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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

Research Methodologies

What are research designs?
What are research methodologies?

What are research methods?

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

Additional Sources

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

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

Constructing Questionnaires

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

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

Quantitative Research Measures

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

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

Focus Groups

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

Advantages of Focus Groups

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

Disadvantages of Focus Groups

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

Observation

There are two ways to conduct research observations:

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

Open-Ended Questionnaires

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

Semi-structured Interviews

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

Theoretical Analysis

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

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

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

Unstructured Interviews

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

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

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

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

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

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Knowledge Base
Methodology

Research Methods | Definition, Types, Examples

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

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

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

Second, decide how you will analyse the data .

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

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

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

Qualitative vs quantitative data

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

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

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

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

Primary vs secondary data

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

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

Descriptive vs experimental data

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

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

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

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

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

Qualitative analysis methods

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

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

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

Quantitative analysis methods

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

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

During an experiment.
Using probability sampling methods .

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

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

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

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

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

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

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

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

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

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

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

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

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

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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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Thanks for your comment.

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

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

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

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Research Methods: What are research methods?

What are research methods?
Searching specific databases

What are research methods

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

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

Types of research

Qualitative Research
Quantitative Research
Mixed Methods Research

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

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

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

Finding Mixed Methods research in the Databases

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

Data collection tools

Sage research methods.

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

Help and Information

Next: Finding qualitative research >>
Last Updated: Apr 18, 2024 11:16 AM
URL: https://libguides.newcastle.edu.au/researchmethods

Research Process

Choosing the Right Research Methodology: A Guide for Researchers

3 minute read

Table of Contents

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

Understanding different research methods:

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

Qualitative vs quantitative research:

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

Qualitative research methodology:

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

Quantitative research methodology:

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

Analysing qualitative vs quantitative data:

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

When to use qualitative vs quantitative research:

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

Conclusion:

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

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Scholarly Sources: What are They and Where can You Find Them?

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University Libraries
Research Guides
Topic Guides
Research Methods Guide
Research Design & Method

Research Methods Guide: Research Design & Method

Introduction
Survey Research
Interview Research
Data Analysis
Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

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

Which research method should I choose ?

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

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

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

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

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

<< Previous: Introduction
Next: Survey Research >>
Last Updated: Aug 21, 2023 10:42 AM

USC Libraries
Research Guides

Organizing Your Social Sciences Research Paper

6. The Methodology
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Reading Research Effectively
Narrowing a Topic Idea
Broadening a Topic Idea
Extending the Timeliness of a Topic Idea
Academic Writing Style
Applying Critical Thinking
Choosing a Title
Making an Outline
Paragraph Development
Research Process Video Series
Executive Summary
The C.A.R.S. Model
Background Information
The Research Problem/Question
Theoretical Framework
Citation Tracking
Content Alert Services
Evaluating Sources
Primary Sources
Secondary Sources
Tiertiary Sources
Scholarly vs. Popular Publications
Qualitative Methods
Quantitative Methods
Insiderness
Using Non-Textual Elements
Limitations of the Study
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism
Footnotes or Endnotes?
Further Readings
Generative AI and Writing
USC Libraries Tutorials and Other Guides
Bibliography

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

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

Importance of a Good Methodology Section

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

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

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

Structure and Writing Style

I. Groups of Research Methods

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

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

II. Content

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

The remainder of your methodology section should describe the following:

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

In addition, an effectively written methodology section should:

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

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

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

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

III. Problems to Avoid

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

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

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

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

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

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

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

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

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

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

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

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

Yet Another Writing Tip

Methods and the Methodology

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

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

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

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Home » Research Techniques – Methods, Types and Examples

Research Techniques – Methods, Types and Examples

Table of Contents

Research Techniques

Definition:

Research techniques refer to the various methods, processes, and tools used to collect, analyze, and interpret data for the purpose of answering research questions or testing hypotheses.

Methods of Research Techniques

The methods of research techniques refer to the overall approaches or frameworks that guide a research study, including the theoretical perspective, research design, sampling strategy, data collection and analysis techniques, and ethical considerations. Some common methods of research techniques are:

Quantitative research: This is a research method that focuses on collecting and analyzing numerical data to establish patterns, relationships, and cause-and-effect relationships. Examples of quantitative research techniques are surveys, experiments, and statistical analysis.
Qualitative research: This is a research method that focuses on collecting and analyzing non-numerical data, such as text, images, and videos, to gain insights into the subjective experiences and perspectives of the participants. Examples of qualitative research techniques are interviews, focus groups, and content analysis.
Mixed-methods research: This is a research method that combines quantitative and qualitative research techniques to provide a more comprehensive understanding of a research question. Examples of mixed-methods research techniques are surveys with open-ended questions and case studies with statistical analysis.
Action research: This is a research method that focuses on solving real-world problems by collaborating with stakeholders and using a cyclical process of planning, action, and reflection. Examples of action research techniques are participatory action research and community-based participatory research.
Experimental research : This is a research method that involves manipulating one or more variables to observe the effect on an outcome, to establish cause-and-effect relationships. Examples of experimental research techniques are randomized controlled trials and quasi-experimental designs.
Observational research: This is a research method that involves observing and recording behavior or phenomena in natural settings to gain insights into the subject of study. Examples of observational research techniques are naturalistic observation and structured observation.

Types of Research Techniques

There are several types of research techniques used in various fields. Some of the most common ones are:

Surveys : This is a quantitative research technique that involves collecting data through questionnaires or interviews to gather information from a large group of people.
Experiments : This is a scientific research technique that involves manipulating one or more variables to observe the effect on an outcome, to establish cause-and-effect relationships.
Case studies: This is a qualitative research technique that involves in-depth analysis of a single case, such as an individual, group, or event, to understand the complexities of the case.
Observational studies : This is a research technique that involves observing and recording behavior or phenomena in natural settings to gain insights into the subject of study.
Content analysis: This is a research technique used to analyze text or other media content to identify patterns, themes, or meanings.
Focus groups: This is a research technique that involves gathering a small group of people to discuss a topic or issue and provide feedback on a product or service.
Meta-analysis: This is a statistical research technique that involves combining data from multiple studies to assess the overall effect of a treatment or intervention.
Action research: This is a research technique used to solve real-world problems by collaborating with stakeholders and using a cyclical process of planning, action, and reflection.
Interviews : Interviews are another technique used in research, and they can be conducted in person or over the phone. They are often used to gather in-depth information about an individual’s experiences or opinions. For example, a researcher might conduct interviews with cancer patients to learn more about their experiences with treatment.

Example of Research Techniques

Here’s an example of how research techniques might be used by a student conducting a research project:

Let’s say a high school student is interested in investigating the impact of social media on mental health. They could use a variety of research techniques to gather data and analyze their findings, including:

Literature review : The student could conduct a literature review to gather existing research studies, articles, and books that discuss the relationship between social media and mental health. This will provide a foundation of knowledge on the topic and help the student identify gaps in the research that they could address.
Surveys : The student could design and distribute a survey to gather information from a sample of individuals about their social media usage and how it affects their mental health. The survey could include questions about the frequency of social media use, the types of content consumed, and how it makes them feel.
Interviews : The student could conduct interviews with individuals who have experienced mental health issues and ask them about their social media use, and how it has impacted their mental health. This could provide a more in-depth understanding of how social media affects people on an individual level.
Data analysis : The student could use statistical software to analyze the data collected from the surveys and interviews. This would allow them to identify patterns and relationships between social media usage and mental health outcomes.
Report writing : Based on the findings from their research, the student could write a report that summarizes their research methods, findings, and conclusions. They could present their report to their peers or their teacher to share their insights on the topic.

Overall, by using a combination of research techniques, the student can investigate their research question thoroughly and systematically, and make meaningful contributions to the field of social media and mental health research.

Purpose of Research Techniques

The Purposes of Research Techniques are as follows:

To investigate and gain knowledge about a particular phenomenon or topic
To generate new ideas and theories
To test existing theories and hypotheses
To identify and evaluate potential solutions to problems
To gather data and evidence to inform decision-making
To identify trends and patterns in data
To explore cause-and-effect relationships between variables
To develop and refine measurement tools and methodologies
To establish the reliability and validity of research findings
To communicate research findings to others in a clear and concise manner.

Applications of Research Techniques

Here are some applications of research techniques:

Scientific research: to explore, investigate and understand natural phenomena, and to generate new knowledge and theories.
Market research: to collect and analyze data about consumer behavior, preferences, and trends, and to help businesses make informed decisions about product development, pricing, and marketing strategies.
Medical research : to study diseases and their treatments, and to develop new medicines, therapies, and medical technologies.
Social research : to explore and understand human behavior, attitudes, and values, and to inform public policy decisions related to education, health care, social welfare, and other areas.
Educational research : to study teaching and learning processes, and to develop effective teaching methods and instructional materials.
Environmental research: to investigate the impact of human activities on the environment, and to develop solutions to environmental problems.
Engineering Research: to design, develop, and improve products, processes, and systems, and to optimize their performance and efficiency.
Criminal justice research : to study crime patterns, causes, and prevention strategies, and to evaluate the effectiveness of criminal justice policies and programs.
Psychological research : to investigate human cognition, emotion, and behavior, and to develop interventions to address mental health issues.
Historical research: to study past events, societies, and cultures, and to develop an understanding of how they shape our present.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other.

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null. The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period.

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them.

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables.

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization.
Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way.

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time.

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures.

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

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

If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

Types of correlation. Strong, weak, and perfect positive correlation, strong, weak, and perfect negative correlation, no correlation. Graphs or charts ...

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved.

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way.

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view.

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
Natural : Here, spontaneous behavior is recorded in a natural setting.
Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.
Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

Test-retest reliability : assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
Primary data is first-hand data collected for the purpose of the investigation.
Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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What is Research Methodology? Definition, Types, and Examples

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

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

What is research methodology ?

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

Why is research methodology important?

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

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

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Types of research methodology.

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

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

What are the types of sampling designs in research methodology?

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

Probability sampling

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

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

What are data collection methods?

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

Qualitative research 5

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

Quantitative research 6

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

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What are data analysis methods.

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

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

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

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

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

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

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

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

How to choose a research methodology?

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

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

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How to write a research methodology .

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

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

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Frequently Asked Questions

Q1. What are the key components of research methodology?

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

Research design
Data collection procedures
Data analysis methods
Ethical considerations

Q2. Why is ethical consideration important in research methodology?

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

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

Q3. What is the difference between methodology and method?

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

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

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

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2.2 Research Methods

Learning objectives.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Field Research

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

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

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

Sociology in the Real World

Beyoncé and lady gaga as sociological subjects.

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

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

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

Participant Observation

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

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

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

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

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

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

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

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

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

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

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

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

Ethnography

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

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

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

Institutional Ethnography

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

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

Sociological Research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Experiments

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

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

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

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

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

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

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

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

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

Secondary Data Analysis

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

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

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

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

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

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

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

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

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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 .

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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.

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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.

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Natural antimicrobial agents such as nisin were used to control the growth of foodborne pathogens in dairy products. The current study aimed to examine the inhibitory effect of pure nisin and nisin nanoparticles (nisin NPs) against methicillin resistant Staphylococcus aureus (MRSA) and E.coli O157:H7 during the manufacturing and storage of yoghurt. Nisin NPs were prepared using new, natural, and safe nano-precipitation method by acetic acid. The prepared NPs were characterized using zeta-sizer and transmission electron microscopy (TEM). In addition, the cytotoxicity of nisin NPs on vero cells was assessed using the 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The minimum inhibitory concentrations (MICs) of nisin and its nanoparticles were determined using agar well-diffusion method. Further, fresh buffalo’s milk was inoculated with MRSA or E.coli O157:H7 (1 × 10 6 CFU/ml) with the addition of either nisin or nisin NPs, and then the inoculated milk was used for yoghurt making. The organoleptic properties, pH and bacterial load of the obtained yoghurt were evaluated during storage in comparison to control group.

The obtained results showed a strong antibacterial activity of nisin NPs (0.125 mg/mL) against MRSA and E.coli O157:H7 in comparison with control and pure nisin groups. Notably, complete eradication of MRSA and E.coli O157:H7 was observed in yoghurt formulated with nisin NPs after 24 h and 5th day of storage, respectively. The shelf life of yoghurt inoculated with nisin nanoparticles was extended than those manufactured without addition of such nanoparticles.

Conclusions

Overall, the present study indicated that the addition of nisin NPs during processing of yoghurt could be a useful tool for food preservation against MRSA and E.coli O157:H7 in dairy industry.

Peer Review reports

Introduction

Using of bacteriocins such as nisin alone or combined with other natural materials such as essential oils, could be represented as a useful candidate for improving the microbiological quality and maintaining the sensory properties of milk and milk products [ 1 , 2 ]. The utility of nisin as a bio preservative in food industry has been approved and this bacteriocins was effective enough to extended shelf life in regions with inadequate preservation facilities such as developing countries [ 3 ]. Nisin is a natural water-soluble antibacterial peptide (AMP) composed of 34 amino acid residues produced by Lactococcus lactis. It has the ability to inhibit the growth of some foodborne pathogens and many of Gram-positive spoilage bacteria [ 4 , 5 ]. This antibacterial peptide is generally regarded as a safe food preservative by the joint Food and Agriculture Organization and World Health Organization (FAO/WHO), also by the US Food and Drug Administration (FDA) [ 6 , 7 ]. Based on aforementioned permissions, it is widely commercialized as a safe and natural food preservative in the food industry in more than 50 countries around the world [ 8 ].

The antibacterial activity of nisin in food is depending on several factors such as its solubility, pH and structural properties of target bacteria. It could exhibit potent antimicrobial activities against many species of Gram-positive pathogens, while it has little effect against Gram-negative bacteria, yeast and fungi due to their outer membrane barriers [ 9 ]. The exact antibacterial mechanism of nisin is attributed to the passage of nisin through the cell wall of bacteria and its interaction with lipid II, which considered as an essential element in the bacterial cell wall [ 9 ].

There are some obstacles that can hinder the antimicrobial efficacy of free nisin as a food bio preservative such as its ability to interact with food components (e.g. proteolytic enzymes, phospholipids, fatty acids and proteins), high pH and many other food additives. These factors could drastically reduce or completely diminish the antimicrobial effect of nisin [ 10 ]. Hence, different strategies were developed to improve the preservative efficacy of nisin such as liposomes [ 11 ] and nanoparticles [ 12 ]. However, these reported techniques are not suitable for applications in food industries due to the utility of inorganic solvents and chemical compounds, in addition to they are expensive and complicated. For these reasons, alternative organic chemicals and solvents or green synthesized nanoparticles were developed to overcome the inactivation of free nisin by many food components through protecting nisin and releasing it in sustained manner [ 13 ]. For instance, acetic acid, a well-known biocompatible organic acid, has no adverse effects, no dietary restrictions and it is generally recognized as a safe food additive. This organic acid is commonly used, as a natural preservative, in the preservation of food especially in cheese and dairy products where it inhibit the development of bacteria, yeast and fungi [ 14 , 15 ]. Besides acetic acid, tween 80 has a great potential to stabilize nanoparticles dispersion through formation of a protective coat around the nanoparticles, so it was used in food without adverse health effect [ 16 , 17 ].

Application of nisin in dairy industry was reported in more than 55 countries due to its prominent antimicrobial, technological characteristics, safety, stability and flavorless. Commercially, nisin was used in several food matrices to ensure safety, extend shelf life, and to improve the microbial quality either through addition of nisin directly in its purified form or through its production in situ by live bacteria [ 18 , 19 , 20 ]. For instance, nisin was added as a bio-preserving ingredient in some kinds of cheese [ 21 , 22 , 23 ], skim milk and whole milk [ 24 , 25 , 26 , 27 ]. Nisin has a potent antibacterial effect against spore-forming bacteria that are the main spoilage concerns in the food industry [ 26 ]. However, several factors such as neutral pH [ 4 ], Fat% [ 25 ], protein% [ 28 ] as well as calcium and magnesium concentrations that can reduce the antimicrobial efficacy of nisin were reported when used directly in dairy foods [ 15 , 29 , 30 ]. Certain previously reported strategies, such as encapsulation and nano-encapsulation of nisin, were applied to increase the antimicrobial efficacy of nisin in dairy industry [ 31 , 32 ]. . Importantly, there is no available data about the use of nisin or nisin NPs as antimicrobial agents during yoghurt preparation.

Accordingly, the current study was designed to prepare nisin NPs by simple nanoprecipitation technique using natural, biocompatible and safe materials. Also the aims of this study were extended to investigate the antibacterial effect of obtained nanoparticles on MRSA and E.coli O157:H7 during manufacturing and storage of yoghurt. Additionally, the effect of the used nisin NPs on the organoleptic properties of yoghurt was addressed.

Materials and methods

Acetic acid (Merck Co., Germany), nisin (Sigma Aldrich from Lactococcus lactis , potency ≥ 900 IU/mg, purity ≥ 95%, CAS Number 1414-45-5), Brain Heart Infusion (BHI) (BBL 11,407, USA), phosphate buffer saline (PBS) (Oxoid, Basingstoke, UK) were purchased and used as received. Polyethylene glycol sorbitan monooleate (Tween 80) was purchased from Sigma Aldrich. Additionally, Mueller Hinton agar (M173) was purchased from HiMedia (Pvt., India), and LAB204 Neogen Company. While, 0.5 McFarland Standard (8.2 log 10 CFU/ml) (Cat. No. TM50) was purchased from Dalynn Biologicals Co. The deionized water was obtained from the Molecular Biology Unit, Assiut University, Egypt.

Preparation of nisin nanoparticles

Nisin (2 mg/mL) was completely dissolved in 100 mL of 0.1 M aqueous acetic acid solution with the aid of sonication using cold probe sonication (UP100H Hielscher Ultrasound). Then, 50 mL of deionized distilled water was gradually added to the nisin solution while maintaining the pH value within the range of 2.5 to 3. Further, 0.01% tween 80 was added as a stabilizer and the mixture was constantly stirred at 25 oC for 7 h to eliminate acetic acid as much as possible. Finally, the nanoparticles suspensions were then sonicated for 5 min before stored at refrigerator temperature for further use. The obtained nanoparticles were examined for size, shape, antibacterial activity and stability after six months.

Characterization of the prepared nisin NPs

Dynamic light scattering (dls).

The prepared nanoparticles was characterized by DLS at a fixed scattered angle of 90° using a Zetasizer, ZS 90 (3000 HS, Malvern Instruments, Malvern, UK) at the Nanotechnology Unit, Al-Azhar University at Assiut, Egypt. Measurements were taken at 25 °C and Zetasizer® software (version 7.03) was used to collect and analyze the data [ 33 ].

Fourier-transform infrared spectroscopy (FTIR)

FTIR was performed at the Chemistry Department at the Faculty of Science, Assiut University. This experiment was used to identify the functional groups and the fingerprint of the molecule. Samples were prepared by compressing potassium bromide with either free nisin or NNPs into small discs. The produced discs were then scanned using FTIR spectrometer (FTIR, NICOLET, iS10, Thermo Scientific) in the wave number ranged from of 4000 to 500 cm − 1 [ 34 ].

High resolution transmission electron microscopy (HRTEM)

The morphology of the prepared nisin NPs was determined using HRTEM (JEM2100, Jeol, Japan) at the Electronic Microscope Unit, National Research Center, Egypt. The sample was diluted with deionized water, and a small drop of nisin NPs was dropped onto 200-mesh copper coated grids at room temperature and negatively stained with uranyl acetate for 3 min. Excess liquid was removed using Whatman filter paper and samples were dried at room temperature [ 35 ].

Bacterial strains and inoculum preparation

The tested pathogens (MRSA and E. coli O157:H7) were previously isolated from dairy products (milk, cheese and yoghurt) samples by culture method and identified using conventional biochemical method and PCR at a certified food lab, Animal Health Research Institute (AHRI), Egypt [ 36 , 37 ]. These isolates were inoculated in trypticase soy broth (Himedia, India) and incubated at 37˚C for 24 h, then co-cultured on selective agars such as MRSA agar base (Acumedia, 7420, USA) and Sorbitol MaCconkey agar (Himedia, India) [ 38 , 39 ] for MRSA and E. coli O157:H7, respectively. The isolates were inoculated in BHI broth and incubated at 37 °C for 24 h until turbidity was comparable to a 0.5 McFarland turbidity standard. Before inoculating bacteria in milk, the inoculum was washed twice in PBS and then re-suspended in skim milk.

Determination of minimum inhibitory concentration (MIC) of free nisin and nisin nanoparticles against MRSA and E. Coli O157:H7

To determine the MIC of nisin NPs against MRSA and E.coli O157:H7, the agar well diffusion method was used according to Suresh et al. [ 40 ] with minor modifications. In brief, 0.1 mL of the previously prepared bacterial suspensions was spread on Mueller Hinton agar plates and left for 10 min to be absorbed. Then, 8 mm wells were punched into the agar plates for testing the antimicrobial activity of nanoparticles. One-hundred µl of different concentrations of free nisin and nisin NPs (from 0.0313 mg/mL to 2 mg/mL) were poured onto the wells. One well in each plate contained 100 µL of sterile deionized water was kept as a negative control. After overnight incubation at 35 ± 2 °C, the diameters of the inhibition zones were observed and measured in mm [ 41 ]. Each concentration was performed in triplicate.

Assessment of nisin nanoparticles cytotoxicity

The biocompatibility and the cytotoxicity of the nisin NPs were evaluated using a MTT assay against a Vero cell line after culture at 37 °C in a humidified incubator with 5% CO 2 in Dulbecco’s Modified Eagle’s Medium supplemented with 10% Fetal Bovine Serum. The cells were seeded into a 96-well plate at a density of 1 × 10 4 cells/well overnight before treatment. Different dilutions (0.5×MIC, MIC, 2×MIC, 4×MIC) of optimized nisin NPs were added to the seeded cells. Cells without nanoparticles served as control group. After 72 h, the consumed media was replaced with phosphate buffered saline, 10 µL from 12 mM MTT stock solution was added to each well and cells were incubated for 4 h at 37 °C. Next, 50 µL DMSO was added to dissolve formazan crystals and then the absorbance was measured at 570 nm using a BMG LABTECH®-FLUO star Omega microplate reader (Ortenberg, Germany). All experiments were performed in triplicate.

Antibacterial efficacy of the free nisin and nisin NPs against MRSA and E. Coli O157:H7 during manufacturing and storage of yoghurt

Fresh milk was heated at 85 °C for 5 min in water bath then suddenly cooled. The prepared inoculums were added to the warmed milk (41 ºC) in a count of 10 6 CFU/mL. The inoculated milk was divided into four parts for further use as following, part 1 is the positive control (contained MRSA or E. coli O157:H7 only, one jar each), part 2 (contained MRSA or E. coli O157:H7 with nisin NPs at MIC and 2×MIC, two jars each), part 3 (contained MRSA or E. coli O157:H7 with free nisin at MIC and 2×MIC, two jars each) and part 4 (negative control; free from pathogens and contained free nisin or nisin NPs only, one jar each). After inoculation of the different treatments, yoghurt was manufactured according to Sarkar [ 42 ] by adding 2% yoghurt starter culture ( Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus ) at 41 °C to milk. The prepared yoghurt was placed in a constant-temperature incubator at 40 °C until pH reached 4.6 to 4.5. Finally, the obtained products were stored at refrigeration temperature (4 ± 1 °C) for 5 days. Samples were collected just after manufacturing of yoghurt and every 2 days during storage, then tested for the count of MRSA using MRSA agar base media [ 43 ], and E. coli O157:H7 using Sorbitol MacConkey (SMAC) agar plates [ 44 ]. In addition, pH values were determined in the examined samples as previously described by Igbabul et al. [ 45 ]. In brief, 10 g o f yoghurt sample was dissolved in 100 mL of distilled water. The mixture was left to equilibrate at room temperature. Then, the pH of the samples was then measured by a pH meter (Microprocessor pH meter, pH 537, WTW, Germany).

Organoleptic assay of manufactured yogurt

Pathogen-free yoghurt jars (negative control) were prepared with two concentrations of either free nisin or nisin NPs (MIC and 2×MIC) as previously mentioned to be used for organoleptic evaluation. Thirty-five panelists were selected in teams of different ages, sex and education. The perception of consumers toward samples with two concentrations of nisin NPs was recorded. Consumers were asked to evaluate the color, flavor, mouth feel, appearance, and overall acceptability (OAA) of the prepared yoghurt samples containing nisin NPs [ 46 ]. The scale points were excellent (5); very good (4); good (3); acceptable (2); and poor (1).

Statistical analysis

One-way analysis of variance (ANOVA) was performed using the SPSS program (SPSS Inc., Chicago, IL, USA, 18) to determine the statistical significance of differences between groups. Results with P < 0.05 were considered statistically significant. The microbiological and cytotoxicity assay data were prepared using Excel software version 2017. While, the FTIR results were performed using Origin Lab 2021 for graphing and analysis. All experiments were carried out in triplicate.

Characterization of the prepared nanoparticles

The freshly prepared nisin NPs had 26.55 nm size and PDI 0.227 as determined by zetasizer. While, the diameter of the same after 6 months at refrigeration temperature was 86.50 nm with a PDI equal to 0.431 (Table 1 ). These results indicated that reasonable small-sized particles of nisin were obtained by precipitation technique using acetic acid. The small size of the prepared particles and the small PDI range (from 0.2 to 0.4) indicated a mono size dispersion and a good stability of the prepared nisin NPs.

The size and morphology of the freshly prepared nisin NPs and after 6 months of storage were measured by HRTEM are presented in Fig. 1 . Both freshly prepared and stored nisin NPs were approximately uniform in size with adequate distribution of particles. The shape of the particles was nearly spherical with slightly a bit of agglomeration just after 6 months of storage. The average size of freshly prepared nisin NPs was 7.35 nm while, after 6 months was 15.4 nm. The size of particles determined by TEM is usually smaller than the dynamic particles determined by zeta-sizer because TEM determine the actual particle diameter while zeta-sizer determine the particles diameter with adjacent moving layers of solvents.

The TEM images of freshly prepared nisin NPs (A) and after 6th months of storage (B)

Figure 2 showed the FTIR of pure and nisin NPs; both spectrum showed the characteristic peaks of nisin at 3425, 1599 and 1493 cm − 1 corresponded to O-H stretching of COOH, C = O stretching of amide I and N-H bending amide II. Bands 1530 cm − 1 in free nisin indicated the stretching of amid II and which, increased to 1549 cm − 1 in nisin NPs that indicated increase the H- bond in nano form than free one. The results of FTIR spectrum confirmed that the formation of nisin NPs did not result in any chemical changes or interaction of nisin with used the materials. These results also demonstrated the suitability of the applied method for the preparation of chemically stable and small-sized nisin NPs.

The FTIR of pure nisin and nisin NPs

Assessment of Nisin nanoparticles cytotoxicity

In the present study, Veros cells were exposed to nisin NPs for 48 and 72 h, and the cytotoxicity was measured by MTT assays. Results showed that the MIC did not exhibit an anti-proliferation effect (Fig. 3 ). Interestingly, even at very high concentrations (4xMIC), there were no cytotoxicity effect as the percentage of viable cells reach 92% and 89.98% after 48 and 72 h, respectively. The obtained findings confirmed the safety and good biocompatibility of the prepared nisin NPs at MIC level.

Cytotoxicity and cell viability of different concentrations nisin NPs using Vero cells after 48 and 72 h using MTT assay

MIC of free nisin and nisin NPs against MRSA and E. Coli O157:H7

The efficacy of the free nisin and prepared nisin NPs against MRSA and E. coli O157:H7 was investigated using agar well diffusion assay (Table 2 ). Nisin and its nanoparticles showed potent antibacterial effect against MRSA than E. coli O157:H7. The MICs of nisin and nisin NPs toward MRSA were 0.0625 and 0.0313 mg/mL, respectively. While, 0.125 mg/mL was the MIC of both nisin and nisin NPs against E. coli O157:H7. Of note, growth inhibition zone was not observed against MRSA at 0.0313 mg/mL of nisin, and toward E. coli O157:H7 at both 0.0625 and 0.0313 mg/mL nisin (Table 2 ). On the other hand, the prepared nisin NPs could produce inhibition zones against MRSA with a mean diameter ranged from 25.4 ± 2.1 mm to 7.1 ± 0.89 mm at concentrations of 2 to 0.0313 mg/mL, respectively. Also, the nisin NPs showed anti- E. coli O157:H7 activity at different concentrations of 2, 1, 0.5, 0.25 and 0.125 mg/mL with average size of 20.1, 15.4, 12.7, 9.5 and 7.2 mm of the inhibitory zones, respectively. There were no inhibition zones against E. coli O157:H7 at 0.0625 and 0.0313 mg/mL of nisin NPs. Overall, the obtained findings indicated that the most effective MICs of nisin and nisin NPs for both organisms were 0.125 mg/mL (Table 2 ).

Antibacterial effect of nisin and nisin NPs against MRSA and E. Coli O157:H7 during manufacturing and storage of yoghurt

Figure 4 presented the antibacterial activity of nisin against the examined foodborne pathogens (MRSA and E. coli O157:H7). Here, nisin at 0.125 and 0.25 mg/ml could induce antibacterial effect against MRSA (3.3 and 3 log 10 CFU/g, respectively) after 24 h of yoghurt storage. However the effect was not higher as in case of nisin NPs (2.3 and 1 log 10 CFU/g) at the same concentrations and time of storage. While, the inhibitory impact of the free nisin on E. coli O157:H7 was observed after 24 h (3.7 log 10 CFU/g) and 3 days (3.8 log 10 CFU/g) of storage at the concentrations of 0.25 and 0.125 mg/mL, respectively. The pathogens were still detected till the end of the experiment in nisin treated yoghurt (Fig. 4 ).

Antibacterial effect of free nisin (A) and nisin NPs (B) on MRSA and E.coli O157:H7 during manufacturing and storage of yoghurt

On the other hand, there was a clear reduction in mean count of MRSA and E.coli O157:H7 in the laboratory-manufactured yoghurt supplemented with different concentrations (0.125 and 0.25 mg/mL) of nisin NPs. A complete inhibition of MRSA was observed after 24 h and at the 3rd day of storage by 0.25 and 0.125 mg/mL of nisin NPs, respectively (Fig. 5 ). While, E. coli O157:H7 was undetectable at the 5th day of storage with 0.25 mg/mL nisin NPs, however it was still detected till the end of the experiment in either yoghurt inoculated with 0.125 mg/mL nisin NPs or in the positive control group (Fig. 4 ). Taken together, the antimicrobial count tests revealed that the free nisin is not effective as the nisin NPs at same time points during processing and storage of yoghurt.

During storage, the pH did not change significantly between different treatments. However, the negative control group showed little decrease in pH in comparison to other groups at the 3rd and 5th day of storage (3.5 and 3, respectively).

Evaluation of pH levels during processing and storage of yoghurt inoculated with different concentrations of free nisin or nisin NPs

Organoleptic evaluation of the laboratory-manufactured yoghurt

Figure 6 clarified that there was no difference in the sensory properties between the different groups (contained 0.125 or 0.25 mg/mL nisin (Fig. 6A) or nisin NPs (Fig. 6B)) in comparison to the control group. The OAA of yoghurt inoculated with 0.125 mg/mL and 0.25 mg/mL of free nisin was 3 and 2.5, respectively (Fig. 6A). While, the control samples had the highest score in mouth feel (4.5), followed in order with yoghurt loaded with 0.125 mg/mL and 0.25 mg/mL nisin NPs (3.8 and 2.7, respectively). Additionally, the overall acceptability (OOA) of control, 0.125 mg/mL and 0.25 mg/mL nisin NPs groups was 4, 3.7 and 3, respectively (Fig. 6B). Such findings indicated the high acceptability of yoghurt containing different concentrations of nisin NPs than those inoculated with free nisin.

Organoleptic properties of yoghurt inoculated with different concentrations of free nisin and nisin NPs

The current study elucidated for the first time the inhibitory effect of free nisin and nisin NPs on two of the most common foodborne pathogens (MRSA and E. coli O157:H7) during processing and storage of laboratory manufactured yoghurt. Strikingly, adding of nisin NPs to yoghurt could induce much higher antibacterial effect on MRSA and E. coli O157:H7 with high consumer acceptability than free nisin. Accordingly, nisin NPs could be a useful and effective bio-preservative candidate against MRSA and E. coli O157:H7 in dairy industry.

The present study revealed that nisin NPs was prepared by a novel and safe method using natural material such as acetic acid which is commonly applied in food products. Chang et al. [ 47 ]. prepared ultra-small sizes of nisin NPs by nanoprecipitation method using HCL while we obtained much smaller particle size of NNPs using acetic acid which is more safer, less toxic and accepted by consumers. The particle size determined by TEM is smaller than the size measured by DLS this difference could be attributed to the removal of solvent and shrinking of nanoparticles during the drying of nisin NPs samples for TEM investigations. In addition, DLS measures the hydrodynamic diameter of the dispersed moving particles with the surrounding moving layers of solvents [ 48 , 49 ].

The result of FTIR was in consistent with that of Flynn et al. [ 50 ]. Herein, we found that the -OH stretching peak of nisin NPs displayed a greater intensity than that of free nisin, which indicated a stronger hydrogen bonding formation within nisin NPs. In case of free nisin, the peak at 1620 cm − 1 corresponding to COO − was shifted to 1610 cm − 1 in nisin NPs indicating that the hydrogen bonding was increased within nisin NPs. In contrast, the amid II band in free nisin appeared at 1530 cm − 1 became more obvious at 1549 cm − 1 in nisin NPs which was in agreement with Webber et al. [ 51 ]. . Band of amide I at wave number of 1632 cm − 1 could be due to the change in the structure of free nisin when converted into nisin NPs by using natural acetic acid.

In food chain, nisin has been approved for use in over 50 countries due to its safety and its potent antimicrobial activity without inducing microbial resistance [ 52 ]. Of particular note, the FAO/WHO Codex Committee and US FDA allow using nisin as a food additive in dairy products at a concentration up to 250 mg/kg [ 1 , 53 ]. Moreover, European Food Safety Authority [ 54 ] reported that nisin has been shown to be non-toxic to humans and it is safe as a food preservative for dairy and meat products. In the current study, the examined organisms (MRSA and E. coli O157:H7) have been involved in many food outbreaks worldwide as well as their resistance to many antibiotics, considered a challenge to be controlled [ 55 , 56 , 57 ]. Therefore, the present study could be a useful alternative strategy to avoid the possible health hazards of these organisms after consumption of yoghurt using either nisin or nisin NPs as natural food preservatives.

The obtained results revealed that the MICs of nisin and nisin NPs against MRSA were lower than that of E. coli O157:H7. This could be due to the ability of nisin to penetrate the cell wall of Gram-positive bacteria, however, it is difficult for nisin to penetrate the outer membrane barrier of Gram-negative bacteria [ 58 ]. Nisin could destroy bacteria through two mechanisms, either by making pores in the plasma membrane or by inhibiting the cell wall biosynthesis through binding to lipid II [ 59 , 60 , 61 ]. Importantly, the obtained results in the current study showed that that MIC of nisin NPs against MRSA was lower than that of pure nisin. Similarly, Zohri et al. [ 62 ] reported that the MICs of nisin and Nisin-Loaded nanoparticles was 2 and 0.5 mg/mL after 72 h of incubation period with the S. aureus samples, respectively. In addition, Moshtaghi et al. [ 63 ] examined the antibacterial effect of nisin on S. aureus and E. coli at different pH values and they found that the MICs against S. aureus were ranged from 19 to 312 µg/mL of nisin at pH levels from 8 to 5.5, respectively. While for E. coli , the MICs were from 78 to 1250 µg/mL at the same range of pH, respectively [ 63 ].

Interestingly, nisin inhibited the pathogenic foodborne bacteria and many other Gram-positive food spoilage microorganisms [ 13 ]. In the present study, evaluation of the kinetic growth of MRSA and E. coli O157:H7 based on the total counts in the laboratory manufactured yoghurt revealed that nisin NPs was able to inhibit more effectively the growth of such foodborne pathogens than free nisin during manufacturing and storage of yoghurt. These findings were in concurrent with those obtained by Zohri et al. [ 62 ] who demonstrated that nisin-loaded chitosan/alginate nanoparticles showed more antibacterial effect than free nisin on the growth of S. aureus in raw and pasteurized milk samples. Additionally, nisin Z in liposomes can provide a powerful tool to improve nisin stability and inhibitory action against Listeria innocua in the cheddar cheese [ 64 ]. In our study, nisin NPs showed a complete inhibition of MRSA after curdling of yoghurt and reduced the survivability of E. coli O157:H7 when applied at two different concentrations during storage of such product. Nisin NPs with high specific surface area could be easily attached to the target cell surface leading to increased permeability of the cell membrane, and finally cause bacterial cell death. Furthermore, nisin NPs were thermo-tolerant because of the internal non-covalent interactions in the nanoparticles [ 4 , 65 ]. Additionally, the decline in the mean count of the examined pathogens (MRSA and E.coli O157: H7) in the current study may be due to the effect of low pH (high acidity) of yoghurt that leads to shrinkage and death of the bacterial cells [ 66 ]. Similarly, Al-Nabulsi et al. [ 67 ] reported that the combination of a starter culture, low temperature, and pH ( ∼ 5.2) had inhibitory effects on the growth of S. aureus .

The effect of adding different levels of nisin and nisin NPs on OAA scores of yoghurt was recorded and the obtained results were in agreement with Hussain et al. [ 68 ], Radha [ 3 ], and Gharsallaoui et al. [ 4 ] who reported that a Nigerian fermented milk product had acceptable sensory scores till 25th day of storage when loaded with nisin at 400 IU/mL. Additionally, Chang et al. [ 47 ] said that the thermal treatments are known to cause undesirable changes in the sensory, nutritional and/or technological properties of milk. Taking advantage of the antimicrobial action of nisin NPs against several spoilage and pathogenic microorganisms, this innovative non-thermal food preservative offers the inactivation of microorganisms with minimal impact on the quality, safety, nutritional values and acceptability of dairy products.

Overall, as the demand for preservative-free food products increased, natural antimicrobials have gained more and more attention because of their effectiveness and safety. Consequently, the current study investigated that the addition of nisin NPs to milk for manufacturing of yoghurt can be used as an innovative preventive measure to inhibit the contamination with foodborne pathogens. However, further researches are required to determine the effective and safe dose of nisin NPs for application in other dairy products.

The present study prepared nisin NPs using acetic acid by precipitation method and the obtained particles were small in size with good stability and consumer acceptability. The antibacterial effect of nisin and nisin NPs against MRSA and E. coli O157:H7 in yoghurt was impressive. Additionally, the studied nanoparticles did not affect the sensory and textural characteristics of the finished product. Hence, this study could be useful for yoghurt makers and dairy products factories through using this novel preservation technology to inhibit the growth of MRSA and E. coli O157:H7, in yoghurt and dairy products, and subsequently avoid food spoilage and foodborne diseases.

Data availability

All data and materials are available here in the current study.

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Acknowledgements

The authors thank the nanotechnology research and synthesis unit at animal health research institute, Assiut, Egypt for their help in preparation of nanomaterials.

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Walaa M. Elsherif & Gamal M. Zayed

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Alshimaa A. Hassanien

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W.M.E., A.A.H., G.M.Z., and S.M.K. conceived and designed the experiment. W.M.E., A.A.H., G.M.Z., and S.M.K. collected the experimental data. W.M.E., A.A.H., and S.M.K. performed the microbiological analysis. A.A.H. and G.M.Z. performed the preparation and analysis of nanoparticles. W.M.E. and S.M.K. performed the statistical analysis. All authors interpreted the data. W.M.E. wrote the first draft of the manuscript. All authors reviewed the manuscript.

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Elsherif, W.M., Hassanien, A.A., Zayed, G.M. et al. Natural approach of using nisin and its nanoform as food bio-preservatives against methicillin resistant Staphylococcus aureus and E.coli O157:H7 in yoghurt. BMC Vet Res 20 , 192 (2024). https://doi.org/10.1186/s12917-024-03985-1

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http://orcid.org/0000-0003-3235-0454 Nicholas S Hopkinson 1 ,
http://orcid.org/0000-0002-6187-0638 Charlotte Vrinten 2 ,
http://orcid.org/0000-0002-4385-2153 Jennie C Parnham 2 ,
Márta K Radó 3 ,
http://orcid.org/0000-0002-2101-2559 Filippos Filippidis 2 ,
Eszter P Vamos 2 ,
http://orcid.org/0000-0003-1318-8439 Anthony A Laverty 2
1 National Heart and Lung Institute , Imperial College London , London , UK
2 Department of Primary Care and Public Health , Imperial College London School of Public Health , London , UK
3 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm , Sweden
Correspondence to Dr Anthony A Laverty, Department of Primary Care and Public Health, Imperial College London School of Public Health, London, W6 8RP, UK; a.laverty{at}imperial.ac.uk

Background Social media may influence children and young people’s health behaviour, including cigarette and e-cigarette use.

Methods We analysed data from participants aged 10–25 years in the UK Household Longitudinal Study 2015–2021. The amount of social media use reported on a normal weekday was related to current cigarette smoking and e-cigarette use. Generalised estimating equation (GEE) logistic regression models investigated associations of social media use with cigarette smoking and e-cigarette use. Models controlled for possible confounders including age, sex, country of UK, ethnicity, household income and use of cigarette/e-cigarettes by others within the home.

Results Among 10 808 participants with 27 962 observations, current cigarette smoking was reported by 8.6% of participants for at least one time point, and current e-cigarette use by 2.5% of participants. In adjusted GEE models, more frequent use of social media was associated with greater odds of current cigarette smoking. This was particularly apparent at higher levels of use (eg, adjusted odds ratio (AOR) 3.60, 95% CI 2.61 to 4.96 for ≥7 hours/day vs none). Associations were similar for e-cigarettes (AOR 2.73, 95% CI 1.40 to 5.29 for ≥7 hours/day social media use vs none). There was evidence of dose–response in associations between time spent on social media and both cigarette and e-cigarette use (both p<0.001). Analyses stratified by sex and household income found similar associations for cigarettes; however, for e-cigarettes associations were concentrated among males and those from higher household income groups.

Conclusions Social media use is associated with increased risk of cigarette smoking and e-cigarette use. There is a need for greater research on this issue as well as potential policy responses.

Tobacco control

Data availability statement

Data are available in a public, open access repository. Data available from UK Data Service https://ukdataservice.ac.uk .

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/thorax-2023-220569

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WHAT IS ALREADY KNOWN ON THIS TOPIC

There is substantial use of social media among children and young people, which has had debated impacts on health outcomes. There are studies examining social media use and associations with cigarette and e-cigarette use in the US but only two such studies in the UK. One study was cross-sectional, while one previous cohort study of data from 2014 to 2018 found that social media use at age 14 years was associated with a greater likelihood of cigarette smoking at age 17 years. This study did not, however, assess the use of e-cigarettes.

WHAT THIS STUDY ADDS

This study examined daily use of social media among 10–25-year-olds from 2015 to 2021. It found that time spent on social media is associated, in a dose-dependent manner, with likelihood both of cigarette smoking and vaping. Those using social media for ≥7 hours/day were more than two and a half times more likely to use both cigarettes and e-cigarettes than those not using social media.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study highlights that more frequent social media use is associated with increased likelihood of using both cigarettes and e-cigarettes in the UK. This reinforces concerns that social media is a vector of direct and indirect marketing and promotion of these products and that policies to curtail this may be warranted.

Introduction

Understanding the mechanisms that drive uptake and use of cigarettes and e-cigarettes is key to developing strategies to prevent harm. The use of social media has been identified as a novel potential vector, with substantial increases in time spent in this activity by young people. 1–5 Social media use increases with age, and girls are more likely to spend longer periods of time on social media than boys. 6 Social media may be driving cigarette smoking and e-cigarette use through both direct, targeted advertisements and the use of paid influencers by the tobacco industry. 7 To date, most evidence on the impact of social media on cigarette and e-cigarette use has focused on America. 8–10 This has found associations with uptake, regular use and reduced perceptions of harm and has included assessment of engagement with different platforms. 11–13 The only two previous UK studies include a cohort study which found that social media use at age 14 years was associated with greater likelihood of cigarette use at age 17 years. 14 A second cross-national study from 42 countries including the UK concluded that there was a link between social media use and substance use but did not examine cigarette use separately from other substances. 15

Previous research has identified links between social media use and both cigarette and e-cigarette use. For example, analyses of Instagram have identified networks of influencers promoting e-cigarettes, often without disclosing financial relationships; while Juul has recently settled a lawsuit over marketing of e-cigarettes to teens, including on social media. 16 17 Comparative analyses in the UK have found good compliance with advertising standards for e-cigarettes on traditional media, but high levels of breaches on social media. 18 Analyses of 11 of the most popular social media platforms have highlighted high levels of tobacco promotion, with few platforms having policies to deal with novel forms of promotion such as sponsored or influencer content. 19 A systematic review of exposure to tobacco promotion and use identified 29 studies (none from the UK) and concluded that there is a need for greater regulation. 20 Any proposal to regulate social media needs to be justified and based on evidence. To contribute to this, we examined the longitudinal relationship of social media use with cigarette smoking and e-cigarette use among children and young people in the UK.

Data come from participants of the UK Household Longitudinal Study (UKHLS), also known as Understanding Society. 21 This is a longitudinal household panel study with annual surveys starting in 2009. The original sample consisted of a clustered and stratified probability sample of approximately 28 000 households in the UK. Data are collected via face-to-face interviews carried out by a trained interviewer in the respondent’s home and via online, self-completion questionnaires. Adults over the age of 16 years or above are asked to complete an individual questionnaire, including a self-completion questionnaire. Household members aged 10–15 years are asked to fill in a shorter self-completion questionnaire, with permission from their parent or carer.

We have focused on children and young adults aged 10–25 years using data from 2015/2016 to 2020/2021 (wave 7 to wave 12). Questions on e-cigarette use were added to UKHLS in 2015/2016. Participation in the panel is voluntary, with a gift voucher sent to encourage completion of questionnaires and a further gift voucher sent when these are completed. All participants provided consent to be interviewed. The University of Essex Ethics Committee approved all data collection. 22

Outcomes and exposure

We used three separate binary outcomes: current cigarette smoking, current e-cigarette use and current dual use of both products. Participants were classified as current cigarette smokers if they responded “I usually smoke between one and six cigarettes a week” or “I usually smoke more than six cigarettes a week”. All other responses were coded as non-users. The same question was used for all waves of data and for all ages.

Current e-cigarette use was first assessed in 2015/2016 with the question “Do you ever use electronic cigarettes (e-cigarettes)?” with response options “Yes” and “No”. From wave 8 (2016/2017) onwards participants were classified as current (weekly) e-cigarette users if they responded “I use e-cigarettes at least once a week”. All other responses were coded as non-users. Dual use was classified as participants currently using both products, with those using only one or no products classed as non-dual users.

The main exposure variable was social media use. Participants were first asked “Do you belong to any social networking websites?” (Yes/No), and if “Yes”, they were also asked how many hours they spend chatting or interacting with friends through a social website on a normal weekday, with the following response options: “None”, “<1 hour/day”, “1–3 hours”, “4–6 hours” and “≥7 hours”. We combined those reporting “None” along with those who were not a member of a social media website into a reference category of “Not a member or no use”. 6

We considered a range of potentially relevant sociodemographics: age, sex, country in UK, self-defined ethnic group (collapsed into White vs non-White due to low numbers in the non-White category), an indicator of living in an urban or rural areas (derived from Office for National Statistics Rural and Urban Classification of Output Areas) and equivalised household net income (based on the Organisation for Economic Co-operation and Development (OECD) equivalence scale, which was used to adjust household income by household composition 23 ).

Statistical analyses

We compared differences in sociodemographics between categories of social media use using ANOVA. We used binary generalised estimating equation (GEE) regression models (family: binomial; link: logit; correlation matrix: exchangeable) to assess relationships between social media use and product use, using separate models for each outcome: cigarette smoking, e-cigarette use and dual use. GEE models assess changes over time and account for the correlation caused by observations being from the same individuals. 24 We also present tests for trend based on frequency of social media use. Analyses were adjusted for time (categorical) as well as the sociodemographic variables listed above. Models of cigarette smoking were additionally adjusted for parental tobacco use, models of e-cigarette use were adjusted for parental e-cigarette use, and models of dual use were adjusted for both. Analyses used survey weights designed by the UKHLS survey team to account for clustered and stratified probability sampling and non-response bias. 25

We tested for interactions of social media use with age (split into above and below 18 years of age), sex and household income (in three groups). This was due to possible differences between those above and below the legal age of sale, greater social media use among women, and potentially differential effects by socioeconomic groups. All interactions were p<0.001 and so we present stratified analyses. Due to the small numbers, we did not test interactions for dual use.

Sensitivity analyses

We performed a range of sensitivity analyses to test the robustness of our findings. As it is possible that those not using social media at all are atypical, we repeated our analyses excluding these participants. Our main analyses used household income as a marker of socioeconomic status. We also performed our analyses using Index of Multiple Deprivation (IMD) (in five groups) as an alternative marker of socioeconomic status. We performed analyses categorising current e-cigarette use as participants who reported using e-cigarettes at least monthly. We also performed analyses controlling for a measure of mental health (the 12-item General Health Questionnaire (GHQ-12)) to consider whether this is a possible pathway, whereby social media impacts mental health, which is then linked to cigarette and e-cigarette use.

Finally, we used fixed effects analyses to directly test if changes in social media use corresponded to uptake of cigarette smoking and e-cigarette use. These adjusted for the time-varying variables parental cigarette/e-cigarette use and household income. These models were on a smaller subset of individuals who were not product users when entering the study and who were found to change their social media use over time.

Outcomes and covariates across categories of social media use are shown in table 1 . Overall, 8.6% of the sample reported current cigarette smoking at one or more data point, 2.5% reported current e-cigarette use, and 1.1% of participants were dual users at one or more data point. Social media use frequency broken down by covariates is shown in online supplemental appendix table 1 .

Supplemental material

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Description of sample observations by social media use 2015–2021

Cigarette smoking, e-cigarette use and dual use were all more common among participants reporting greater social media use (all p<0.001) ( figure 1 ). Some 2.0% of participants who used social media “None or not a member” reported being a current cigarette smoker compared with 15.7% among those using social media for ≥7 hours/day. Current e-cigarette use ranged from 0.8% among those not using social media to 2.5% among those using it for ≥7 hours/day.

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Cigarette smoking, e-cigarette use and dual use by social media use.

Differences between categories of social media use were apparent for all variables studied (all p<0.001). Males were less likely to be in higher social media use groups than females (57.5% of the “None or not a member” social media group compared with 39.9% of the “≥7 hours/day” group). Social media use was more frequent at older ages (mean age of “None or not a member” social media group 12.0 years vs 17.7 years for the “≥7 hours/day” group). Parental cigarette smoking was more common among those using social media more frequently (17.0% for the “None or not a member” social media group vs 25.2% for the “≥7 hours/day” group) as was parental e-cigarette use (7.3% and 10.5%, respectively).

Table 2 shows results of our GEE models of social media use and cigarette smoking. Cigarette smoking was more common among those using social media more frequently (p for trend <0.001). Those using social media for “<1 hour/day” were more likely to be current cigarette smokers than those using social media “None or not a member” (adjusted odds ratio (AOR) 1.92, 95% CI 1.43 to 2.58) ( table 2 ). Those using social media for “≥7 hours/day” were substantially more likely to be current cigarette smokers than those using social media “None or not a member” (AOR 3.60, 95% CI 2.61 to 4.96).

Associations of social media use with current cigarette use from generalised estimating equation model

Table 3 shows results for e-cigarette use. E-cigarette use was more common among those using social media more frequently (p for trend <0.001). E-cigarette use was more common among those using social media “1–3 hours per day” compared with those using it “None or not a member” (AOR 1.92, 95% CI 1.07 to 3.46). E-cigarette use was considerably more likely among participants using social media “≥7 hours/day” than those using social media “None or not a member” (AOR 2.73, CI 1.40 to 5.29).

Associations of social media use and current e-cigarette use from generalised estimating equation model

Table 4 shows results for dual cigarette and e-cigarette use. Models have wide confidence intervals reflecting low levels of dual use. Those using social media more frequently were more likely to be dual users (p for trend <0.001). Those using social media “1–3 hours per day” were more likely to be dual users compared with those using it “None or not a member” (AOR 3.28, 95% CI 1.24 to 8.70). Dual use was more likely among participants using social media “≥7 hours/day” than among those using social media “None or not a member” (AOR 4.96, 95% CI 1.71 to 14.34).

Associations of social media use with current e-cigarette and cigarette dual use from generalised estimating equation model

Interactions of social media and sex were statistically significant for both cigarettes and e-cigarettes (both p<0.001). In stratified models ( table 5 ) AORs were similar between the sexes for current cigarette smoking. For e-cigarettes, associations between social media use and e-cigarette use were statistically significant for males but not for females (AOR 4.10, 95% CI 1.90 to 8.87 for males for “≥7 hours/day” vs “None or not a member” social media use).

Associations of social media use with current e-cigarette and cigarette use from gender stratified generalised estimating equation models

Interactions with household income categories were statistically significant (p<0.001 for both cigarettes and e-cigarettes) ( table 6 ). In stratified analyses of cigarette smoking, point estimates for the richest income group were higher than for the lowest income group, although these overlapped (eg, AOR 5.22 for “≥7 hours/day”, 95% CI 2.82 to 9.67 for the richest income group vs AOR 4.17, 95% CI 2.27 to 7.65 for the lowest income group). For e-cigarette use, associations were statistically significant for the highest income groups (eg, AOR 7.85, 95% CI 1.72 to 35.82 for “≥7 hours/day” vs no social media use) but were not statistically significant for the lowest income group.

Associations of social media use with current e-cigarette and cigarette use from household income stratified generalised estimating equation models

Analyses stratified by age found similar results to main analyses for cigarettes ( online supplemental appendix table 2 ). Models for e-cigarette use were only statistically significant among those <18 years old.

GEE analyses excluding those not using any social media were similar to main analyses ( online supplemental appendix table 3 ). Analyses using IMD as a marker of socioeconomic status rather than household income also gave similar results ( online supplemental appendix table 4 ). Analyses classifying current e-cigarette use as participants using them at least monthly also gave similar results although with larger point estimates ( online supplemental appendix table 5 ). Analyses controlling for GHQ-12 as a measure of mental health were similar for cigarettes but did not find statistically significant associations between social media and e-cigarette or dual use. This may indicate that social media use impacts mental health, which in turn impacts likelihood of using cigarettes or e-cigarettes, although this result should be treated with caution ( online supplemental appendix table 6 ).

Fixed effect analyses gave similar results to main analyses for uptake of cigarette smoking ( online supplemental appendix table 7 ). It should be noted that sample size was much reduced for this model (n=864). These analyses found some evidence that changes in social media use are linked to uptake of cigarette smoking in a dose–response manner (p for trend=0.053). For example, changing to using social media for ≥7 hours/day was associated with more than double the odds of taking up cigarette smoking (AOR 2.33, 95% CI 1.28 to 4.24).

Associations between changes in social media use and uptake of e-cigarettes did not reveal associations between changes in social media use and uptake of e-cigarettes. These analysis models had even lower sample sizes (n=564). For example, AORs of e-cigarette uptake ranged from 0.71 (95% CI 0.34 to 1.48) for participants using social media “<1 hour/day” to AOR 0.84 (95% CI 0.38 to 1.85) for those using social media “≥7 hours/day”. The test for trend was not statistically significant (p=0.584).

The main finding of the present study is that in children and young adults more frequent social media use was associated with a higher likelihood of both current use of cigarettes and e-cigarettes. This association was independent of other factors associated with increasing smoking and vaping including age, gender, socioeconomic status and parental smoking and vaping. These findings were robust to sensitivity analyses, while in stratified analyses there were more consistent associations for e-cigarette use among those under the legal age of sale, males and those with higher household incomes.

While we were unable to assess use of specific social media platforms or what content was being accessed, we propose a number of possible, non-exclusive explanations for this relationship. First, and most straightforwardly, there is evidence that the corporations behind cigarette smoking and vaping make use of social media to advertise and promote their products. 8–10 16 This includes direct advertising which is algorithmically targeted and the use of paid social media influencers who present smoking and vaping as a fashionable and desirable activity. Greater time spent on social media is likely to increase exposure to these forms of influence. While cigarettes and e-cigarettes are likely promoted differently, we found association with use of both products, highlighting the need for greater understanding of such corporate behaviours. Second, social media use has been shown to have features in common with reward-seeking addictive behaviour. 26 High social media use may increase susceptibility to other addictive behaviours like smoking. Alternatively, both behaviours may be driven by a common susceptibility. Third, as a space that is largely unsupervised by parents/caregivers, social media use may encourage behaviours that are transgressive, including cigarette smoking and vaping. There is evidence that peer smoking is a strong influence on child uptake of smoking 27 and social media is one of the ways in which peer smoking and vaping will be experienced, both by seeing others’ behaviour and by sharing “influencer content” that promotes these behaviours.

Stratified analyses revealed more consistent associations for cigarettes, while for e-cigarettes statistically significant associations were only found for those under the legal age of sale, among males, and those from richer households. Analyses of cigarette smoking did not identify changes over time, which fits with other evidence that smoking prevalence has been reasonably consistent over this time frame. 28 Analyses of e-cigarette use found reduced odds of these outcomes after 2015/2016, likely caused by changes in e-cigarette use ascertainment, although our main findings were robust to reclassification to examine monthly use. Our main analyses focused on weekly use of e-cigarettes; as any health impacts are probably related to amounts of vapour inhaled, this measure of regular use is more important for health than e-cigarette experimentation.

Strengths and limitations

This study uses a nationally representative cohort to examine social media use and use of cigarettes and e-cigarettes over time. UKHLS households are sampled based on geographical areas, population densities and ethnic composition, with survey weight adjusting for differential non-response across groups. 29 We conducted a range of sensitivity analyses, although other potential factors such as education may also be important. All data are based on self-report, and specifically we do not have information about which social media platforms were being used or how individuals were using them, for example, the extent to which they are interacting socially with individuals they know or consuming content from influencers, personalities or media corporations, etc. Precise pathways remain to be fully elucidated: our sensitivity analyses point to a possible role for mental health, although it should be noted that a formal mediation analysis was outside the scope of this article. As cigarette smoking is linked to poorer mental health, these relationships could well be bidirectional. 30 This, as well as potential targeted advertising, are among pathways that should be investigated in both quantitative and qualitative research.

Policy implications

Although we do not have data on the specific platforms used or content used, there is compelling evidence that vape companies are using social media to market their products. 2–5 The content that social media users are exposed to is to a substantial extent algorithmically controlled, both through targeted advertising and by the promotion of material that maximises engagement in order to increase revenue to the platform. This can be controlled. For example, far right imagery which is otherwise widely available is largely inaccessible in Germany, as a consequence of German law which social media platforms are bound to enforce. The companies that own social media platforms have substantial power to modify exposure to material that promotes smoking and vaping if they choose to or are compelled to. Voluntary codes seem unlikely to achieve this, and the introduction and enforcement on bans on material that promote this should be considered. In general, we think that algorithms should not be promoting products to individuals that they cannot legally buy. Legislation and enforcement around this and other corporate determinants of health concerns should be considered a core part of online safety and child protection.

This longitudinal analysis of children and young people in the UK found that more frequent social media use is associated with an increased risk of cigarette and e-cigarette use.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

Royal Society for Public Health
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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

Data supplement 1

X @COPDdoc, @cvrinten, @anthonylav

Contributors AAL conceived the project. JCP, CV and AAL cleaned data and performed the analyses with guidance from FF and MKR. AAL and NSH wrote the first draft and all authors contributed to this process. AAL is guarantor

Funding This study was supported by Cancer Research UK (CRUK PPRCTAGPJT\100005).

Competing interests NSH is Chair of Action on Smoking and Health and Medical Director of Asthma and Lung UK. AAL is a Trustee of Action on Smoking and Health.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Linked Articles

Editorial Strengthening the evidence base to support stronger regulation of social media based advertising of e-cigarette products to youth Kim L Lavoie Thorax 2024; - Published Online First: 16 May 2024. doi: 10.1136/thorax-2023-221169

Read the full text or download the PDF:

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Medications for Opioid Use Disorder (MOUD) Study

In 2022, an estimated 6.1 million people ages 12 or older reported having an opioid use disorder (OUD).
Common treatment options for OUD include medications for opioid use disorder (MOUD) (including methadone, buprenorphine, naltrexone) and counseling without medication.
CDC's National Center for Injury Prevention and Control conducted a study of Medications for Opioid Use Disorder (MOUD) to better understand treatment engagement and factors that may influence treatment experiences and outcomes.

MOUD study objectives

The objectives of this 18-month observational cohort study in outpatient settings without random assignment to OUD treatment were to:

Identify factors that influence the type of OUD treatment offered to patients and the patient's treatment choice.
Better understand patient and outpatient treatment facility factors associated with key OUD treatment outcomes.
Identify relevant antecedents or co-occurring conditions that could be addressed through primary prevention.
Inform evidence-based practices and OUD treatment policies.

Types of data

The collected data are available for the public to access. The publicly available data from the MOUD Study include:

Type of OUD treatment
Substance use
Drug overdose
Health-related quality of life
Socioeconomic status
Side effects (e.g., patients reported experiencing an adverse reaction to the current or most recent MOUD treatment)
Resources utilization (e.g., patients reported receiving peer-to-peer recovery support services)
COVID-related information (e.g., patients reported completing the survey during the COVID-19 pandemic)
Distance to treatment facility
Treatment facility ID
Region of treatment facility
Mortality outcomes

Patients from around the United States were included

All sites were outpatient treatment facilities. 1 Information about individual treatment site will not be released to protect patient and staff privacy.

Cities included in the study:

Birmingham, Alabama
Boston, Massachusetts
Chicago, Illinois
Cincinnati, Ohio
Dallas, Texas
Denver, Colorado
Huntington, West Virginia
Los Angeles, California
New York, New York
Phoenix, Arizona
Raleigh-Durham, North Carolina
Salt Lake City, Utah
San Francisco, California
Seattle, Washington
Washington, DC Metro Area

Data collection methods

Opioid use disorder (oud)‎.

The MOUD Study was a longitudinal, observational cohort study. The study sample included 1,974 adults with opioid use disorder (OUD) receiving various types of OUD treatment at 62 outpatient facilities over 18 months.

Participants were followed from March 2018 through May 2021.
The types of OUD treatment included MOUD (i.e., methadone, buprenorphine, naltrexone), or counseling without MOUD.
Patients in this study were asked to complete five web-based, self-administered questionnaires—baseline and follow up at 3-, 6-, 12-, and 18-months post-baseline.

The response rate was 100% at baseline, 72% at 3 months, 68% at 6 months, 52% at 12 months, and 53% at 18 months. 1

Researchers might consider imputation methods to deal with missing data. Researchers can use treatment facility ID to address the correlation between observations within the same treatment facility.

How the data is interpreted

Please note, CDC staff are unable to provide analytic or technical assistance to researchers who use this publicly available data.

Contact Information‎

Published manuscripts using the MOUD Study Data:

Dever, Jill A., et al. "The Medications for Opioid Use Disorder Study: Methods and Initial Outcomes From an 18-Month Study of Patients in Treatment for Opioid Use Disorder." Public Health Reports (2023): 00333549231222479. https://journals.sagepub.com/doi/epub/10.1177/00333549231222479
Villamil, Vanessa I., et al. "Barriers to retention in medications for opioid use disorder treatment in real-world practice." Journal of Substance Use and Addiction Treatment (2024): 209310. https://www.sciencedirect.com/science/article/pii/S2949875924000225
Nataraj, Nisha., et al. "Public Health Interventions and Overdose-Related Outcomes Among Persons With Opioid Use Disorder." JAMA Netw Open. 2024;7(4):e244617. DOI: 10.1001/jamanetworkopen.2024.4617

Data reporting

There are no costs for accessing the publicly available MOUD Study data.

Download Data Files, Patient Questionnaires, and Codebooks:

The MOUD Study Codebook

Contains a list of variables, their labels and distributions from baseline to 18 months post-baseline.

Patient questionnaires

Contains questions the enrolled patients were asked from baseline to 18 months post-baseline.

Publicly available MOUD Study Data:

Patient Baseline Data
Patient 3-Month Data
Patient 6-Month Data
Patient 12-Month Data
Patient 18-Month Data

Data quality

Data limitations can be found in the MOUD Study publications listed below.

More information on:

Medications for Opioid Use Disorder (MOUD)
Find Help & Treatment
Preventing Overdose
Preventing Opioid Use Disorder
Treating Opioid Use Disorder
Linking People to Opioid Use Disorder Treatment
Addiction Medicine Toolkit
Substance Abuse and Mental Health Services Administration. (2023). Key substance use and mental health indicators in the United States: Results from the 2022 National Survey on Drug Use and Health (HHS Publication No. PEP23-07-01-006, NSDUH Series H-58). Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. https://www.samhsa.gov/data/report/2022-nsduh-annual-national-report

Overdose Prevention

Overdose prevention is a CDC priority that impacts families and communities. Drug overdose is a leading cause of preventable death in the U.S.

For Everyone

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