f), Staff, A, Pre.

As a result of the desire to be person-centred in the use of such interventions, one staff member noted that they weighed-up such consequences for the patient before using the BWC and would make decisions not to use the camera where they thought it may have a negative impact.

Actually, with this body worn camera, as I did mention, if a patient is unwell, that doesn’t, the patient will not have the capacity to I mean, say yes, you cannot just put it on like that. Yeah, I know it’s for evidence, but when something happens, you first have to attend to the patient. You first have to attend to the patient before this camera is, for me. Ruby (f), Staff, I, Post.

Some staff questioned the existing evidence and theories as to why BWCs work to reduce incidents, and instead noted that for some people it will instigate an incident, while others may be triggered by a camera.

I’m on the fence of how that is going to work because I know the evidence is that by telling a patient ‘look if you keep escalating I’m gonna have to turn this on’, but I know several of our patients would kind of take that as a dare and escalate just to spite so that you would turn it on. Diana (f), Staff, A, Pre.

In contrast, some staff felt the cameras helped them feel safer on wards due to transparency of footage as evidence for both staff and patients.

They [staff] need to use it for protection, for recording evidence, that type of thing… They can record instances for later evidence. Yeah, for them as well. Safer for them and for patients because you can also have the right to get them to record, because a patient might be in the wrong but sometimes it may be the staff is in the wrong position. And that’s achieving safety for patients as well. Yeah, I think it works both ways. Dylan (m), Patient, A, Post.

Positive buy-in was also related to the potential use of the intervention as a training, learning or reflective tool for staff to improve practice and care and promote positive staff behaviour.

If you know that your actions might be filmed one way or the other, that would make me to step up your behaviour to patients… if you know that your actions can be viewed, if the authority wants to, then you behave properly with patients so I think that will improve the quality of the care to patient. Davide (m), Staff, I, Pre.

While there were some positive attitudes towards the cameras, there remained considerable concerns among participants regarding the transparency of camera use to collate evidence in relation to incidents as it was widely noted that the cameras remain in staff control therefore there is an issue in relation to bias and power.

I do think my gut would say that it wouldn’t necessarily be well received. Because also I think people feel like prisoners in here, that’s how some of the patients have described their experience, so in terms of the power dynamic and also just – I think that can make one feel a bit, even worse, basically, you know? Leslie (m), Staff, A, Pre.

These issues lead to staff reporting they didn’t want to wear the camera.

I’d feel quite uncomfortable wearing one to be honest. Leslie (m), Staff, A, Pre.

The staff control of the cameras had a particular impact on patient acceptability of the intervention as it led to some patients viewing BWCs as being an intervention for staff advantage and staff safety, thus increasing a ‘them and us’ culture and leading to patient resistance to the cameras. This was particularly salient for those with prior negative experiences of police use of cameras or mistrust in staff.

I feel like the fact that the body worn cameras is gonna be similar to how the police use them, if a staff member has negative intent toward a patient, they would be able to instigate an incident and then turn the camera on and use the consequences of what they’ve instigated to expect restraint or injection or whatever else might happen. So, I feel like it would be putting all the power and trust into the hands of the staff and I feel that it would be better to have CCTV on the ward because CCTV is neutral. Whereas, the body worn camera, especially with some of the personality conflicts/bad attitudes, impressions I’ve had from certain members of staff since I’ve been here, I feel like body worn cameras might be abused in that way possible. Harry (m), Patient, A, Pre.

Perceived unintended consequences and impact on care

Prior to the implementation there were concerns from staff that the introduction of BWCs could have consequences beyond the intended use of reducing violence and aggression, unintentionally affecting a range of factors that may impact on the overall delivery of care. There was a key concern regarding the potential negative impact that cameras may have for patients who have paranoia or psychosis as well as for those who may have prior traumatic experiences of being filmed.

It might have negative impacts on these patients because I’m thinking about kind of patients with schizophrenia and things like that who already have paranoid delusions, thinking that people are after them, thinking that people are spying on them, people are watching them, and then seeing kind of cameras around. It might have negative impacts on them. Tayla (f), Staff, I, Pre. When I was admitted I was going through psychosis… I don’t want to be filmed and things like that. So you just see a camera, a guy with a camera on, you are like, are you filming me? Elijah (m), Patient, A, Post.

There was also a considerable concern among both staff and patients that the use of cameras would have a negative impact on the therapeutic relationship between staff and patients. This was felt to be related to the implication that the cameras enhanced a ‘them and us’ dynamic due to the power differential that staff controlling the cameras can create, likened to policing and criminalisation of patients. With the potential of a negative impact on relationships between staff and patients, staff suggested they may be disinclined to use BWCs if it would stop patients speaking to them or approaching them if they needed support.

Yeah, I think it would probably damage [the therapeutic relationship] because I think what’s probably quite helpful is things that maybe create less of a power difference. I think to some extent, [the BWC] might hinder that ability. Like for example imagine going to a therapist and them just like ‘I’ve got this camera in the corner of the room and it’s gonna be filming our session and just in case – or like, just in case I feel that you might get aggressive with me’. Um, I don’t think that’s going to help the therapeutic relationship! Jamal (m), Staff, A, Pre. When you get body worn cameras on there, the relationship as well between staff and patients, is just gonna instantly change because you’re looking like police! Elijah (m), Patient, A, Post.

In contrast, a minority of staff felt that the presence of cameras may improve relationships as they provide transparency of staff behaviour and would encourage staff to behave well and provide high quality care for patients.

It will also help how, improve the way we look at the patients… because if you know that your actions might be filmed one way or the other, that would make me to step up your behaviour you know… you behave properly with patients so I think that will improve the quality of the care to patient. More efficiently, more caring to patient. Davide (m), Staff, I, Pre.

The process of implementation

Planning: top-down implementation.

Staff perceived that BWC implementation directives had been given by senior management or policy stakeholders whom they felt viewed the process from a position of limited understanding due to a lack of ‘frontline’ mental health service experience. This led to a lack of faith amongst staff, and a perception that funds were being misspent.

They sit up there, they just roll it out and see how it works, how it goes. They waste a whole lot of money, millions or whatever, thousands of pounds in it, and then they see that ‘Oh, it’s not gonna work’. They take it back and all of that. Before coming out with it, you need to come speak to us… they just sit up there drinking tea and coffee, and then they’re just like, Oh, yeah, well, let’s do it this way…come stay with these people, work with them, for just I give you a 12 h shift, stay with them. Richard (m), Staff, I, Post.

This was exacerbated when staff felt there was a lack of consultation or explanation.

we don’t always get the ins and outs of certain things…We know that the cameras are coming in and stuff like that, but you know, and obviously it’s gone through every avenue to make sure that it’s fine. But then sometimes we don’t always know the ins and outs to then explain to people why we have the cameras. Patrick (m), Staff, A, Post.

It was also highlighted that due to multiple initiatives being implemented and directives handed down in parallel, staff felt negative towards interventions more widely, with the BWCs being ‘ just another thing to do’ , adding to workload for staff and reducing enthusiasm to use the cameras.

it’s not just to do with the camera, I just think there’s lots of changes happening at once, and there’s loads of new things being constantly introduced that people are just thinking oh it’s another thing. I think that’s what it is more than the camera itself. Alice (f), Staff, A, Pre.

Execution: training, Use and Ward Visibility

Overall, there was a lack of consistency amongst staff in their understanding of the purpose and processes of using the BWCs on the wards.

What do you do, do you record every single thing or, I don’t know. Do you record like, if a patient said, I want to talk to you, confidential, you go sit in a room, do you record things like those or is it just violence and aggression? Ada (f), Staff, A, Post.

The lack of clarity regarding the purpose of the intervention and the appropriate use of the cameras was felt to impact staffs’ attitudes and acceptance of using them and contributed to a lack of transparency or perhaps trust regarding the use of any subsequent video footage.

I think if the importance of the recording was explained a bit more…and how it would improve things, I think people would use it more… that’s why I don’t think it’s always used sometimes… if you’re not sure why some of it’s important, then you’re not going to see the value…I think if you’re gonna keep with them, it’s about updating the training, teaching staff when to use it, then where does that information go? How does that look in terms of improving? Just a bit of transparency, I think. But when you don’t know certain things it’s a bit hard to get behind something or back it, you know? Patrick (m), Staff, A, Post.

The lack of information about the purpose and processes related to the intervention was also seen among patients, with most patients noting that they hadn’t received information about the cameras during their admissions.

No information at all. I don’t think any of the patients know about it. Toby (m), Patient, A, Post.

While training was provided it was widely felt that it was insufficient to provide understanding about the purpose of the cameras or the more in-depth processes beyond operational aspects such as charging and docking. Several staff interviewed were unaware of the training, while others noted that they had an informal run-through by colleagues rather than anything formal.

What training are you talking about?… I wasn’t here, so I was taught by my colleague. I mean, from what I was taught, to operate the camera, and to give a warning to the patient that you’re going to use the camera. Nevis (f), Staff, A, Post.

Longer training with further details beyond operational use was felt to be needed by staff.

I think the training should have to be longer, even if it’s like an hour or something… Like what situations deem the camera to be… more information on the cameras, when to use it, why it’s used, and I think if the importance of the recording was explained a bit more and what it was doing and how that recording would go and how it would improve things. Patrick (m), Staff, A, Post.

Furthermore, there was a need for training to be on a rolling basis given the use of bank staff who were not trained to use the cameras or to understand the proper processes or purpose of using the BWCs, which could leave them vulnerable to misuse or abuse.

We have bank staff [who aren’t trained] so they say ‘I don’t know how to use that camera you are giving me’. Nevis (f), Staff, A, Post.

The inner setting

Ward context: acceptance of violence and aggression is part of the job.

It was widely believed by staff that the nature of working on a mental health ward included accepting that violence and aggression was part of the job. This was not seen as an acceptance of violence but more that the job was providing care for individuals who are mentally unwell, and confusion, fear, frustration and aggression can be part of that. As a result, there was an ambivalence among some staff that the introduction of cameras would change this.

I think like in this line of work, there’s always that potential for like risky behaviours to happen. I’m not sure if putting the camera on will make much difference. Patrick (m), Staff, A, Post.

Staff noted that because of the nature of the job, staff are used to managing these situations and they understood that it was part of the job; therefore, it was unlikely that they would record everything that on paper might be considered an incident.

There’s also enough things that happen here, so I don’t think they would record [the incidents] because it’s just another day here. You know what I’m saying… [staff] can just say, ‘Stop, go back to your room and leave it at that and that kind of be the end of it’. Dylan (m), Patient, A, Post. We are trained for it. Eveline (f), Staff, I, Pre.

This acceptance that incidents are a hazard of mental healthcare was linked to staff’s acknowledgment that many factors make up the complexity of violence and aggression including the nature of individual patients, acuity levels, ward atmosphere, staffing levels, access to activities, leave and outside space. The interplay of multiple factors creates a context in which frustrations and incidents are likely, thus become part of the everyday and ‘normal’ life on the ward for staff and patients alike.

I feel like, you know, how in GP services you say, zero tolerance to abusive language, or any kind of harassment. I don’t think there is that on a psychiatric ward you are kind of expected to take all the abuse and just get on with it. Petra (f), Staff, A, Post.

With staff reported having a higher threshold for these behaviours it was perceived that this was likely to impact on the efficiency of the intervention as staff would be less likely to consider a situation as violent but more ‘ part of the job’ .

Reactive nature of the ward and incidents

Most participants noted that the ward context is always changing with people being admitted and discharged, with daily staff changes and wider turnover of staff, so things are never static and can change at any point. This reflects the dynamic nature of the ward which creates a complex moving picture that staff need to consider and react to.

[the atmosphere] it’s very good at the moment. If you had asked me this two weeks ago, I would say, ‘Oh, my gosh’. But it changes… The type of patient can make your whole ward change… it depends on the client group we have at the time. Nevis (f), Staff, A, Post.

Staff noted that a key limitation of using the cameras to reduce incidents was the reactive nature of the environment and care being provided. This was felt to impact on the feasibility and use of the cameras as staff noted that they often react to what is happening rather than thinking to ‘ put the camera on first ’. It was felt by staff with experience of reacting to incidents that the failure to use BWCs during these processes were linked to staff’s instincts and training to focus on patients as a priority.

Say for instance, you’re in the office, and two patients start fighting, or a patient attacks someone and, all you’re thinking about is to go there to stop the person. You’re not thinking about putting on any camera. You understand? So sometimes it’s halfway through it, somebody might say, ‘Has anybody switched the camera on’? And that’s the time you start recording… If something happens immediately, you’re not thinking about the camera at that time, you’re just thinking to just go, so yeah. Nevis (f), Staff, A, Post.

Incidents happen quickly and often surprise staff, therefore staff react instantly so are not thinking about new processes such as recording on the cameras as this would slow things down or is not in the reactive nature needed by staff during such incidents.

When you’re in the middle of an incident and your adrenaline’s high, you’re focusing on the incident itself. It’s very difficult for you to now remember, remind yourself to switch on the camera because you’re thinking, patient safety, staff safety, who’s coming to relieve you? What’s going on? Who’s at the door? Petra (f), Staff, A, Post.

In addition, the need for an immediate response meant that it was felt that by the time staff remember to, or have the chance to, switch the camera on it was often too late.

Sometimes in the heat of moments and stuff like that, or if the situation’s happening, sometimes you don’t always think to, you know, put your camera on. Patrick (m), Staff, A, Post.

Outer setting

Resources: staffing.

Issues related to staffing were highlighted by several participants as a key problem facing mental health wards thus leading to staff having higher workloads, and higher rates of bank and agency staff being used on shift and feeling burnt-out.

Out of all the wards I’ve been on I’d say this is the worst. It’s primarily because the staff are overworked…it seems like they spend more time doing paperwork than they do interacting with the patients. Harry (m), Patient, A, Pre. We’re in a bit of a crisis at the minute, we’re really, really understaffed. We’re struggling to cover shifts, so the staff are generally quite burnt out. We’ve had a number of people that have just left all at once, so that had an impact… Staff do get frustrated if they’re burnt out from lack of staff and what have you. Alice (f), Staff, A, Pre.

It was noted by one participant that the link of a new intervention with extra workload was likely to have a negative impact on its acceptability due to these increasing demands.

People automatically link the camera to then the additional paperwork that goes alongside it. It’s like, ‘Oh god, if we do this, we’ve got to do that’, and that could play a part. Petra (f), Staff, A, Post.

One staff member noted that the staffing issue meant there were more likely to be bank staff on wards so the care of patients may be affected as temporary staff may be less able to build meaningful therapeutic relationships.

So obviously there is the basic impact on safety of not having adequate staffing, but then there’s the impact of having a lot of bank staff. So obviously when you have permanent staff they get to know the patients more, we’re able to give them the more individualised care that we ideally should be giving them, but we can’t do that with bank staff. Diana (f), Staff, A, Pre.

It was also suggested that staffing levels and mix often made it more difficult to provide activities or facilitate escorted leave which can lead to patients feeling frustrated and becoming more aggressive.

So you know there is enough staff to facilitate the actual shift, so you know when there’s less staff like you say you’ve got people knocking at the door, but then you don’t have staff to take people out on leave straight away, that all has a rippling effect! Serene (f), Staff, A, Pre.

Wider systemic issues

Overall, there was a concern that the introduction of BWCs would not impact on wider, underlying factors that may contribute to frustration, aggression and incidents on wards. Providing a more enhanced level of care and better addressing the needs of patients was felt to be central to helping people but also reducing the frustration that patients feel when on the ward.

… for violence and aggression, [focus on] the mental health side of things like therapy and psychology should be compulsory. It shouldn’t be something you apply for and have to wait three or four weeks for. I think every person should, more than three or four weeks even, months even… we need psychology and therapists. That’s what will stop most violence, because psychologists and a therapist can edit the way that they speak to people because they’ve been given that skill depending on the way the person behaves. So that’s what we need regularly… not like all this dancing therapy, yoga therapy. That’s a person, that you come and you actually sit down and talk through your shit with them. That will help! Elijah (m), Patient, A, Post. There’s a lack of routine and I think there’s a lack of positive interaction between the patient and the staff as well. The only time you interact with a member of staff is if you’re hassling them for something, you have to hassle for every little thing, and it becomes a sort of, frustration inducing and like I’m a very calm person, but I found myself getting very fucking angry, to be honest, on this ward just because out of pure frustration… there’s bigger problems than body worn cameras going on. Harry (m), Patient, A, Pre.

Staff agreed that there was a need to invest in staff and training rather than new technologies or innovations as it is staff and their skills behind the camera.

It’s not the camera that will do all of that. It’s not making the difference. It’s a very good, very beautiful device, probably doing its job in its own way. But it’s more about investing in the staff, giving them that training and making them reflect on every day-to-day shift. Richard (m), Staff, I, Post.

There was felt to be a need to support staff more in delivering care within wards that can be challenging and where patients are unwell to ensure that staff feel safe. While in some circumstances the cameras made some staff feel safer, greater support from management would be more beneficial in making staff feel valued.

In this study exploring the implementation and use of body-worn cameras on mental health wards, we employed two methods for collecting and comparing data on incidents and use of containment measures, including BWCs, on one acute ward and one psychiatric intensive care unit. We found no clear relationship between the use of BWCs and rates or severity of incidents on either ward. While BWCs may be used when there are incidents of both physical and verbal aggression, results indicate that they may also provoke verbal aggression, as was suggested during some interviews within this study. This should be a concern, as strong evidence that being repeatedly subject to verbal aggression and abuse can lead to burnout and withdrawal of care by staff [ 30 ]. These mixed findings reflect results that were reported in two earlier studies of BWCs on mental health wards [ 12 , 13 ]. However, the very low use of the cameras, on just 10 per cent of the shifts where data was obtained, makes it even more difficult to draw any conclusions.

While the data shows limited impact of using BWCs on levels of incidents, we did find that during the pilot period BWC use tended to occur alongside physical restraint, but the direction of relationship is unclear as staff were asked to use BWCs when planning an intervention such as restraint. This relationship with restraint reflected the findings on several wards in a previous study [ 13 ], while contrasting with those reported in a second study that found reductions in incidents involving restraint during the evaluation period [ 12 ]. Such a mix of findings highlights the complexity of using BWCs as a violence reduction method within a busy healthcare setting in which several interacting components and contextual factors, and behaviours by staff and patients can affect outcomes [ 31 ]. The qualitative data collected during this pilot period highlighted the potential systemic and contextual factors such as low staffing that may have a confounding impact on the incident data presented in this simple form.

The findings presented within this evaluation provide some insights into the process of implementing BWCs as a safety intervention in mental health services and highlight some of the challenges and barriers faced. The use of implementation science to evaluate the piloting of BWCs on wards helps to demonstrate how multiple elements including a variety of contextual and systemic factors can have a considerable impact and thus change how a technology may vary not only between hospitals, but even across wards in the same hospital. By understanding the elements that may and do occur during the process of implementing such interventions, we can better understand if and how BWCs might be used in the future.

Within this pilot, extensive preparatory work conducted at a directorate and senior management level did not translate during the process of implementation at a ward level, which appeared to impact on the use of BWCs by individuals on the wards. This highlights that there is a need to utilise implementation science approaches in planning the implementation of new technologies or interventions and to investigate elements related to behavioural change and context rather than just the desired and actual effects of the intervention itself.

While ward staff and patients identified the potential for BWCs to enhance safety on the wards, participants distrusted their deployment and expressed concerns about ethical issues and possible harmful consequences of their use on therapeutic relationships, care provided and patient wellbeing. These themes reflect previous findings from a national interview study of patient and staff perspectives and experiences of BWCs in inpatient mental health wards [ 14 ]. Given these issues, alternatives such as increasing de-escalation skills were identified by staff as possible routes that may be more beneficial in these settings. Furthermore, other approaches such as safety huddles have also been highlighted within the literature as potential means to improve patient safety by looking ahead at what can be attended to or averted [ 32 ].

Furthermore, it is important to consider that the presence of power imbalances and the pre-existing culture on the ward have considerable implications for safety approaches and must be considered, as exemplified by the preferences by both staff and patients in this evaluation for more perceived ‘impartial’ interventions such as CCTV. As identified within previous studies [ 14 ], BWCs can have different implications for psychological safety, particularly for vulnerable patients who already feel criminalised in an environment with asymmetrical power imbalances between staff and patients. This is particularly salient when considering aspects of identity such as race, ethnicity, and gender both in terms of the identities of the patient group but also in terms of the staff/patient relationship.

While preferences in this study note CCTV as more ‘impartial’, work by Desai [ 33 ] draws on the literature about the use of surveillance cameras in other settings (such as public streets) as well as on psychiatric wards and concludes that CCTV monitoring is fraught with difficulties and challenges, and that ‘watching’ patients and staff through the lens of a camera can distort the reality of what is happening within a ward environment. In her recently published book, Desai [ 34 ] develops this theme to explore the impacts of being watched on both patients and staff through her ethnographic research in psychiatric intensive care units. She highlights concerns over the criminalisation of patient behaviour, safeguarding concerns in relation to the way women’s bodies and behaviours are viewed and judged, and the undermining by CCTV of ethical mental health practice by staff who attempt to engage in thoughtful, constructive, therapeutic interactions with patients in face-to-face encounters. Appenzeller et al.’s [ 35 ] review found that whilst the presence of CCTV appeared to increase subjective feelings of safety amongst patients and visitors, there was no objective evidence that video surveillance increases security, and that staff may develop an over-reliance on the technology.

In addition, our findings add to the existing literature which notes that alternative interventions and approaches that address underlying contextual and systemic issues related to improving care on inpatient wards require attention to address the underlying factors related to incidents, e.g., flashpoints [ 36 ]. Evidence suggests that factors leading to incidents can be predicted; therefore, there is a need to enable staff to work in a proactive way to anticipate and prevent incidents rather than view incidents as purely reactive [ 37 , 38 , 39 ]. Such skills-based and relational approaches are likely to impact more on improving safety and reducing incidents by addressing the complex and multi-faceted issue of incidents on inpatient mental health wards [ 40 ].

These findings highlight that interventions such as BWCs are not used within a vacuum, and that hospitals are complex contexts in which there are a range of unique populations, processes, and microsystems that are multi-faceted [ 41 ]. As a result, interventions will encounter both universal, specific, and local barriers that will impact on its functioning in the real world. This is salient because research suggests that camera use inside mental health wards is based on a perception of the violent nature of the mental health patient, a perception that not only influences practice but also impacts how patients experience the ward [ 33 ]. As a result, there needs to be careful consideration of the use of any new and innovative intervention aimed at improving safety within mental health settings that have limited research supporting their efficacy.

Limitations

While the study provides important insights into the efficacy and acceptability of introducing BWCs onto inpatient mental health wards, there were several limitations. Firstly, the analysis of incident data is limited in its nature as it only presents surface level information about incidents without wider contextual information. Results using such data should be cautiously interpreted as they do not account for confounding factors, such as staffing, acuity, ward culture or ward atmosphere, that are likely to contribute to incidents of violence and aggression. For example, while there was a statistically significant decrease in restrictive practice on the PICU across the study period, we know that BWCs were not widely used on that ward, so this is likely due to a confounding variable that was not accounted for in the study design.

Secondly, the study faced limitations in relation to recruitment, particularly with patients. Researchers’ access to wards was challenging due to high staff turnover and high rates of acuity, meaning many patients were not deemed well enough to be able to consent to take part in the study. In addition, the low use of the cameras on wards meant that many patients, and some staff, had not seen the BWCs in use. Similarly, patients had been provided limited information about the pilot, so their ability to engage in the research and describe their own experiences with BWCs was restricted.

Thirdly, analysis captures the active use of the BWC, however it does not fully capture the impact of staff wearing the cameras even where they do not actively use them. While our qualitative analysis provides insight into the limitation of such passive use, it is likely that the presence of the cameras being worn by staff, even when turned off, may have an impact on both staff and patient behaviours. This may explain trends in the data that did not reach significance but warrant further investigation in relation to the presence of BWCs, nonetheless.

Finally, researchers had planned to collect quantitative surveys from staff and patients in relation to their experiences of the ward atmosphere and climate, views related to therapeutic relationships on the ward, levels of burnout among staff, views on care, and attitudes to containment measures. Due to issues related to staff time, patient acuity, and poor engagement from staff leading to challenges accessing the wards, the collection of such survey data was unfeasible, and this element of the study was discontinued. As a result, we have not reported this aspect in our paper. This limitation reflects the busy nature of inpatient mental health wards with pressures on staff and high levels of ill health among patients. As such, traditional methodologies for evaluation are unlikely to elicit data that is comprehensive and meaningful. Alternative approaches may need to be considered.

Future directions

With BWCs being increasingly used across inpatient mental health services [ 14 ], it is important that further research and evaluation is conducted. To date, there is limited data regarding the effectiveness of this technology in relation to violence reduction; however, there may be other beneficial uses in relation to safeguarding and training [ 13 ]. Future research should consider alternative methods that ensure contextual factors are accounted for and that patient voices can be maximised. For example, focus groups with patients currently admitted to a mental health ward or interviews with those who have recently been on a ward that has used the cameras, would bypass problems encountered with capacity to consent in the present study. Furthermore, ethnographic approaches may provide a deeper understanding of the implementation, deployment and impact that BWCs have on wards.

Overall, this research sheds light on the complexities of using BWCs as a tool for ‘maximising safety’ in mental health settings. The findings suggest that BWCs have a limited impact on levels of incidents on wards, something that is likely to be largely influenced by the process of implementation as well as a range of contextual factors, including the staff and patient populations on the wards. As a result, it is likely that while BWCs may see successes in one hospital site this is not guaranteed for another site as such factors will have a considerable impact on efficacy, acceptability, and feasibility. Furthermore, the findings point towards the need for more consideration to be placed on processes of implementation and the complex ethical discussions regarding BWC use from both a patient and a staff perspective.

In conclusion, while there have been advances in digital applications and immersive technologies showing promise of therapeutic benefits for patients and staff more widely, whether BWCs and other surveillance approaches are to be part of that picture remains to be seen and needs to be informed by high-quality, co-produced research that focuses on wider therapeutic aspects of mental healthcare.

Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

We would like to thank The Burdett Trust for Nursing for funding this work. We would also like to acknowledge our wider Lived Experience Advisory Panel and Project Advisory Panel for their contributions and support and would like to thank the staff and service users on the wards we attended for their warmth and participation.

Funding was provided by The Burdett Trust of Nursing. Funders were independent of the research and did not impact findings.

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All authors have read and approved the manuscript. Authors AS, UF, KW, GB created the protocol for the study. KW, JJ, UF conducted the recruitment for the study, and conducted the interviews. UF, JJ, JB, LMA, LU, SMK, KB, ET coded data, and contributed to the analysis. All authors supported drafting and development of the manuscript.

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Foye, U., Wilson, K., Jepps, J. et al. Exploring the use of body worn cameras in acute mental health wards: a mixed-method evaluation of a pilot intervention. BMC Health Serv Res 24 , 681 (2024). https://doi.org/10.1186/s12913-024-11085-x

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DOI : https://doi.org/10.1186/s12913-024-11085-x

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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

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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Wire snare removal in protected areas is labor-intensive but effective -- and essential to solving the Southeast Asian snaring crisis

Snaring -- a non-selective method of poaching using wire traps -- is widespread in tropical forests in Southeast Asia. Snaring decimates wildlife populations and has pushed many larger mammals to local or even global extinction. Eleven years of data from ranger patrols in the Thua Thien Hue and Quang Nam Saola Nature Reserves in Viet Nam show that intensive removal efforts are labour-intensive and costly but brought snaring down by almost 40 percent and therefore reduced imminent threats to wildlife. Further reductions were difficult to achieve despite continued removal efforts. Snare removal is therefore necessary but by itself not sufficient to save the threatened wildlife diversity in tropical forests, scientists conclude in the journal Conservation Letters.

From 2011 to 2021, WWF-Viet Nam and local authorities removed close to 120,000 snares from the contiguous Thua Thien Hue and Quang Nam Saola Nature Reserves in Central Viet Nam. The reserves are home to several endemic, rare, and threatened species, including Annamite Striped Rabbit ( Nesolagus timminsi ) and Annamite Crested Argus ( Rheinardia ocellata ). Snare removal is labour-intensive and costly, since rangers need to cover large areas on foot in rugged and inaccessible terrain. Snare removal is an often-employed strategy because it is straightforward and non-controversial compared to other responses such as arrests and prosecutions. To date, few studies have assessed the impact of snare removal on snaring levels over long time periods.

An international team of scientists from the Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW), WWF-Viet Nam and WWF Asia Pacific as well as the Universities of Exeter and Montpellier analysed 11 years of patrol data and concluded that intensive snare removal significantly reduced this threat: Over the 11-year time frame, the presence of snares in the two reserves declined by 37 percent. The effect was more pronounced in easily accessible locations, presumably because patrolling was more frequent in these areas. "What we also found is that, when patrolled, an area is less likely to have many snares afterwards," says Jürgen Niedballa, a data scientist from the Leibniz-IZW. "Patrolling acts as a deterrent to future snaring, and therefore is an important measure to counteract the snaring crisis in Southeast Asia." On the other hand, snaring levels remained relatively high in more remote parts of the forest. "The spatial analysis of the patrol data is of great importance for our daily management," adds Hung Luong Viet, project manager of WWF-Viet Nam. "The maps showing the distribution of snares within the protected areas help us to target our patrolling activities to those parts of the reserves that need the most attention."

The scientists also found that the decrease in snaring levels was mainly achieved within the first 6 years of patrolling. After that, despite continuous efforts, snare presence remained stable. "Our results show snare removal alone is unlikely to protect wildlife in Southeast Asian protected areas," says Andrew Tilker, scientist at the Leibniz-IZW and Species Conservation Coordinator at Re:wild. "This is especially true for rare or snaring-sensitive species, many of which are now on the brink of extinction in Viet Nam."

The findings show that it is important to view snare removal as part of a wider, multi-faceted conservation response that addresses the underlying drivers, the authors conclude in the paper. "Relying solely on snare removal will not be sufficient to address the threat at scale," says Tin Nguyen Van Tri, Wildlife Practice Lead of WWF-Viet Nam. "In collaboration with other conservation partners, we are now engaged in larger multi-disciplinary conservation initiatives such as cross-border cooperation in tackling illegal wildlife trade; livelihood improvement, awareness raising and behaviour change programmes to complement snare removal to prevent snares being set in the first place. With these additional efforts, we can address the root cause of the problem and make the forests of the Central Annamite Mountains in Viet Nam safe again for wildlife." One of these initiatives is the CarBi II project, which is implemented over a 5-year period (2019 -- 2024) by WWF-Viet Nam and Laos through the German Development Bank (KfW), and part of the International Climate Initiative (IKI).

Although the wildlife of Southeast Asia is facing an unprecedented threat from snaring, there is hope that, when intensive snare removal is combined with more holistic approaches, the threat can be addressed at the scale needed for large-scale wildlife recovery, the authors conclude. Reducing snaring across Southeast Asia's protected areas will take substantial resources and an as-yet unrealized level of political commitment from the region's governments -- but it is a future that is within reach.

Snaring in the Annamite Mountains

One of the major causes of wildlife declines in tropical forests is the use of nonselective wire snares. Snares are cheap, easy to set in large numbers, and highly effective at capturing terrestrial vertebrate species. They are nonselective, because every ground-dwelling animal may step into a wire snare and get caught, whether targeted or not. Wire snares can remain active for months. Snaring is particularly severe in Southeast Asia, where it has depressed wildlife populations in many protected and unprotected areas, and remains a significant and ongoing threat. A recent scientific investigation showed that snaring is a more immediate and severe threat to Southeast Asian wildlife communities than forest degradation in some areas, and can empty forests of wildlife. In the Annamites, a mountain chain straddling the Viet Nam and Laos border, snaring has driven many endemic ground-dwelling species to the brink of extinction; the Saola ( Pseudoryx nghetinhensis ), Large-antlered muntjac ( Muntiacus vuquangensis ), Annamite striped rabbit (Nesolagus timminsi), and Silver-backed chevrotain ( Tragulus versicolor ) are all highly threatened from industrial-scale snaring across the region.

• Wild Animals
• Endangered Animals
• Rainforests
• Deforestation
• Wildlife gardening
• Slash and burn
• Pesticide poisoning
• Biodiversity
• Common Chimpanzee

Story Source:

Materials provided by Leibniz Institute for Zoo and Wildlife Research (IZW) . Note: Content may be edited for style and length.

Journal Reference :

• Andrew Tilker, Jürgen Niedballa, Hung Luong Viet, Jesse F. Abrams, Lucile Marescot, Nicholas Wilkinson, Benjamin M. Rawson, Rahel Sollmann, Andreas Wilting. Addressing the Southeast Asian snaring crisis: Impact of 11 years of snare removal in a biodiversity hotspot . Conservation Letters , 2024; DOI: 10.1111/conl.13021

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