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• Mixed Methods Research | Definition, Guide & Examples

Mixed Methods Research | Definition, Guide & Examples

Published on August 13, 2021 by Tegan George . Revised on June 22, 2023.

Mixed methods research combines elements of quantitative research and qualitative research in order to answer your research question . Mixed methods can help you gain a more complete picture than a standalone quantitative or qualitative study, as it integrates benefits of both methods.

Mixed methods research is often used in the behavioral, health, and social sciences, especially in multidisciplinary settings and complex situational or societal research.

• To what extent does the frequency of traffic accidents ( quantitative ) reflect cyclist perceptions of road safety ( qualitative ) in Amsterdam?
• How do student perceptions of their school environment ( qualitative ) relate to differences in test scores ( quantitative ) ?
• How do interviews about job satisfaction at Company X ( qualitative ) help explain year-over-year sales performance and other KPIs ( quantitative ) ?
• How can voter and non-voter beliefs about democracy ( qualitative ) help explain election turnout patterns ( quantitative ) in Town X?
• How do average hospital salary measurements over time (quantitative) help to explain nurse testimonials about job satisfaction (qualitative) ?

Table of contents

When to use mixed methods research, mixed methods research designs, advantages of mixed methods research, disadvantages of mixed methods research, other interesting articles, frequently asked questions.

Mixed methods research may be the right choice if your research process suggests that quantitative or qualitative data alone will not sufficiently answer your research question. There are several common reasons for using mixed methods research:

• Generalizability : Qualitative research usually has a smaller sample size , and thus is not generalizable. In mixed methods research, this comparative weakness is mitigated by the comparative strength of “large N,” externally valid quantitative research.
• Contextualization: Mixing methods allows you to put findings in context and add richer detail to your conclusions. Using qualitative data to illustrate quantitative findings can help “put meat on the bones” of your analysis.
• Credibility: Using different methods to collect data on the same subject can make your results more credible. If the qualitative and quantitative data converge, this strengthens the validity of your conclusions. This process is called triangulation .

As you formulate your research question , try to directly address how qualitative and quantitative methods will be combined in your study. If your research question can be sufficiently answered via standalone quantitative or qualitative analysis, a mixed methods approach may not be the right fit.

But mixed methods might be a good choice if you want to meaningfully integrate both of these questions in one research study.

Keep in mind that mixed methods research doesn’t just mean collecting both types of data; you need to carefully consider the relationship between the two and how you’ll integrate them into coherent conclusions.

Mixed methods can be very challenging to put into practice, and comes with the same risk of research biases as standalone studies, so it’s a less common choice than standalone qualitative or qualitative research.

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There are different types of mixed methods research designs . The differences between them relate to the aim of the research, the timing of the data collection , and the importance given to each data type.

As you design your mixed methods study, also keep in mind:

• Your research approach ( inductive vs deductive )
• Your research questions
• What kind of data is already available for you to use
• What kind of data you’re able to collect yourself.

Here are a few of the most common mixed methods designs.

Convergent parallel

In a convergent parallel design, you collect quantitative and qualitative data at the same time and analyze them separately. After both analyses are complete, compare your results to draw overall conclusions.

• On the qualitative side, you analyze cyclist complaints via the city’s database and on social media to find out which areas are perceived as dangerous and why.
• On the quantitative side, you analyze accident reports in the city’s database to find out how frequently accidents occur in different areas of the city.

In an embedded design, you collect and analyze both types of data at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.

This is a good approach to take if you have limited time or resources. You can use an embedded design to strengthen or supplement your conclusions from the primary type of research design.

Explanatory sequential

In an explanatory sequential design, your quantitative data collection and analysis occurs first, followed by qualitative data collection and analysis.

You should use this design if you think your qualitative data will explain and contextualize your quantitative findings.

Exploratory sequential

In an exploratory sequential design, qualitative data collection and analysis occurs first, followed by quantitative data collection and analysis.

You can use this design to first explore initial questions and develop hypotheses . Then you can use the quantitative data to test or confirm your qualitative findings.

“Best of both worlds” analysis

Combining the two types of data means you benefit from both the detailed, contextualized insights of qualitative data and the generalizable , externally valid insights of quantitative data. The strengths of one type of data often mitigate the weaknesses of the other.

For example, solely quantitative studies often struggle to incorporate the lived experiences of your participants, so adding qualitative data deepens and enriches your quantitative results.

Solely qualitative studies are often not very generalizable, only reflecting the experiences of your participants, so adding quantitative data can validate your qualitative findings.

Method flexibility

Mixed methods are less tied to disciplines and established research paradigms. They offer more flexibility in designing your research, allowing you to combine aspects of different types of studies to distill the most informative results.

Mixed methods research can also combine theory generation and hypothesis testing within a single study, which is unusual for standalone qualitative or quantitative studies.

Mixed methods research is very labor-intensive. Collecting, analyzing, and synthesizing two types of data into one research product takes a lot of time and effort, and often involves interdisciplinary teams of researchers rather than individuals. For this reason, mixed methods research has the potential to cost much more than standalone studies.

Differing or conflicting results

If your analysis yields conflicting results, it can be very challenging to know how to interpret them in a mixed methods study. If the quantitative and qualitative results do not agree or you are concerned you may have confounding variables , it can be unclear how to proceed.

Due to the fact that quantitative and qualitative data take two vastly different forms, it can also be difficult to find ways to systematically compare the results, putting your data at risk for bias in the interpretation stage.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

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Dissertation Research—Planning, Researching, Publishing

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• Qualitative
• Quantitive Research
• Mixed Methods Research
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Mixed methods research is an approach that combines both quantitative and qualitative forms. It involves philosophical assumptions, and the mixing of qualitative and quantitative approaches in tandem so that the overall strength of a study is greater than either qualitative or quantitative methods ( Creswell, 2007 ) .

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• What is mixed methods research?

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By blending both quantitative and qualitative data, mixed methods research allows for a more thorough exploration of a research question. It can answer complex research queries that cannot be solved with either qualitative or quantitative research .

Analyze your mixed methods research

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Mixed methods research combines the elements of two types of research: quantitative and qualitative.

Quantitative data is collected through the use of surveys and experiments, for example, containing numerical measures such as ages, scores, and percentages. 

Qualitative data involves non-numerical measures like beliefs, motivations, attitudes, and experiences, often derived through interviews and focus group research to gain a deeper understanding of a research question or phenomenon.

Mixed methods research is often used in the behavioral, health, and social sciences, as it allows for the collection of numerical and non-numerical data.

• When to use mixed methods research

Mixed methods research is a great choice when quantitative or qualitative data alone will not sufficiently answer a research question. By collecting and analyzing both quantitative and qualitative data in the same study, you can draw more meaningful conclusions. 

There are several reasons why mixed methods research can be beneficial, including generalizability, contextualization, and credibility. 

For example, let's say you are conducting a survey about consumer preferences for a certain product. You could collect only quantitative data, such as how many people prefer each product and their demographics. Or you could supplement your quantitative data with qualitative data, such as interviews and focus groups , to get a better sense of why people prefer one product over another.

It is important to note that mixed methods research does not only mean collecting both types of data. Rather, it also requires carefully considering the relationship between the two and method flexibility.

You may find differing or even conflicting results by combining quantitative and qualitative data . It is up to the researcher to then carefully analyze the results and consider them in the context of the research question to draw meaningful conclusions.

When designing a mixed methods study, it is important to consider your research approach, research questions, and available data. Think about how you can use different techniques to integrate the data to provide an answer to your research question.

• Mixed methods research design

A mixed methods research design  is   an approach to collecting and analyzing both qualitative and quantitative data in a single study.

Mixed methods designs allow for method flexibility and can provide differing and even conflicting results. Examples of mixed methods research designs include convergent parallel, explanatory sequential, and exploratory sequential.

By integrating data from both quantitative and qualitative sources, researchers can gain valuable insights into their research topic . For example, a study looking into the impact of technology on learning could use surveys to measure quantitative data on students' use of technology in the classroom. At the same time, interviews or focus groups can provide qualitative data on students' experiences and opinions.

• Types of mixed method research designs

Researchers often struggle to put mixed methods research into practice, as it is challenging and can lead to research bias. Although mixed methods research can reveal differences or conflicting results between studies, it can also offer method flexibility.

Designing a mixed methods study can be broken down into four types: convergent parallel, embedded, explanatory sequential, and exploratory sequential.

Convergent parallel

The convergent parallel design is when data collection and analysis of both quantitative and qualitative data occur simultaneously and are analyzed separately. This design aims to create mutually exclusive sets of data that inform each other. 

For example, you might interview people who live in a certain neighborhood while also conducting a survey of the same people to determine their satisfaction with the area.

Embedded design

The embedded design is when the quantitative and qualitative data are collected simultaneously, but the qualitative data is embedded within the quantitative data. This design is best used when you want to focus on the quantitative data but still need to understand how the qualitative data further explains it.

For instance, you may survey students about their opinions of an online learning platform and conduct individual interviews to gain further insight into their responses.

Explanatory sequential design

In an explanatory sequential design, quantitative data is collected first, followed by qualitative data. This design is used when you want to further explain a set of quantitative data with additional qualitative information.

An example of this would be if you surveyed employees at a company about their satisfaction with their job and then conducted interviews to gain more information about why they responded the way they did.

Exploratory sequential design

The exploratory sequential design collects qualitative data first, followed by quantitative data. This type of mixed methods research is used when the goal is to explore a topic before collecting any quantitative data.

An example of this could be studying how parents interact with their children by conducting interviews and then using a survey to further explore and measure these interactions.

Integrating data in mixed methods studies can be challenging, but it can be done successfully with careful planning.

No matter which type of design you choose, understanding and applying these principles can help you draw meaningful conclusions from your research.

• Strengths of mixed methods research

Mixed methods research designs combine the strengths of qualitative and quantitative data, deepening and enriching qualitative results with quantitative data and validating quantitative findings with qualitative data. This method offers more flexibility in designing research, combining theory generation and hypothesis testing, and being less tied to disciplines and established research paradigms.

Take the example of a study examining the impact of exercise on mental health. Mixed methods research would allow for a comprehensive look at the issue from different angles. 

Researchers could begin by collecting quantitative data through surveys to get an overall view of the participants' levels of physical activity and mental health. Qualitative interviews would follow this to explore the underlying dynamics of participants' experiences of exercise, physical activity, and mental health in greater detail.

Through a mixed methods approach, researchers could more easily compare and contrast their results to better understand the phenomenon as a whole.  

Additionally, mixed methods research is useful when there are conflicting or differing results in different studies. By combining both quantitative and qualitative data, mixed methods research can offer insights into why those differences exist.

For example, if a quantitative survey yields one result while a qualitative interview yields another, mixed methods research can help identify what factors influence these differences by integrating data from both sources.

Overall, mixed methods research designs offer a range of advantages for studying complex phenomena. They can provide insight into different elements of a phenomenon in ways that are not possible with either qualitative or quantitative data alone. Additionally, they allow researchers to integrate data from multiple sources to gain a deeper understanding of the phenomenon in question.  

• Challenges of mixed methods research

Mixed methods research is labor-intensive and often requires interdisciplinary teams of researchers to collaborate. It also has the potential to cost more than conducting a stand alone qualitative or quantitative study . 

Interpreting the results of mixed methods research can be tricky, as it can involve conflicting or differing results. Researchers must find ways to systematically compare the results from different sources and methods to avoid bias.

For example, imagine a situation where a team of researchers has employed an explanatory sequential design for their mixed methods study. After collecting data from both the quantitative and qualitative stages, the team finds that the two sets of data provide differing results. This could be challenging for the team, as they must now decide how to effectively integrate the two types of data in order to reach meaningful conclusions. The team would need to identify method flexibility and be strategic when integrating data in order to draw meaningful conclusions from the conflicting results.

• Advanced frameworks in mixed methods research

Mixed methods research offers powerful tools for investigating complex processes and systems, such as in health and healthcare.

Besides the three basic mixed method designs—exploratory sequential, explanatory sequential, and convergent parallel—you can use one of the four advanced frameworks to extend mixed methods research designs. These include multistage, intervention, case study , and participatory. 

This framework mixes qualitative and quantitative data collection methods in stages to gather a more nuanced view of the research question. An example of this is a study that first has an online survey to collect initial data and is followed by in-depth interviews to gain further insights.

Intervention

This design involves collecting quantitative data and then taking action, usually in the form of an intervention or intervention program. An example of this could be a research team who collects data from a group of participants, evaluates it, and then implements an intervention program based on their findings .

This utilizes both qualitative and quantitative research methods to analyze a single case. The researcher will examine the specific case in detail to understand the factors influencing it. An example of this could be a study of a specific business organization to understand the organizational dynamics and culture within the organization.

Participatory

This type of research focuses on the involvement of participants in the research process. It involves the active participation of participants in formulating and developing research questions, data collection, and analysis.

An example of this could be a study that involves forming focus groups with participants who actively develop the research questions and then provide feedback during the data collection and analysis stages.

The flexibility of mixed methods research designs means that researchers can choose any combination of the four frameworks outlined above and other methodologies , such as convergent parallel, explanatory sequential, and exploratory sequential, to suit their particular needs.

Through this method's flexibility, researchers can gain multiple perspectives and uncover differing or even conflicting results when integrating data.

When it comes to integration at the methods level, there are four approaches.

Connecting involves collecting both qualitative and quantitative data during different phases of the research.

Building involves the collection of both quantitative and qualitative data within a single phase.

Merging involves the concurrent collection of both qualitative and quantitative data.

Embedding involves including qualitative data within a quantitative study or vice versa.

• Techniques for integrating data in mixed method studies

Integrating data is an important step in mixed methods research designs. It allows researchers to gain further understanding from their research and gives credibility to the integration process. There are three main techniques for integrating data in mixed methods studies: triangulation protocol, following a thread, and the mixed methods matrix.

Triangulation protocol

This integration method combines different methods with differing or conflicting results to generate one unified answer.

For example, if a researcher wanted to know what type of music teenagers enjoy listening to, they might employ a survey of 1,000 teenagers as well as five focus group interviews to investigate this. The results might differ; the survey may find that rap is the most popular genre, whereas the focus groups may suggest rock music is more widely listened to. 

The researcher can then use the triangulation protocol to come up with a unified answer—such as that both rap and rock music are popular genres for teenage listeners. 

Following a thread

This is another method of integration where the researcher follows the same theme or idea from one method of data collection to the next. 

A research design that follows a thread starts by collecting quantitative data on a specific issue, followed by collecting qualitative data to explain the results. This allows whoever is conducting the research to detect any conflicting information and further look into the conflicting information to understand what is really going on.

For example, a researcher who used this research method might collect quantitative data about how satisfied employees are with their jobs at a certain company, followed by qualitative interviews to investigate why job satisfaction levels are low. They could then use the results to explore any conflicting or differing results, allowing them to gain a deeper understanding of job satisfaction at the company. 

By following a thread, the researcher can explore various research topics related to the original issue and gain a more comprehensive view of the issue.

Mixed methods matrix

This technique is a visual representation of the different types of mixed methods research designs and the order in which they should be implemented. It enables researchers to quickly assess their research design and adjust it as needed. 

The matrix consists of four boxes with four different types of mixed methods research designs: convergent parallel, explanatory sequential, exploratory sequential, and method flexibility. 

For example, imagine a researcher who wanted to understand why people don't exercise regularly. To answer this question, they could use a convergent parallel design, collecting both quantitative (e.g., survey responses) and qualitative (e.g., interviews) data simultaneously.

If the researcher found conflicting results, they could switch to an explanatory sequential design and collect quantitative data first, then follow up with qualitative data if needed. This way, the researcher can make adjustments based on their findings and integrate their data more effectively.

Mixed methods research is a powerful tool for understanding complex research topics. Using qualitative and quantitative data in one study allows researchers to understand their subject more deeply. 

Mixed methods research designs such as convergent parallel, explanatory sequential, and exploratory sequential provide method flexibility, enabling researchers to collect both types of data while avoiding the limitations of either approach alone.

However, it's important to remember that mixed methods research can produce differing or even conflicting results, so it's important to be aware of the potential pitfalls and take steps to ensure that data is being correctly integrated. If used effectively, mixed methods research can offer valuable insight into topics that would otherwise remain largely unexplored.

What is an example of mixed methods research?

An example of mixed methods research is a study that combines quantitative and qualitative data. This type of research uses surveys, interviews, and observations to collect data from multiple sources.

Which sampling method is best for mixed methods?

It depends on the research objectives, but a few methods are often used in mixed methods research designs. These include snowball sampling, convenience sampling, and purposive sampling. Each method has its own advantages and disadvantages.

What is the difference between mixed methods and multiple methods?

Mixed methods research combines quantitative and qualitative data in a single study. Multiple methods involve collecting data from different sources, such as surveys and interviews, but not necessarily combining them into one analysis. Mixed methods offer greater flexibility but can lead to differing or conflicting results when integrating data.

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Ten steps to producing a successful mixed methods dissertation in Evidence-Based Health Care

9 June 2017

Tips for students

This blog is part of a series for Evidence-Based Health Care MSc students undertaking their dissertations, by Research Assistant Alice Tompson.

I thought Margaret Głogowska would be a great person to chat to about the opportunities and challenges of writing a mixed-methods thesis. Margaret has loads of research experience and co-coordinates the  Mixed Methods  Evidence-Based Health Care module.

Here are her top tips for writing a successful mixed methods dissertation:

1) Start writing as soon as you can

Beginning to write your dissertation can be daunting – a blank screen can be very intimidating! Margaret suggests the methods section can be a good place to start. Writing what you are doing, and how to you are doing it is, often more straightforward than writing why you’re doing it or describing or discussing your results. Plus it’ll help you identify any holes in your research plans.

2) Mixed methods isn’t a game of two halves

Margaret explains that a common mistake is to think of mixed methods studies as having to have two components. In fact, they have three: in addition to the quantitative and qualitative strands, successful dissertations will pull these together to provide insight greater than the sum of the parts. This doesn’t only relate to the results: be sure to include your plans for integration in your methods section too. This article by  Jenny Burt  gives some further advice on “ following the mixed methods trail ”.

3) Think about the structure

When writing up her own work, Margaret reflects on,  “What’s a good way to bring this together to answer my research question? ” You could follow a typical quantitative approach where each component is reported sequentially (i.e. quantitative, qualitative, integration). However, you could adopt a more qualitative approach organising your results by themes, each illustrated with qualitative and quantitative data. Think about which structure will enable you to present the fullest picture of the issue you are investigating.  In this article,  Alicia O’Cathain  and colleagues describe three approaches to integrating mixed methods data.

4) It’s not about the “right answer”

Don’t be disheartened if the results from the different components of your study are not in agreement. Instead of attempting to establish which is more valid, use dissonant findings as an opportunity to return to your datasets to explore the reasons for these differences. This will enrich your understanding and enable a full account to be presented.

5) Embrace the flexibility

Mixed methods studies are a relatively recent development that can take many forms. As a result, there are not currently any reporting standards that students can use to structure their work. Although this can be daunting, Margaret encourages students to use this freedom to work to their advantage. Be creative and flexible to enable you to present a rich, complete account of your work.

6) Be systematic and rigorous

Although mixed methods offer flexibility, this must not be at the expense of rigor or transparency.  When writing up provide enough detail for your examiners/ readers to be able to replicate your methods and analyses. Justify the approaches you took and the decisions you made.  Enable them to follow the story.

7) Read the literature

The field of mixed methods is advancing all the time. Refer to the literature for methodological developments, for example how to display data, and also to see how published studies reported their mixed method projects.

To get you started, here are three helpful papers Margaret uses as teaching examples:

• Van den Bruel et al  (2016) C-reactive protein point-of-care testing in acutely ill children: a mixed methods study in primary care. Archives of Disease in Childhood 10.1136/archdischild-2015-309228
• Moffat et al  (2006) Using quantitative and qualitative data in health services research – what happens when mixed method findings conflict? BMC Health Services Research, 6:28 doi:10.1186/1472-6963-6-28
• Casey et al  (2014) A mixed methods study exploring the factors and behaviours that affect glycemic control following a structured education program: the Irish DAFNE study. Journal of Mixed Methods Research 10(2):182-203

She also recommends the work of  Alan Bryman , a pioneer in combining qualitative and quantitative research.

8) Fortune favours the prepared!

Keep your research notebook with you: it will allow you to keep track of ideas, useful references, and helpful conversations. Fortune favours the prepared so always keep your notebook close to hand!

9) Be concise

Word limits are a perennial issue in mixed methods research. Two methods plus integration means there is a lot of information to convey. No word can be superfluous and it may take several drafts to cut out the clutter. Use tables and appendices to  “make the most of your precious word count”.

10) Final steps – publishing your thesis

The value of mixed methods, particularly in applied health research, is increasingly being recognised.  Based on her own experience, Margaret suggests contacting journal editors for advice on how to tailor your manuscript for their particular audience to increase your chances of it being accepted.

  If you are interested in learning more about the Evidence-Based Health Care module: “Mixed- Methods in Health Research” take a look  here .

Doctoral Dissertation. Title: A mixed methods study of online course facilitators' perceptions of mobile technology, design, and TPaCK affordances

• Thesis for: Doctorate of Education in Learning Technologies, Pepperdine University
• Advisor: Dr. Jack McManus, Dissertation Chair

• Eastern Washington University

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Home » Dissertation Methodology – Structure, Example and Writing Guide

Dissertation Methodology – Structure, Example and Writing Guide

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Dissertation Methodology

In any research, the methodology chapter is one of the key components of your dissertation. It provides a detailed description of the methods you used to conduct your research and helps readers understand how you obtained your data and how you plan to analyze it. This section is crucial for replicating the study and validating its results.

Here are the basic elements that are typically included in a dissertation methodology:

• Introduction : This section should explain the importance and goals of your research .
• Research Design : Outline your research approach and why it’s appropriate for your study. You might be conducting an experimental research, a qualitative research, a quantitative research, or a mixed-methods research.
• Data Collection : This section should detail the methods you used to collect your data. Did you use surveys, interviews, observations, etc.? Why did you choose these methods? You should also include who your participants were, how you recruited them, and any ethical considerations.
• Data Analysis : Explain how you intend to analyze the data you collected. This could include statistical analysis, thematic analysis, content analysis, etc., depending on the nature of your study.
• Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your study. For instance, you could discuss measures taken to reduce bias, how you ensured that your measures accurately capture what they were intended to, or how you will handle any limitations in your study.
• Ethical Considerations : This is where you state how you have considered ethical issues related to your research, how you have protected the participants’ rights, and how you have complied with the relevant ethical guidelines.
• Limitations : Acknowledge any limitations of your methodology, including any biases and constraints that might have affected your study.
• Summary : Recap the key points of your methodology chapter, highlighting the overall approach and rationalization of your research.

Types of Dissertation Methodology

The type of methodology you choose for your dissertation will depend on the nature of your research question and the field you’re working in. Here are some of the most common types of methodologies used in dissertations:

Experimental Research

This involves creating an experiment that will test your hypothesis. You’ll need to design an experiment, manipulate variables, collect data, and analyze that data to draw conclusions. This is commonly used in fields like psychology, biology, and physics.

Survey Research

This type of research involves gathering data from a large number of participants using tools like questionnaires or surveys. It can be used to collect a large amount of data and is often used in fields like sociology, marketing, and public health.

Qualitative Research

This type of research is used to explore complex phenomena that can’t be easily quantified. Methods include interviews, focus groups, and observations. This methodology is common in fields like anthropology, sociology, and education.

Quantitative Research

Quantitative research uses numerical data to answer research questions. This can include statistical, mathematical, or computational techniques. It’s common in fields like economics, psychology, and health sciences.

Case Study Research

This type of research involves in-depth investigation of a particular case, such as an individual, group, or event. This methodology is often used in psychology, social sciences, and business.

Mixed Methods Research

This combines qualitative and quantitative research methods in a single study. It’s used to answer more complex research questions and is becoming more popular in fields like social sciences, health sciences, and education.

Action Research

This type of research involves taking action and then reflecting upon the results. This cycle of action-reflection-action continues throughout the study. It’s often used in fields like education and organizational development.

Longitudinal Research

This type of research involves studying the same group of individuals over an extended period of time. This could involve surveys, observations, or experiments. It’s common in fields like psychology, sociology, and medicine.

Ethnographic Research

This type of research involves the in-depth study of people and cultures. Researchers immerse themselves in the culture they’re studying to collect data. This is often used in fields like anthropology and social sciences.

Structure of Dissertation Methodology

The structure of a dissertation methodology can vary depending on your field of study, the nature of your research, and the guidelines of your institution. However, a standard structure typically includes the following elements:

• Introduction : Briefly introduce your overall approach to the research. Explain what you plan to explore and why it’s important.
• Research Design/Approach : Describe your overall research design. This can be qualitative, quantitative, or mixed methods. Explain the rationale behind your chosen design and why it is suitable for your research questions or hypotheses.
• Data Collection Methods : Detail the methods you used to collect your data. You should include what type of data you collected, how you collected it, and why you chose this method. If relevant, you can also include information about your sample population, such as how many people participated, how they were chosen, and any relevant demographic information.
• Data Analysis Methods : Explain how you plan to analyze your collected data. This will depend on the nature of your data. For example, if you collected quantitative data, you might discuss statistical analysis techniques. If you collected qualitative data, you might discuss coding strategies, thematic analysis, or narrative analysis.
• Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your research. This might include steps you took to reduce bias or increase the accuracy of your measurements.
• Ethical Considerations : If relevant, discuss any ethical issues associated with your research. This might include how you obtained informed consent from participants, how you ensured participants’ privacy and confidentiality, or any potential conflicts of interest.
• Limitations : Acknowledge any limitations in your research methodology. This could include potential sources of bias, difficulties with data collection, or limitations in your analysis methods.
• Summary/Conclusion : Briefly summarize the key points of your methodology, emphasizing how it helps answer your research questions or hypotheses.

How to Write Dissertation Methodology

Writing a dissertation methodology requires you to be clear and precise about the way you’ve carried out your research. It’s an opportunity to convince your readers of the appropriateness and reliability of your approach to your research question. Here is a basic guideline on how to write your methodology section:

1. Introduction

Start your methodology section by restating your research question(s) or objective(s). This ensures your methodology directly ties into the aim of your research.

2. Approach

Identify your overall approach: qualitative, quantitative, or mixed methods. Explain why you have chosen this approach.

• Qualitative methods are typically used for exploratory research and involve collecting non-numerical data. This might involve interviews, observations, or analysis of texts.
• Quantitative methods are used for research that relies on numerical data. This might involve surveys, experiments, or statistical analysis.
• Mixed methods use a combination of both qualitative and quantitative research methods.

3. Research Design

Describe the overall design of your research. This could involve explaining the type of study (e.g., case study, ethnography, experimental research, etc.), how you’ve defined and measured your variables, and any control measures you’ve implemented.

4. Data Collection

Explain in detail how you collected your data.

• If you’ve used qualitative methods, you might detail how you selected participants for interviews or focus groups, how you conducted observations, or how you analyzed existing texts.
• If you’ve used quantitative methods, you might detail how you designed your survey or experiment, how you collected responses, and how you ensured your data is reliable and valid.

5. Data Analysis

Describe how you analyzed your data.

• If you’re doing qualitative research, this might involve thematic analysis, discourse analysis, or grounded theory.
• If you’re doing quantitative research, you might be conducting statistical tests, regression analysis, or factor analysis.

Discuss any ethical issues related to your research. This might involve explaining how you obtained informed consent, how you’re protecting participants’ privacy, or how you’re managing any potential harms to participants.

7. Reliability and Validity

Discuss the steps you’ve taken to ensure the reliability and validity of your data.

• Reliability refers to the consistency of your measurements, and you might discuss how you’ve piloted your instruments or used standardized measures.
• Validity refers to the accuracy of your measurements, and you might discuss how you’ve ensured your measures reflect the concepts they’re supposed to measure.

8. Limitations

Every study has its limitations. Discuss the potential weaknesses of your chosen methods and explain any obstacles you faced in your research.

9. Conclusion

Summarize the key points of your methodology, emphasizing how it helps to address your research question or objective.

Example of Dissertation Methodology

An Example of Dissertation Methodology is as follows:

Chapter 3: Methodology

• Introduction

This chapter details the methodology adopted in this research. The study aimed to explore the relationship between stress and productivity in the workplace. A mixed-methods research design was used to collect and analyze data.

Research Design

This study adopted a mixed-methods approach, combining quantitative surveys with qualitative interviews to provide a comprehensive understanding of the research problem. The rationale for this approach is that while quantitative data can provide a broad overview of the relationships between variables, qualitative data can provide deeper insights into the nuances of these relationships.

Data Collection Methods

Quantitative Data Collection : An online self-report questionnaire was used to collect data from participants. The questionnaire consisted of two standardized scales: the Perceived Stress Scale (PSS) to measure stress levels and the Individual Work Productivity Questionnaire (IWPQ) to measure productivity. The sample consisted of 200 office workers randomly selected from various companies in the city.

Qualitative Data Collection : Semi-structured interviews were conducted with 20 participants chosen from the initial sample. The interview guide included questions about participants’ experiences with stress and how they perceived its impact on their productivity.

Data Analysis Methods

Quantitative Data Analysis : Descriptive and inferential statistics were used to analyze the survey data. Pearson’s correlation was used to examine the relationship between stress and productivity.

Qualitative Data Analysis : Interviews were transcribed and subjected to thematic analysis using NVivo software. This process allowed for identifying and analyzing patterns and themes regarding the impact of stress on productivity.

Reliability and Validity

To ensure reliability and validity, standardized measures with good psychometric properties were used. In qualitative data analysis, triangulation was employed by having two researchers independently analyze the data and then compare findings.

Ethical Considerations

All participants provided informed consent prior to their involvement in the study. They were informed about the purpose of the study, their rights as participants, and the confidentiality of their responses.

Limitations

The main limitation of this study is its reliance on self-report measures, which can be subject to biases such as social desirability bias. Moreover, the sample was drawn from a single city, which may limit the generalizability of the findings.

Where to Write Dissertation Methodology

In a dissertation or thesis, the Methodology section usually follows the Literature Review. This placement allows the Methodology to build upon the theoretical framework and existing research outlined in the Literature Review, and precedes the Results or Findings section. Here’s a basic outline of how most dissertations are structured:

• Acknowledgements
• Literature Review (or it may be interspersed throughout the dissertation)
• Methodology
• Results/Findings
• References/Bibliography

In the Methodology chapter, you will discuss the research design, data collection methods, data analysis methods, and any ethical considerations pertaining to your study. This allows your readers to understand how your research was conducted and how you arrived at your results.

Advantages of Dissertation Methodology

The dissertation methodology section plays an important role in a dissertation for several reasons. Here are some of the advantages of having a well-crafted methodology section in your dissertation:

• Clarifies Your Research Approach : The methodology section explains how you plan to tackle your research question, providing a clear plan for data collection and analysis.
• Enables Replication : A detailed methodology allows other researchers to replicate your study. Replication is an important aspect of scientific research because it provides validation of the study’s results.
• Demonstrates Rigor : A well-written methodology shows that you’ve thought critically about your research methods and have chosen the most appropriate ones for your research question. This adds credibility to your study.
• Enhances Transparency : Detailing your methods allows readers to understand the steps you took in your research. This increases the transparency of your study and allows readers to evaluate potential biases or limitations.
• Helps in Addressing Research Limitations : In your methodology section, you can acknowledge and explain the limitations of your research. This is important as it shows you understand that no research method is perfect and there are always potential weaknesses.
• Facilitates Peer Review : A detailed methodology helps peer reviewers assess the soundness of your research design. This is an important part of the publication process if you aim to publish your dissertation in a peer-reviewed journal.
• Establishes the Validity and Reliability : Your methodology section should also include a discussion of the steps you took to ensure the validity and reliability of your measurements, which is crucial for establishing the overall quality of your research.

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Qualitative, quantitative and mixed methods dissertations

What are they and which one should i choose.

In the sections that follow, we briefly describe the main characteristics of qualitative, quantitative and mixed methods dissertations. Rather than being exhaustive, the main goal is to highlight what these types of research are and what they involve. Whilst you read through each section, try and think about your own dissertation, and whether you think that one of these types of dissertation might be right for you. After reading about these three types of dissertation, we highlight some of the academic, personal and practical reasons why you may choose to take on one type over another.

• Types of dissertation: Qualitative, quantitative and mixed methods dissertations
• Choosing between types: Academic, personal and practical justifications

Types of dissertation

Whilst we describe the main characteristics of qualitative, quantitative and mixed methods dissertations, the Lærd Dissertation site currently focuses on helping guide you through quantitative dissertations , whether you are a student of the social sciences, psychology, education or business, or are studying medical or biological sciences, sports science, or another science-based degree. Nonetheless, you may still find our introductions to qualitative dissertations and mixed methods dissertations useful, if only to decide whether these types of dissertation are for you. We discuss quantitative dissertations , qualitative dissertations and mixed methods dissertations in turn:

Quantitative dissertations

When we use the word quantitative to describe quantitative dissertations , we do not simply mean that the dissertation will draw on quantitative research methods or statistical analysis techniques . Quantitative research takes a particular approach to theory , answering research questions and/or hypotheses , setting up a research strategy , making conclusions from results , and so forth. Classic routes that you can follow include replication-based studies , theory-driven research and data-driven dissertations . However, irrespective of the particular route that you adopt when taking on a quantitative dissertation, there are a number of core characteristics to quantitative dissertations:

They typically attempt to build on and/or test theories , whether adopting an original approach or an approach based on some kind of replication or extension .

They answer quantitative research questions and/or research (or null ) hypotheses .

They are mainly underpinned by positivist or post-positivist research paradigms .

They draw on one of four broad quantitative research designs (i.e., descriptive , experimental , quasi-experimental or relationship-based research designs).

They try to use probability sampling techniques , with the goal of making generalisations from the sample being studied to a wider population , although often end up applying non-probability sampling techniques .

They use research methods that generate quantitative data (e.g., data sets , laboratory-based methods , questionnaires/surveys , structured interviews , structured observation , etc.).

They draw heavily on statistical analysis techniques to examine the data collected, whether descriptive or inferential in nature.

They assess the quality of their findings in terms of their reliability , internal and external validity , and construct validity .

They report their findings using statements , data , tables and graphs that address each research question and/or hypothesis.

They make conclusions in line with the findings , research questions and/or hypotheses , and theories discussed in order to test and/or expand on existing theories, or providing insight for future theories.

If you choose to take on a quantitative dissertation , go to the Quantitative Dissertations part of Lærd Dissertation now. You will learn more about the characteristics of quantitative dissertations, as well as being able to choose between the three classic routes that are pursued in quantitative research: replication-based studies , theory-driven research and data-driven dissertations . Upon choosing your route, the Quantitative Dissertations part of Lærd Dissertation will help guide you through these routes, from topic idea to completed dissertation, as well as showing you how to write up quantitative dissertations.

Qualitative dissertations

Qualitative dissertations , like qualitative research in general, are often associated with qualitative research methods such as unstructured interviews, focus groups and participant observation. Whilst they do use a set of research methods that are not used in quantitative dissertations, qualitative research is much more than a choice between research methods. Qualitative research takes a particular approach towards the research process , the setting of research questions , the development and use of theory , the choice of research strategy , the way that findings are presented and discussed, and so forth. Overall, qualitative dissertations will be very different in approach, depending on the particular route that you adopt (e.g., case study research compared to ethnographies). Classic routes that you can follow include autoethnographies , case study research , ethnographies , grounded theory , narrative research and phenomenological research . However, irrespective of the route that you choose to follow, there are a number of broad characteristics to qualitative dissertations:

They follow an emergent design , meaning that the research process , and sometimes even the qualitative research questions that you tackle, often evolve during the dissertation process.

They use theory in a variety of ways - sometimes drawing on theory to help the research process; on other occasions, using theory to develop new theoretical insights ; sometimes both - but the goal is infrequently to test a particular theory from the outset.

They can be underpinned by one of a number of research paradigms (e.g., interpretivism , constructivism , critical theory , amongst many other research paradigms).

They follow research designs that heavily influence the choices you make throughout the research process, as well as the analysis and discussion of 'findings' (i.e., such research designs differ considerably depending on the route that is being followed, whether an autoethnography , case study research , ethnography , grounded theory , narrative research , phenomenological research , etc.).

They try to use theoretical sampling - a group of non-probability sampling techniques - with the goal of studying cases (i.e., people or organisations) that are most appropriate to answering their research questions.

They study people in-the-field (i.e., in natural settings ), often using multiple research methods , each of which generate qualitative data (e.g., unstructured interviews , focus groups , participant observation , etc.).

They interpret the qualitative data through the eyes and biases of the researcher , going back-and-forth through the data (i.e., an inductive process ) to identify themes or abstractions that build a holistic/gestalt picture of what is being studied.

They assess the quality of their findings in terms of their dependability , confirmability , conformability and transferability .

They present (and discuss ) their findings through personal accounts , case studies , narratives , and other means that identify themes or abstracts , processes , observations and contradictions , which help to address their research questions.

They discuss the theoretical insights arising from the findings in light of the research questions, from which tentative conclusions are made.

If you choose to take on a qualitative dissertation , you will be able to learn a little about appropriate research methods and sampling techniques in the Fundamentals section of Lærd Dissertation. However, we have not yet launched a dedicated section to qualitative dissertations within Lærd Dissertation. If this is something that you would like us to do sooner than later, please leave feedback .

Mixed methods dissertations

Mixed methods dissertations combine qualitative and quantitative approaches to research. Whilst they are increasingly used and have gained greater legitimacy, much less has been written about their components parts. There are a number of reasons why mixed methods dissertations are used, including the feeling that a research question can be better addressed by:

Collecting qualitative and quantitative data , and then analysing or interpreting that data, whether separately or by mixing it.

Conducting more than one research phase ; perhaps conducting qualitative research to explore an issue and uncover major themes, before using quantitative research to measure the relationships between the themes.

One of the problems (or challenges) of mixed methods dissertations is that qualitative and quantitative research, as you will have seen from the two previous sections, are very different in approach. In many respects, they are opposing approaches to research. Therefore, when taking on a mixed methods dissertation, you need to think particularly carefully about the goals of your research, and whether the qualitative or quantitative components (a) are more important in philosophical, theoretical and practical terms, and (b) should be combined or kept separate.

Again, as with qualitative dissertations, we have yet to launch a dedicated section of Lærd Dissertation to mixed methods dissertations . However, you will be able to learn about many of the quantitative aspects of doing a mixed methods dissertation in the Quantitative Dissertations part of Lærd Dissertation. You may even be able to follow this part of our site entirely if the only qualitative aspect of your mixed methods dissertation is the use of qualitative methods to help you explore an issue or uncover major themes, before performing quantitative research to examine such themes further. Nonetheless, if you would like to see a dedicated section to mixed methods dissertations sooner than later, please leave feedback .

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The Growing Importance of Mixed-Methods Research in Health

Sharada prasad wasti.

1,2 School of Human and Health Sciences, University of Huddersfield, United Kingdom

Padam Simkhada

3 Centre for Midwifery, Maternal and Perinatal Health, Bournemouth University, Bournemouth, United Kingdom

Edwin R. van Teijlingen

Brijesh sathian.

4 Geriatrics and long term care Department, Rumailah Hospital, Hamad Medical Corporation, Doha, Qatar

Indrajit Banerjee

5 Sir Seewoosagur Ramgoolam Medical College, Belle Rive, Mauritius

All authors have made substantial contributions to all of the following: (1) the conception and design of the study (2) drafting the article or revising it critically for important intellectual content, (3) final approval of the version to be submitted

There is no conflict of interest for any author of this manuscript.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector.

This paper illustrates the growing importance of mixed-methods research to many health disciplines ranging from nursing to epidemiology. Mixed-methods approaches requires not only the skills of the individual quantitative and qualitative methods but also a skill set to bring two methods/datasets/findings together in the most appropriate way. Health researchers need to pay careful attention to the ‘best’ approach to designing, implementing, analysing, integrating both quantitative (number) and qualitative (word) information and writing this up in a way offers greater insights and enhances its applicability. This paper highlights the strengths and weaknesses of mixed-methods approaches as well as some of the common mistakes made by researchers applying mixed-methods for the first time.

Quantitative and qualitative research methods each address different types of questions, collect different kinds of data and deliver different kinds of answers. Each set of methods has its own inherent strengths and weaknesses, and each offers a particular approach to address specific types of research questions (and agendas). Health disciplines such as dentistry, nursing, speech and language therapy, and physiotherapy often use either quantitative or qualitative research methods on their own. However, there is a steadily growing literature showing the advantages of mixed-methods research is used in the health care and health service field [ 1-2 ]. Although we have advocated the use of mixed-methods in this journal eight years ago [ 3 ], there is still not enough mixed-methods research training in the health research field, particularly for health care practitioners, such as nurses, physiotherapists, midwives, and doctors, wanting to do research. Mixed-methods research has been popular in the social sciences since the twentieth century [ 4 ], and it has been growing in popularity among healthcare professionals [ 5 ], although it is still underdeveloped in disciplines such nursing and midwifery [ 6 , 7 ].

Underpinning philosophies

To help understand that mixed-methods research is not simply employing two different methods in the same study, one needs to consider their underpinning research philosophies (also called paradigms). First, quantitative research is usually underpinned by positivism. This includes most epidemiological studies; such research is typically based on the assumption that there is one single real world out there that can be measured. For example, quantitative research would address the question “What proportion of the population of India drinks coffee?” Secondly, qualitative research is more likely to be based on interpretivism. This includes research based on interviews and focus groups, research which us is typically based on the assumption that we all experience the world differently. Since we all live in a slightly different world in our heads the task of qualitative research is to analyse the interpretations of the people in the sample. For example, qualitative research would address the question “How do people experience drinking coffee in India?”, and “What does drinking coffee mean to them?”

Mixed-methods research brings together questions from two different philosophies in what is being referred to as the third path [ 8 ], third research paradigm [ 9 , 10 ], the third methodology movement [ 11 , 12 ] and pragmatism [ 5 ]. The two paradigms differ in key underlying assumptions that ultimately lead to choices in research methodology and methods and often give a breadth by answering more complicated research questions [ 4 ]. The roles of mixed-methods are clear in an understanding of the situation (the what), meaning, norms, values (the why or how) within a single research question which combine the strength of two different method and offer multiple ways of looking at the research question [ 13 ]. Epidemiology sits strongly in the quantitative research corner, with a strong emphasis on large data sets and sophisticated statistical analysis. Although the use of mixed methods in health research has been discussed widely researchers raised concerns about the explanation of why and how mixed methods are used in a single research question [ 5 ].

The relevance of mixed-methods in health research

The overall goal of the mixed-methods research design is to provide a better and deeper understanding, by providing a fuller picture that can enhance description and understanding of the phenomena [ 4 ]. Mixed-methods research has become popular because it uses quantitative and qualitative data in one single study which provides stronger inference than using either approach on its own [ 4 ]. In other words, a mixed-methods paper helps to understand the holistic picture from meanings obtained from interviews or observation to the prevalence of traits in a population obtained from surveys, which add depth and breadth to the study. For example, a survey questionnaire will include a limited number of structured questions, adding qualitative methods can capture other unanticipated facets of the topic that may be relevant to the research problem and help in the interpretation of the quantitative data. A good example of a mixed-methods study, it one conducted in Australia to understand the nursing care in public hospitals and also explore what factors influence adherence to nursing care [ 14 ]. Another example is a mixed-methods study that explores the relationship between nursing care practices and patient satisfaction. This study started with a quantitative survey to understand the general nursing services followed by qualitative interviews. A logistic regression analysis was performed to quantify the associations between general nursing practice variables supplemented with a thematic analysis of the interviews [ 15 ]. These research questions could not be answered if the researchers had used either qualitative or quantitative alone. Overall, this fits well with the development of evidence-based practice.

Despite the strengths of mixed-methods research but there is not much of it in nursing and other fields [ 7 ]. A recent review paper shows that the prevalence of mixed-methods studies in nursing was only 1.9% [ 7 ]. Similarly, a systematic review synthesised a total of 20 papers [ 16 ], and 16 papers [ 17 ] on nursing-related research paper among these only one mixed-methods paper was identified. Worse, a further two mixed-methods review recently revealed that out of 48 [ 18 , 19 ] synthesised nursing research papers, not one single mixed-methods paper was identified. This clearly depicts that mixed-methods research is still in its infancy stage in nursing but we can say there is huge scope to implement it to understand research questions on both sides of coin [ 4 ]. Therefore, there is a great need for mixed-methods training to enhance the evidence-based decision making in health and nursing practices.

Strengths and weaknesses of mixed-methods

There are several challenges in identifying expertise of both methods and in working with a multidisciplinary, interdisciplinary, or transdisciplinary team [ 20 ]. It increases costs and resources, takes longer to complete as mixed-methods design often involves multiple stages of data collection and separate data analysis [ 4 , 5 ]. Moreover, conducting mixed-methods research does not necessarily guarantee an improvement in the quality of health research. Therefore, mixed-methods research is only appropriate when there are appropriate research questions [ 4 , 6 ].

Identifying an appropriate mixed-methods journal can also be challenging when writing mixed-methods papers [ 21 ]. Mixed-methods papers need considerably more words than single-methods papers as well as sympathetic editors who understand the underlying philosophy of a mixed-methods approach. Such papers, simply require more words. The mixed-methods researcher must be reporting two separate methods with their own characteristics, different samples, and ways of analysing, therefore needs more words to describe both methods as well as both sets of findings. Researcher needs to find a journal that accepts longer articles to help broaden existing evidence-based practice and promote its applicability in the nursing field [ 22 ].

Common mistakes in applying mixed-methods

Not all applied researchers have insight into the underlying philosophy and/or the skills to apply each set of methods appropriately. Younas and colleagues’ review identified that around one-third (29%) of mixed-methods studies did not provide an explicit label of the study design and 95% of studies did not identify the research paradigm [ 7 ]. Whilst several mixed-methods publications did not provide clear research questions covering both quantitative and qualitative approaches. Another common issue is how to collect data either concurrent or sequential and the priority is given to each approach within the study where equal or dominant which are not clearly stated in writing which is important to mention while writing in the methods section. Similarly, a commonly overlooked aspect is how to integrate both findings in a paper. The responsibility lies with the researcher to ensure that findings are sufficiently plausible and credible [ 4 ]. Therefore, intensive mixed-methods research training is required for nursing and other health practitioners to ensure its appropriate.

The way forward

Despite the recognised strengths and benefits of doing mixed-methods research, there is still only a limited number of nursing and related-health research publications using such this approach. Researchers need training in how to design, conduct, analyse, synthesise and disseminate mixed-methods research. Most importantly, they need to consider appropriate research questions that can be addressed using a mixed methods approach to add to our knowledge in evidence-based practice. In short, we need more training on mixed-methods research for a range of health researchers and health professionals.

Acknowledgement

Sample Dissertations

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Mixed Method Research Design

The mixed method in research design.

The mixed method approach to evaluating research data may be applicable to studies that are designed to gather both qualitative and quantitative information. This technique is often used in disciplines such as psychology, sociology or certain types of medicine. The continued development of these fields may depend on data that is derived from standardized scales or rating systems in addition to that gleaned from interviews, ‘focus group’ sessions and other similar tools. Therefore, the mixed method may be appropriate in a new project on a complex issue or situation that generates complex and highly individualized answers to research questions. Examples of these may include the societal impact of homelessness or the treatment of a lost or diminished sense. The data here may need to cover detailed and varied feedback (or ‘self-reports’) on the effect(s) of these target variables, as well as scores from formal quantitative tools typically used within the research community in question. One data type does not give a complete ‘picture’ of the outcome(s) without the other. Therefore, a methodology that incorporates both to analyse the data set as a whole is necessary.

The mixed method may combine and synthesize this data through a process called triangulation. This may involve the conversion of qualitative data into quantitative data. Such a form of triangulation is most applicable to data resulting from the administration of structured interviews or surveys, provided that data is sufficiently standard or homogeneous across respondents to be coded or scored effectively (i.e. without bias or other forms or statistical inadequacy). In this way, it may be converted to quantitative data, and compared or analysed in accordance with the requirements of the study design (e.g. subjected to a form of analysis such as a paired t-test). On the other hand, the qualitative data may be too individualized and/or complex to be coded. In this case, a thematic analytical technique may be used, incorporating findings such as significant differences among the quantitative data points as a theme or concept.

The aim of triangulation is the full integration of both data types to generate contiguous concepts or conclusions. This leads to another advantage of the mixed method: i.e. that it can address research aims that do not stem from standard null hypotheses. Questions, in other words, along the lines of ‘Does this novel treatment result in an improvement in the life quality of patients with hearing loss?’ rather than statements such as ‘This treatment improves hearing loss [in comparison to an existing alternative]’ to be confirmed or denied.

The mixed method is not, however, without disadvantages or detractors. Critics of this methodology often cite the risk of the ‘incompatibility paradox’; the probability that one data type will be inadequately analysed compared to the other. A prominent example of this risk is known as ‘pragmatism’, or the perception that researchers who use the mixed method value ‘experiential data’ (i.e. self-reports recorded from respondents) at the expense of quantitative data. The use of the mixed method may also be subject to preconceptions, judgement or other forms of observer bias that a researcher may impose on qualitative data in the course of its collection. These risks can be ameliorated, mainly through the skill and training of the individual researcher. Under these conditions, the mixed-method technique can be applied to generating full, comprehensive conclusions for non-standard research questions.

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Brown RA, Kennedy DP, Tucker JS, Golinelli D, Wenzel SL. Monogamy on the Street: A Mixed Methods Study of Homeless Men. Journal of Mixed Methods Research. 2013;7(4):328-346

Windsor LC. Using Concept Mapping in Community-Based Participatory Research A Mixed Methods Approach. Journal of mixed methods research. 2013;7(3):274-293

Robson C. Real World Research. 2 ed. Oxford: Blackwell; 2002

Mertens DM, Hesse-Biber S. Triangulation and Mixed Methods Research: Provocative Positions. Journal of Mixed Methods Research. 2012;6(2):75-79

Lieber E, Weisner TS. Meeting the practical challenges of mixed methods research. SAGE handbook of mixed methods in social and behavioral research. 2010;2:559-579

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• http://orcid.org/0000-0002-4570-6686 Ramya Kumar 1 , 2 ,
• http://orcid.org/0000-0003-4076-0170 Deepa Rao 3 ,
• http://orcid.org/0000-0002-8189-0732 Anjali Sharma 1 ,
• Jamia Phiri 1 ,
• Martin Zimba 4 ,
• Maureen Phiri 4 ,
• Ruth Zyambo 5 ,
• Gwen Mulenga Kalo 5 ,
• Louise Chilembo 5 ,
• Phidelina Milambo Kunda 6 ,
• Chama Mulubwa 1 ,
• Benard Ngosa 1 ,
• http://orcid.org/0000-0001-5208-7468 Kenneth K Mugwanya 7 ,
• Wendy E Barrington 8 ,
• http://orcid.org/0000-0002-3629-3867 Michael E Herce 1 , 9 ,
• http://orcid.org/0000-0001-9968-7540 Maurice Musheke 1
• 1 Centre for Infectious Disease Research in Zambia , Lusaka , Zambia
• 2 Epidemiology , University of Washington School of Public Health , Seattle , Washington , USA
• 3 University of Washington School of Public Health , Seattle , Washington , USA
• 4 Zambia Sex Workers Alliance , Lusaka , Zambia
• 5 Tithandizeni Umoyo Network , Lusaka , Zambia
• 6 Lusaka District Health Office , Zambia Ministry of Health , Lusaka , Zambia
• 7 Epidemiology, Global Health , University of Washington School of Public Health , Seattle , Washington , USA
• 8 Epidemiology; Child, Family, and Population Health Nursing; Health Systems and Population Health , University of Washington School of Public Health , Seattle , Washington , USA
• 9 Institute for Global Health and Infectious Diseases , University of North Carolina at Chapel Hill , Chapel Hill , North Carolina , USA
• Correspondence to Dr Ramya Kumar; ramya.kumar.mlk{at}gmail.com

Introduction Women engaging in sex work (WESW) have 21 times the risk of HIV acquisition compared with the general population. However, accessing HIV pre-exposure prophylaxis (PrEP) remains challenging, and PrEP initiation and persistence are low due to stigma and related psychosocial factors. The WiSSPr (Women in Sex work, Stigma and PrEP) study aims to (1) estimate the effect of multiple stigmas on PrEP initiation and persistence and (2) qualitatively explore the enablers and barriers to PrEP use for WESW in Lusaka, Zambia.

Methods and analysis WiSSPr is a prospective observational cohort study grounded in community-based participatory research principles with a community advisory board (CAB) of key population (KP) civil society organi sations (KP-CSOs) and the Ministry of Health (MoH). We will administer a one-time psychosocial survey vetted by the CAB and follow 300 WESW in the electronic medical record for three months to measure PrEP initiation (#/% ever taking PrEP) and persistence (immediate discontinuation and a medication possession ratio). We will conduct in-depth interviews with a purposive sample of 18 women, including 12 WESW and 6 peer navigators who support routine HIV screening and PrEP delivery, in two community hubs serving KPs since October 2021. We seek to value KP communities as equal contributors to the knowledge production process by actively engaging KP-CSOs throughout the research process. Expected outcomes include quantitative measures of PrEP initiation and persistence among WESW, and qualitative insights into the enablers and barriers to PrEP use informed by participants’ lived experiences.

Ethics and dissemination WiSSPr was approved by the Institutional Review Boards of the University of Zambia (#3650-2023) and University of North Carolina (#22-3147). Participants must give written informed consent. Findings will be disseminated to the CAB, who will determine how to relay them to the community and stakeholders.

• MENTAL HEALTH
• HIV & AIDS
• EPIDEMIOLOGIC STUDIES
• Health Equity
• QUALITATIVE RESEARCH
• SOCIAL MEDICINE

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjopen-2023-080218

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STRENGTHS AND LIMITATIONS OF THIS STUDY

The Women in Sex work, Stigma and PrEP (WiSSPr) study uses a mixed-methods approach which is ideal for intersectional stigma research because it allows quantitative research to be grounded in the lived experiences of people, while ensuring that aspects of stigma that emerge at the intersections of identities are measured in testable ways.

Qualitative aim enrolls peer navigators to capture the perspectives of women who are at the unique interface of recipients of care as sex workers themselves, and supporters of health service delivery.

Uses core principles of community-based participatory research which value key populations as equal contributors to the knowledge production process.

Limitations include an inability to longitudinally assess the alignment of pre-exposure prophylaxis (PrEP) adherence and persistence with HIV risk, and limitations in measuring PrEP adherence by self-report and pharmacy dispensations instead of by drug biomarkers.

Introduction

Women engaging in sex work (WESW) are a key population (KP) that experiences an unacceptably high risk of HIV infection. In 2019, the Joint United Nations Programme on HIV/AIDS (UNAIDS) estimate WESW have 21 times the risk of HIV acquisition compared with the general population of adults aged 15 – 49 years old. 1 In Southern and East Africa, KPs and their sexual partners account for 25% of all new HIV infections. 2 To reduce the burden of HIV in Africa, HIV prevention strategies tailored to the unique needs of WESW are critical to safeguarding their health, as well as the health of people in their sexual networks. 3 4

While HIV pre-exposure prophylaxis (PrEP) is highly effective in preventing HIV infection, its real-world efficacy is closely linked to adherence, which is a complex process for WESW. A systematic review of PrEP usage and adherence among WESW reveals complex interrelationships between individual perceptions of HIV risk, social support and fear of healthcare provider stigma. 5 WESW may experience multiple stigmatised identities, conditions or behaviours, such as participating in sex work, having a substance use disorder, and taking HIV prevention medication. 6

Zambia has a generalised HIV epidemic, and the capital city of Lusaka is a major regional transit hub attracting WESW from the region. Approximately 3,396 live in Lusaka with over half (53%) living with HIV, underscoring the need to urgently tailor prevention strategies for this population. 7 WESW in Zambia are subject to violence and discrimination in the form of verbal, physical and sexual abuse from strangers, acquaintances, clients, intimate partners and even law enforcement. 8 Surveys among WESW in Zambia have identified healthcare provider stigma and discrimination, as well as a lack of confidential care as main barriers to HIV prevention services at public health facilities. 7 9 Therefore, a better understanding of the multiple stigmas that WESW experience is a critical first step to designing interventions to meet their HIV prevention needs.

In recent years, Zambia has made significant progress in reaching WESW and providing them with comprehensive HIV prevention services. Since May 2019, the PEPFAR-funded Key Population Investment Fund (KPIF) has been successfully engaging with KP in Lusaka Province and providing them with community-based HIV prevention and treatment services. KPIF is implemented by the Centre for Infectious Disease Research in Zambia (CIDRZ) in partnership with the Zambian Ministry of Health (MoH), US Centers for Disease Control and Prevention and importantly, key population civil society organisations (KP-CSOs). A key objective of the KPIF programme is to improve PrEP initiation, persistence and adherence for HIV-negative WESW. For this study, we propose to leverage existing KPIF infrastructure to enhance study feasibility and ensure its real-world relevance to achieving this key objective.

Although PrEP initiations are high in the KPIF programme, they may not accurately reflect PrEP effectiveness. 10 A systematic review of 41 studies found high discontinuation rates at 1 month. 11 Despite WHO recommendations and national PrEP guidelines for regular HIV testing and follow-up visits, maintaining client engagement with PrEP has been challenging. 12 13 This has resulted in a lack of data on short-term PrEP persistence among WESW in Zambia. Assessing the percentage of clients who do not return for their first follow-up visit is crucial for determining PrEP effectiveness. Current prevention strategies do not adequately address the multiple stigmas and psychosocial stress that hinder PrEP persistence.

Specific objectives

The Women in Sex work, Stigma and PrEP (WiSSPr) mixed-methods study aims to (1) measure the association between multiple stigmas on PrEP initiation and persistence among HIV-negative adult WESW and (2) qualitatively explore the enablers and barriers (interpersonal, psychosocial and structural) to initiating and persisting on PrEP. The qualitative aim will complement and contextualise 14–16 findings from the quantitative results. We hypothesize that WESW with high levels of any type of stigma will be less likely to initiate and persist on PrEP.

Conceptual framework

Interview guides will be informed by the Community, Opportunity, Motivation – Behaviour (COM-B) framework to assess how these components drive engagement with PrEP services. 17 18 The COM-B model is commonly used in HIV prevention because it offers a framework to guide the development and implementation of targeted interventions, thereby enhancing the efficacy and reach of HIV prevention programmes. 19 This framework will guide us to identify deficits in knowledge or skills (Capability), environmental and social contexts (Opportunity), and personal motivations and attitudes (Motivation). This integrated approach ensures that all relevant aspects of behaviour change are considered, leading to more effective and sustainable health outcomes.

Directed acyclic graph

Directed acyclic graphs (DAG) visually synthesise a priori knowledge about the hypothesised relationships between variables of interest, helping to identify causal pathways and potential confounders that could bias the results. We propose confounders based on their known association with stigmas and PrEP persistence, using evidence from published studies addressing similar questions. Controlling for the following variables will be sufficient to block any unconditionally open, non-causal backdoor paths that could lead to confounding: age, community hub, duration of sex work, and education ( figure 1 ).

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Directed acyclic graph illustrating the causal effect of stigma on PrEP persistence. PrEP, pre-exposure prophylaxis.

Methods and analysis

Study design.

We will use a prospective observational cohort study design with mixed methods to characterise PrEP outcomes for HIV-negative WESW in Lusaka, Zambia. Trained research assistants will administer a one-time, 75-item psychosocial survey to participants and follow them prospectively in the electronic medical record. For the qualitative aim, we will conduct in-depth interviews (IDIs) with WESW to get perspectives of prevention services with peer navigators who are both recipients of care and supporters of health service delivery.

Mixed-methods integration

We will use the NIH ‘Best Practices for Mixed Methods’ guidelines to design, analyse and interpret qualitative and quantitative data in mixed-methods research. 20 Specifically, we will employ a convergent parallel design that collects both qualitative and quantitative data concurrently and separately, prioritising both the quantitative and qualitative strands equally but keeping them independent during analysis. We will interpret the extent to which the two sets of results converge, diverge, relate to each other and/or combine to create a better understanding in response to the study’s overall purpose. 20

Study setting

The study population is composed of adult WESW who are living or working within the catchment areas of two community hubs located within urban Lusaka. Based on CIDRZ’s prior published work, we anticipate that the study population will be comprised largely (63%) of younger women (18 – 29 years old). 10

Study exposures and outcomes

Table 1 identifies the primary outcomes of PrEP initiation and persistence from pharmacy dispensations records in the last 90 days for survey participants. Several studies have accessed this data from the national electronic medical record system SmartCare. 21 22 CIDRZ is a key Smartcare implementing partner and routinely leveraging this data to optimise service delivery for KP in KPIF in order to better understand outcomes for HIV treatment and prevention in the national HIV programme. 23–28 Table 2 identifies the independent variables of interest including sociodemographic history, intersectional stigma (everyday discrimination scale), 29 substance use (ASSIST), 30 depressive symptoms (Patient Health Questionnaire, PHQ), 31 as well as sex work, HIV and PrEP-related stigmas and resulting discrimination using established questionnaires. 32–34 The qualitative outcomes are insights into the enablers and barriers to PrEP use informed by participants’ lived experiences according to the COM-B model.

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WiSSPr study outcomes

WiSSPr study independent variables

Sample size

We determined the minimum sample size using Demidenko’s method for logistic regression with binary interactions, informed by effect size and variance data from Witte et al ’s study on PrEP acceptability among women in Uganda. 35–37 Sample size considerations are based on our primary outcome of PrEP initiation and informed by preliminary programmatic data that formed assumptions about baseline HIV prevalence and estimated PrEP initiations. Each site tests an average of 200 WESW per month, which will allow an estimated 800 women to be tested during the 2-month enrolment period. We project approximately 56% (448) will test HIV-negative, and of these, we estimate 403 (90%) will be eligible, and 350 (87%) will agree to initiate PrEP. Due to time and resource limitations, we seek to enroll a sample of 300 eligible WESW. Assuming 5% of participant medical records cannot be found, a total cohort of 285 PrEP users would allow us to estimate the prevalence ratio of stigma on PrEP initiation of 1.98 or higher (positive association), or 0.50 or lower (negative association) at 80% power with a significance level of 0.05. We aim to recruit 18 participants for IDIs, based on prior research with this population and qualitative methodology guidelines suggesting that 6 – 10 interviews per subgroup are sufficient to reach thematic saturation 14 20

Participant recruitment

The study will start in July 2023. WiSSPr will recruit 300 participants from a convenience sample of WESW who are receiving HIV services from two community-based hubs which have been functioning as MoH drop-in wellness centres since October 2021. All HIV testing and prevention services at these community hubs are led by teams of KP and MoH staff. Outreach activities take place in venues where WESW socialise, such as brothels, bars, or the home of a KP. Recruitment activities will take place during these outreach activities. KPIF programming leverages KP social networks to mobilise WESW for recruitment into the study. A total of 18 participants, including 6 peer navigators, 6 WESW who discontinue PrEP after initiation, and 6 WESW who continue on PrEP, will be purposively sampled for IDIs, or until we achieve thematic saturation. 38 Qualitative data collection will take place at least 30 days after the quantitative recruitment begins, in order to sample women who initiate a 30 day supply of PrEP but do not return to pick up another refill. Figure 2 outlines the WiSSPr study recruitment process.

The WiSSPr study flow diagram summarises the stages of participant recruitment and follow-up. PrEP, pre-exposure prophylaxis; WiSSPr, Women in Sex work, Stigma and PrEP.

Recruitment will end when 300 participants have been enrolled for the survey and 18 participants enrolled for interviews. PrEP event data will be abstracted from SmartCare approximately 3 months after the final participant’s enrollment. Study activities, including qualitative data collection, data quality control and assurance, and data analysis, are anticipated to continue until the planned end of the study in September 2024.

We will engage the community advisory board (CAB) in collaborative decision-making on: (1) how best to conduct outreach to venues that WESW frequent, (2) how to engage leaders in the sex work community to inform them about this study, and (3) to encourage WESW participation in a way that minimises social harms. Box 1 identifies the inclusion and exclusion criteria for the study. Written informed consent in English or local languages (ChiNyanja or IchiBemba) will be obtained before enrollment. As an added measure of protection for this marginalised population, participants must complete an informed consent quiz to ensure that they understand the potential risks of study participation. Participants will receive the Zambia Kwacha equivalent of US$5 per survey and interview as compensation for their time.

Inclusion and exclusion criteria

Cohort inclusion and exclusion criteria are as follows:

Inclusion criteria: (1) identify as a cis-gendered or transgendered woman, (2) age ≥ 18 years, (3) earns a significant amount of income from exchanging sex for money or goods in the last 3 months, (4) HIV-negative status and eligible for PrEP according to national guidelines, (5) not planning to transfer care to another site within the next 30 days, (6) speaks English or ChiNyanja or IchiBemba and (7) willing and able to provide written informed consent

Exclusion criteria: (1) do not identify as a woman, (2) age < 18 years old, (3) has not earned a significant amount of income from exchanging sex for money or goods or has earned for < 3 months, (4) HIV-positive status or status is unknown or ineligible for PrEP, (5) planning to transfer care to another site within the next 30 days, (6) unable to speak English or ChiNyanja or IchiBemba and (7) not willing or able to provide written informed consent

In-depth interviews will be conducted with cohort members, as well as peer navigators. The inclusions and exclusion criteria for peer navigators is as follows:

Inclusion criteria: (1) age ≥ 18 years old, (2) history working as a peer health navigator, (3) history of providing HIV services to women engaging in sex work, (4) speaks English or ChiNyanja or IchiBemba and (5) willing and able to provide written informed consent.

Exclusion criteria: (1) age < 18 years, (2) does not have a history working as a peer health navigator, (3) does not have a history of providing HIV services to women engaging in sex work, (4) unable to speak English or ChiNyanja or IchiBemba and (5) not willing or able to provide written informed consent.

Quantitative data collection

A team of 3–5 trained research assistants will administer a tablet-based survey ( online supplemental file 1 ) for quicker data entry, real-time quality control and logic checks to reduce data entry errors and immediate data backup compared with paper. Surveys, estimated to take 60 min each, will be conducted in English, ChiNyanja or IchiBemba, based on participant preference. The survey tool will be piloted with CAB members and peer navigators. Patient medical records are routinely entered by KPIF programme staff into a secure, standardised electronic data capture system, from which we will extract relevant deidentified data using the participants’ SmartCare ID numbers.

Supplemental material

Qualitative data collection.

We will use a semi-structured interview guide ( online supplemental file 1 ) with open-ended questions and probes to explore specific themes related to HIV prevention and intersectional stigma. This guide allows some flexibility for participants to follow topics of interest to them. The themes we will explore are informed by the COM-B conceptual framework which include perceived and enacted stigma, the impact of intersectional stigmas on health service utilisation service needs, enablers such as psychosocial support or the trustworthiness of the healthcare system. The guide also includes modules on PrEP where the interviewer will explain oral and long-acting injectable PrEP and assess participants perceptions of the advantages and disadvantages and willingness to use these different PrEP options. Participants will be asked about their own perceptions as well as their perceived opinions of their peers, as this approach has yielded richer responses in previous studies. 39 Interviews are estimated to take 60 minutes and will be conducted in English, ChiNyanja, or IchiBemba in a private location at a community safe space or other similarly secure location determined by participant preference. We will request permission to audio record interviews for transcription and translation. All interviews will be conducted by a single trained interviewer. The interview guides will be piloted with CAB members before implementation.

Data management

SmartCare serves as a repository of clinical data for WESW accessing KPIF services. A secure server will be used to store encrypted study data, including the study database. Quantitative data collected on tablets will be transmitted to the server at the end of each day. To ensure data safety, there will be daily backups, and data will also be stored on secure drives.

All IDIs will be audio recorded. Audio recordings will be transcribed verbatim and then translated into English in a single step by qualified research staff. The audio recordings will not be marked with any identifying information. Instead, interviewers will use unique participant codes to label the audio recordings. No personal identifiers will be used, and any identifiers inadvertently mentioned during interviews will be purged from the transcripts prior to analysis.

All medical records that contain participant identities are treated as confidential in accordance with the Zambian Data Protection Act. All study documents related to the participants will only include an assigned participant code. Only research staff will have access to linkable information, which will be kept strictly confidential. All records will be archived in a secure storage facility for 3 years after the completion of the study per local regulatory guidelines, after which time all electronic data will be deleted from project servers and hard drives, and all paper-based records will be disposed of.

Quantitative data analysis

We will conduct univariable analyses to examine whether there are differences in the levels of stigma, discrimination, depressive symptoms and substance use disorders among those who initiate PrEP versus those who do not, stratified by community hub. We will report the prevalences of HIV and PrEP stigmas, discrimination due to intersectional stigma identified by the Everyday Discrimination scale, depression and suicidal ideation identified by PHQ, and substance use disorders identified by ASSIST. We will sum all items within a screener to a total score before collapsing data into categorical variables. For cases where missing data are more limited (approximately < 5%), for single items and measures, we will use mean imputation to derive a score. If there is substantial missingness (> 10%) then we will use missing data methods such as multiple imputation.

A PHQ-9 score ≥ 10 is commonly used in primary care settings as a cut-off for probable major depression. 40 PHQ-9 cut-off scores of 5, 10, 15 and 20 will be categorised as mild, moderate, moderately severe and severe depression, respectively. The ASSIST gives 10 risk scores for tobacco, alcohol, cannabis, cocaine, amphetamine-type stimulants, inhalants, sedatives, hallucinogens, opioids and other drugs. The score is higher the more frequently the participant reports using substances. For alcohol use, we will use cut-offs of 11 and 27 for moderate and high risk of substance use disorder. For all other substances cut-offs of 4, and 27 for moderate and high risk. 30

PrEP initiation will be calculated using the total number of individuals initiated on PrEP over the total number of HIV-negative individuals who were enrolled and eligible for PrEP. We refer to the complement of discontinuation as PrEP persistence. 41 We define immediate discontinuation for those who initiate a 30 day supply of PrEP and do not return for any refills over the 108 day observation period in alignment with national antiretroviral therapy (ART) programme guidelines on continuity of care and management of missed appointments. 21 42 We will calculate a medication possession ratio (MPR) of total days with medication in patient possession to the observation period, as a measure of engagement in services and report both the MPR and IQR ( table 1 ).

We will use Stata (V.16.1, StataCorp) for analysis, reporting descriptive statistics to characterise the study population and bivariate associations between key exposures and immediate discontinuation with Pearson’s χ 2 statistics. We will fit Poisson regression models, which will estimate prevalence ratios of discrimination, PrEP stigma and HIV stigma on immediate discontinuation of PrEP over a 3-month follow-up period, controlling for confounders identified by the DAG. Adjusted prevalence ratio estimates will be reported with 95% CIs and p-values at the alpha = 0.05 significance level.

Qualitative data analysis

We will analyse the qualitative data using established analytical software (NVivo, QSR International, Melbourne, Australia) through deductive reasoning based on our conceptual model and inductive reasoning to identify major and minor themes emerging from audio recordings and transcripts. The process of eliciting themes will involve familiarisation with interview transcripts and noting emergent themes, adapting our conceptual framework as necessary, performing open coding, developing a codebook, performing data reduction, data display using matrices and/or tables, and interpretation to map out relationships in the data. Two coders will review these data, independently identify emergent themes, and confer to agree on final coding and findings. We will apply established qualitative research principles in our analyses, including negative case analysis and respondent validation. 43 44

Participant attitudes and preferences relating to elements of future stigma-reduction intervention, psychosocial support provision and long-acting injectable PrEP will be described qualitatively. We will strive for critical reflexivity by outlining our point of view in relation to the interviewees of the study during data collection and will state how positionality and context may have affected the findings. The credibility and trustworthiness of qualitative data will be assured through member-checking by participants themselves. 45

Ethics and dissemination

WiSSPr was approved by the Institutional Review Boards of the University of Zambia (#3650 -2023) and University of North Carolina, the Zambia National Health Research Authority and the Lusaka Provincial and District Health Offices. A final study notification will be sent on completion of the study, or in the event of early termination. Participants are free to withdraw from the study at any time without affecting their right to medical care.

The study findings will be disseminated to KP community members, providers, researchers and policy-makers. The CAB will review preliminary results and advise on meaningful dissemination to the KP community, National AIDS Council, National HIV and Mental Health Technical Working Groups, investigators and stakeholders. The information will be presented at conferences or published in peer-reviewed journals. Participants’ personal information will not be included in any publications.

Patient and public involvement

We will use principles of community-based participatory research (CBPR) to ensure patient and public involvement in this study. CBPR is a research paradigm that focuses on relationships between academic and community partners, with principles of co-learning, mutual benefit and long-term commitment. 46 CBPR incorporates community theories, participation, and practices into the research efforts and plays a role in expanding the reach of implementation science to influence practice and policies for eliminating health disparities. 46 47

To collaboratively develop this study with clients and the public, we will use CBPR principles and create a CAB with Lusaka District Health Office and two KP-CSOs working in the study sites: Zambia Sex Workers Alliance and Tithandizeni Umoyo Network. As a study team, our first priority is to develop trust with people engaging in sex work. Trust development is a construct of CBPR and has also emerged as a synthesising theory. 48 49 Trust types are ordered along a relative continuum from least (trust deficit) to most (critical reflective) trust which reflects an ability to discuss and move on after a misstep. 48 Given the historical marginalisation and stigmatisation of WESW in Zambia, we anticipate a trust deficit and have allocated time and budget to nurture and develop trust along this continuum. We will build trust through ‘role-based trust’ as researchers, ‘proxy trust’ from the reputation of CIDRZ and KP CSO team members’ work with KPs in Zambia, and ultimately aim to establish ‘critical reflective’ trust.

The research questions and outcome measures were developed in collaboration with the CAB, ensuring they reflect the priorities, experiences and preferences of the sex worker community. Input from the CAB helped tailor the study to address the most pressing issues identified by the community. The study team will work with the CAB to adapt the study within complex systems of organisational and cultural context and knowledge. Collaborative decision-making will occur prior to the study launch, throughout the recruitment period, and during dissemination. The CAB will provide feedback on the potential burden of the intervention and the time required for participation, so that the study minimises inconvenience and respected participants’ time constraints. All partners will decide what it means to have a ‘collaborative, equitable partnership’ and how to make that happen. 50 The CAB will advise on which community hub to recruit from first, and how to work with community leaders to adapt study standard operating procedures to not disrupt service implementation at study sites. They will also advise on how to minimise potential risks to participants, including ways to reduce emotional distress and ensure physical safety. Participants experiencing emotional distress will be referred for psychosocial support with evidence-based mental health therapy specialised for those with depression and substance abuse, with the KPIF providing transportation and a peer navigator accompanying them to the facility providing these services. The CAB will be actively involved in planning the dissemination of study results to participants and the wider community, helping decide what information to share, the timing of the dissemination and the most appropriate formats for communicating the findings.

The WiSSPr study is significant as it addresses the limitations of HIV interventions that focus solely on HIV-related stigma, without considering co-occurring stigmas linked to other identities or conditions. This study will inform the design of PrEP service delivery programmes for WESW in Zambia and the region. Understanding stigmas and related psychosocial factors is crucial for developing effective, evidence-based stigma-reduction interventions for WESW in Africa. Our long-term goal is to optimise person-centred HIV prevention by implementing inclusive, affirming practices for individuals facing multiple barriers.

Strengths of this study include (1) a mixed-methods approach which grounds quantitative research in the lived experiences of people and measures aspects of stigma that emerge at the intersections of identities, (2) qualitative data from peer navigators capturing perspectives of women at the unique interface of being recipients of care as sex workers as well as direct supporters of health service delivery, and (3) incorporation of core principles of CBPR which value KP-CSOs as equal contributors to the knowledge production process.

Several methodological limitations are also inherent in the study. First, we are unable to longitudinally assess the alignment of PrEP adherence and persistence with HIV risk. We will be limited to measuring PrEP adherence by self-report and pharmacy dispensations instead of by biomarkers of tenofovir use. Secondly, recruitment might fall short at some sites, necessitating expansion to additional community outreach venues leveraging our network of KPs. Lastly, cohort studies may have bias, due to recall and social desirability bias of self-reported measures, and missing data.

Ethics statements

Patient consent for publication.

Not applicable.

Acknowledgments

The authors would like to acknowledge the infrastructure support provided by the Centre for Infectious Disease Research in Zambia (CIDRZ) and the Key Populations Investment Fund (KPIF) programme. The authors would also like to thank peer navigators and leaders in the sex work community for their assistance in developing the study approach and recruiting study participants.

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MEH and MM are joint senior authors.

X @idlidosa2, @kenmugwanya, @webarrington

Contributors RK, DR, AS, MM, MH, KKM and WB conceived and designed the study. RK, DR, AS, MM, MH, JP, MZ, MP, RZ, GMK, LC, PMK, CM and BN created the interview guides and survey. All authors revised drafts and gave final approval for publication. MM is the guarantor of the study and accepts full responsibility for the finished work and the conduct of the study, had access to the data and controlled the decision to publish.

Funding The study is being supported by the NIH Fogarty Global Health Fellowship awarded by the NIH Fogarty International Center Grant #D43TW009340.

Competing interests None declared.

Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

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.

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