research design rationale

How to Write the Rationale for a Research Paper

• Research Process
• Peer Review

A research rationale answers the big SO WHAT? that every adviser, peer reviewer, and editor has in mind when they critique your work. A compelling research rationale increases the chances of your paper being published or your grant proposal being funded. In this article, we look at the purpose of a research rationale, its components and key characteristics, and how to create an effective research rationale.

Updated on September 19, 2022

The rationale for your research is the reason why you decided to conduct the study in the first place. The motivation for asking the question. The knowledge gap. This is often the most significant part of your publication. It justifies the study's purpose, novelty, and significance for science or society. It's a critical part of standard research articles as well as funding proposals.

Essentially, the research rationale answers the big SO WHAT? that every (good) adviser, peer reviewer, and editor has in mind when they critique your work.

A compelling research rationale increases the chances of your paper being published or your grant proposal being funded. In this article, we look at:

• the purpose of a research rationale
• its components and key characteristics
• how to create an effective research rationale

What is a research rationale?

Think of a research rationale as a set of reasons that explain why a study is necessary and important based on its background. It's also known as the justification of the study, rationale, or thesis statement.

Essentially, you want to convince your reader that you're not reciting what other people have already said and that your opinion hasn't appeared out of thin air. You've done the background reading and identified a knowledge gap that this rationale now explains.

A research rationale is usually written toward the end of the introduction. You'll see this section clearly in high-impact-factor international journals like Nature and Science. At the end of the introduction there's always a phrase that begins with something like, "here we show..." or "in this paper we show..." This text is part of a logical sequence of information, typically (but not necessarily) provided in this order:

Here's an example from a study by Cataldo et al. (2021) on the impact of social media on teenagers' lives.

Note how the research background, gap, rationale, and objectives logically blend into each other.

The authors chose to put the research aims before the rationale. This is not a problem though. They still achieve a logical sequence. This helps the reader follow their thinking and convinces them about their research's foundation.

Elements of a research rationale

We saw that the research rationale follows logically from the research background and literature review/observation and leads into your study's aims and objectives.

This might sound somewhat abstract. A helpful way to formulate a research rationale is to answer the question, “Why is this study necessary and important?”

Generally, that something has never been done before should not be your only motivation. Use it only If you can give the reader valid evidence why we should learn more about this specific phenomenon.

A well-written introduction covers three key elements:

• What's the background to the research?
• What has been done before (information relevant to this particular study, but NOT a literature review)?
• Research rationale

Now, let's see how you might answer the question.

1. This study complements scientific knowledge and understanding

Discuss the shortcomings of previous studies and explain how'll correct them. Your short review can identify:

• Methodological limitations . The methodology (research design, research approach or sampling) employed in previous works is somewhat flawed.

Example : Here , the authors claim that previous studies have failed to explore the role of apathy “as a predictor of functional decline in healthy older adults” (Burhan et al., 2021). At the same time, we know a lot about other age-related neuropsychiatric disorders, like depression.

Their study is necessary, then, “to increase our understanding of the cognitive, clinical, and neural correlates of apathy and deconstruct its underlying mechanisms.” (Burhan et al., 2021).

• Contextual limitations . External factors have changed and this has minimized or removed the relevance of previous research.

Example : You want to do an empirical study to evaluate the effects of the COVID-19 pandemic on the number of tourists visiting Sicily. Previous studies might have measured tourism determinants in Sicily, but they preceded COVID-19.

• Conceptual limitations . Previous studies are too bound to a specific ideology or a theoretical framework.

Example : The work of English novelist E. M. Forster has been extensively researched for its social, political, and aesthetic dimensions. After the 1990s, younger scholars wanted to read his novels as an example of gay fiction. They justified the need to do so based on previous studies' reliance on homophobic ideology.

This kind of rationale is most common in basic/theoretical research.

2. This study can help solve a specific problem

Here, you base your rationale on a process that has a problem or is not satisfactory.

For example, patients complain about low-quality hospital care on weekends (staff shortages, inadequate attention, etc.). No one has looked into this (there is a lack of data). So, you explore if the reported problems are true and what can be done to address them. This is a knowledge gap.

Or you set out to explore a specific practice. You might want to study the pros and cons of several entry strategies into the Japanese food market.

It's vital to explain the problem in detail and stress the practical benefits of its solution. In the first example, the practical implications are recommendations to improve healthcare provision.

In the second example, the impact of your research is to inform the decision-making of businesses wanting to enter the Japanese food market.

This kind of rationale is more common in applied/practical research.

3. You're the best person to conduct this study

It's a bonus if you can show that you're uniquely positioned to deliver this study, especially if you're writing a funding proposal .

For an anthropologist wanting to explore gender norms in Ethiopia, this could be that they speak Amharic (Ethiopia's official language) and have already lived in the country for a few years (ethnographic experience).

Or if you want to conduct an interdisciplinary research project, consider partnering up with collaborators whose expertise complements your own. Scientists from different fields might bring different skills and a fresh perspective or have access to the latest tech and equipment. Teaming up with reputable collaborators justifies the need for a study by increasing its credibility and likely impact.

When is the research rationale written?

You can write your research rationale before, or after, conducting the study.

In the first case, when you might have a new research idea, and you're applying for funding to implement it.

Or you're preparing a call for papers for a journal special issue or a conference. Here , for instance, the authors seek to collect studies on the impact of apathy on age-related neuropsychiatric disorders.

In the second case, you have completed the study and are writing a research paper for publication. Looking back, you explain why you did the study in question and how it worked out.

Although the research rationale is part of the introduction, it's best to write it at the end. Stand back from your study and look at it in the big picture. At this point, it's easier to convince your reader why your study was both necessary and important.

How long should a research rationale be?

The length of the research rationale is not fixed. Ideally, this will be determined by the guidelines (of your journal, sponsor etc.).

The prestigious journal Nature , for instance, calls for articles to be no more than 6 or 8 pages, depending on the content. The introduction should be around 200 words, and, as mentioned, two to three sentences serve as a brief account of the background and rationale of the study, and come at the end of the introduction.

If you're not provided guidelines, consider these factors:

• Research document : In a thesis or book-length study, the research rationale will be longer than in a journal article. For example, the background and rationale of this book exploring the collective memory of World War I cover more than ten pages.
• Research question : Research into a new sub-field may call for a longer or more detailed justification than a study that plugs a gap in literature.

Which verb tenses to use in the research rationale?

It's best to use the present tense. Though in a research proposal, the research rationale is likely written in the future tense, as you're describing the intended or expected outcomes of the research project (the gaps it will fill, the problems it will solve).

Example of a research rationale

Research question : What are the teachers' perceptions of how a sense of European identity is developed and what underlies such perceptions?

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology , 3(2), 77-101.

Burhan, A.M., Yang, J., & Inagawa, T. (2021). Impact of apathy on aging and age-related neuropsychiatric disorders. Research Topic. Frontiers in Psychiatry

Cataldo, I., Lepri, B., Neoh, M. J. Y., & Esposito, G. (2021). Social media usage and development of psychiatric disorders in childhood and adolescence: A review. Frontiers in Psychiatry , 11.

CiCe Jean Monnet Network (2017). Guidelines for citizenship education in school: Identities and European citizenship children's identity and citizenship in Europe.

Cohen, l, Manion, L., & Morrison, K. (2018). Research methods in education . Eighth edition. London: Routledge.

de Prat, R. C. (2013). Euroscepticism, Europhobia and Eurocriticism: The radical parties of the right and left “vis-à-vis” the European Union P.I.E-Peter Lang S.A., Éditions Scientifiques Internationales.

European Commission. (2017). Eurydice Brief: Citizenship education at school in Europe.

Polyakova, A., & Fligstein, N. (2016). Is European integration causing Europe to become more nationalist? Evidence from the 2007–9 financial crisis. Journal of European Public Policy , 23(1), 60-83.

Winter, J. (2014). Sites of Memory, Sites of Mourning: The Great War in European Cultural History . Cambridge: Cambridge University Press.

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Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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How to Write the Rationale of the Study in Research (Examples)

What is the Rationale of the Study?

The rationale of the study is the justification for taking on a given study. It explains the reason the study was conducted or should be conducted. This means the study rationale should explain to the reader or examiner why the study is/was necessary. It is also sometimes called the “purpose” or “justification” of a study. While this is not difficult to grasp in itself, you might wonder how the rationale of the study is different from your research question or from the statement of the problem of your study, and how it fits into the rest of your thesis or research paper. 

The rationale of the study links the background of the study to your specific research question and justifies the need for the latter on the basis of the former. In brief, you first provide and discuss existing data on the topic, and then you tell the reader, based on the background evidence you just presented, where you identified gaps or issues and why you think it is important to address those. The problem statement, lastly, is the formulation of the specific research question you choose to investigate, following logically from your rationale, and the approach you are planning to use to do that.

Table of Contents:

How to write a rationale for a research paper , how do you justify the need for a research study.

• Study Rationale Example: Where Does It Go In Your Paper?

The basis for writing a research rationale is preliminary data or a clear description of an observation. If you are doing basic/theoretical research, then a literature review will help you identify gaps in current knowledge. In applied/practical research, you base your rationale on an existing issue with a certain process (e.g., vaccine proof registration) or practice (e.g., patient treatment) that is well documented and needs to be addressed. By presenting the reader with earlier evidence or observations, you can (and have to) convince them that you are not just repeating what other people have already done or said and that your ideas are not coming out of thin air. 

Once you have explained where you are coming from, you should justify the need for doing additional research–this is essentially the rationale of your study. Finally, when you have convinced the reader of the purpose of your work, you can end your introduction section with the statement of the problem of your research that contains clear aims and objectives and also briefly describes (and justifies) your methodological approach. 

When is the Rationale for Research Written?

The author can present the study rationale both before and after the research is conducted. 

• Before conducting research : The study rationale is a central component of the research proposal . It represents the plan of your work, constructed before the study is actually executed.
• Once research has been conducted : After the study is completed, the rationale is presented in a research article or  PhD dissertation  to explain why you focused on this specific research question. When writing the study rationale for this purpose, the author should link the rationale of the research to the aims and outcomes of the study.

What to Include in the Study Rationale

Although every study rationale is different and discusses different specific elements of a study’s method or approach, there are some elements that should be included to write a good rationale. Make sure to touch on the following:

• A summary of conclusions from your review of the relevant literature
• What is currently unknown (gaps in knowledge)
• Inconclusive or contested results  from previous studies on the same or similar topic
• The necessity to improve or build on previous research, such as to improve methodology or utilize newer techniques and/or technologies

There are different types of limitations that you can use to justify the need for your study. In applied/practical research, the justification for investigating something is always that an existing process/practice has a problem or is not satisfactory. Let’s say, for example, that people in a certain country/city/community commonly complain about hospital care on weekends (not enough staff, not enough attention, no decisions being made), but you looked into it and realized that nobody ever investigated whether these perceived problems are actually based on objective shortages/non-availabilities of care or whether the lower numbers of patients who are treated during weekends are commensurate with the provided services.

In this case, “lack of data” is your justification for digging deeper into the problem. Or, if it is obvious that there is a shortage of staff and provided services on weekends, you could decide to investigate which of the usual procedures are skipped during weekends as a result and what the negative consequences are. 

In basic/theoretical research, lack of knowledge is of course a common and accepted justification for additional research—but make sure that it is not your only motivation. “Nobody has ever done this” is only a convincing reason for a study if you explain to the reader why you think we should know more about this specific phenomenon. If there is earlier research but you think it has limitations, then those can usually be classified into “methodological”, “contextual”, and “conceptual” limitations. To identify such limitations, you can ask specific questions and let those questions guide you when you explain to the reader why your study was necessary:

Methodological limitations

• Did earlier studies try but failed to measure/identify a specific phenomenon?
• Was earlier research based on incorrect conceptualizations of variables?
• Were earlier studies based on questionable operationalizations of key concepts?
• Did earlier studies use questionable or inappropriate research designs?

Contextual limitations

• Have recent changes in the studied problem made previous studies irrelevant?
• Are you studying a new/particular context that previous findings do not apply to?

Conceptual limitations

• Do previous findings only make sense within a specific framework or ideology?

Study Rationale Examples

Let’s look at an example from one of our earlier articles on the statement of the problem to clarify how your rationale fits into your introduction section. This is a very short introduction for a practical research study on the challenges of online learning. Your introduction might be much longer (especially the context/background section), and this example does not contain any sources (which you will have to provide for all claims you make and all earlier studies you cite)—but please pay attention to how the background presentation , rationale, and problem statement blend into each other in a logical way so that the reader can follow and has no reason to question your motivation or the foundation of your research.

Background presentation

Since the beginning of the Covid pandemic, most educational institutions around the world have transitioned to a fully online study model, at least during peak times of infections and social distancing measures. This transition has not been easy and even two years into the pandemic, problems with online teaching and studying persist (reference needed) . 

While the increasing gap between those with access to technology and equipment and those without access has been determined to be one of the main challenges (reference needed) , others claim that online learning offers more opportunities for many students by breaking down barriers of location and distance (reference needed) .  

Rationale of the study

Since teachers and students cannot wait for circumstances to go back to normal, the measures that schools and universities have implemented during the last two years, their advantages and disadvantages, and the impact of those measures on students’ progress, satisfaction, and well-being need to be understood so that improvements can be made and demographics that have been left behind can receive the support they need as soon as possible.

Statement of the problem

To identify what changes in the learning environment were considered the most challenging and how those changes relate to a variety of student outcome measures, we conducted surveys and interviews among teachers and students at ten institutions of higher education in four different major cities, two in the US (New York and Chicago), one in South Korea (Seoul), and one in the UK (London). Responses were analyzed with a focus on different student demographics and how they might have been affected differently by the current situation.

How long is a study rationale?

In a research article bound for journal publication, your rationale should not be longer than a few sentences (no longer than one brief paragraph). A  dissertation or thesis  usually allows for a longer description; depending on the length and nature of your document, this could be up to a couple of paragraphs in length. A completely novel or unconventional approach might warrant a longer and more detailed justification than an approach that slightly deviates from well-established methods and approaches.

Consider Using Professional Academic Editing Services

Now that you know how to write the rationale of the study for a research proposal or paper, you should make use of Wordvice AI’s free AI Grammar Checker , or receive professional academic proofreading services from Wordvice, including research paper editing services and manuscript editing services to polish your submitted research documents.

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How do you Write the Rationale for Research?

• By DiscoverPhDs
• October 21, 2020

What is the Rationale of Research?

The term rationale of research means the reason for performing the research study in question. In writing your rational you should able to convey why there was a need for your study to be carried out. It’s an important part of your research paper that should explain how your research was novel and explain why it was significant; this helps the reader understand why your research question needed to be addressed in your research paper, term paper or other research report.

The rationale for research is also sometimes referred to as the justification for the study. When writing your rational, first begin by introducing and explaining what other researchers have published on within your research field.

Having explained the work of previous literature and prior research, include discussion about where the gaps in knowledge are in your field. Use these to define potential research questions that need answering and explain the importance of addressing these unanswered questions.

The rationale conveys to the reader of your publication exactly why your research topic was needed and why it was significant . Having defined your research rationale, you would then go on to define your hypothesis and your research objectives.

Final Comments

Defining the rationale research, is a key part of the research process and academic writing in any research project. You use this in your research paper to firstly explain the research problem within your dissertation topic. This gives you the research justification you need to define your research question and what the expected outcomes may be.

A thesis and dissertation appendix contains additional information which supports your main arguments. Find out what they should include and how to format them.

An academic transcript gives a breakdown of each module you studied for your degree and the mark that you were awarded.

Find out the differences between a Literature Review and an Annotated Bibliography, whey they should be used and how to write them.

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• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

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

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

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Shona McCombes

Setting Rationale in Research: Cracking the code for excelling at research

Knowledge and curiosity lays the foundation of scientific progress. The quest for knowledge has always been a timeless endeavor. Scholars seek reasons to explain the phenomena they observe, paving way for development of research. Every investigation should offer clarity and a well-defined rationale in research is a cornerstone upon which the entire study can be built.

Research rationale is the heartbeat of every academic pursuit as it guides the researchers to unlock the untouched areas of their field. Additionally, it illuminates the gaps in the existing knowledge, and identifies the potential contributions that the study aims to make.

Table of Contents

What Is Research Rationale and When Is It Written

Research rationale is the “why” behind every academic research. It not only frames the study but also outlines its objectives , questions, and expected outcomes. Additionally, it helps to identify the potential limitations of the study . It serves as a lighthouse for researchers that guides through data collection and analysis, ensuring their efforts remain focused and purposeful. Typically, a rationale is written at the beginning of the research proposal or research paper . It is an essential component of the introduction section and provides the foundation for the entire study. Furthermore, it provides a clear understanding of the purpose and significance of the research to the readers before delving into the specific details of the study. In some cases, the rationale is written before the methodology, data analysis, and other sections. Also, it serves as the justification for the research, and how it contributes to the field. Defining a research rationale can help a researcher in following ways:

1. Justification of a Research Problem

• Research rationale helps to understand the essence of a research problem.
• It designs the right approach to solve a problem. This aspect is particularly important for applied research, where the outcomes can have real-world relevance and impact.
• Also, it explains why the study is worth conducting and why resources should be allocated to pursue it.
• Additionally, it guides a researcher to highlight the benefits and implications of a strategy.

2. Elimination of Literature Gap

• Research rationale helps to ideate new topics which are less addressed.
• Additionally, it offers fresh perspectives on existing research and discusses the shortcomings in previous studies.
• It shows that your study aims to contribute to filling these gaps and advancing the field’s understanding.

3. Originality and Novelty

• The rationale highlights the unique aspects of your research and how it differs from previous studies.
• Furthermore, it explains why your research adds something new to the field and how it expands upon existing knowledge.
• It highlights how your findings might contribute to a better understanding of a particular issue or problem and potentially lead to positive changes.
• Besides these benefits, it provides a personal motivation to the researchers. In some cases, researchers might have personal experiences or interests that drive their desire to investigate a particular topic.

4. An Increase in Chances of Funding

• It is essential to convince funding agencies , supervisors, or reviewers, that a research is worth pursuing.
• Therefore, a good rationale can get your research approved for funding and increases your chances of getting published in journals; as it addresses the potential knowledge gap in existing research.

Overall, research rationale is essential for providing a clear and convincing argument for the value and importance of your research study, setting the stage for the rest of the research proposal or manuscript. Furthermore, it helps establish the context for your work and enables others to understand the purpose and potential impact of your research.

5 Key Elements of a Research Rationale

Research rationale must include certain components which make it more impactful. Here are the key elements of a research rationale:

By incorporating these elements, you provide a strong and convincing case for the legitimacy of your research, which is essential for gaining support and approval from academic institutions, funding agencies, or other stakeholders.

How to Write a Rationale in Research

Writing a rationale requires careful consideration of the reasons for conducting the study. It is usually written in the present tense.

Here are some steps to guide you through the process of writing a research rationale:

After writing the initial draft, it is essential to review and revise the research rationale to ensure that it effectively communicates the purpose of your research. The research rationale should be persuasive and compelling, convincing readers that your study is worthwhile and deserves their attention.

How Long Should a Research Rationale be?

Although there is no pre-defined length for a rationale in research, its length may vary depending on the specific requirements of the research project. It also depends on the academic institution or organization, and the guidelines set by the research advisor or funding agency. In general, a research rationale is usually a concise and focused document.

Typically, it ranges from a few paragraphs to a few pages, but it is usually recommended to keep it as crisp as possible while ensuring all the essential elements are adequately covered. The length of a research rationale can be roughly as follows:

1. For Research Proposal:

A. Around 1 to 3 pages

B. Ensure clear and comprehensive explanation of the research question, its significance, literature review , and methodological approach.

2. Thesis or Dissertation:

A. Around 3 to 5 pages

B. Ensure an extensive coverage of the literature review, theoretical framework, and research objectives to provide a robust justification for the study.

3. Journal Article:

A. Usually concise. Ranges from few paragraphs to one page

B. The research rationale is typically included as part of the introduction section

However, remember that the quality and content of the research rationale are more important than its length. The reasons for conducting the research should be well-structured, clear, and persuasive when presented. Always adhere to the specific institution or publication guidelines.

Example of a Research Rationale

In conclusion, the research rationale serves as the cornerstone of a well-designed and successful research project. It ensures that research efforts are focused, meaningful, and ethically sound. Additionally, it provides a comprehensive and logical justification for embarking on a specific investigation. Therefore, by identifying research gaps, defining clear objectives, emphasizing significance, explaining the chosen methodology, addressing ethical considerations, and recognizing potential limitations, researchers can lay the groundwork for impactful and valuable contributions to the scientific community.

So, are you ready to delve deeper into the world of research and hone your academic writing skills? Explore Enago Academy ‘s comprehensive resources and courses to elevate your research and make a lasting impact in your field. Also, share your thoughts and experiences in the form of an article or a thought piece on Enago Academy’s Open Platform .

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Frequently Asked Questions

A rationale of the study can be written by including the following points: 1. Background of the Research/ Study 2. Identifying the Knowledge Gap 3. An Overview of the Goals and Objectives of the Study 4. Methodology and its Significance 5. Relevance of the Research

Start writing a research rationale by defining the research problem and discussing the literature gap associated with it.

A research rationale can be ended by discussing the expected results and summarizing the need of the study.

A rationale for thesis can be made by covering the following points: 1. Extensive coverage of the existing literature 2. Explaining the knowledge gap 3. Provide the framework and objectives of the study 4. Provide a robust justification for the study/ research 5. Highlight the potential of the research and the expected outcomes

A rationale for dissertation can be made by covering the following points: 1. Highlight the existing reference 2. Bridge the gap and establish the context of your research 3. Describe the problem and the objectives 4. Give an overview of the methodology

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Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Research Design Considerations

Associated data.

Editor's Note: The online version of this article contains references and resources for further reading and the authors' professional information.

The Challenge

“I'd really like to do a survey” or “Let's conduct some interviews” might sound like reasonable starting points for a research project. However, it is crucial that researchers examine their philosophical assumptions and those underpinning their research questions before selecting data collection methods. Philosophical assumptions relate to ontology, or the nature of reality, and epistemology, the nature of knowledge. Alignment of the researcher's worldview (ie, ontology and epistemology) with methodology (research approach) and methods (specific data collection, analysis, and interpretation tools) is key to quality research design. This Rip Out will explain philosophical differences between quantitative and qualitative research designs and how they affect definitions of rigorous research.

What Is Known

Worldviews offer different beliefs about what can be known and how it can be known, thereby shaping the types of research questions that are asked, the research approach taken, and ultimately, the data collection and analytic methods used. Ontology refers to the question of “What can we know?” Ontological viewpoints can be placed on a continuum: researchers at one end believe that an observable reality exists independent of our knowledge of it, while at the other end, researchers believe that reality is subjective and constructed, with no universal “truth” to be discovered. 1,2 Epistemology refers to the question of “How can we know?” 3 Epistemological positions also can be placed on a continuum, influenced by the researcher's ontological viewpoint. For example, the positivist worldview is based on belief in an objective reality and a truth to be discovered. Therefore, knowledge is produced through objective measurements and the quantitative relationships between variables. 4 This might include measuring the difference in examination scores between groups of learners who have been exposed to 2 different teaching formats, in order to determine whether a particular teaching format influenced the resulting examination scores.

In contrast, subjectivists (also referred to as constructionists or constructivists ) are at the opposite end of the continuum, and believe there are multiple or situated realities that are constructed in particular social, cultural, institutional, and historical contexts. According to this view, knowledge is created through the exploration of beliefs, perceptions, and experiences of the world, often captured and interpreted through observation, interviews, and focus groups. A researcher with this worldview might be interested in exploring the perceptions of students exposed to the 2 teaching formats, to better understand how learning is experienced in the 2 settings. It is crucial that there is alignment between ontology (what can we know?), epistemology (how can we know it?), methodology (what approach should be used?), and data collection and analysis methods (what specific tools should be used?). 5

Key Differences in Qualitative and Quantitative Approaches

Use of theory.

Quantitative approaches generally test theory, while qualitative approaches either use theory as a lens that shapes the research design or generate new theories inductively from their data. 4

Use of Logic

Quantitative approaches often involve deductive logic, starting off with general arguments of theories and concepts that result in data points. 4 Qualitative approaches often use inductive logic or both inductive and deductive logic, start with the data, and build up to a description, theory, or explanatory model. 4

Purpose of Results

Quantitative approaches attempt to generalize findings; qualitative approaches pay specific attention to particular individuals, groups, contexts, or cultures to provide a deep understanding of a phenomenon in local context. 4

Establishing Rigor

Quantitative researchers must collect evidence of validity and reliability. Some qualitative researchers also aim to establish validity and reliability. They seek to be as objective as possible through techniques, including cross-checking and cross-validating sources during observations. 6 Other qualitative researchers have developed specific frameworks, terminology, and criteria on which qualitative research should be evaluated. 6,7 For example, the use of credibility, transferability, dependability, and confirmability as criteria for rigor seek to establish the accuracy, trustworthiness, and believability of the research, rather than its validity and reliability. 8 Thus, the framework of rigor you choose will depend on your chosen methodology (see “Choosing a Qualitative Research Approach” Rip Out).

View of Objectivity

A goal of quantitative research is to maintain objectivity, in other words, to reduce the influence of the researcher on data collection as much as possible. Some qualitative researchers also attempt to reduce their own influence on the research. However, others suggest that these approaches subscribe to positivistic ideals, which are inappropriate for qualitative research, 6,9,10 as researchers should not seek to eliminate the effects of their influence on the study but to understand them through reflexivity . 11 Reflexivity is an acknowledgement that, to make sense of the social world, a researcher will inevitably draw on his or her own values, norms, and concepts, which prevent a totally objective view of the social world. 12

Sampling Strategies

Quantitative research favors using large, randomly generated samples, especially if the intent of the research is to generalize to other populations. 6 Instead, qualitative research often focuses on participants who are likely to provide rich information about the study questions, known as purposive sampling . 6

How You Can Start TODAY

• Consider how you can best address your research problem and what philosophical assumptions you are making.
• Consider your ontological and epistemological stance by asking yourself: What can I know about the phenomenon of interest? How can I know what I want to know? W hat approach should I use and why? Answers to these questions might be relatively fixed but should be flexible enough to guide methodological choices that best suit different research problems under study. 5
• Select an appropriate sampling strategy. Purposive sampling is often used in qualitative research, with a goal of finding information-rich cases, not to generalize. 6
• Be reflexive: Examine the ways in which your history, education, experiences, and worldviews have affected the research questions you have selected and your data collection methods, analyses, and writing. 13

How You Can Start TODAY—An Example

Let's assume that you want to know about resident learning on a particular clinical rotation. Your initial thought is to use end-of-rotation assessment scores as a way to measure learning. However, these assessments cannot tell you how or why residents are learning. While you cannot know for sure that residents are learning, consider what you can know—resident perceptions of their learning experiences on this rotation.

Next, you consider how to go about collecting this data—you could ask residents about their experiences in interviews or watch them in their natural settings. Since you would like to develop a theory of resident learning in clinical settings, you decide to use grounded theory as a methodology, as you believe asking residents about their experience using in-depth interviews is the best way for you to elicit the information you are seeking. You should also do more research on grounded theory by consulting related resources, and you will discover that grounded theory requires theoretical sampling. 14,15 You also decide to use the end-of-rotation assessment scores to help select your sample.

Since you want to know how and why students learn, you decide to sample extreme cases of students who have performed well and poorly on the end-of-rotation assessments. You think about how your background influences your standpoint about the research question: Were you ever a resident? How did you score on your end-of-rotation assessments? Did you feel this was an accurate representation of your learning? Are you a clinical faculty member now? Did your rotations prepare you well for this role? How does your history shape the way you view the problem? Seek to challenge, elaborate, and refine your assumptions throughout the research.

As you proceed with the interviews, they trigger further questions, and you then decide to conduct interviews with faculty members to get a more complete picture of the process of learning in this particular resident clinical rotation.

What You Can Do LONG TERM

• Familiarize yourself with published guides on conducting and evaluating qualitative research. 5,16–18 There is no one-size-fits-all formula for qualitative research. However, there are techniques for conducting your research in a way that stays true to the traditions of qualitative research.
• Consider the reporting style of your results. For some research approaches, it would be inappropriate to quantify results through frequency or numerical counts. 19 In this case, instead of saying “5 respondents reported X,” you might consider “respondents who reported X described Y.”
• Review the conventions and writing styles of articles published with a methodological approach similar to the one you are considering. If appropriate, consider using a reflexive writing style to demonstrate understanding of your own role in shaping the research. 6

Supplementary Material

• How it works

How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On June 24, 2024

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

Also, see; Research methods, design, and analysis .

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• Statistical analysis, research methodology, discussion of the results or conclusion – our experts can help you no matter how complex the requirements are.

Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

You May Also Like

Repository of ten perfect research question examples will provide you a better perspective about how to create research questions.

How to write a hypothesis for dissertation,? A hypothesis is a statement that can be tested with the help of experimental or theoretical research.

Here we explore what is research problem in dissertation with research problem examples to help you understand how and when to write a research problem.

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Establishing Rationale and Significance of Research

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This chapter builds on the first five chapters in this handbook that explained the research design typology. The focus here is on establishing rationale and significance of research. This chapter is intended to serve as a guide for practitioners to apply and integrate the research design typology layers into a scholarly manuscript. In contrast to the broad scope of the first five chapters, this chapter concentrates on how to integrate specific components of the typology regardless of which ideology the researcher holds on the continuum (positivist, post-positivist, pragmatist, interpretivist, or constructivist).

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Research Questions and Research Design

Reflections on Methodological Issues

Katz, M. (2010). Toward a new moral paradigm in health care delivery: Accounting for individuals. American Journal Of Law and Medicine , 36 (1), 78–135.

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Matteson, L. & Lacey F. M. (2011). Doing your literature review: Traditional and systematic techniques . London: Sage

Mclean, T. R. (2006). The future of telemedicine and its Faustian reliance on regulatory trade barriers for protection. Health Matrix: Journal of Law-Medicine , 16 (2), 443–509.

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The Ethics of Using QI Methods to Improve Health Quality and Safety (2006). Hastings Center Report , Vol. 36, pp. S1–S40.

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Hahn, J. (2015). Establishing Rationale and Significance of Research. In: Strang, K.D. (eds) The Palgrave Handbook of Research Design in Business and Management. Palgrave Macmillan, New York. https://doi.org/10.1057/9781137484956_7

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How to write the Rationale for your research

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• 19 November, 2021

The rationale for one’s research is the justification for undertaking a given study. It states the reason(s) why a researcher chooses to focus on the topic in question, including what the significance is and what gaps the research intends to fill. In short, it is an explanation that rationalises the need for the study. The rationale is typically followed by a hypothesis/ research question (s) and the study objectives.

When is the rationale for research written?

The rationale of a study can be presented both before and after the research is conducted. 

• Before : The rationale is a crucial part of your research proposal , representing the plan of your work as formulated before you execute your study.
• After : Once the study is completed, the rationale is presented in a research paper or dissertation to explain why you focused on the particular question. In this instance, you would link the rationale of your research project to the study aims and outcomes.

Basis for writing the research rationale

The study rationale is predominantly based on preliminary data . A literature review will help you identify gaps in the current knowledge base and also ensure that you avoid duplicating what has already been done. You can then formulate the justification for your study from the existing literature on the subject and the perceived outcomes of the proposed study.

Length of the research rationale

In a research proposal or research article, the rationale would not take up more than a few sentences . A thesis or dissertation would allow for a longer description, which could even run into a couple of paragraphs . The length might even depend on the field of study or nature of the experiment. For instance, a completely novel or unconventional approach might warrant a longer and more detailed justification.

Basic elements of the research rationale

Every research rationale should include some mention or discussion of the following: 

• An overview of your conclusions from your literature review
• Gaps in current knowledge
• Inconclusive or controversial findings from previous studies
• The need to build on previous research (e.g. unanswered questions, the need to update concepts in light of new findings and/or new technical advancements). 

Example of a research rationale

Note: This uses a fictional study.

Abc xyz is a newly identified microalgal species isolated from fish tanks. While Abc xyz algal blooms have been seen as a threat to pisciculture, some studies have hinted at their unusually high carotenoid content and unique carotenoid profile. Carotenoid profiling has been carried out only in a handful of microalgal species from this genus, and the search for microalgae rich in bioactive carotenoids has not yielded promising candidates so far. This in-depth examination of the carotenoid profile of Abc xyz will help identify and quantify novel and potentially useful carotenoids from an untapped aquaculture resource .

In conclusion

It is important to describe the rationale of your research in order to put the significance and novelty of your specific research project into perspective. Once you have successfully articulated the reason(s) for your research, you will have convinced readers of the importance of your work!

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Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

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What is Research Design? Understand Types of Research Design, with Examples

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Are you unsure about the research design elements or which of the different types of research design best suit your study? Don’t worry! In this article, we’ve got you covered!   

Table of Contents

What is research design?  

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Don’t worry! In this article, we’ve got you covered!  

A research design is the plan or framework used to conduct a research study. It involves outlining the overall approach and methods that will be used to collect and analyze data in order to answer research questions or test hypotheses. A well-designed research study should have a clear and well-defined research question, a detailed plan for collecting data, and a method for analyzing and interpreting the results. A well-thought-out research design addresses all these features.  

Research design elements  

Research design elements include the following:  

• Clear purpose: The research question or hypothesis must be clearly defined and focused.  
• Sampling: This includes decisions about sample size, sampling method, and criteria for inclusion or exclusion. The approach varies for different research design types .  
• Data collection: This research design element involves the process of gathering data or information from the study participants or sources. It includes decisions about what data to collect, how to collect it, and the tools or instruments that will be used.  
• Data analysis: All research design types require analysis and interpretation of the data collected. This research design element includes decisions about the statistical tests or methods that will be used to analyze the data, as well as any potential confounding variables or biases that may need to be addressed.  
• Type of research methodology: This includes decisions about the overall approach for the study.  
• Time frame: An important research design element is the time frame, which includes decisions about the duration of the study, the timeline for data collection and analysis, and follow-up periods.  
• Ethical considerations: The research design must include decisions about ethical considerations such as informed consent, confidentiality, and participant protection.  
• Resources: A good research design takes into account decisions about the budget, staffing, and other resources needed to carry out the study.  

The elements of research design should be carefully planned and executed to ensure the validity and reliability of the study findings. Let’s go deeper into the concepts of research design .    

Characteristics of research design  

Some basic characteristics of research design are common to different research design types . These characteristics of research design are as follows:  

• Neutrality : Right from the study assumptions to setting up the study, a neutral stance must be maintained, free of pre-conceived notions. The researcher’s expectations or beliefs should not color the findings or interpretation of the findings. Accordingly, a good research design should address potential sources of bias and confounding factors to be able to yield unbiased and neutral results.   
•   Reliability : Reliability is one of the characteristics of research design that refers to consistency in measurement over repeated measures and fewer random errors. A reliable research design must allow for results to be consistent, with few errors due to chance.   
•   Validity : Validity refers to the minimization of nonrandom (systematic) errors. A good research design must employ measurement tools that ensure validity of the results.  
•   Generalizability: The outcome of the research design should be applicable to a larger population and not just a small sample . A generalized method means the study can be conducted on any part of a population with similar accuracy.   
•   Flexibility: A research design should allow for changes to be made to the research plan as needed, based on the data collected and the outcomes of the study  

A well-planned research design is critical for conducting a scientifically rigorous study that will generate neutral, reliable, valid, and generalizable results. At the same time, it should allow some level of flexibility.  

Different types of research design  

A research design is essential to systematically investigate, understand, and interpret phenomena of interest. Let’s look at different types of research design and research design examples .  

Broadly, research design types can be divided into qualitative and quantitative research.  

Qualitative research is subjective and exploratory. It determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc.  

Quantitative research is objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research is usually done using surveys and experiments.  

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

The following will familiarize you with the research design categories in qualitative research:  

• Grounded theory: This design is used to investigate research questions that have not previously been studied in depth. Also referred to as exploratory design , it creates sequential guidelines, offers strategies for inquiry, and makes data collection and analysis more efficient in qualitative research.   

Example: A researcher wants to study how people adopt a certain app. The researcher collects data through interviews and then analyzes the data to look for patterns. These patterns are used to develop a theory about how people adopt that app.  

•   Thematic analysis: This design is used to compare the data collected in past research to find similar themes in qualitative research.  

Example: A researcher examines an interview transcript to identify common themes, say, topics or patterns emerging repeatedly.  

• Discourse analysis : This research design deals with language or social contexts used in data gathering in qualitative research.   

Example: Identifying ideological frameworks and viewpoints of writers of a series of policies.  

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

• Descriptive research design : This quantitative research design is applied where the aim is to identify characteristics, frequencies, trends, and categories. It may not often begin with a hypothesis. The basis of this research type is a description of an identified variable. This research design type describes the “what,” “when,” “where,” or “how” of phenomena (but not the “why”).   

Example: A study on the different income levels of people who use nutritional supplements regularly.  

• Correlational research design : Correlation reflects the strength and/or direction of the relationship among variables. The direction of a correlation can be positive or negative. Correlational research design helps researchers establish a relationship between two variables without the researcher controlling any of them.  

Example : An example of correlational research design could be studying the correlation between time spent watching crime shows and aggressive behavior in teenagers.  

•   Diagnostic research design : In diagnostic design, the researcher aims to understand the underlying cause of a specific topic or phenomenon (usually an area of improvement) and find the most effective solution. In simpler terms, a researcher seeks an accurate “diagnosis” of a problem and identifies a solution.  

Example : A researcher analyzing customer feedback and reviews to identify areas where an app can be improved.    

• Explanatory research design : In explanatory research design , a researcher uses their ideas and thoughts on a topic to explore their theories in more depth. This design is used to explore a phenomenon when limited information is available. It can help increase current understanding of unexplored aspects of a subject. It is thus a kind of “starting point” for future research.  

Example : Formulating hypotheses to guide future studies on delaying school start times for better mental health in teenagers.  

•   Causal research design : This can be considered a type of explanatory research. Causal research design seeks to define a cause and effect in its data. The researcher does not use a randomly chosen control group but naturally or pre-existing groupings. Importantly, the researcher does not manipulate the independent variable.   

Example : Comparing school dropout levels and possible bullying events.  

•   Experimental research design : This research design is used to study causal relationships . One or more independent variables are manipulated, and their effect on one or more dependent variables is measured.  

Example: Determining the efficacy of a new vaccine plan for influenza.  

Benefits of research design  

 T here are numerous benefits of research design . These are as follows:  

• Clear direction: Among the benefits of research design , the main one is providing direction to the research and guiding the choice of clear objectives, which help the researcher to focus on the specific research questions or hypotheses they want to investigate.  
• Control: Through a proper research design , researchers can control variables, identify potential confounding factors, and use randomization to minimize bias and increase the reliability of their findings.
• Replication: Research designs provide the opportunity for replication. This helps to confirm the findings of a study and ensures that the results are not due to chance or other factors. Thus, a well-chosen research design also eliminates bias and errors.  
• Validity: A research design ensures the validity of the research, i.e., whether the results truly reflect the phenomenon being investigated.  
• Reliability: Benefits of research design also include reducing inaccuracies and ensuring the reliability of the research (i.e., consistency of the research results over time, across different samples, and under different conditions).  
• Efficiency: A strong research design helps increase the efficiency of the research process. Researchers can use a variety of designs to investigate their research questions, choose the most appropriate research design for their study, and use statistical analysis to make the most of their data. By effectively describing the data necessary for an adequate test of the hypotheses and explaining how such data will be obtained, research design saves a researcher’s time.   

Overall, an appropriately chosen and executed research design helps researchers to conduct high-quality research, draw meaningful conclusions, and contribute to the advancement of knowledge in their field.

Frequently Asked Questions (FAQ) on Research Design

Q: What are th e main types of research design?

Broadly speaking there are two basic types of research design –

qualitative and quantitative research. Qualitative research is subjective and exploratory; it determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc. Quantitative research , on the other hand, is more objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research design is usually done using surveys and experiments.

Q: How do I choose the appropriate research design for my study?

Choosing the appropriate research design for your study requires careful consideration of various factors. Start by clarifying your research objectives and the type of data you need to collect. Determine whether your study is exploratory, descriptive, or experimental in nature. Consider the availability of resources, time constraints, and the feasibility of implementing the different research designs. Review existing literature to identify similar studies and their research designs, which can serve as a guide. Ultimately, the chosen research design should align with your research questions, provide the necessary data to answer them, and be feasible given your own specific requirements/constraints.

Q: Can research design be modified during the course of a study?

Yes, research design can be modified during the course of a study based on emerging insights, practical constraints, or unforeseen circumstances. Research is an iterative process and, as new data is collected and analyzed, it may become necessary to adjust or refine the research design. However, any modifications should be made judiciously and with careful consideration of their impact on the study’s integrity and validity. It is advisable to document any changes made to the research design, along with a clear rationale for the modifications, in order to maintain transparency and allow for proper interpretation of the results.

Q: How can I ensure the validity and reliability of my research design?

Validity refers to the accuracy and meaningfulness of your study’s findings, while reliability relates to the consistency and stability of the measurements or observations. To enhance validity, carefully define your research variables, use established measurement scales or protocols, and collect data through appropriate methods. Consider conducting a pilot study to identify and address any potential issues before full implementation. To enhance reliability, use standardized procedures, conduct inter-rater or test-retest reliability checks, and employ appropriate statistical techniques for data analysis. It is also essential to document and report your methodology clearly, allowing for replication and scrutiny by other researchers.

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Research Design

Same as research approach, different textbooks place different meanings on research design. Some authors consider research design as the choice between qualitative and quantitative research methods. Others argue that research design refers to the choice of specific methods of data collection and analysis. Research design is also placed as a master plan for conducting a research project and this appears to be the most authentic explanation of the term.

 In your dissertation you can define research design as a general plan about what you will do to answer the research question. [1] It is a framework for choosing specific methods of data collection and data analysis.

Research design can be divided into two groups:  exploratory  and  conclusive . Exploratory research, according to its name merely aims to explore specific aspects of the research area. Exploratory research does not aim to provide final and conclusive answers to research questions. The researcher may even change the direction of the study to a certain extent, however not fundamentally, according to new evidences gained during the research process.

Conclusive research, on the contrary, generate findings that can be practically useful for decision-making. The following Table 1 illustrates the main differences between exploratory and conclusive research in relation to important components of a dissertation.

Table 1 Major differences between exploratory and conclusive research design [2]

The following can be mentioned as examples with exploratory design:

• A critical analysis of argument of mandatory CSR for UK private sector organisations
• A study into contradictions between CSR program and initiatives and business practices: a case study of Philip Morris USA
• An investigation into the ways of customer relationship management in mobile marketing environment

Studies listed above do not aim to generate final and conclusive evidences to research questions. These studies merely aim to explore their respective research areas.

Conclusive research  can be divided into two categories:  descriptive  and  causal . Descriptive research design, as the name suggests, describes specific elements, causes, or phenomena in the research area.

Table 2 Examples for descriptive research design

Causal research design , on the other hand, is conducted to study cause-and-effect relationships.  Table 3 below illustrates some examples for studies with causal research design.

Table 3 Examples for studies with causal design

My e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance  contains discussions of theory and application of research designs. The e-book also explains all stages of the  research process  starting from the  selection of the research area  to writing personal reflection. Important elements of dissertations such as  research philosophy ,  research approach ,  methods of data collection ,  data analysis  and  sampling  are explained in this e-book in simple words.

John Dudovskiy

[1] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6 th  edition, Pearson Education Limited

[2] Source: Pride and Ferrell (2007)

• Open access
• Published: 21 August 2024

Rationale and design of the BeyeOMARKER study: prospective evaluation of blood- and eye-based biomarkers for early detection of Alzheimer’s disease pathology in the eye clinic

• Ilse Bader 1 , 2 , 3 ,
• Colin Groot 1 , 2 ,
• H. Stevie Tan 3 , 4 , 5 , 6 ,
• Jean-Marie A. Milongo 2 , 3 ,
• Jurre den Haan 1 , 2 ,
• Inge M. W. Verberk 7 ,
• Keir Yong 8 ,
• Julie Orellina 9 ,
• Shannon Campbell 9 ,
• David Wilson 10 ,
• Argonde C. van Harten 1 , 2 ,
• Pauline H. B. Kok 3 ,
• Wiesje M. van der Flier 1 , 2 , 11 ,
• Yolande A. L. Pijnenburg 1 , 2 ,
• Frederik Barkhof 5 , 12 , 13 ,
• Elsmarieke van de Giessen 5 , 12 ,
• Charlotte E. Teunissen 1 , 2 , 7 ,
• Femke H. Bouwman 1 , 2 &
• Rik Ossenkoppele 1 , 2 , 14  

Alzheimer's Research & Therapy volume  16 , Article number:  190 ( 2024 ) Cite this article

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Metrics details

Alzheimer’s disease (AD) is a common, complex and multifactorial disease that may require screening across multiple routes of referral to enable early detection and subsequent future implementation of tailored interventions. Blood- and eye-based biomarkers show promise as low-cost, scalable and patient-friendly tools for early AD detection given their ability to provide information on AD pathophysiological changes and manifestations in the retina, respectively. Eye clinics provide an intriguing real-world proof-of-concept setting to evaluate the performance of these potential AD screening tools given the intricate connections between the eye and brain, presumed enrichment for AD pathology in the aging population with eye disorders, and the potential for an accelerated diagnostic pathway for under-recognized patient groups.

The BeyeOMARKER study is a prospective, observational, longitudinal cohort study aiming to include individuals visiting an eye-clinic. Inclusion criteria entail being ≥ 50 years old and having no prior dementia diagnosis. Excluded eye-conditions include traumatic insults, superficial inflammation, and conditions in surrounding structures of the eye that are not engaged in vision. The BeyeOMARKER cohort ( n  = 700) will undergo blood collection to assess plasma p-tau217 levels and a brief cognitive screening at the eye clinic. All participants will subsequently be invited for annual longitudinal follow-up including remotely administered cognitive screening and questionnaires. The BeyeOMARKER + cohort ( n  = 150), consisting of 100 plasma p-tau217 positive participants and 50 matched negative controls selected from the BeyeOMARKER cohort, will additionally undergo Aβ-PET and tau-PET, MRI, retinal imaging including hyperspectral imaging (primary), widefield imaging, optical coherence tomography (OCT) and OCT-Angiography (secondary), and cognitive and cortical vision assessments.

We aim to implement the current protocol between April 2024 until March 2027. Primary outcomes include the performance of plasma p-tau217 and hyperspectral retinal imaging to detect AD pathology (using Aβ- and tau-PET visual read as reference standard) and to detect cognitive decline. Initial follow-up is ~ 2 years but may be extended with additional funding.

Conclusions

We envision that the BeyeOMARKER study will demonstrate the feasibility of early AD detection based on blood- and eye-based biomarkers in alternative screening settings, and will improve our understanding of the eye-brain connection.

Trial registration

The BeyeOMARKER study (Eudamed CIV ID: CIV-NL-23–09-044086; registration date: 19th of March 2024) is approved by the ethical review board of the Amsterdam UMC.

The hallmark pathophysiological processes of Alzheimer’s disease (AD; i.e., amyloid β [Aβ] plaques and neurofibrillary tau tangles) may emerge 20–30 years prior to the onset of dementia, and the earliest incipient symptoms often go unnoticed by patients and their caregivers [ 1 , 2 , 3 ]. Early AD, prior to extensive atrophy and cognitive impairment, is the optimal window for intervention and may be essential to achieve the most beneficial long-term outcomes [ 4 , 5 , 6 ]. This notion has led to a paradigm shift towards a focus on early biomarker-confirmed diagnosis and biological staging of AD [ 1 ], which is further fueled by the first regulatory approvals of monoclonal antibodies against Aβ [ 7 , 8 ] and by clinical trial results that have hinted towards more beneficial outcomes in the early, pre-symptomatic stages of AD [ 9 ]. These developments are major advances in the field but also emphasize longstanding challenges concerning the rising demand for large-scale accessibility of early AD detection to facilitate early intervention [ 10 ]. The current diagnostic process in memory clinics is inadequate to accommodate large-scale early detection of AD pathology due to the reliance on expensive and invasive procedures (i.e., a lumbar puncture or Positron Emission Tomography [PET]) [ 1 , 3 ]. Furthermore, PET and cerebrospinal fluid (CSF) biomarkers are only clinically approved (e.g., European Commission [CE-marked] or US Food and Drug Administration [FDA] approved) to diagnose individuals at symptomatic stages of AD and are only accessible in highly specialized clinics that are mainly situated in high-income countries. To prepare for a future wherein disease-modifying treatment may become widely available, there is a need towards building an efficient and inclusive infrastructure to detect individuals at risk of AD. This will require low-cost, patient-friendly and scalable biomarkers for AD that are also suitable for implementation outside of a specialized memory clinic setting, such as blood-based and eye-based biomarkers [ 11 ]. Blood-based biomarkers for AD have advanced rapidly and hold promise for future real-world clinical implementation to detect AD pathophysiology [ 12 , 13 ]. Eye-based biomarkers derived from retinal imaging are emerging to screen for AD-associated structural changes and Aβ- or tau-related lesions, which may be of particular relevance in ophthalmological settings [ 14 , 15 , 16 ]. The BeyeOMARKER study aims to evaluate the real-world implementation of blood-based biomarkers, and the potential (additional) value of eye-based biomarkers, to screen for AD pathophysiology in eye-clinics. In this design paper, we provide a rationale for early detection of AD in eye clinics, present the BeyeOMARKER study design and population, and elaborate on several aspects of the study including ethical considerations, potential challenges, and future opportunities.

Based on previous epidemiological and pathophysiological evidence, eye clinics provide a prime opportunity to investigate the feasibility of blood- and eye-based biomarkers to detect early AD. From an epidemiological perspective, eye clinics are known for a high-throughput of patients within the typical age-range when AD pathological changes first manifest, highlighted by the overlap in age-of-onset (i.e., > 50 years of age) for acquired eye-disorders [ 17 , 18 , 19 ] and AD [ 1 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Moreover, epidemiological investigations indicate that eye patients may be at increased risk for dementia and AD [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] (Table  1 ). These associations are reported for glaucoma, age-related macular degeneration, diabetic retinopathy, cataract, and for vision impairment as a whole. Possible mechanisms underlying this increased risk may differ per eye condition, and could be related to embryological, anatomical, physiological and functional connections between the eyes and the brain. Through these intricate connections, diseases affecting the brain may affect the eye and vice versa [ 36 , 37 ]. Indeed, ocular manifestations of AD are myriad and include the retinal presence of AD pathology, neurodegenerative changes and vascular changes [ 15 , 16 , 37 , 38 , 39 , 40 , 41 ] (Table  2 ). Various hypotheses have been postulated to explain the association between eye disorders and AD, such as shared (genetic) risk factors, the common-cause hypothesis, or the sensory deprivation and information degradation hypotheses [ 29 , 34 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ] (Table  3 ). For example, glaucoma and age-related macular degeneration are neurodegenerative diseases of the eye that share pathological features with AD, such as the presence of Aβ- and tau deposits and inflammatory and neurodegenerative processes [ 49 , 50 , 51 ]. For cataract on the other hand, alternative reversible cognitive or psychosocial processes may be involved given that cataract extraction appears to reverse dementia risk [ 52 , 53 ]. Taking together these close connections between the eyes and the brain, the eye is considered an accessible ‘window to the brain’ and eye-based biomarkers have potential as a prognostic tool to identify risk of cognitive impairment due to neurodegenerative disease [ 15 , 36 , 37 , 39 ]. Moreover, vison impairment represents an established modifiable risk factor (population attributable fraction 1.8% [ 54 ]) and early and effective treatment of eye disorder may hence lower the odds of developing dementia [ 55 , 54 , 56 ].

Another highly relevant factor contributing to the suitability of eye clinics as a screening setting for AD is related to the potential for an accelerated diagnostic pathway for currently under-recognized or underserved patient groups. First, individuals with an eye disorder represent a large portion of the aging population (e.g. prevalence of mild and moderate/severe visual impairment in individuals ≥ 50 years is estimated to be 7.7% and 11.2%, respectively [ 57 ]), and they appear to be disproportionately affected by AD [ 28 , 29 , 30 , 31 ]. This group experiences particular diagnostic challenges and underrepresentation in clinical research and trials due to accessibility issues (e.g., difficulties in traveling) and confounding of visually-mediated neuropsychological assessment [ 58 , 59 , 60 , 61 , 62 ]. Second, individuals with a low income, relatively low education attainment and a minority status are known to be disproportionally affected by AD [ 63 , 64 , 65 ]. These individuals typically experience difficulties in cognitive testing due to cultural bias and/or language barriers [ 66 ] and are currently underrepresented in memory clinic populations [ 67 ] and in clinical trial samples [ 62 , 68 ]. Eye clinics provide an alternative route to connect with individuals who are otherwise unlikely to seek help if they experience cognitive complaints, for example due to dementia-related stigma or lack of awareness in some diverse communities [ 69 ]. Third, individuals with an atypical clinical presentation of AD generally experience significant morbidity and impact on daily life, but are diagnosed relatively late due to their atypical (non-amnestic) clinical presentation and overrepresentation in younger-onset AD [ 70 , 71 , 72 ]. Of particular interest in the eye clinic are individuals suffering from posterior cortical atrophy (PCA), also referred to as the visual-variant AD. PCA is characterized by early and prominent impairment in visual perception or visuospatial processing accompanied by pathology and atrophy that disproportionally affects the visual and visual association cortices [ 73 , 74 ]. These individuals may present at the eye clinic due to their visual impairments but, as the cause is rooted in the brain rather than the eye, the complaints often remain unexplained by an ophthalmologist [ 72 , 75 , 76 ]. These factors may contribute to the long interval of on average 3.8 years between symptom onset and a formal PCA diagnosis [ 74 ]. Shortening this interval is essential to provide these patients with more equal access to patient management and to move towards clinical trial opportunities [ 75 ]. For all of the aforementioned individuals, eye clinics may provide an accelerated diagnostic pathway where the use of a biological (rather than cognitive) marker for AD could mitigate cognitive test(ing) bias, and the use of patient-friendly tools may reduce barriers to participation in research [ 77 , 78 ]. By exploring the potential for AD detection in diverse and alternative setting, the BeyeOMARKER study aims to contribute to a more inclusive healthcare system.

Screening biomarkers in the BeyeOMARKER study

The main biomarkers of interest for the BeyeOMARKER study are the blood-based plasma phosphorylated tau (p-tau217) biomarker and eye-based hyperspectral (HS) retinal scans.

Blood-biomarker measurement: plasma p-tau217

Blood-based biomarkers have seen a rapid rise to prominence as minimally invasive tools to detect AD pathology [ 12 ]. Emerging blood-based biomarkers for AD include markers for the hallmark pathologies (p-tau isoforms and Aβ) and markers of axonal degeneration (neurofilament light; NfL) or astrocytosis (glial fibrillary acidic protein; GFAP [ 12 ]). Since future high-throughput analysis of blood-based AD biomarkers will require the use of standardized and commercially available assays [ 12 ], we will screen participants based on the commercially available Quanterix single-molecule array (Simoa) for plasma p-tau217. Several p-tau isoforms exhibit high analytical and clinical performance [ 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ], are specific to AD [ 87 ], and have adequate predictive value for atrophy and cognitive measures [ 82 , 83 , 88 , 89 , 90 ]. However, p-tau217 appears to be most accurate in detecting the earliest AD pathological changes [ 91 , 92 , 93 , 94 , 95 ] and correlates strongly with postmortem Aβ plaques and tau tangle load [ 93 ].

Eye-based screening: hyperspectral retinal scanning

Eye-based biomarkers have gained attention over the years within the field of neurodegenerative diseases since the retina shares many characteristics with the brain [ 96 ] (Table  2 ). Moreover, it is the only part of the central nervous system that is not shielded by bone which makes non-invasive and high-resolution imaging relatively easy. In the BeyeOMARKER study, a subset of participants will undergo retinal scanning including a HS retinal scan developed by Optina Diagnostics (Canada). Standard retinal imaging techniques provide spatial information and have been used to show vascular and neurodegenerative changes in AD [ 14 , 15 , 16 , 37 , 38 , 39 ]. HS retinal imaging additionally incorporates reflective properties of the retina in response to monochromatic light waves, and thereby produces retinal images containing both spectral and spatial information [ 97 ]. Retinal spectral differences (i.e., differences in reflection in response to certain wavelengths) have been detected between control and AD mouse models that accumulate amyloid, both in vivo [ 98 , 99 ] and ex vivo [ 100 , 101 ]. The data-rich retinal images provided by the HS retinal scan were used to train an artificial intelligence (AI) algorithm to detect retinal features associated with AD. This AI paradigm has demonstrated good discriminative ability between amyloid negative and amyloid positive individuals [ 97 , 98 , 102 , 103 , 104 ], as well as between clinically diagnosed AD cases versus cognitively unimpaired participants [ 105 ]. These earlier preliminary findings using HS retinal imaging highlight the potential of this biomarker in a prospective screening setting.

Knowledge gaps

Despite the promising performance of blood- and eye-based biomarkers for AD, several aspects remain to be evaluated to ascertain their (potentially complementary) utility as early AD screening tools outside specialized memory clinics. First, clinical performance studies on blood-based biomarkers to date have included relatively homogeneous samples with high diagnostic certainty, were mostly retrospective in design, and did not use a priori defined cut-offs [ 13 ]. These study design aspects could have favored biomarker performance and hamper generalizability to many real-world clinical settings. Similarly, validation studies of HS retinal imaging against Aβ-PET have only been performed in selected populations without eye conditions and with a high diagnostic certainty for AD [ 15 , 97 , 98 , 102 , 103 , 104 , 106 ]. Secondly, the clinical value of blood- and eye-based biomarkers has been studied separately but they have not yet been examined as potentially complementary markers in a combined prediction model. We hypothesize that combining these biomarkers into an integrative or step-wise model will provide complementary or even additive diagnostic and prognostic value for AD since plasma p-tau217 allows highly specific detection of a hallmark of AD pathology whereas the (HS) retinal scans also allow minimally invasive visualization of a multitude of neurodegenerative, inflammatory, vascular, and AD-related pathological changes that are reflective of changes in the brain [ 10 , 13 , 14 , 107 ]. Of note, the efficacy of AD screening in an eye clinic population also partially relies on whether this population is indeed enriched for AD pathology. Although individuals with an eye disorder are at increased risk for (AD) dementia [ 28 , 29 , 30 , 31 , 32 , 33 , 34 ], risk estimates vary, and a precise prevalence estimate for AD biomarker positivity within the eye clinic population is currently lacking.

Study objectives

The primary aim of the BeyeOMARKER study is to evaluate and compare the performance of plasma p-tau217 and HS retinal scans to predict AD pathophysiology and cognitive decline (1). In addition, we envision that the BeyeOMARKER will provide a multimodal dataset for a diverse sample of patients visiting the eye clinic to secondarily (2) assess the individual and complementary clinical predictive value of other blood- and eye-based biomarkers, (3) explore the potential mechanisms contributing to the link between AD and conditions in the visual system, and (4) investigate enrichment for AD in an eye clinic population. The specific aims and their corresponding endpoints are also listed in Table  4 and visualized in Fig.  1 . Findings of the BeyeOMARKER could ultimately aid in providing a roadmap for future studies on minimally invasive early detection of AD in alternative diagnostic settings.

BeyeOMARKER study aims. Abbreviations : AD = Alzheimer’s Disease, PET = Position Emission Tomography, MRI = magnetic resonance imaging, Aβ = Amyloid beta, PCA = Posterior Cortical Atrophy

Study design

The BeyeOMARKER study is a single-center prospective, observational, longitudinal cohort study aiming to include individuals from a clinic for comprehensive eye-care who have no prior dementia diagnosis and are ≥ 50 years of age. As illustrated in Fig.  2 , the BeyeOMARKER study comprises an initial screening phase, including the plasma p-tau217 assessment, followed by two longitudinal arms for subsequent follow-up. All BeyeOMARKER participants (prospected n  = 700) will be followed-up remotely at T1 (9–12 months after screening) and T2 (9–12 months after T1). This will include online questionnaires and a web-based cognitive test (cCOG; [ 108 ]) partly in collaboration with the online ABOARD (“A Personalized Medicine Approach for Alzheimer's Disease”) platform [ 109 ], and cognitive screening via telephone (Fig.  2 , blue route). In addition, from the full BeyeOMARKER cohort a BeyeOMARKER + subcohort will be recruited, which will consist of 100 plasma p-tau217 positive individuals and 50 plasma p-tau217 negative individuals matched on age, sex and eye condition. The BeyeOMARKER + cohort ( n  = 150) will be invited to the Amsterdam UMC for assessment at T0 (± 3 months and maximum 6 months after screening) and T2 (21–24 months after T0). Assessment at T0 includes standard and HS retinal imaging, structural MRI, Aβ-PET, tau-PET, and a cognitive and cortical vision test battery. Assessment at T2 includes a follow-up MRI and cognitive and cortical vision assessment (Fig.  2 , green route). Outcomes available for the BeyeOMARKER and BeyeOMARKER + cohort are listed in Table S2 and will be described in further detail below. Additional funding will be sought to allow extended follow-up and repeated assessments.

Study design including study visits, study procedures, time-intervals and the study population for all participants (blue route) and the BeyeOMARKER + cohort (green route). Abbreviations : MoCA = Montreal Cognitive Assessment, MRI = magnetic resonance imaging, HS = hyperspectral, ABOARD = A Personalized Medicine Approach for Alzheimer's Disease cohort study, yellow and black individuals represent the estimated plasma p-tau217 positive and negative individuals, respectively. *only applicable if the required optional consent has been provided

Targeted sample size

A conservative estimate of the prevalence of plasma p-tau217 positivity in cognitively unimpaired subjects between 65 to 69 years of age is 17.0% based on the lower bound of the 95%-confidence interval derived from a large meta-analysis on amyloid abnormality across the AD spectrum [ 110 ]. We estimated the plasma p-tau217 prevalence based on amyloid-based estimates since the two are strongly related to each other [ 111 ]. Based on an open access sample size calculator for prevalence studies [ 112 ], we subsequently estimated a required screening sample size of n  = 700 (prevalence = 17.0%, level of confidence (Z) = 95%, precision estimate (D) = 3.0%, expected attrition rate = 10%). Given the expected prevalence of amyloid positivity (i.e., 17.0%), the screening sample of 700 subjects is expected to be sufficient to identify 100 p-tau217 positive cases for the BeyeOMARKER + cohort, and to determine a reliable prevalence estimate of AD pathology in our eye-clinic population.

Participants

Participants will be recruited from a clinic for comprehensive eye-care (i.e., Bergman clinics) located in an area of Amsterdam known for its socio-culturally and socio-economically diverse population. To be eligible to participate, a subject 1) must be ≥ 50 years of age, and 2) did not receive a formal dementia diagnosis. Individuals visiting the eye clinic based on solely the following reasons are excluded from participation: 1) a traumatic insult, 2) a superficial inflammatory eye disease, and 3) a condition in a structure surrounding the eye that is not directly involved in visual processing (e.g. the tear-ducts and eye muscles). Individuals who are eligible and express their interest in the BeyeOMARKER study will receive written and oral information and are invited to the eye clinic for informed consent procedures and a screening visit at the Bergman eye-clinic after the mandatory consideration time (i.e., one week after receiving the participant information form).

For enrolment in the BeyeOMARKER + cohort, results of the plasma p-tau217 measurement will be prospectively evaluated based on an a priori defined cut-off for plasma p-tau217 positivity, established in a large independent data-set of patients and controls from the Amsterdam Dementia Cohort [ 113 ]. Subsequently, all p-tau217 positive participants ( n  = 100) and a group of matched p-tau217 negative controls (ratio 2:1, n  = 50) will be selected to be included in the BeyeOMARKER + cohort ( n  = 150). Matching will be based on age, sex and eye condition categorized into 1) anterior eye conditions, 2) posterior eye conditions, 3) refractive errors, and 4) unexplained visual impairment to allow identification of individuals with suspected PCA(Table S1). Selected participants who are eligible (e.g., based on safety criteria described in Text S1) to participate will receive additional written and oral information on the BeyeOMARKER + study and will be invited to the Amsterdam UMC (location VUmc) for informed consent procedures and additional assessments after the mandatory consideration time.

Base clinical dataset for all BeyeOMARKER participants

Pre-specified blood-based ad biomarkers: screening for ad pathology.

For each participant, at least one EDTA blood tube (6 mL) is collected. This will primarily be used for evaluating the plasma p-tau217 level and secondarily for assessing the levels of plasma Aβ40, Aβ42, GFAP, and NfL using the N4PE (Neurology 4-Plex E) assay [ 114 ]. The complete panel of plasma p-tau217, Aβ40, Aβ42, GFAP and NfL has demonstrated diagnostic and prognostic performance for AD and neurodegenerative diseases, and their combined use has the potential to further improve the diagnostic and prognostic performance of blood tests [ 12 , 115 , 116 , 117 ]. Both assays will be performed using the Simoa HD-X automated platform in line with standard lab procedures and in accordance with pre-analytical handling recommendations [ 114 ].

Future blood-biomarkers and genetic analyses: the BeyeOMARKER biobank

For participants who provide consent for the BeyeOMARKER biobank, three additional 6 mL EDTA blood tubes will be collected for storage of plasma and whole-blood in the BeyeOMARKER biobank. This will serve to conduct future genetic and biomarker research into (risk factors for) AD and dementia, for instance by investigating newly emerging plasma biomarkers and by exploring genetic risk modifiers. For example, APOE4 carriership is a known genetic risk factor for AD but findings related to the visual system have been counterintuitive. First, compared to amnestic AD, the prevalence of APOE4-carriership is lower in visual-variant AD and associations appear weaker [ 74 , 118 ]. Second, even though eye diseases like age-related macular degeneration [ 50 , 119 ] and glaucoma [ 120 , 121 ] are associated with increased AD risk, APOE4-carriership appears a protective factor for these eye conditions. The BeyeOMARKER biobank will enable a rapid response to developments in the field to further optimize biomarker-based diagnostic algorithms, and may provide more insight into genetic risk factors for AD and conditions of the visual system.

Sociodemographic and medical data collection

The collection of sociodemographic information serves to evaluate how representative our study sample is to the general population, and to investigate whether there are group-differences associated with sociodemographic factors that call for stratification and/or tailored interpretation of AD risk-estimates. Variables include sex, age, marital status, socio-economic status (SES), country of birth (age of immigration, if applicable) and country of birth of the parents and ancestors. Collection of country of birth is based on the updated guidelines provided by the Dutch central bureau of statistics (CBS) in 2022 [ 122 ]. SES is based on overall SES of the resident living community (information provided by the CBS), educational attainment [ 123 , 124 ] and occupational attainment [ 125 ].

General and ophthalmological medical history will be collected to evaluate their associations with biomarker measurements and to investigate shared risk factors and pathological features between eye-disease and dementia. General medical history includes current diagnoses, medication use, relevant family history, and an assessment of cardiovascular risk factors (e.g. length and weight for body mass index, smoking, alcohol use, diabetes, blood pressure, treatment status [ 126 ]). Ophthalmological medical history includes presence of eye disorders, ophthalmological interventions and self-reported (functional) visual impairment with use of visual aids based on the Dutch EyeQ itembank [ 127 ].

Repeated cognitive screening and questionnaires

Cognitive screening will be performed using the Dutch or English version of the Montreal Cognitive Assessment (MoCA) standard or MoCA blind. The MoCA is a validated tool to screen for cognitive impairment and covers all cognitive domains (visuospatial function, executive function, language, memory and attention/processing speed [ 128 ]). The MoCA blind [ 129 , 130 ] is similar to the standard MoCA but leaves out the vision-dependent subtasks making it suitable to administer to visually impaired participants. The MoCA blind also allows annual remote cognitive screening via telephone, which will be combined with online follow-up questionnaires to track medical and ophthalmological changes. Additional questionnaires including patient-centered outcomes (e.g. health, mobility, work-status, social environment and use of healthcare) and a web-based cognitive test (cCOG; [ 108 ]) can be incorporated from the ABOARD platform [ 109 ].

Extended clinical dataset for the BeyeOMARKER + cohort

(hyperspectral) retinal imaging.

In the current study, HS retinal imaging will be performed using the Optina Mydriatic Hyperspectral Retinal Camera (MHRC). Unlike conventional retinal cameras, the Optina MHRC contains an integrated light source that emits monochromatic light of different wavelengths onto the retinal surface. The camera images a 31° field-of-view of the retina and acquires 92 retinal images for successive monochromatic wavelengths in one second (5 nm increments across a visible to near-infrared spectral range of 450–905 nm). This way, a HS retinal scan provides a stack of monochromatic images containing both spatial and spectral information (i.e., each spatial locus has an associated reflectance across wavelengths). Parameters from these data-rich retinal images have been correlated to amyloid status (positive or negative) to build a ‘Retinal Deep Phenotyping’ model. This model incorporates phenotypic features that provides a probability of amyloid positivity [ 97 , 98 , 102 , 103 , 104 ]. Optina’s existing model will be used to predict the Aβ-PET and Tau-PET status of BeyeOMARKER participants.

Other imaging modalities that have been extensively reviewed [ 15 , 16 , 37 , 38 , 39 ] and are in line with a recommended minimum data set framework provided by experts in neuroscience, neurology, optometry and ophthalmology [ 16 ] are optical coherence tomography (OCT; Heidelberg spectralis), OCT-A (OCT-angiography; Zeiss plex elite 9000), and (blue autofluorescence) widefield fundus imaging (Optos). OCT provides structural information, such as the thickness of the retinal layers at the macular region and at the optic disc. The OCT-A yields vascular parameters, such as vessel density in the macular area and around the optical nerve head. In addition, a widefield fundus photo allows visualization of the far periphery of the retina (i.e., 200 degrees or 80% of the retinal surface), which has been shown to contain significant AD pathology as well [ 42 ]. Finally, blue autofluorescence imaging adds information on fluorescent properties of pigments in the retina, which is informative for various retinal disorders (e.g. age related macular degeneration, macular dystrophies) and potentially AD-related pathological changes [ 15 , 131 , 132 ]. Altogether, these imaging techniques could provide more insight into the eye-brain connection and in which of the parameters provided by a HS retinal scan contribute (the most) to the classification of AD biomarker status, particularly since HS imaging specifically for AD detection purposes has been validated in populations without eye conditions.

To ensure retinal image quality, participants first undergo pupil mydriasis achieved by administration of Tropicamide 0.5% drops into both eyes according to standard procedure ophthalmological clinical practice. If one eye is not suitable for retinal imaging, pupil mydriasis and subsequent scanning is performed on a single eye.

Structural MRI

Structural MRI will be performed to assess associations with our primary screening biomarkers (plasma p-tau217 and HS retinal scans) and to gain a deeper understanding of the interplay between conditions of the visual system, AD pathology and the down-stream effects of pathology (e.g. atrophy and white matter damage). Images are acquired on a 3T MR scanner at the Amsterdam UMC (location VUmc). To minimize participant burden we only include the following standard sequences: sagittal 3D T1, axial T2, Axial Susceptibility Weighted Image (SWI), Axial Diffusion Weighted Image (DWI) and Sagittal 3D Fluid-attenuated inversion recovery (FLAIR). These sequences are part of the standard diagnostic protocol for dementia at the Amsterdam UMC and provide neurodegenerative markers including cortical thickness, grey matter volume, white matter volume, and cerebrovascular outcomes such as white matter hyperintensities, lacunes and microbleeds.

Aβ-PET and tau-PET visual read and quantification

Aβ-PET and tau-PET are a validated reference standard to evaluate novel AD biomarkers [ 13 ]. Abnormality on both Aβ-PET and tau-PET is strongly associated with short-term subsequent cognitive decline [ 133 ] and, beyond binary classification, PET allows valuable insight into the extent and regional distribution of pathology [ 134 , 135 ]. PET scans will be performed on a Siemens Whole-Body PET-CT-scanner (Biograph Vision Quadra) as this scanner provides excellent imaging results at lower tracer dosages. For the Aβ-PET scan acquisition, participants receive a single intravenous bolus injection of approximately 140 MBq [ 18 F]florbetapir and undergo a static scan from 50 until 70 min post-injection. For the tau-PET scan, participants receive a single intravenous bolus of approximately 140 MBq [ 18 F]flortaucipir and undergo a static scan from 80 until 100 min post-injection. Scanning procedures also include acquisition of a low-dose Computerized Tomography (CT) scan prior to the PET scan for attenuation and motion correction. After PET scan acquisition, the scans will be reconstructed into 4 × 5-min frames, corrected for movement when necessary, co-registered to the corresponding T1 MR image, and reoriented to remove head tilt. Visual reads will then be performed in correspondence with company guidelines for [ 18 F]florbetapir (Amyvid) and [ 18 F]flortaucipir (Tauvid) [ 136 , 137 ]. Furthermore, semi-quantification will be performed by calculating standardized uptake value ratios (SUVR) to address our secondary study objectives [ 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 ].

Cognitive and cortical vision assessment

Cognitive and cortical vision assessment will be performed to assess the clinical effects of AD pathophysiological changes, to assess clinical trajectories in the BeyeOMARKER cohort and to the determine the presence of suspected PCA based on positive AD biomarkers and adherence to clinical criteria for PCA (i.e., based on cognitive and cortical vision tests) [ 73 ].

The comprehensive cognitive test battery (Table S3) covers all cognitive domains based on vision-dependent as well as non-vision-dependent tasks (with exception of the visuospatial domain, which includes the Visual Object and Space Perception Battery [VOSP] and is inherently vision dependent). Of note, given the expected cultural and educational diversity of the study population, a short 20-item version of the Naming Assessment in Multicultural Europa (NAME) task will be administered [ 146 ], which is less culture- and education-dependent compared to other naming tasks. Furthermore, most tasks are suitable administer and execute in English when appropriate (e.g., Rey-complex figure, digit-span task, trail making task, and the VOSP). Additional cortical vision tests (Table S4) will cover all basic visual perception and visual spatial processing domains based on tasks from the Cortical Vision Screening Test (CORVIST) and the self-report Colorado screening questionnaire for posterior cortical symptoms [ 147 ] as recommended by the Atypical AD Professional Interest Area of the Alzheimer’s association [ 148 ].

Outcome measures

The performance of plasma p-tau217 and AI-based Aβ-status classification from the HS retinal scan will be evaluated for detecting AD pathophysiology and cognitive decline. First, it is essential to evaluate novel AD biomarkers against an extensively validated reference standard like PET [ 13 ]. Therefore, the primary pathophysiological outcome of interest is the visual read of the Aβ-PET and tau-PET scan to determine positivity for AD biomarkers. Visual examination will be performed by by a trained nuclear medicine physician in accordance with the company guidelines for [ 18 F]florbetapir (Amyvid) and [ 18 F]flortaucipir (Tauvid) [ 136 , 137 ]. Second, the primary clinical outcome of interest is change on the modified preclinical Alzheimer cognitive composite 5 (mPACC5 [ 133 , 149 ]) across a 21–24 month interval (i.e., timepoint T0 to T2). For the BeyeOMARKER study, the mPACC5 will be compiled as a vision-independent composite of the Rey Auditory verbal Learning test delayed recall (episodic memory), digit-span backward (executive function), animal fluency (semantic memory) and the MoCA blind (global cognition).

Statistical analyses will be performed using R studio. First, the performance of plasma p-tau217 and AI-based Aβ-status classification from the HS retinal scan will be determined based on logistic regression and Receiver operating characteristic (ROC) analysis for 1) presence of AD pathophysiology defined as a positive Aβ-PET and/or tau-PET visual read and 2) clinical decline defined as ≤ -1 versus > -1 standard deviation decline on the mPACC5). The logistic regression models will be performed including plasma p-tau217, the HS scan, and both methods combined to compare their performance to detect cognitive decline and AD pathophysiology. Models will be corrected for age and sex, and additionally for educational attainment when assessing cognitive outcomes. The ROC curve will be calculated using the predicted probabilities from the logistic regression models and sensitivity, specificity, accuracy, positive predictive value, negative predictive value and the Area Under the Curve (AUC) will be derived to assess the models’ discriminative power. Appropriate tests will be used to compare the performance between biomarkers (e.g., the DeLong test to compare AUCs).

In secondary analyses (Table  4 ), general linear and non-linear models will be explored to assess and compare the performance of p-tau217 and the retinal scan to predict down-stream effects of AD (e.g. MRI markers and cognitive and cortical vision outcomes). We will additionally compare MRI features and cognitive measures between the BeyeOMARKER cohort and an independent reference sample from the Amsterdam Dementia Cohort [ 113 ] to explore how comorbid eye-disease affects the neurobiological and clinical manifestations of AD. Since these outcomes may also be affected by other comorbid conditions (e.g. other neurological or psychiatric conditions), this will be evaluated in post-hoc assessments. Lastly, we aim to report the observed prevalence of plasma p-tau217 positivity in the BeyeOMARKER cohort and compare our findings with a memory clinic cohort and the general population, while also exploring the effect of demographic features (such as age, sex, SES and APOE genotype) using general linear models.

Ethical considerations

General ethical considerations.

The BeyeOMARKER study will be conducted in accordance with the Medical Research Involving Human Subjects Act (WMO) and according to the principles of the World Medical Association (WMA) Declaration of Helsinki, version 64 WMA General Assembly, Fortaleza October 2013. The study will be conducted in compliance with the protocol Clinical Trials Regulation No 536/2014 and with the principles of good clinical practice (GCP). Data and human material will be handled confidentially and in agreement with the Dutch Act on Implementation of the General Data Protection (GDPR) (in Dutch: algemene verordening gegevensbescherming; AVG).

The study has been reviewed and approved by the Medical Ethics Committee from the Amsterdam UMC. Adequate time, a week at minimum, will be given for the subject to consider his or her decision to participate in the study. Consent procedures will clarify that consent can be withdrawn at any stage, and research participants can refuse participation in any of the BeyeOMARKER study procedures at any time without consequence. Optional consent will be obtained with regard to sharing of data for countries outside the European Union. Consent procedures make it clear that data protection is either at an adequate level of data protection based on article 45 of Regulation (EU) 2016/679 (Adequacy decisions (europa.eu) (e.g. for Canada) or will be at the best possible level of confidence when other standards apply (e.g. for the United States).

Ethical considerations around biomarker disclosure

For all personal data, BeyeOMARKER follows a non-disclosure policy, meaning that one’s own personal data will never be automatically disclosed to the individual. However, participants may still learn their study results when the treating physician considers it clinically relevant and responsible to disclose a result or when legal requirements around personal data oblige the study to return personal data to the participant when this is requested.

A recent systematic review reported high interest in biomarker disclosure (72–81% for individuals involved in research and 50% in the general population) and no significant short-term psychological effects. Moreover, disclosure was generally considered actionable in terms of implementing lifestyle changes, seeking clinical trial participation and preparing for the future (e.g. financial, legal and living arrangements) [ 150 ]. However, the personal attitude towards biomarker disclosure and the consequent impact is highly personal and remains dependent on the clinical, personal and societal context. Furthermore, as the landscape around Alzheimer biomarkers and care will continue to change, so will the ethical considerations around biomarkers disclosure. In the BeyeOMARKER study we aim to further minimize the risk of negative impacts. First, the BeyeOMARKER study is initiated by a specialized memory clinic with longstanding experience at the forefront of innovative biomarker research, which has provided extensive experience with novel biomarker interpretation, disclosure and communication. Second, the BeyeOMARKER study implements a disclosure protocol in order to standardize procedures that ensure understanding and mitigate the impact of receiving information on one’s own AD biomarker status. With these strategies in place, participants are supported in making informed decisions concerning their own biomarker data.

The Medical Ethics Committee approved the BeyeOMARKER study in March 2024. We aim to implement the current protocol between April 2024 and March 2027 and are intending to seek additional funding for extended annual follow-up. Primary outcomes include the performance of plasma p-tau217 and HS retinal scanning for 1) Aβ-PET and tau-PET visual read as reference standard, and 2) cognitive change (Table  4 ).

The BeyeOMARKER study is a single-center prospective, observational, longitudinal cohort study that aims to evaluate both blood- and eye-based screening tools for early detection of AD in a cohort of patients from a clinic for comprehensive eye-care. First, the implementation of optimized multimodal screening outside of a specialized memory clinic setting has the potential to make early AD detection more accessible and cost-effective, thereby reducing the per-person cost for an AD diagnosis compared to existing tools [ 84 ]. This will aid in facilitating accessibility of early interventions that improve patient- and caregiver wellbeing [ 4 , 5 , 6 ], which will in turn reduce long-term care costs [ 151 , 152 ]. Second, the multimodal dataset in a unique study population of eye patients could increase our understanding of the eye-brain connection and provide new routes for early intervention, potentially even for both classes of disease (i.e., brain and eye disease). Recently, the population attributable fraction (PAF) of vision impairment of dementia was estimated to be 1.8%, meaning that a proportion of these dementia cases could have been prevented by appropriate management of eye disorders [ 54 ]. Despite this seemingly low percentage, vision impairment is deemed an important factor to consider in life-course models of potentially modifiable dementia risk factors [ 54 ] given that 9 out of 10 cases of vision impairment are preventable or treatable by relatively simple and cost-effective interventions (e.g. corrective lenses or cataract surgery). The observed co-existence of visual and cognitive impairment and the availability of effective, yet underused, ophthalmological interventions suggest an important interplay between ophthalmological and memory clinic practice that could allow relatively easily obtainable health and quality of life benefits [ 52 , 153 ].

Complementary value of blood- and eye-based biomarkers

Thus far, blood- and eye-biomarkers have not been applied in a combined multimodal screening approach. Hence, the (extent of) added value of applying these biomarker modalities in conjunction remains a key question to be addressed in the BeyeOMARKER study. Multimodal biomarker approaches for AD are gaining traction to improve AD detection, prognosis, and monitoring. After all, AD is a complex disease with many pathophysiological contributors and each modality has its own strengths and limitations in capturing different aspects and stages of AD-related pathophysiological changes [ 13 , 14 , 154 , 155 ]. Currently, several blood tests allow detection of AD-pathology with high accuracy, including the core pathophysiological hallmarks, as well as neurodegenerative and inflammatory markers [ 12 ]. However, the interpretation of blood-based biomarkers may be affected by variability due to interindividual differences in general systemic metabolism, or comorbidities (e.g., obesity, chronic kidney disease, cardiovascular conditions) and/or sociodemographic factors (e.g., sex, diversity in race or ethnicity) that potentially affect metabolic rates [ 156 , 157 ]. In contrast, retinal imaging provides an accessible way to directly visualize the retinal component of the CNS, thereby offering a direct insight into molecular changes (e.g., protein depositions) and structural changes (e.g., neurodegenerative and vascular changes) [ 14 , 107 ]. Interindividual differences in, and dynamic changes of, systemic metabolism will less likely impact structural retinal imaging parameters compared to dynamic blood-biomarker concentrations. However, retinal changes may occur in other (neurodegenerative) diseases and are less AD-specific [ 36 , 39 , 107 ] than markers of plasma p-tau. We therefore hypothesize that retinal markers should not be regarded as an alternative to blood-based biomarkers but rather that combining eye- and blood-based could have complementary value in detecting AD pathophysiology and cognitive decline.

Future opportunities

The characterization of the BeyeOMARKER cohort provides multiple avenues for future research beyond the objectives outlined in this report. First, the field of blood-biomarkers is evolving rapidly and creating a biobank allows future assessment of novel and potentially better performing biomarkers. Secondly, questionnaires implemented in the online ABOARD platform [ 109 ] provides low burden collection of long-term functional outcomes in relation to AD(-related) blood-biomarkers or to eye disease and visual impairment. Third, multiple opportunities exist for AI-based classification of HS retinal scans. For example, it is thus far unclear which of the myriad of parameters provided by a HS retinal scan contribute (the most) to the classification of AD biomarker status, and whether these parameters are directly reflective of amyloid pathology or of other pathological processes like iron accumulation, mitochondrial dysfunction, or inflammation [ 98 , 158 , 159 ]. Furthermore, retinal depositions of tau are observed in glaucoma [ 160 ] as well as in AD [ 161 , 162 ] and a recent study suggests spectral signature related to retinal tau ex vivo [ 163 ]. Currently, the question remains whether AI-driven classification of data-rich HS images could provide retinal-indices that 1) relate to tau-PET status or to a combination of Aβ-PET and tau-PET status, and 2) remain specific to AD in cases with a simultaneous eye disease affecting the retina. These developments, alongside the rapid developments of novel blood-based biomarkers, may provide novel multimodal screening approaches for optimized prognostication. Fourth, implementation of PCA screening tasks may give an estimate on the number of patients that present at the eye clinic with cortical (rather than ocular) vision complaints, indicative of early PCA [ 164 ]. Depending on the sample size, this subgroup is highly suitable to examine the role ophthalmological practice in identifying potential PCA cases and to further characterize the first symptoms and progression of these early PCA cases. Other future ambitions include the implementation of additional longitudinal follow-up for blood-based and eye-based assessment to study the dynamics of these markers and to assess the predictive value of changes over time.

Given the novelty and ambitious nature of the BeyeOMARKER study design, a number of challenges are anticipated. First, although screening for cognitive complaints in eye care settings has been proposed before [ 34 , 46 ], little is known about the willingness of patients to undergo screening, or of eye care professionals to perform this screening. Recent literature suggests that out of 210 participants from a senior center, 194 (92.4%) would want to know their dementia risk based on retinal scanning, particularly to be able to plan for the future [ 165 ]. A supportive attitude towards cognitive screening was also reported for audiology services, but training of the audiologist and sufficient explanation was deemed important [ 166 ]. The latter finding points out the general challenge regarding investment of time and staff resources, and the degree of willingness to make these investments is currently unknown among ophthalmologists. Secondly, the targeted sample size of 700 participants is ambitious, particularly in currently under-represented socio-culturally and socio-economically diverse populations where enrollment barriers are relatively high [ 62 , 167 , 168 ]. Recruitment will be continuously monitored, and our criteria and recruitment strategies may be adapted throughout the study when deemed necessary. Alternatively, the BeyeOMARKER project will continue as planned but with reduced sample sizes. Third, additional study procedures for the BeyeOMARKER + cohort can be experienced as relatively burdensome. Even though the procedures are standard clinical procedures with known and acceptable risks, in this part of the study we may encounter reduced willingness to participate [ 169 ]. Therefore, we aim to minimize study burden where possible by scheduling visits at a familiar location (i.e., the eye clinic), implementation of home-based online questionnaires, providing flexibility in scheduling, providing a clear and accessible point of contact and ensuring understanding of the relevance and burdens of study procedures. The latter may be particularly relevant for the PET scan procedures as this is a known study enrolment barrier, especially in some previously underrepresented groups [ 170 ]. Therefore, the study team will follow recommendations on the communication regarding PET-scanning, such as efforts to improve understanding of the (minimal) risks of radiotracers by avoiding jargon, using visualization aids, providing understandable risk estimates and implementing active listening strategies [ 171 ]. Finally, challenges remain in cognitive assessment of participants with a visual impairment or culturally diverse background, particularly as the solutions can be counteracting. For example, tasks adapted for participants with a visual impairment are often more language-dependent, while tasks adapted to culturally and linguistically diverse populations are often more vision-dependent. Any potential language- or vision-dependent bias in cognitive testing will be documented and will be taken into account through sensitivity analyses when evaluating the clinical outcomes. We will report on our findings with regard to the performance of our clinical measures to inform future investigations.

The BeyeOMARKER study will provide a well-characterized cohort to 1) investigate the feasibility of early AD detection based on blood- and eye-based biomarkers in alternative screening settings, and 2) improve our understanding of the eye-brain connection. Findings, future opportunities, challenges and limitations of the BeyeOMARKER study will be integrated into a roadmap for large-scale implementation of early AD detection, which will aid towards building an efficient and inclusive infrastructures to detect individuals at risk of AD and allow intervention to those who need it.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

A Personalized Medicine Approach for Alzheimer's Disease

Alzheimer's disease

Amyloid beta

Artificial Intelligence

Apolipoprotein E

Dutch central bureau of statistics

Cortical Vision Screening Test

European Union

Good Clinical Practice

General Data Protection (GDPR) (in Dutch: algemene verordening gegevensbescherming (AVG))

Hazard Ratio

Hyperspectral

Mydriatic Hyperspectral Retinal Camera

Montreal Cognitive Assessment

Magnetic Resonance Imaging

Neurology 4-Plex E assay

Optical coherence tomography

OCT-Angiography

Population Attributable Fraction

Posterior Cortical Atrophy

Positron Emission Tomography

Phosphorylated tau

Relative Risk

Socioeconomic status

Single-molecule assay

Trail Making Task

• Visual impairment

Visual Object and Space Perception Battery

VU University Medical Center

World Medical Association

Medical Research Involving Human Subjects Act

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Acknowledgements

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The BeyeOMARKER project has received funding from the Alzheimer’s Association (AARG-22–924466). The project also received funding from Health Holland (2012714) which includes in-cash and in-kind contributions from Quanterix and Optina Diagnostics. This communication reflects the views of the authors and neither the Alzheimer’s Association nor Health Holland are liable for any use that may be made of the information contained herein.

Research of Alzheimer Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting Steun Alzheimercentrum Amsterdam. The clinical database structure was developed with funding from Stichting Dioraphte.

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Ilse Bader, Colin Groot, Jurre den Haan, Argonde C. van Harten, Wiesje M. van der Flier, Yolande A. L. Pijnenburg, Charlotte E. Teunissen, Femke H. Bouwman & Rik Ossenkoppele

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IB, CG, ST, JH, IV, KY, AH, PK, WF, YP, FB, EG, CT, FH, and RO contributed intellectually to the study protocol and have made a substantial contribution to the conception and design of the current study. IB and JM contributed to project administration and set-up of the study infrastructure. ST supported crucial access to the location of recruitment. PK provides study support at this location of recruitment. JO, SC and DW supported crucial access to study instrumentation. RO is principal investigator of the BeyeOMARKER study and supervises the project. All authors have approved the final manuscript.

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CT is editor-in-chief of Alzheimer Research and Therapy. RO is part of the editorial board of Alzheimer’s Research and Therapy.

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Bader, I., Groot, C., Tan, H.S. et al. Rationale and design of the BeyeOMARKER study: prospective evaluation of blood- and eye-based biomarkers for early detection of Alzheimer’s disease pathology in the eye clinic. Alz Res Therapy 16 , 190 (2024). https://doi.org/10.1186/s13195-024-01545-1

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