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• 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.

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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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Home » Research Design – Types, Methods and Examples

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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Researcher, Academic Writer, Web developer

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Research Methods Guide: Research Design & Method

• Introduction
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• Data Analysis
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Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

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

Which research method should I choose ?

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

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

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

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

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

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• Last Updated: Aug 21, 2023 10:42 AM

Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

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Research Design Qualitative, Quantitative, and Mixed Methods Approaches

• John W. Creswell - Department of Family Medicine, University of Michigan
• J. David Creswell - Carnegie Mellon University, USA
• Description

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“A long time ago, I participated in one of Dr. Creswell’s workshops on mixed methods research.... I am still learning from Dr. Creswell. I appreciate how he takes complex topics and makes them accessible to everyone. But I must caution my students that Dr. Creswell’s easygoing cadence and elegant descriptions sometimes mask the depth of the material. This reminds me of why he is such a highly respected researcher and teacher.”

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“This book is based around dissertation chapters, and that's why I love it using in my class. Practical, concise, and to the point!”

“This book is easy to use. The information and additional charts are also helpful.”

The book provides a comprehensive overview and does well at demystifying the research philosophy. I have recommended it to my level 7 students for their dissertation project.

This book will be added to next academic year's reading list.

I am fed up with trying to get access to this "inspection copy". You don't respond to emails (and the email addresses you provide do not work). I get regular emails from you saying my ebook order is ready, but it does not appear in VitalSource and I cannot access it through any link on this web page. I am not willing to waste any more time on this. There are good alternatives.

Excellent introduction for research methods.

Creswell has always had excellent textbooks. Sixth Edition is no exception!

I really like the authors' explanations. This book is used as a resource throughout my 4-course research sequence.

• Fully updated for the 7th edition of the Publication Manual of the American Psychological Association.
• More inclusive and supportive language throughout helps readers better see themselves in the research process.
• Learning Objectives provide additional structure and clarity to the reading process.
• The latest information on participatory research, evaluating literature for quality, using software to design literature maps, and additional statistical software types is newly included in this edition.
• Chapter 4: Writing Strategies and Ethical Considerations now includes information on indigenous populations and data collection after IRB review.
• An updated Chapter 8: Quantitative Methods now includes more foundational details, such as Type 1 and Type 2 errors and discussions of advantages and disadvantages of quantitative designs.
• A restructured and revised Chapter 10: Mixed Methods Procedures brings state-of-the-art thinking to this increasingly popular approach.
• Chapters 8, 9, and 10 now have parallel structures so readers can better compare and contrast each approach.
• Reworked end-of-chapter exercises offer a more straightforward path to application for students.
• New research examples throughout the text offer students contemporary studies for evaluation.
• Current references and additional readings are included in this new edition.
• Compares qualitative, quantitative, and mixed methods research in one book for unparalleled coverage.
• Highly interdisciplinary examples make this book widely appealing to a broad range of courses and disciplines.
• Ethical coverage throughout consistently reminds students to use good judgment and to be fair and unbiased in their research.
• Writing exercises conclude each chapter so that readers can practice the principles learned in the chapter; if the reader completes all of the exercises, they will have a written plan for their scholarly study.
• Numbered points provide checklists of each step in a process.
• Annotated passages help reinforce the reader's comprehension of key research ideas.

Sample Materials & Chapters

Chapter 1: The Selection of a Research Approach

Chapter 2: Review of the Literature

For instructors

Select a purchasing option, related products.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

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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5 Research design

Research design is a comprehensive plan for data collection in an empirical research project. It is a ‘blueprint’ for empirical research aimed at answering specific research questions or testing specific hypotheses, and must specify at least three processes: the data collection process, the instrument development process, and the sampling process. The instrument development and sampling processes are described in the next two chapters, and the data collection process—which is often loosely called ‘research design’—is introduced in this chapter and is described in further detail in Chapters 9–12.

Broadly speaking, data collection methods can be grouped into two categories: positivist and interpretive. Positivist methods , such as laboratory experiments and survey research, are aimed at theory (or hypotheses) testing, while interpretive methods, such as action research and ethnography, are aimed at theory building. Positivist methods employ a deductive approach to research, starting with a theory and testing theoretical postulates using empirical data. In contrast, interpretive methods employ an inductive approach that starts with data and tries to derive a theory about the phenomenon of interest from the observed data. Often times, these methods are incorrectly equated with quantitative and qualitative research. Quantitative and qualitative methods refers to the type of data being collected—quantitative data involve numeric scores, metrics, and so on, while qualitative data includes interviews, observations, and so forth—and analysed (i.e., using quantitative techniques such as regression or qualitative techniques such as coding). Positivist research uses predominantly quantitative data, but can also use qualitative data. Interpretive research relies heavily on qualitative data, but can sometimes benefit from including quantitative data as well. Sometimes, joint use of qualitative and quantitative data may help generate unique insight into a complex social phenomenon that is not available from either type of data alone, and hence, mixed-mode designs that combine qualitative and quantitative data are often highly desirable.

Key attributes of a research design

The quality of research designs can be defined in terms of four key design attributes: internal validity, external validity, construct validity, and statistical conclusion validity.

Internal validity , also called causality, examines whether the observed change in a dependent variable is indeed caused by a corresponding change in a hypothesised independent variable, and not by variables extraneous to the research context. Causality requires three conditions: covariation of cause and effect (i.e., if cause happens, then effect also happens; if cause does not happen, effect does not happen), temporal precedence (cause must precede effect in time), and spurious correlation, or there is no plausible alternative explanation for the change. Certain research designs, such as laboratory experiments, are strong in internal validity by virtue of their ability to manipulate the independent variable (cause) via a treatment and observe the effect (dependent variable) of that treatment after a certain point in time, while controlling for the effects of extraneous variables. Other designs, such as field surveys, are poor in internal validity because of their inability to manipulate the independent variable (cause), and because cause and effect are measured at the same point in time which defeats temporal precedence making it equally likely that the expected effect might have influenced the expected cause rather than the reverse. Although higher in internal validity compared to other methods, laboratory experiments are by no means immune to threats of internal validity, and are susceptible to history, testing, instrumentation, regression, and other threats that are discussed later in the chapter on experimental designs. Nonetheless, different research designs vary considerably in their respective level of internal validity.

External validity or generalisability refers to whether the observed associations can be generalised from the sample to the population (population validity), or to other people, organisations, contexts, or time (ecological validity). For instance, can results drawn from a sample of financial firms in the United States be generalised to the population of financial firms (population validity) or to other firms within the United States (ecological validity)? Survey research, where data is sourced from a wide variety of individuals, firms, or other units of analysis, tends to have broader generalisability than laboratory experiments where treatments and extraneous variables are more controlled. The variation in internal and external validity for a wide range of research designs is shown in Figure 5.1.

Some researchers claim that there is a trade-off between internal and external validity—higher external validity can come only at the cost of internal validity and vice versa. But this is not always the case. Research designs such as field experiments, longitudinal field surveys, and multiple case studies have higher degrees of both internal and external validities. Personally, I prefer research designs that have reasonable degrees of both internal and external validities, i.e., those that fall within the cone of validity shown in Figure 5.1. But this should not suggest that designs outside this cone are any less useful or valuable. Researchers’ choice of designs are ultimately a matter of their personal preference and competence, and the level of internal and external validity they desire.

Construct validity examines how well a given measurement scale is measuring the theoretical construct that it is expected to measure. Many constructs used in social science research such as empathy, resistance to change, and organisational learning are difficult to define, much less measure. For instance, construct validity must ensure that a measure of empathy is indeed measuring empathy and not compassion, which may be difficult since these constructs are somewhat similar in meaning. Construct validity is assessed in positivist research based on correlational or factor analysis of pilot test data, as described in the next chapter.

Statistical conclusion validity examines the extent to which conclusions derived using a statistical procedure are valid. For example, it examines whether the right statistical method was used for hypotheses testing, whether the variables used meet the assumptions of that statistical test (such as sample size or distributional requirements), and so forth. Because interpretive research designs do not employ statistical tests, statistical conclusion validity is not applicable for such analysis. The different kinds of validity and where they exist at the theoretical/empirical levels are illustrated in Figure 5.2.

Improving internal and external validity

The best research designs are those that can ensure high levels of internal and external validity. Such designs would guard against spurious correlations, inspire greater faith in the hypotheses testing, and ensure that the results drawn from a small sample are generalisable to the population at large. Controls are required to ensure internal validity (causality) of research designs, and can be accomplished in five ways: manipulation, elimination, inclusion, and statistical control, and randomisation.

In manipulation , the researcher manipulates the independent variables in one or more levels (called ‘treatments’), and compares the effects of the treatments against a control group where subjects do not receive the treatment. Treatments may include a new drug or different dosage of drug (for treating a medical condition), a teaching style (for students), and so forth. This type of control is achieved in experimental or quasi-experimental designs, but not in non-experimental designs such as surveys. Note that if subjects cannot distinguish adequately between different levels of treatment manipulations, their responses across treatments may not be different, and manipulation would fail.

The elimination technique relies on eliminating extraneous variables by holding them constant across treatments, such as by restricting the study to a single gender or a single socioeconomic status. In the inclusion technique, the role of extraneous variables is considered by including them in the research design and separately estimating their effects on the dependent variable, such as via factorial designs where one factor is gender (male versus female). Such technique allows for greater generalisability, but also requires substantially larger samples. In statistical control , extraneous variables are measured and used as covariates during the statistical testing process.

Finally, the randomisation technique is aimed at cancelling out the effects of extraneous variables through a process of random sampling, if it can be assured that these effects are of a random (non-systematic) nature. Two types of randomisation are: random selection , where a sample is selected randomly from a population, and random assignment , where subjects selected in a non-random manner are randomly assigned to treatment groups.

Randomisation also ensures external validity, allowing inferences drawn from the sample to be generalised to the population from which the sample is drawn. Note that random assignment is mandatory when random selection is not possible because of resource or access constraints. However, generalisability across populations is harder to ascertain since populations may differ on multiple dimensions and you can only control for a few of those dimensions.

Popular research designs

As noted earlier, research designs can be classified into two categories—positivist and interpretive—depending on the goal of the research. Positivist designs are meant for theory testing, while interpretive designs are meant for theory building. Positivist designs seek generalised patterns based on an objective view of reality, while interpretive designs seek subjective interpretations of social phenomena from the perspectives of the subjects involved. Some popular examples of positivist designs include laboratory experiments, field experiments, field surveys, secondary data analysis, and case research, while examples of interpretive designs include case research, phenomenology, and ethnography. Note that case research can be used for theory building or theory testing, though not at the same time. Not all techniques are suited for all kinds of scientific research. Some techniques such as focus groups are best suited for exploratory research, others such as ethnography are best for descriptive research, and still others such as laboratory experiments are ideal for explanatory research. Following are brief descriptions of some of these designs. Additional details are provided in Chapters 9–12.

Experimental studies are those that are intended to test cause-effect relationships (hypotheses) in a tightly controlled setting by separating the cause from the effect in time, administering the cause to one group of subjects (the ‘treatment group’) but not to another group (‘control group’), and observing how the mean effects vary between subjects in these two groups. For instance, if we design a laboratory experiment to test the efficacy of a new drug in treating a certain ailment, we can get a random sample of people afflicted with that ailment, randomly assign them to one of two groups (treatment and control groups), administer the drug to subjects in the treatment group, but only give a placebo (e.g., a sugar pill with no medicinal value) to subjects in the control group. More complex designs may include multiple treatment groups, such as low versus high dosage of the drug or combining drug administration with dietary interventions. In a true experimental design , subjects must be randomly assigned to each group. If random assignment is not followed, then the design becomes quasi-experimental . Experiments can be conducted in an artificial or laboratory setting such as at a university (laboratory experiments) or in field settings such as in an organisation where the phenomenon of interest is actually occurring (field experiments). Laboratory experiments allow the researcher to isolate the variables of interest and control for extraneous variables, which may not be possible in field experiments. Hence, inferences drawn from laboratory experiments tend to be stronger in internal validity, but those from field experiments tend to be stronger in external validity. Experimental data is analysed using quantitative statistical techniques. The primary strength of the experimental design is its strong internal validity due to its ability to isolate, control, and intensively examine a small number of variables, while its primary weakness is limited external generalisability since real life is often more complex (i.e., involving more extraneous variables) than contrived lab settings. Furthermore, if the research does not identify ex ante relevant extraneous variables and control for such variables, such lack of controls may hurt internal validity and may lead to spurious correlations.

Field surveys are non-experimental designs that do not control for or manipulate independent variables or treatments, but measure these variables and test their effects using statistical methods. Field surveys capture snapshots of practices, beliefs, or situations from a random sample of subjects in field settings through a survey questionnaire or less frequently, through a structured interview. In cross-sectional field surveys , independent and dependent variables are measured at the same point in time (e.g., using a single questionnaire), while in longitudinal field surveys , dependent variables are measured at a later point in time than the independent variables. The strengths of field surveys are their external validity (since data is collected in field settings), their ability to capture and control for a large number of variables, and their ability to study a problem from multiple perspectives or using multiple theories. However, because of their non-temporal nature, internal validity (cause-effect relationships) are difficult to infer, and surveys may be subject to respondent biases (e.g., subjects may provide a ‘socially desirable’ response rather than their true response) which further hurts internal validity.

Secondary data analysis is an analysis of data that has previously been collected and tabulated by other sources. Such data may include data from government agencies such as employment statistics from the U.S. Bureau of Labor Services or development statistics by countries from the United Nations Development Program, data collected by other researchers (often used in meta-analytic studies), or publicly available third-party data, such as financial data from stock markets or real-time auction data from eBay. This is in contrast to most other research designs where collecting primary data for research is part of the researcher’s job. Secondary data analysis may be an effective means of research where primary data collection is too costly or infeasible, and secondary data is available at a level of analysis suitable for answering the researcher’s questions. The limitations of this design are that the data might not have been collected in a systematic or scientific manner and hence unsuitable for scientific research, since the data was collected for a presumably different purpose, they may not adequately address the research questions of interest to the researcher, and interval validity is problematic if the temporal precedence between cause and effect is unclear.

Case research is an in-depth investigation of a problem in one or more real-life settings (case sites) over an extended period of time. Data may be collected using a combination of interviews, personal observations, and internal or external documents. Case studies can be positivist in nature (for hypotheses testing) or interpretive (for theory building). The strength of this research method is its ability to discover a wide variety of social, cultural, and political factors potentially related to the phenomenon of interest that may not be known in advance. Analysis tends to be qualitative in nature, but heavily contextualised and nuanced. However, interpretation of findings may depend on the observational and integrative ability of the researcher, lack of control may make it difficult to establish causality, and findings from a single case site may not be readily generalised to other case sites. Generalisability can be improved by replicating and comparing the analysis in other case sites in a multiple case design .

Focus group research is a type of research that involves bringing in a small group of subjects (typically six to ten people) at one location, and having them discuss a phenomenon of interest for a period of one and a half to two hours. The discussion is moderated and led by a trained facilitator, who sets the agenda and poses an initial set of questions for participants, makes sure that the ideas and experiences of all participants are represented, and attempts to build a holistic understanding of the problem situation based on participants’ comments and experiences. Internal validity cannot be established due to lack of controls and the findings may not be generalised to other settings because of the small sample size. Hence, focus groups are not generally used for explanatory or descriptive research, but are more suited for exploratory research.

Action research assumes that complex social phenomena are best understood by introducing interventions or ‘actions’ into those phenomena and observing the effects of those actions. In this method, the researcher is embedded within a social context such as an organisation and initiates an action—such as new organisational procedures or new technologies—in response to a real problem such as declining profitability or operational bottlenecks. The researcher’s choice of actions must be based on theory, which should explain why and how such actions may cause the desired change. The researcher then observes the results of that action, modifying it as necessary, while simultaneously learning from the action and generating theoretical insights about the target problem and interventions. The initial theory is validated by the extent to which the chosen action successfully solves the target problem. Simultaneous problem solving and insight generation is the central feature that distinguishes action research from all other research methods, and hence, action research is an excellent method for bridging research and practice. This method is also suited for studying unique social problems that cannot be replicated outside that context, but it is also subject to researcher bias and subjectivity, and the generalisability of findings is often restricted to the context where the study was conducted.

Ethnography is an interpretive research design inspired by anthropology that emphasises that research phenomenon must be studied within the context of its culture. The researcher is deeply immersed in a certain culture over an extended period of time—eight months to two years—and during that period, engages, observes, and records the daily life of the studied culture, and theorises about the evolution and behaviours in that culture. Data is collected primarily via observational techniques, formal and informal interaction with participants in that culture, and personal field notes, while data analysis involves ‘sense-making’. The researcher must narrate her experience in great detail so that readers may experience that same culture without necessarily being there. The advantages of this approach are its sensitiveness to the context, the rich and nuanced understanding it generates, and minimal respondent bias. However, this is also an extremely time and resource-intensive approach, and findings are specific to a given culture and less generalisable to other cultures.

Selecting research designs

Given the above multitude of research designs, which design should researchers choose for their research? Generally speaking, researchers tend to select those research designs that they are most comfortable with and feel most competent to handle, but ideally, the choice should depend on the nature of the research phenomenon being studied. In the preliminary phases of research, when the research problem is unclear and the researcher wants to scope out the nature and extent of a certain research problem, a focus group (for an individual unit of analysis) or a case study (for an organisational unit of analysis) is an ideal strategy for exploratory research. As one delves further into the research domain, but finds that there are no good theories to explain the phenomenon of interest and wants to build a theory to fill in the unmet gap in that area, interpretive designs such as case research or ethnography may be useful designs. If competing theories exist and the researcher wishes to test these different theories or integrate them into a larger theory, positivist designs such as experimental design, survey research, or secondary data analysis are more appropriate.

Regardless of the specific research design chosen, the researcher should strive to collect quantitative and qualitative data using a combination of techniques such as questionnaires, interviews, observations, documents, or secondary data. For instance, even in a highly structured survey questionnaire, intended to collect quantitative data, the researcher may leave some room for a few open-ended questions to collect qualitative data that may generate unexpected insights not otherwise available from structured quantitative data alone. Likewise, while case research employ mostly face-to-face interviews to collect most qualitative data, the potential and value of collecting quantitative data should not be ignored. As an example, in a study of organisational decision-making processes, the case interviewer can record numeric quantities such as how many months it took to make certain organisational decisions, how many people were involved in that decision process, and how many decision alternatives were considered, which can provide valuable insights not otherwise available from interviewees’ narrative responses. Irrespective of the specific research design employed, the goal of the researcher should be to collect as much and as diverse data as possible that can help generate the best possible insights about the phenomenon of interest.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Design: Qualitative, Quantitative, and Mixed Methods Approaches

You are here, student resources, welcome to the companion website.

Welcome to the SAGE edge site for Research Design, Fifth Edition .

The SAGE edge site for Research Design by John W. Creswell and J. David Creswell offers a robust online environment you can access anytime, anywhere, and features an array of free tools and resources to keep you on the cutting edge of your learning experience.

This best-selling text pioneered the comparison of qualitative, quantitative, and mixed methods research design. For all three approaches, John W. Creswell and new co-author J. David Creswell include a preliminary consideration of philosophical assumptions, key elements of the research process, a review of the literature, an assessment of the use of theory in research applications, and reflections about the importance of writing and ethics in scholarly inquiry.

The  Fifth   Edition  includes more coverage of: epistemological and ontological positioning in relation to the research question and chosen methodology; case study, PAR, visual and online methods in qualitative research; qualitative and quantitative data analysis software; and in quantitative methods more on power analysis to determine sample size, and more coverage of experimental and survey designs; and updated with the latest thinking and research in mixed methods.

Acknowledgments

We gratefully acknowledge John W. Creswell and J. David Creswell for writing an excellent text. Special thanks are also due to Tim Guetterman of the University of Michigan, Shannon Storch of the University of Creighton, and Tiffany J. Davis of the University of Houston for developing the ancillaries on this site.

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Research Methodology & Design

Research methodology.

• Seminal Authors
• Qualitative Research Methods
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If you are looking for a document in the Dissertation Center or Applied Doctoral Center and can't find it please contact your Chair or The Center for Teaching and Learning at [email protected]

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Once you are well into your literature review, it is time to start thinking about the study you will design to answer the gap you identified. Which methodology will you use to gather the data for your research? Will you use a qualitative, quantitative, or mixed methods methodology? You will choose a research method that best aligns with your research question.

To evaluate which type of methodology will be most appropriate, you will work closely with your Dissertation Chair. However, as you are reading the literature, take a look at past studies that focus on your topic, or a similar topic. What kind of research methodology do you see being used most often? Once you have an idea about the general methodology type that would suit your research, consult with your Dissertation Chair on the possibility of using that methodology.

Finding a research design strategy is similar to the research process as a whole: first, locate general information on research design and methodologies, then gain background knowledge on the methodology you feel would most appropriately address the type of data you will be collecting, and finally choose a methodology and test/measurement to use in your research. The following techniques outline how to locate information about research methodology from reference books, scholarly articles and dissertations.

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After you have located background information about your research design, you may want to locate scholarly journal articles on your research topic that use a particular type of methodology. By looking at research articles that use a particular methodology you can learn a lot about your field. What types of research studies are prevalent? What methodologies are appropriate for a specific research question? How do you construct a research study? What methodologies should you consider for your dissertation research? Few databases allow you to limit your search by research methodology. APA PsycArticles  and APA PsycInfo  are the exceptions; these databases do allow you to limit your search results to show articles that use a particular methodology. 

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Research Design Considerations

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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

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• Volume 14, Issue 4
• Collaborative design of a health research training programme for nurses and midwives in Tshwane district, South Africa: a study protocol
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• http://orcid.org/0000-0002-8761-2055 Rodwell Gundo ,
• Mavis Fhumulani Mulaudzi
• Department of Nursing Science , University of Pretoria , Pretoria , South Africa
• Correspondence to Dr Rodwell Gundo; rodwell.gundo{at}up.ac.za

Introduction Nurses are essential for implementing evidence-based practices to improve patient outcomes. Unfortunately, nurses lack knowledge about research and do not always understand research terminology. This study aims to develop an in-service training programme for health research for nurses and midwives in the Tshwane district of South Africa.

Methods and analysis This protocol outlines a codesign study guided by the five stages of design thinking proposed by the Hasso-Plattner Institute of Design at Stanford University. The participants will include nurses and midwives at two hospitals in the Tshwane district, Gauteng Province. The five stages will be implemented in three phases: Phase 1: Stage 1—empathise and Stage 2—define. Exploratory sequential mixed methods including focus group discussions with nurses and midwives (n=40), face-to-face interviews (n=6), and surveys (n=330), will be used in this phase. Phase 2: Stage 3—ideate and Stage 4—prototype. A team of research experts (n=5), nurses and midwives (n=20) will develop the training programme based on the identified learning needs. Phase 3: Stage 5—test. The programme will be delivered to clinical nurses and midwives (n=41). The training programme will be evaluated through pretraining and post-training surveys and face-to-face interviews (n=4) following training. SPSS V.29 will be used for quantitative analysis, and content analysis will be used to analyse qualitative data.

Ethics and dissemination The protocol was approved by the Faculty of Health Sciences Research Ethics Committee of the University of Pretoria (reference number 123/2023). The protocol is also registered with the National Health Research Database in South Africa (reference number GP_202305_032). The study findings will be disseminated through conference presentations and publications in peer-reviewed journals.

• Nursing Care
• Patient-Centered Care
• Patient Satisfaction

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

https://doi.org/10.1136/bmjopen-2023-076959

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STRENGTHS AND LIMITATIONS OF THIS STUDY

This study will be strengthened through the use of quantitative and qualitative methods to understand the research problem.

The inclusion of two hospitals and the participation of different nurses and midwives will ensure the credibility of the findings.

Local research experts, nurses and midwives will collaborate to develop a training programme appropriate to the context of the setting.

The findings will be limited to two hospitals; therefore, the findings may not be generalisable to other hospitals.

Introduction

Evidence-based practice (EBP) has gained prominence in health services internationally over the past three decades. 1 EBP integrates individual clinical expertise with clinical evidence generated from systematic research. 2 EBP aims to deliver appropriate, efficient patient care. 3 Consequently, generating evidence that informs care delivery has become increasingly important for improving patient-centred care, patient safety, patient outcomes and the healthcare system. 1 3 In healthcare, nurses are well positioned to implement EBP because they constitute the largest proportion of the health workforce. 1 4 Nurses thus have to be proactive in acquiring, synthesising and using research knowledge and the best evidence to inform their practice and decision-making. 3 4

Recognising the need for EBP, many nursing organisations worldwide have developed best practice guidelines for patient-care decision-making. 4 In South Africa, the roadmap for strengthening nursing and midwifery acknowledges that nurses are vital for providing safe and effective patient care. Strategically, investing in nurse-led research will help develop nurse-led models of care. 5 Similarly, the South African Nursing Council expects nurses to actively participate in research activities, including academic writing, reading and reviewing, as part of continuing professional development. 6 Training nurses and midwives can enhance their research capacity and enable them to use available resources for research, ultimately leading to changes in EBP in clinical settings.

Nurses need to gain research knowledge and become comfortable with research terminology. 7 8 Although undergraduate nursing training includes a research component, this training does not always translate into a strong understanding of research. 7 As such, there needs to be more nurse-led patient-centred research. A recent review of nursing research from 2000 to 2019 showed that most nursing research is conducted by nurses working at higher education institutions. Research output and collaboration are also disproportionately more prominent in high-income countries across North America, Europe, and Oceania than in low-income and middle-income countries. 9 The other challenges that affect health research include limited time, lack of research facilities, research culture, mentors, access to mentors, and workforce capacity. 10

Little is known about the research literacy of nurses and midwives and research training programmes for practicing nurses and midwives in South Africa. Therefore, we developed a protocol to develop a research training programme for nurses and midwives in the Tshwane district of South Africa. This protocol is guided by the following research questions: (a) what are the levels of nurses’ and midwives’ knowledge, attitudes and involvement in research?; (b) what are the learning needs of nurses and midwives regarding research design and implementation?; (c) what content should be included in a research training programme for nurses and midwives?; (d) how does the developed training programme impact nurses’ knowledge about research?

Theoretical framework

The principles of constructivism learning theory will guide this study. This theory is rooted in the work of Piaget and Vygotsky. 11 This paradigm explains how people might acquire and retain knowledge. 12 Through the lens of constructivism learning theory, adult educators acknowledge learners’ previous experiences, appreciate multiple perspectives and embed learning in social contexts. The instructor is a mentor who helps learners understand new information. Constructivism learning theory has three dimensions, namely, individual constructivism, social constructivism and contextualism. In individual constructivism, learners are self-directed and construct knowledge via personal experience. Social constructivism assumes that learning is socially mediated, and that knowledge is constructed through social interaction. In contextualism, learning should be tied to real-life contexts. 13 Some benefits of constructivism theory are that learners enjoy learning because they are actively engaged and have ownership over what they learn. 12 The theory was considered appropriate because the study will be conducted at two research-intensive hospitals. Therefore, nurses and midwives are familiar with the research process.

Methods and analysis

Research design.

We will use a codesign approach guided by the stages of design thinking proposed by the Hasso-Plattner Institute of Design at Stanford University. 14 15 The design originated from participatory research and involves active engagement of the participants to identify needs and collaboratively propose solutions. 14 16 The approach is considered appropriate because it ensures meaningful involvement of end-users, thereby creating meaningful benefits. 17 A codesign approach ensures fewer challenges when implementing the initiative because stakeholders are fully engaged throughout the process. 14 Underpinned by the African philosophy of Ubuntu, the process will promote the culture of working together and collective solidarity. 18

The study will be guided by the five stages of design thinking: empathise, define, ideate, prototype and test. Empathise aims to understand the deeper issues, needs and challenges needed to solve the problem. Define involves data analysis and prioritising the needs of the end users of the training programme. Ideate includes brainstorming for innovative solutions to address the identified needs. In the prototype stage, the idea or innovation is shown to the end users and other stakeholders. Finally, testing involves checking what works in a real-world setting. 14 15

Study setting

The study will be conducted at two public hospitals in the Tshwane district of Gauteng Province in South Africa. The province has the highest population density, the most hospitals and the greatest number of nurses and midwives. 19 According to a 2016 community survey, Gauteng has a population of 13.4 million people. 20 Tshwane is one of the five districts in the province and the third most populous district, accounting for 24% of the population in the province. 21 There are three district hospitals, namely, Tshwane, Pretoria West, Jubilee and ODI; one regional hospital, Mamelodi; and three tertiary hospitals, namely, Steve Biko Academic Hospital, Dr George Mukhari Hospital and Khalafong Hospital. The two hospitals were selected due to their proximity to the University of Pretoria. One of the hospitals is a tertiary hospital with 800 beds. The second hospital is a 240-bed district hospital linked to the University of Pretoria’s Faculty of Health Sciences. 22

Target population

The population will comprise nurses and midwives working at the two hospitals. In South Africa, there are six categories of nurses and midwives based on qualifications as follows: registered auxiliary nurse (higher certificate), registered general nurse (diploma in nursing), registered midwife (advanced diploma), registered professional nurse and midwife (bachelor’s degree), nurse specialist or midwife specialist (postgraduate diploma), advanced specialist nurse (master’s degree) and those with doctorate degrees. 5 Nurses working at academic hospitals are expected to engage in research activities, including academic writing, reading and reviewing, as part of continuing professional development. 6 A preliminary audit revealed 1900 nurses and midwives working at the two hospitals.

Inclusion and exclusion criteria

Participation will be limited to registered auxiliary nurses, registered general nurses, registered midwives, registered professional nurses and midwives older than 18 years, those registered with the South African Nursing Council, and those with more than 3 months of experience. All people older than 18 years are mandated to give legal consent in South Africa. Nurses with less than 3 months of experience or undergoing orientation will be excluded from the study.

As illustrated in table 1 , the study will be implemented in three phases and five stages to address the four objectives. Stage 1 is currently underway. The collection of the qualitative data started in December 2023 at one of the two hospitals. This will proceed at the second hospital until April 2024. The whole study is expected to be completed by September 2024.

• View inline

Illustration of the research process guided by the stages of design thinking

In this phase, we aim to understand the nurses’ and midwives’ perceived knowledge, attitudes and involvement in research and their learning needs. We will base our investigation on empathising and defining. An exploratory sequential mixed methods design will be used. This design begins with collecting and analysing qualitative data. The qualitative findings are used to develop quantitative measures or instruments to test the identified variables. 23 In this study, the qualitative findings will be used to revise a questionnaire for the subsequent quantitative strand.

Strand 1—qualitative study

Qualitative methods are appropriate for investigating the who, what and where of events or experiences of informants of a poorly understood phenomenon. 24 25

Sample size and sampling

Forty-six participants (n=46) will be selected from nurses and midwives working at the two hospitals. The sample size was pragmatically determined according to the mode of data collection and the volume of data to be collected. However, the final sample size will be determined by data saturation.

We will purposively sample nurses and midwives from the following cadres: registered auxiliary nurses, registered general nurses, registered midwives, and registered professional nurses and midwives. As presented in table 2 , two focus group discussions (FGDs) will be held at each hospital and will involve 10 participants each. Due to power differences that can cause a halo effect among the participants, 26 one FGD will include senior professional nurses and midwives. In contrast, the other FDG will include junior nurses and midwives with either diplomas or certificates. For the individual interviews, three participants (one registered auxiliary nurse, one registered general nurse with a diploma and one professional nurse (with either a bachelor’s or postgraduate qualification)) will be invited to participate. The participants will be expected to share their knowledge of the competencies needed for conducting health research.

Sampling plan for the qualitative strand

Data collection

The study information will be communicated through nursing and midwifery managers. Participation will be voluntary. Nurses and midwives willing to participate will be invited for either FGDs or individual interviews. The participants will be given the details of the study and a consent form. The interviews will be conducted in English in hospitals in private settings at times and places that are most convenient for participants. The participants will be requested to use pseudonyms during interviews. A semistructured interview guide will be used for the interviews (refer to online supplemental file 1 ). The interviews will be audiotaped and later transcribed verbatim in English.

Supplemental material

Data analysis.

The data will be analysed manually using conventional content analysis as described by Hsieh and Shannon. 27 The steps of the analysis will be as follows: (a) repeatedly reading the data to achieve immersion and a sense of the whole; (b) deriving and labelling codes by highlighting the words that capture critical thoughts and concepts; (c) sorting the related codes into categories; (d) organising numerous subcategories into fewer categories; (e) defining each category; and (f) identifying the relationship of the categories in terms of their concurrence, antecedents or consequences. To ensure the reliability of the qualitative coding, tHead2he two researchers will code the first transcript independently. The online Coding Analysis Toolkits 28 will be used to calculate intercoder reliability. The two researchers will discuss differences and agree on the coding before proceeding to the next transcript.

Methodological rigour

Trustworthiness will be achieved through credibility, transferability, dependability and confirmability. 24 29 Credibility will be achieved through spatial and personal triangulation. Spatial triangulation refers to collecting data on the same phenomenon from multiple sites, while personal triangulation refers to collecting data from different types and levels of people. 29 This study will collect data from different cadres of nurses and midwives at two hospitals. Transferability will be enhanced by providing sufficient study details. Dependability and confirmability will be achieved by establishing an audit trail describing the procedures and processes. Additionally, reflexivity will be used to ensure the transparency and quality of the study. 29 30 Reflexivity is where researchers critique, appraise and evaluate the influence of subjectivity and context on the research process. 30 In some branches of qualitative inquiries, researchers use reflexive bracketing to prevent subjective influences. However, Olmos-Vega et al 30 observed that this approach is no longer favoured in modern qualitative research because setting aside certain aspects of subjectivity is problematic. In this study, reflexivity will be ensured by keeping memos and field notes to document interpersonal dynamics and critical decisions made throughout the study.

Strand 2—quantitative study

A cross-sectional survey will be used to assess nurses’ and midwives’ perceived knowledge, attitudes and involvement in research.

The sample size was calculated using Yamane’s formula 31 as follows: n=N/(1+N(e2), where n is the sample, N is the population size, and e is the level of precision. Assuming a 95% CI and the estimated proportion of an attribute p=0.5, the calculated sample size for a population N=1900 with ±5% precision is 330. In this study, a convenience sampling technique will be used to select participants.

The researchers will brief nurse managers about the study. Furthermore, posters inviting nurses and midwives to participate in the study will be placed in each department. The poster will include details of the study and relevant contact details. The nurses and midwives willing to participate will be given an information sheet, consent form and questionnaire. They will be requested to leave the completed questionnaire in a designated box in the unit manager’s office.

Data collection instrument

The data will be collected using the Edmonton Research Orientation Survey (EROS). The EROS was developed in Canada and is a valid and reliable self-reported instrument for measuring perceived knowledge, attitudes and involvement in research. The tool has four subscales with 43 items. The four subscales are the value of research, value of innovation, research involvement and research utilisation (EBP). Valuing research is a positive attitude towards research; the value of innovation refers to being on the leading edge or keeping up to date with information; research involvement relates to active participation in research; and research utilisation (EBP) pertains to whether respondents use research to guide their day-to-day practice. Additionally, there is a category for the barriers and support for research. 32–34

The EROS items are measured using a 5-point Likert scale ranging from 1—strongly disagree to 5—strongly agree. The maximum score is 215. Higher overall scores indicate a stronger research orientation. The scores will be categorised into high (between 143 and 215 points), medium (73–142 points) or low (0–72 points). 32 33 The tool has been extensively used to assess the research orientation of health professionals, including physiotherapists, 35 midwives, 36 occupational therapists, 33 academics 32 and undergraduate students. 34 Previous studies reported high internal reliability with Cronbach’s alpha coefficients of 0.95 37 and 0.92. 34

Although the tool has been previously used among South African occupational therapists, 33 the copyright author observed that the tool had been developed at a time when there was no access to information via the internet, hence the need to find ways of incorporating such issues. This study will use qualitative findings to identify items not included in the tool but relevant to the South African context.

The quantitative data will be entered into Microsoft Excel and imported to IBM SPSS statistics V.29. Descriptive statistics will be used to summarise demographic characteristics and questionnaire scores. Mean scores and SD will be calculated for individual items, subgroup scores and overall scores. Independent sample t-tests, Mann-Whitney U tests, and multiple regression will be used to compare the scores of different groups of nurses and midwives. The assumptions for each test will be assessed before analysis. The level of significance will be set at 0.05.

During this phase, we will develop the training programme based on the learning needs identified in Phase 1. Research experts (n=5) will participate in a one-design studio workshop to brainstorm the content to be included in the training programme. Although there is limited literature on the definition and characteristics of an expert, Bruce et al 38 defined an expert as a person who is knowledgeable or informed in a particular discipline. Bruce et al 38 further observed that maximum variation or heterogeneity in sampling experts yields rich information. This study will select experts based on the criteria proposed by Davis 39 and Rubio et al . 40 The characteristics include clinical experience in the setting, professional certification in a related area, research experience, work experience, conference presentation and publication in the topic area.

A design studio workshop is a process in which participants create, and critique proposed interventions. 16 The researcher will share the findings of Phase 1 and explain the workshop’s goal to the participants. Participants will be provided with pens, sticky notes and flip-chart paper. The researcher will facilitate discussion and capture feedback. At the end of the workshop, the researcher will consolidate the ideas, create a more detailed programme design and communicate with the participants.

Next, we will develop a prototype to be discussed in a consultative meeting and validation meeting. An iterative process will be used to validate the developed training programme. The consultative meeting will be held with research experts (n=5). A validation exercise will also be conducted with nurses and midwives (n=20), the programme’s end-users. The nurses and midwives will be identified in consultation with nurse managers at the two hospitals to avoid disruption of services. During the validation exercise, the participants will be grouped into smaller idea groups to review and discuss the developed programme. Each group will be requested to identify a representative to report on behalf of the group. The feedback from the consultative and validation meeting will help to improve the developed programme.

The purpose of this phase is to assess the impact of the developed training programme. The developed training will be delivered to 41 nurses and midwives in the Tshwane district. The sample is based on similar studies that have implemented interventions for health professionals. For example, a study by Gundo et al 41 used G-Power software 42 to calculate the sample size based on a conservative effect size of d=0.5, a power of 80% and an alpha=0.05. The calculated sample size was 34, but 41 participants were invited to participate in training to allow for a dropout rate of at most 20%. The identification and invitation of the participants will be negotiated with nurse managers at the two hospitals to avoid service disruptions. The selection process will ensure the representation of the different cadres of nurses and midwives. We will invite a team of research experts to facilitate the training. The impact of the training will be assessed by comparing pre-survey and post-survey EROS scores, FGDs with participants, and evaluations at the end of the training. A paired-sample t-test will be used to compare the pretest and post-test scores.

This protocol aims to develop a research training programme for nurses and midwives in the Tshwane district of South Africa. Initially, we will investigate the learning needs of nurses and midwives. The learning needs will inform a training programme to improve research capacity. As observed by Hines et al , 7 implementing a training programme will improve nurses’ research knowledge, critical appraisal ability and research efficacy. Building capacity for health research in Africa will enhance the ownership of research activities that target relevant topics.

Furthermore, findings relevant to local populations will be communicated in a culturally acceptable manner. Research recommendations may also resonate better and have a better uptake among African policymakers than research produced by internationally led teams. 43–45 This research training programme could be used in other hospitals with similar contexts and other categories of healthcare professionals. However, this will require a larger, multicentre validation study. Our findings will be limited to the two hospitals; therefore, the findings may not be generalisable to other hospitals.

Ethics and dissemination

The protocol was approved by the Research Ethics Committee, Faculty of Health Sciences at the University of Pretoria (reference number: 123/2023). The protocol is registered with the National Health Research Database in South Africa (reference number GP_202305_032). The two hospitals also provided permission for the study. Permission to use the EROS was obtained from the copyright authors, Dr Kerrie Pain and Dr Paul Hagler.

The participants will receive an information leaflet and be required to provide written informed consent. The researcher will ensure that the participants’ personal information is anonymised. Participants can give the researcher written permission to share their personal information. During the FGDs and individual interviews in Phase 1, the participants will be asked to use pseudonyms of their choice. In Phases 2 and 3, anonymity will not be possible because the meetings will be in person. However, the participants will be requested to maintain confidentiality. The data will be stored in compliance with the research ethics committee’s guidelines. The findings of the study will be disseminated through conference presentations and publications in peer-reviewed journals. The preparation of this manuscript followed the standards for reporting qualitative research 46 and the guidelines for reporting observational studies. 47

Ethics statements

Patient consent for publication.

Not applicable.

Acknowledgments

The manuscript was written during a writing retreat that was funded by the National Research Foundation through the Ubuntu Community Model of Nursing Project at the University of Pretoria in South Africa. We also thank Dr Cheryl Tosh for editing the manuscript.

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Contributors RG and MFM conceptualised the study, developed the proposal, drafted and revised the manuscript.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

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

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• Study protocol
• Open access
• Published: 05 February 2022

Towards optimal treatment selection for borderline personality disorder patients (BOOTS): a study protocol for a multicenter randomized clinical trial comparing schema therapy and dialectical behavior therapy

• Carlijn J. M. Wibbelink 1 ,
• Arnoud Arntz 1 ,
• Raoul P. P. P. Grasman 1 ,
• Roland Sinnaeve 2 ,
• Michiel Boog 3 , 4 ,
• Odile M. C. Bremer 5 ,
• Eliane C. P. Dek 6 ,
• Sevinç Göral Alkan 7 ,
• Chrissy James 8 ,
• Annemieke M. Koppeschaar 9 ,
• Linda Kramer 10 ,
• Maria Ploegmakers 11 ,
• Arita Schaling 12 ,
• Faye I. Smits 13 &
• Jan H. Kamphuis 1  

BMC Psychiatry volume  22 , Article number:  89 ( 2022 ) Cite this article

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Specialized evidence-based treatments have been developed and evaluated for borderline personality disorder (BPD), including Dialectical Behavior Therapy (DBT) and Schema Therapy (ST). Individual differences in treatment response to both ST and DBT have been observed across studies, but the factors driving these differences are largely unknown. Understanding which treatment works best for whom and why remain central issues in psychotherapy research. The aim of the present study is to improve treatment response of DBT and ST for BPD patients by a) identifying patient characteristics that predict (differential) treatment response (i.e., treatment selection) and b) understanding how both treatments lead to change (i.e., mechanisms of change). Moreover, the clinical effectiveness and cost-effectiveness of DBT and ST will be evaluated.

The BOOTS trial is a multicenter randomized clinical trial conducted in a routine clinical setting in several outpatient clinics in the Netherlands. We aim to recruit 200 participants, to be randomized to DBT or ST. Patients receive a combined program of individual and group sessions for a maximum duration of 25 months. Data are collected at baseline until three-year follow-up. Candidate predictors of (differential) treatment response have been selected based on the literature, a patient representative of the Borderline Foundation of the Netherlands, and semi-structured interviews among 18 expert clinicians. In addition, BPD-treatment-specific (ST: beliefs and schema modes; DBT: emotion regulation and skills use), BPD-treatment-generic (therapeutic environment characterized by genuineness, safety, and equality), and non-specific (attachment and therapeutic alliance) mechanisms of change are assessed. The primary outcome measure is change in BPD manifestations. Secondary outcome measures include functioning, additional self-reported symptoms, and well-being.

The current study contributes to the optimization of treatments for BPD patients by extending our knowledge on “Which treatment – DBT or ST – works the best for which BPD patient, and why?”, which is likely to yield important benefits for both BPD patients (e.g., prevention of overtreatment and potential harm of treatments) and society (e.g., increased economic productivity of patients and efficient use of treatments).

Trial registration

Netherlands Trial Register, NL7699 , registered 25/04/2019 - retrospectively registered.

Peer Review reports

Borderline personality disorder (BPD) is a complex and severe mental disorder, characterized by a pervasive pattern of instability in emotion regulation, self-image, interpersonal relationships, and impulse control [ 1 , 2 ]. The prevalence in the general population is estimated to be between 1 and 3% [ 3 , 4 , 5 ], and 10 to 25% among psychiatric outpatient and inpatient individuals [ 3 ]. BPD is associated with severe functional impairment, high rates of comorbid mental disorders, and physical health problems [ 5 , 6 , 7 ]. In addition, BPD is characterized by low quality of life; lower compared to other common mental disorders such as depressive disorder, and comparable to that of patients with severe physical conditions, such as Parkinson’s disease and stroke [ 8 ]. Moreover, BPD is related to a high risk of suicide (3–6%, or even up to 10% [ 9 , 10 ]) and suicide attempts or threats (up to 84% [ 11 , 12 ]), and an increased mortality rate [ 13 ]. Besides the detrimental effects of BPD on the individual patient, BPD also poses a high financial burden to society. BPD patients make extensive use of treatment services resulting in markedly higher healthcare costs of people with BPD compared to people with other mental disorders, such as other personality disorders [ 14 ] and depressive disorder [ 15 ]. BPD is also associated with high non-healthcare costs, including costs related to productivity losses, informal care, and out-of-pocket costs [ 16 , 17 ].

Interventions: dialectical behavior therapy and schema therapy

BPD has traditionally been viewed as one of the most difficult mental disorders to treat [ 18 ]. During recent years, a number of promising treatments have been developed and evaluated, including Dialectical Behavior Therapy (DBT) [ 19 , 20 ] and Schema Therapy (ST) [ 21 , 22 ]. DBT is a comprehensive cognitive behavioral treatment for BPD, rooted in behaviorism, Zen and dialectical philosophy [ 19 ]. ST is based on an integrative cognitive therapy, combining cognitive behavior and experiential therapy techniques with concepts derived from developmental theories, including attachment theory, and psychodynamic concepts [ 23 ]. For detailed information about these treatments, the reader is referred to the Methods/design section.

Several studies have demonstrated the effectiveness and the efficacy of DBT and ST for BPD, although the evidence is mostly based on low-to-moderate-quality evidence, and trials focusing on DBT, but especially ST, are limited [ 24 , 25 ]. In addition, substantial reductions in direct and indirect healthcare costs have been found for both treatments [ 26 ]. However, research on the comparative effectiveness and cost-effectiveness of the two interventions is lacking. Moreover, research on mediators and moderators of treatment effects is limited. This gap warrants attention, as treatment effectiveness can be optimized by identifying mechanisms within treatments that are associated with improvement and patient characteristics that predict (differential) treatment response [ 27 ]. Optimizing treatment effectiveness of DBT and ST for BPD is highly needed since a substantial proportion of patients does not respond fully to either DBT or ST. A systematic review found a mean percentage of non-response of 46% among BPD patients treated with specialized psychotherapies, including DBT and ST [ 28 ]. In addition, more than one-third of the patients did not achieve a reliable change in BPD symptoms or even showed an increase in BPD severity after DBT or ST [ 29 , 30 , 31 ]). Finally, dropout rates up to 30% have been found for DBT and ST [ 32 , 33 ]. Individual differences in responses to both ST and DBT have been observed across studies, but the factors driving these differences in treatment response among BPD patients are largely unknown. This state of affairs leaves the principal question “What treatment, by whom, is most effective for this individual with that specific problem, under which set of circumstances?” ([ 34 ], p111), historically one of the key questions dominating the psychotherapy research agenda, fully open in the treatment of BPD individuals [ 35 , 36 ]. Identifying factors that specify which patients will benefit most from which treatment (i.e., treatment selection, or also known as precision medicine or personalized medicine; [ 37 , 38 ]) will lead to fewer mismatches between patients and treatments, and in turn to better outcome and more efficient use of healthcare resources.

• Treatment selection

Several factors predicting treatment response irrespective of type of treatment (i.e., prognostic factors; [ 35 ]) among BPD patients have been reported in the literature. The overwhelming list of candidate variables and the general lack of replication hampers the research among BPD patients on prognostic factors [ 39 ]. Research among BPD patients on prescriptive factors (i.e., factors that predict different outcomes depending on the treatment; moderators) is very scarce indeed. Arntz et al. [ 39 ] examined the effect of several potential predictors of (differential) treatment response across ST and Transference Focused Psychotherapy (TFP) among BPD patients. The authors failed to find prescriptive factors, but it should be noted that the sample size was inadequate to detect subtle differences between treatments. In addition, Verheul et al. [ 40 ] found that patients with a high frequency of self-mutilating behavior before treatment were more likely to benefit from DBT compared to treatment as usual, whereas for patients with a low frequency of self-mutilating behavior effectiveness did not differ.

Historically, research has focused on a single variable to predict treatment response, but often failed to find consistent and clinically meaningful moderators [ 41 , 42 , 43 , 44 ]. However, it is highly unlikely that a single variable is responsible for the differences in treatment response [ 43 , 45 , 46 ]. In recent decades, novel approaches combining multiple predictors to determine the optimal treatment for a particular patient have been introduced, including the methods of Kraemer ([ 47 ]; optimal composite moderator) and DeRubeis and colleagues ([ 35 ]; statistically derived selection algorithm). Several studies have found that a combination of predictors was predictive of differential treatment response (e.g., [ 48 , 49 , 50 ]). For example, by using the method of DeRubeis and colleagues, it was investigated in an effectiveness study among BPD patients which of two different treatments (DBT and General Psychiatric Management; GPM) would have been the optimal treatment option for a particular patient in terms of long term outcome [ 45 ]. The authors found that BPD patients with childhood emotional abuse, social adjustment problems, and dependent personality traits were more likely to benefit from DBT compared to GPM, whereas GPM excelled for patients with more severe problems related to impulsivity. The authors also provided an estimate of the advantage that might be gained if patients had been allocated to the optimal treatment option. The average difference in outcomes between the predicted optimal treatment and non-optimal treatment for all patients was small-to-medium ( d  = 0.36), while the advantage for patients with a relatively stronger prediction increased to a medium-to-large effect ( d  = 0.61). This suggests that treatment allocation based on a treatment selection procedure may substantially improve outcomes for BPD patients.

• Mechanisms of change

Another principal way to improve treatment response is to capitalize on mechanisms underlying change in treatments [ 27 , 45 , 51 , 52 ]. Studying mechanisms of change helps to identify core ingredients of interventions and points the way to enhancing crucial elements, while discarding redundant elements. Presumably, this would maximize (cost-)effectiveness and efficiency as well. Since the 1950s, research on change processes has increased exponentially [ 53 ]. However, the majority of the trials on BPD have focused on outcomes, and only a few addressed how treatments exerted a positive effect on patient outcomes [ 54 , 55 ]. Rudge et al. [ 56 ] reviewed studies on mechanisms of change in DBT. They concluded that there is empirical support for behavioral control, emotion regulation, and skills use as mechanisms underlying change in DBT. Recently, Yakın et al. [ 57 ] examined schema modes as mechanisms of change in ST for cluster C, histrionic, paranoid, and narcissistic personality disorders. They found that a strengthening of a functional schema mode (i.e., healthy adult mode) and weakening of four maladaptive schema modes (i.e., vulnerable child mode, impulsive child mode, avoidant protector mode, and self-aggrandizer mode) predicted improvements in PD symptomatology. However, changes in these schema modes, except for self-aggrandizer mode, also predicted improvements in outcome in treatment-as-usual and clarification-oriented psychotherapy, suggesting that modifying the strength of schema modes might reflect common mechanisms of change. The question of specificity of mechanisms of change is interesting, especially since both DBT and ST have their roots in cognitive behavior therapy and show similarity in certain treatment parameters, but differ substantially in techniques, explanatory model, and terminology [ 58 ]. Clarifying the treatment-specific and non-specific mechanisms of change may be key to furthering the effectiveness of both DBT and ST, and potentially also for psychotherapy in general.

Current study

BPD-tailored treatments, like DBT and ST, are considered treatments of choice for BPD [ 25 ]. However, knowledge on the comparative (cost-)effectiveness of DBT and ST is lacking, as is knowledge on mechanisms of change and patient characteristics that predict (differential) treatment response. We will therefore perform a multicenter randomized clinical trial (RCT) comparing DBT and ST for BPD patients to elucidate the question “Which treatment – DBT or ST – works the best for which BPD patient, and why?”. The main aim of the BOOTS (Borderline Optimal Treatment Selection) study is to improve treatment response of DBT and ST for BPD patients by optimizing treatment selection through the identification of a prediction model based on patient characteristics that predict (differential) treatment response. By doing so, this study is a first step into the development of a treatment selection procedure for BPD patients. Moreover, the results of this study can serve as a starting point for future studies with the ultimate goal of implementing a treatment selection procedure that can be used in clinical practice to guide BPD patients and clinicians in selecting the optimal treatment. In addition, we aim to elucidate the mechanisms by which DBT and ST lead to change, thus pursuing the other main avenue towards improving BPD treatments.

This study has four primary objectives. The first objective of this study is to develop a treatment selection model based on a combination of patient characteristics that predict (differential) treatment response across DBT and ST. Candidate predictors of (differential) treatment response have been selected based on the literature, suggestions of a patient representative of the Borderline Foundation of the Netherlands, and clinicians’ appraisals of BPD patient characteristics that predict (differential) treatment response across DBT and ST. Semi-structured interviews were conducted among 18 expert clinicians to identify patient characteristics they deemed predictive of (differential) treatment response. The extensive investment in the identification of pertinent predictors is a lesson learned from Meehl [ 34 ], who noted that actuarial methods will not outperform clinical judgment when the actuarial method is based on inadequate knowledge of relevant variables. According to Westen and Weinberger [ 59 ], clinical expertise can serve the important function of identifying relevant variables for use in research. In addition, the majority of studies examining predictors of treatment response are based on randomized controlled trials with a primary focus on treatment effectiveness [ 60 ], which could result in the preclusion of potentially relevant predictors due to the lack of instruments assessing these constructs [ 39 , 61 ]. Moreover, findings in the literature may be affected by publication bias, since statistically significant predictors of treatment response are more likely to be published [ 46 ]. Therefore, candidate predictors of (differential) treatment response are not only based on the literature, but also on clinical expertise and experience-based knowledge. We hypothesize that a combination of multiple patient characteristics will predict and moderate treatment effectiveness of DBT and ST. Hypotheses on the effects of single patient characteristics will not be formulated as research among BPD patients often failed to find consistent prognostic factors, while research on prescriptive factors or a combination between factors is scarce. In addition, there was in general a lack of consensus between the 18 expert clinicians on patient characteristics predicting (differential) treatment response across DBT and ST.

Second, we aim to elucidate how DBT and ST exert their effect by gaining a better understanding of the mechanisms of change of DBT and ST. A first step towards more insight into mechanisms of change is the identification of mediators. Mediators are easily confused with mechanisms of change, despite important differences [ 62 ]. A mediator is an intervening variable (partly) accounting for the statistical relationship between the intervention and outcome, and might serve as a statistical proxy for a mechanism of change [ 63 ]. In this study, we will examine potential BPD-treatment-specific, BPD-treatment-generic, and non-specific mediators. Based on empirical research and the presumed mechanisms of change (e.g., [ 55 , 56 , 57 ]), we hypothesize that change in skills use and emotion regulation are the mechanisms underlying change in DBT, and that change in schema modes and beliefs are the mechanisms of change in ST (i.e., BPD-treatment-specific mechanisms of change). In addition, a therapeutic environment characterized by genuineness of the therapists and group members, safety, and equality is considered to be especially important for BPD treatment [ 64 , 65 , 66 , 67 ] and is, therefore, assumed to be a BPD-treatment-generic mechanism of change. Finally, attachment and therapeutic alliance are the presumed non-specific mechanisms of change [ 68 , 69 ].

Third, the comparative effectiveness of DBT and ST will be examined. Accumulating evidence suggests that symptoms and psychosocial functioning are only loosely associated [ 70 , 71 ]. Patients with BPD are characterized by significant impairments in vocational functioning, relationships, and leisure [ 72 ]. In addition, social adjustment of BPD patients is considerably lower than social adjustment seen in other mental disorders, such as major depressive disorder and bipolar I disorder [ 73 ]. Moreover, although several studies found that even as psychopathology after treatment of BPD decreased, impairments in quality of life and functioning often (partly) persist [ 74 , 75 ]. A more comprehensive view of recovery is therefore needed. This notion is underscored by qualitative research that has shown that patients define recovery by personal well-being, social inclusion, and satisfaction with life [ 76 , 77 ]. Therefore, the current trial will track outcomes in multiple domains including symptoms, functioning, and well-being.

Finally, the cost-effectiveness of DBT and ST will be compared. Individual ST seems a cost-effective treatment [ 78 , 79 ]. However, although group ST combined with individual ST is widely used in clinical practice, the cost-effectiveness of this combined program is yet unknown. An international RCT evaluating the (cost-)effectiveness of group ST for BPD is currently in progress [ 80 ]. More economic evaluations of DBT are available and support the cost-effectiveness of DBT. However, the studies vary highly in their design and the number of trials is still somewhat limited [ 26 , 81 , 82 ]. Therefore, an economic evaluation will be performed and a societal perspective will be applied, including indirect and direct healthcare costs.

In addition to these primary objectives, several secondary investigations will be performed, including (but not limited to): 1) the heterogeneity of BPD, 2) substance use (disorders) among patients with BPD, 3) perspectives of patients and therapists in key areas, including predictors, mechanisms of change, the treatments, and the implementation of the results in clinical practice, and 4) psychometric evaluations of several Dutch questionnaires (e.g., Dialectical Behavior Therapy-Ways of Coping Checklist, Ultrashort BPD Checklist).

Methods/design

The study is a multicenter RCT with two active conditions (DBT or ST). The study is set at various Dutch mental healthcare centers accessible through the public health system, including Antes (Rotterdam), GGZ inGeest (Amsterdam), GGZ NHN (Heerhugowaard), GGZ Rivierduinen (Leiden), NPI (Amsterdam), Pro Persona (Ede and Tiel), PsyQ (Rotterdam-Kralingen), and PsyQ/i-psy (Amsterdam). For an overview of the study design, including the enrollment, randomization, interventions, and assessments, see Fig. 1 .

Flow chart of the study design. DBT = Dialectical Behavior Therapy; ST = Schema Therapy. *An extra assessment after wait is included for patients with a waitlist period of more than three months after the baseline assessment

The Medical Ethics Committee of the Academic Medical Center (MEC-AMC) Amsterdam approved the study protocol (registration number NL66731.018.18). The study is registered at the Netherlands Trial Register, part of the Dutch Cochrane Center (registration number NL7699), and complies with the World Health Organization Trial Registration Data Set. Modifications to the protocol require a formal amendment to the protocol which will be examined by the MEC-AMC. The trial adheres to the SPIRIT methodology and guidelines [ 83 ], see Additional file 1 .

Patients are eligible if they 1) are between 18 and 65 years old, 2) have a primary diagnosis of BPD (diagnosed with the Structural Clinical Interview for DSM-5 Personality Disorders; SCID-5-PD), 3) have a BPD severity score > 20 on the Borderline Personality Disorder Severity Index, version 5 (BPDSI-5), 4) have an adequate proficiency in the Dutch language, and 5) are motivated to participate in (group) treatment for a maximum of 25 months and are willing and able to complete the assessments over a period of three years. Patients will be excluded if they 1) fulfill the criteria of a psychotic disorder in the past year (diagnosed with the Structural Clinical Interview for DSM-5 Syndrome Disorders; SCID-5-S), 2) have current substance dependence needing clinical detoxification, 3) have been diagnosed with a bipolar I disorder with at least one manic episode in the past year, 4) have been diagnosed with antisocial personality disorder (diagnosed with the SCID-5-PD), in combination with a history of physical violence against multiple individuals in the past two years, 5) have an IQ below 80, 6) have a travel time to the mental healthcare center longer than 45 min (except when the patient lives in the same city), 7) have no fixed address, and 8) have received ST or DBT in the past year.

Sample size

We aim to include 200 participants. Each center intends to recruit at least 18 patients. For the power analysis, we adopted the minimal statistically detectable effect approach [ 84 ]. A sample size of 200 will be sufficient to have 80% power to detect moderators of treatment effects that have an effect size of Cohen’s f of .20 (small to medium effect size), based on a two-tailed significance level of p  < .05. In addition, the study has 80% power to detect medium effect-sized (i.e., Cohen’s f  = .25) moderators of treatment effects, based on a two-tailed significance level of p  < .01.

Regarding the effectiveness study, with a sample size of N  = 200 the study is powered at 82% to detect a group difference with a medium effect size of Cohen’s d  = .50 at a two-tailed significance level of p  < .05 and assuming a model with center as random effect and an intraclass correlation value of 0.05 corresponding to the center by treatment interaction [ 85 , 86 ].

Finally, a sample size of N  = 200 will be sufficient to have 98% power to detect a medium effect size of the mediation effect ( rr  = .09; [ 87 , 88 , 89 ]), assuming path a (relation between the predictor and mediator) and path b (relation between the mediator and outcome measure) both have a medium effect size ( r  = .30), and based on a simplified trivariate mediation model [ 90 ].

Recruitment

Patients are recruited in the respective participating mental healthcare centers. Patients diagnosed with BPD or for whom this is deemed likely are invited to participate in the screening process. After reading and hearing information about the study and signing an informed consent (see Additional file 2 , Appendix A), patients will start with the screening process. Not only new referrals can be included, but also patients who are already receiving treatment for mental disorders (except patients receiving ST or DBT).

Randomization

A central independent research assistant randomizes the patients per center after a final check of the inclusion and exclusion criteria, and after all baseline measures have been completed. Generally, patients will be randomized using computerized covariate adaptive randomization [ 91 , 92 , 93 ], taking into account gender and severity of BPD (BPDSI-5 score ≤ 24; BPDSI-5 score > 24). By using this method, the imbalance of baseline characteristics between the treatments will be minimized. Patients are allocated to the treatment group that results in the least imbalance between the treatments with an allocation probability of 0.8 to preserve unpredictability [ 94 ]. Groups in both treatments are semi-open which implies that new patients can enter the group if treatment slots are available. Therefore, treatment capacity will be taken into account by using unequal ratios if needed (e.g., 2:1 or 1:3).

In exceptional cases, an alternative randomization method will be used if one or more treatment slots are available in only one condition and there is no available treatment slot in the other condition. To prevent long waiting times for treatment and empty places in the groups, the available treatment slot(s) in one condition will be randomized over 2* k patients whereby k stands for the number of available treatment slots, and randomization is done in the subsample of k patients that wait the longest. Randomization over 2* k patients guarantees unpredictable outcomes. For example, if one treatment slot is available in DBT and there is no available treatment slot in ST at that moment, nor within the foreseeable future, the available treatment slot in DBT will be randomized over two patients waiting for treatment. Sensitivity analyses will be performed by excluding patients that have been randomized using the alternative randomization method.

Procedure and assessments

Patients with BPD or suspected of BPD are invited to the screening process by the research assistant or intake staff member. After providing written informed consent, patients are assessed for eligibility to participate in the study based on the inclusion and exclusion criteria. First, to assess DSM-5 syndrome disorders, the SCID-5-S is administered. The SCID-5-PD will also be administered in case the SCID-5-PD is not part of the standard intake procedure of the mental healthcare center. Second, the BPDSI-5 and a screening interview to assess the motivation and availability of the patient are conducted. A simple “yes” answer to the questions posed by the interviewer (e.g., “Are you motivated and available for treatment, including individual and group sessions?”) is not sufficient. Patients need to elaborate on their answers and follow-up questions are asked if needed. Patients who are eligible for participation will be invited for the baseline assessment, including interviews and computer-based self-report questionnaires, and intake staff members will fill out a questionnaire (i.e., intake questionnaire; see the Measures section) about these patients. After completing the baseline assessment, patients will be randomized as soon as treatment slots become available. Patients will be informed that they have been allocated to one of the treatment conditions, but the name of the treatment will not be communicated to the patient until the first treatment session. If patients cannot be randomized within several months after completing the baseline assessment because of unavailability of treatment slots, the BPDSI-5 will be re-assessed after three months and the BPDSI-5 and cost interview will be re-assessed after six months.

After the treatment phase has started, patients are reassessed every six months during the two years of treatment. These assessments are a combination of interviews and computer-based self-report questionnaires. In addition, a selection of measures are also assessed every three months, by computer-based self-report questionnaires. After end of the treatment, two follow-up assessments (six and 12 months after end of the treatment) will be administered. An overview of the measures is presented in Table 1 . Candidate predictors of (differential) treatment response that are assessed only once at baseline are not included in Table 1 . These measures can be found in the Measures section.

All assessments are performed by trained local research assistants blind to the patients’ treatment condition, with exception of the SCID-5 interviews, demographic interview, and cost interview. The SCID-5 interviews can be administered by trained research assistants as well as trained intake staff members, both blind for condition. The demographic interview and cost interview contain questions on healthcare utilization and are therefore performed by non-blinded local research assistants. Due to the nature of the interventions, blinding of therapists and patients is not possible. All interviews, except for the SCID interviews, are audio-recorded. Participants receive financial compensation for their involvement in the study. Patients who discontinue their treatment or deviate from the treatment protocol will be encouraged to continue the assessments.

For patients of both DBT and ST, treatment has a maximum duration of 25 months and starts with a pretreatment phase of approximately four weeks consisting of several (ST: ± three; DBT: ± five) individual sessions in which patients are prepared for the group sessions and become accustomed to their therapists and the treatment model. After the pretreatment phase, patients receive a combined program of individual sessions and group sessions (i.e., treatment phase). Group sessions of both treatments are offered in a semi-open format. If treatment slots are available, new patients can enter the ST group every 10 weeks and for DBT groups at the start of a mindfulness skills module. In DBT, the treatment phase has a maximum duration of 12 months and consists of weekly group sessions (i.e., skills training groups; 150 min), weekly individual psychotherapy sessions (50 min), and between-session consultation. The between-session consultation, often called telephone consultation although all kinds of technology can be used [ 95 ], is offered to the patient within limitations set by the individual therapist, varying between access to between-session support within working hours to 24/7 access to between-session support, which is officially the standard in DBT. In ST, the treatment phase has a maximum duration of 18 months consisting of weekly group (90 min) and individual (45 min) psychotherapy sessions for a period of 12 months, continued by weekly group psychotherapy sessions and biweekly individual psychotherapy sessions for a period of six months. Following the treatment phase, patients continue their treatment with a maintenance phase. The maintenance phase of DBT is a recently developed blended aftercare program with a maximum duration of 12 months. The blended aftercare program was developed based on results of previous studies (e.g., [ 31 , 96 ]) and recommendations by several authors (e.g., [ 96 , 97 , 98 ]) to extend the duration of DBT to sustain or even enhance treatment effects. The DBT aftercare program consists of monthly individual psychotherapy sessions, three-monthly group sessions, and an eHealth intervention in which patients have online access to DBT handouts and worksheets [ 99 ]. The maintenance phase of ST consists of biweekly individual psychotherapy sessions for a period of three months, continued by three months of one individual session each month. Disregarding the time spent on telephone consultation, homework assignments, and eHealth, and based on 48 working weeks a year, patients will receive about 167 h of treatment if they follow the treatment protocol. Patients who have completed treatment successfully before they reach the maximum number of treatment sessions are allowed to complete treatment earlier, although the assessments will be conducted at the originally planned assessment points. Early termination of treatment requires substantial improvements in the primary and secondary outcomes and is decided in joint decision by the patient and therapist. The treatments are covered by the public health insurance. See Table 2 for an overview of the treatment formats.

Schema therapy (ST)

ST, developed by Jeffrey Young [ 22 , 100 ], is based on an integrative cognitive model, combining cognitive behavior therapy and experiential techniques with insights from developmental theories, including attachment theory, and psychodynamic concepts [ 23 ]. Central concepts are early maladaptive schemas and schema modes. Early maladaptive schemas can be defined as broad, pervasive patterns of thoughts, emotions, memories, and cognitions regarding oneself and relationships with others, developed during childhood [ 22 ]. ST assumes that the frustration of core needs and early traumatic experiences lead to the development of early maladaptive schemas. A schema mode refers to an activated set of schemas and the associated coping response (i.e., overcompensation, avoidance, and surrender), and describes the momentary emotional, cognitive, and behavioral state of the patient. The following schema modes are characteristic of BPD [ 101 ]: 1) vulnerable child mode, associated with a fear of abandonment and strong emotions, such as loneliness, sadness, and helplessness, 2) angry and impulsive child mode, characterized by anger, frustration, hostility, and impulsivity, 3) punitive parent mode, representing the internalized voice of very punitive and critical attachment figures and associated with self-criticism, self-hatred, guilt, and self-denial, 4) detached protector mode, characterized by attempts to cut off the self from needs and feelings, resulting in symptoms of detachment, substance misuse, social withdrawal, and self-harm, and 5) healthy modes, reflecting in functional thoughts, cognitions, and behavior (i.e., healthy adult mode) and the feeling that core needs are been fulfilled (i.e., happy child mode). The first four modes are maladaptive schema modes and central to BPD. The last two modes are functional and often only weakly present at the beginning of the treatment [ 102 ]. Idiosyncratic schema mode models usually cover additional modes, depending on the specific problems and comorbidity of the patient.

ST aims to enable patients to fulfill their needs, reduce maladaptive schema modes, and strengthen adaptive schema modes. In this study, ST is offered in a combined group-individual format developed by Farrell and Shaw [ 103 ]. The group acts as an analogue of a family with the other patients as “siblings” and the two therapists as “parents” [ 103 ]. The group may speed up and amplify the effect of treatment by offering corrective emotional experiences, peer support, opportunities for in vivo practice, and a sense of understanding [ 104 ]. The individual ST follows the protocol as described by Arntz and Van Genderen [ 105 ].

Dialectical behavior therapy (DBT)

DBT is a comprehensive cognitive behaviorally based treatment for BPD, integrating strategies from cognitive and behavioral treatments, Zen-based acceptance strategies, and dialectical strategies [ 19 , 106 ]. Linehan [ 19 , 20 ] proposed a skills deficit model in which emotion regulation is central. More specifically, the model holds that the problematic behaviors associated with BPD (e.g., suicide attempts, self-injury, substance use) are in fact best understood as dysfunctional attempts to regulate emotions. Emotion dysregulation results from the complex transaction between dispositional emotional vulnerabilities and an adverse invalidating environment. Therefore, the treatment involves balancing problem solving strategies with loads of validation. DBT aims to help patients develop new skills, enhance motivation, ensure generalization of skills use, and change their environment if needed. In addition, DBT aims to enhance therapists’ motivation to deliver effective treatment [ 20 ].

DBT involves skills training groups, individual therapy, between-session consultation, and therapist consultation team meetings. DBT skills training groups teach patients behavioral skills in four different, yet inter-related, areas: mindfulness, interpersonal effectiveness, emotion regulation, and distress tolerance / radical acceptance. Individual therapy focuses on motivational issues and the acquisition and use of skills in daily life. A predetermined ordering of treatment targets is used in individual sessions and part of different stages of the treatment. Stage 1 focuses on stabilizing the patient and behavior control. Targets in this stage of the treatment include: life-threatening behavior, therapy-interfering behavior, quality-of-life-interfering behavior, and behavior skills. Stage 2 focuses on reducing posttraumatic stress and requires exposure to trauma-related cues [ 19 ]. Finally, Stages 3 and 4 target self-respect and the sense of incompleteness. However, due to time constraints, some patients might not enter all stages and most studies have focused on Stage 1 DBT [ 107 ]. Individual therapists provide between-session (telephone) consultation if needed. According to the guidelines of DBT, access to between-session consultation outside of office hours, preferably by the individual therapist, is part of DBT [ 19 ]. In this trial, between-session consultation by the individual therapist will be within limitations set by the therapist, which can vary between support provided within working hours to 24/7 access to telephone consultation. As access to between-session (telephone) consultation will vary between centers and individual therapists, the effect of therapist’s availability for between-session support will be examined. Finally, DBT therapists meet weekly in a DBT consultation team to motivate and support each other.

Therapists, training, and supervision

The therapists in this study will be licensed psychologists, psychotherapists, psychiatrists, or psychiatric nurses. Individual and group schema therapists must have completed a basic training in individual ST. Group schema therapists must have also completed a four-day training in the group schema therapy model of Farrell and Shaw [ 103 ]. All schema therapists receive a one-day training in experiential techniques by a certified ST trainer. DBT therapists are required to complete a three-day training in DBT and at least one member of the DBT team must have completed the 10-day intensive DBT training. In addition, DBT therapists receive a two-day kick-off training by certified DBT trainers to expand their knowledge of DBT. Moreover, DBT-therapists were given the opportunity to participate in a one-day training in imaginal exposure. According to Linehan [ 19 ], reducing behaviors and stress response patterns related to traumatic life events is a primary DBT target. Reducing posttraumatic stress is mostly part of Stage 2 of DBT and involves exposure to trauma-associated cues [ 19 , 108 ]. However, some of the DBT therapists expressed concerns about their ability to apply the principles and procedures of exposure to treat traumatic memories in BPD patients. Therefore, the opportunity to participate in a one-day exposure training was offered to the therapists.

Before the start of the study, schema therapists should have received at least 10 individual supervision sessions by a licensed supervisor. There is no requirement for the minimum number of DBT supervision sessions. During the study, therapists receive supervision over a period of two years by certified supervisors. ST supervision is provided through teleconferencing biweekly in the beginning, then (two-)monthly after six to 12 months, depending on the experience of the therapists. DBT therapists receive supervision at location every three months. Moreover, there will be weekly DBT team meetings (i.e., DBT consultation team meetings) and biweekly ST team meetings. All individual ST sessions will be audiotaped, while individual DBT sessions and ST and DBT group sessions will be videotaped. These recordings are used for supervision and treatment adherence ratings. Treatment adherence, a component of treatment integrity (i.e., the extent to which a treatment is implemented as intended; [ 109 ]), refers to the extent to which the therapist utilizes prescribed techniques and procedures and avoids the use of proscribed techniques and procedures [ 110 ]. Adherence will be assessed in a random selection of session recordings by trained raters (master psychology students) blind for condition. Master psychology students will be trained by ST and DBT experts by using session recordings not used in the final adherence rating to practice with the instruments. Individual ST sessions will be rated on an adapted version of the Therapy Adherence and Competence scale for ST for BPD [ 111 ] and group ST sessions will be rated on the Group Schema Therapy Rating Scale – Revised [ 112 ]. Individual DBT sessions will be rated on the Dutch translation of the observer-rated version of the DBT Adherence Checklist for Individual Therapy [ 113 ]. An observer-rated instrument will be developed to assess the skills training groups.

Other treatment

During the treatment, patients are not allowed to engage in any other form of psychological treatment. However, in case of acute crisis, the crisis procedures of the treatments will be followed (e.g., telephone consultation by the therapist, contact a crisis line, visit the emergency room, hospitalization, individual crisis management sessions). Any additional treatment will be recorded and included in the analyses. Patients will only be withdrawn from the study at their request.

Coronavirus disease (COVID-19) pandemic

This study is conducted during the COVID-19 pandemic. The pandemic is expected to have adverse effects on patients with mental health disorders [ 114 ]. In addition, in case face-to-face treatment is restricted in mental healthcare centers because of government and healthcare center policy, the treatment will be delivered via videoconferencing. Consequently, differences between patients will arise regarding the amount of treatment sessions delivered during the pandemic and/or via videoconferencing. We will control for a potential influence of the COVID-19 pandemic by, for example, adding dynamic regression parameters that include the impact of time in treatment during the pandemic. The definition of the indicator variable indicating the COVID-19 pandemic will be decided before start of the data-analyses (e.g., dummy variable indicating pandemic/no pandemic or continuous variable indicating the amount of time in treatment during the pandemic), given the unpredictability of the current situation. Moreover, exploratory analyses may be conducted to investigate the potential influence of the deviating treatment format (i.e., online vs. face-to-face individual sessions and/or group sessions) on the treatment effectiveness.

In addition, the assessments will be conducted via videoconferencing or phone, and the computer-based questionnaires will be completed by participants at home, if face-to-face assessments are not allowed. Before receiving the treatment and/or assessments via videoconferencing, patients will sign an additional informed consent form (see Additional file 2 , Appendix B).

Data management, storage, monitoring, and dissemination

Data is collected with a unique identifier for each patient (i.e., pseudonym) using the online survey software program Qualtrics [ 115 ] and the web tool Lotus, which has been especially developed for longitudinal research by the University of Amsterdam. The list of pseudonyms and personal information of patients within a particular mental healthcare center is securely stored at the center and only accessible for the research assistant and coordinator of this center. A different set of pseudonyms is used for data collected by clinicians (i.e., intake questionnaire and recordings). The list with the combination of both pseudonyms of patients is only accessible for the research assistant and coordinator of the center and the authorized researchers. The data is stored on a secure storage server of the University of Amsterdam, accessible only to authorized researchers.

All (serious) adverse events reported by the patient or observed by clinicians or researchers will be recorded. There is no data monitoring committee and the study will not be audited. The results of the study will be disseminated in scientific journals and presentations at (inter)national scientific conferences.

The instruments include screening measures, measures to assess potential predictors and mediators of treatment response, and outcome measures. The instruments that were not available in Dutch were translated (i.e., Brief Experiential Avoidance Questionnaire, Dialectical Behavior Therapy-Ways of Coping Checklist, Gordon Test of Visual Imagery Control, Positive Mental Health scale, and social problems) by bi-lingual experts. The translations were checked for consistency with the original version. Items, questionnaires, and interviews that have been developed or modified by the authors are available upon request by the first author.

Mental disorders

The SCID-5 is a semi-structured interview used to diagnose DSM-5 disorders. Personality disorders are assessed with the SCID-5-PD [ 116 ] and syndrome disorders are assessed using the SCID-5-S [ 117 ], which is an extended version of the SCID-5 Clinician Version (SCID-5-CV; [ 118 ]). Additional file 3 offers an overview of all syndrome disorders that are assessed by the SCID-5-S. Based on a first psychometric evaluation in a psychiatric patient sample, Somma et al. [ 119 ] found an adequate interrater reliability of the SCID-5-PD. In addition, the SCID-5-CV has demonstrated good psychometric properties [ 120 , 121 , 122 ].

Before administering the SCID-5-S and/or SCID-5-PD, self-report screening questionnaires (SCID-5-SPQ; [ 123 ], and SCID-5-SV; [ 124 ]) may be administered. In accordance with the instructions for administering the SCID, disorders and criteria of disorders not affirmed by the screening questionnaires and not considered as false negatives by the clinician will be assumed to be absent. The SCID-5 will be assessed during the screening phase and 12 months after end of the treatment.

Motivation and availability

A 13-item semi-structured motivation interview is used to assess several exclusion criteria (e.g., no fixed address, have received ST or DBT in the past year) and patient’s motivation and availability.

As mentioned, candidate predictor variables of (differential) treatment response have been selected using a multi-method approach (i.e., literature, suggestions of a patient representative of the Borderline Foundation of the Netherlands, and semi-structured interviews with 18 expert clinicians). Additional file 4 , Table 1 offers an overview of the predictors that have emerged during the semi-structured interviews with clinicians. Additional file 4 , Table 2 offers an overview of the predictors based on the literature and suggestions of a patient representative. The candidate predictors of (differential) treatment response are assessed at baseline. Only the measures that are not part of the screening, mediator or outcome measures will be briefly described in this paragraph.

Autistic traits

Autistic traits are assessed by the abbreviated version of the Autism Spectrum Quotient, the AQ-10 [ 125 ]. The AQ-10 consists of 10 items rated on a four point Likert scale. The AQ-10 has demonstrated acceptable psychometric properties in an adult general population sample [ 126 ].

Patient commitment to treatment is measured with a selection of items of the subscale Motivation to Engage of the Treatment Motivation Scales for forensic outpatient treatment (TMS-F; [ 127 ]). The four items can be rated on a seven point Likert scale.

Experiential avoidance

The Brief Experiential Avoidance Questionnaire (BEAQ; [ 128 ]) is a 15-item scale assessing experiential avoidance across six domains (i.e., behavioral avoidance, distress aversion, suppression, procrastination, repression/denial, and distress endurance). The items can be rated on a six point Likert scale. The BEAQ has shown good psychometric properties among psychiatric outpatients [ 128 ].

Frustration intolerance

Frustration intolerance is assessed by the Frustration Tolerance subscale of the Severity Indices of Personality Problems (SIPP-118; [ 129 ]). This subscale consists of eight 4-point Likert scale items measuring the capacity to cope with setbacks and disappointments. In previous research among Dutch patients with a personality disorder, the subscale demonstrated moderate to good reliability [ 129 ].

A modified version of the Self-Reflection and Insight Scale (SRIS; [ 130 , 131 ]) is used to assess self-reflection and insight. The SRIS contains 20 five point Likert scale items. The SRIS has shown good reliability and validity in student samples [ 130 , 132 ].

Internal locus of control

Internal locus of control, defined as the extent to which a person experiences an outcome as the result of their own behavior or personal characteristics rather than external circumstance, is assessed by the Locus of Control scale (IE; [ 133 ]). The IE contains 10 five point Likert scale items. Previous research has demonstrated adequate psychometric properties [ 133 , 134 ].

Level of personality functioning

The Level of Personality Functioning Scale-Brief Form 2.0 (LPFS-BF 2.0; [ 135 ]) assesses impairment in personality functioning according to the DSM-5 alternative model for personality disorders. The LPFS-BF 2.0 contains 12 four point Likert scale items. Based on a first psychometric evaluation among Dutch patients referred to a specialized mental healthcare center for personality disorders, the LPFS-BF 2.0 has demonstrated satisfactory psychometric properties [ 135 ].

Mental imagery capacity

Mental imagery capacity is assessed with the 12-item Gordon Test of Visual Imagery Control (TVIC; [ 136 ]). The TVIC assesses the ability to visualize and manipulate a given scenario in response to a set of cues. Participants can response on a three point Likert scale. In addition to the 12 Likert scale items, we measure the time it takes the participant to visualize the scenarios. Finally, we have added two 100 mm visual analog scale (VAS) items measuring how well participants see the scenarios that were described and how difficult it was for the participant to visualize the different scenarios. The TVIC has demonstrated fair to satisfactory internal consistency and validity among community samples and undergraduates [ 137 , 138 , 139 , 140 ].

Mentalizing capacity

Mentalizing capacity is measured using an eight-item version of the Reflective Functioning Questionnaire (RFQ-8; [ 141 ]). The RFQ-8 comprises two dimensions: uncertainty about mental states, reflecting hypomentalizing, and certainty about mental states, indicating hypermentalizing. The RFQ-8 uses a seven point Likert scale. In previous research among BPD patients, the questionnaire has demonstrated satisfactory psychometric properties [ 141 , 142 , 143 ].

Perfectionism

The eight-item Frost Multidimensional Perfectionism Scale-Brief (F-MPS-Brief; [ 144 ]) assesses perfectionism across two dimensions (evaluative concerns and striving). Items are rated on a five point Likert scale. Psychometric properties of the F-MPS-Brief were found to be good in clinical and community samples [ 144 ].

Personality traits

Personality traits are measured, among others, with the Ten-Item Personality Inventory (TIPI; [ 145 , 146 ]), which is a brief measure of the Big-Five personality dimensions. The 10 items can be rated on a seven point Likert scale. The TIPI has shown low to moderate internal consistency and adequate validity among students [ 145 , 146 ].

Positive mental health

Positive mental health, often referred to as mental well-being, is assessed using the nine-item Positive Mental Health scale (PMH-scale; [ 147 ]). The items can be rated on a nine point Likert scale. Based on a previous study on the psychometric properties of the PMH-scale in student, patient and general samples, the PMH-scale was found to be a reliable and valid instrument [ 147 ].

Psychopathology and maladaptive personality traits

The Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; [ 148 ]) measures a wide range of psychopathology symptoms, personality characteristics, and behavioral proclivities. The MMPI-2-RF consists of 338 true-false items aggregating onto 51 individual scales. The psychometric properties of the MMPI-2-RF varied from inadequate to good among normative, outpatient, and inpatients samples, as documented in detail in the Technical Manual [ 149 ].

Readiness to change

Readiness to change is assessed by two subscales (contemplation and action) of the 24-item version of the University of Rhode Island Change Assessment (URICA; [ 150 , 151 , 152 ]). Both subscales are measured by six 5-point Likert scale items and have demonstrated good reliability across a diversity of studies (e.g., [ 153 , 154 , 155 ]).

Rigidity is measured by the Rigidity subscale of the Computerized Adaptive Test of Personality Disorder-Static Form (CAT-PD-SF; [ 156 ]). The Rigidity subscale contains 10 five point Likert scale items reflecting an unwillingness to consider alternative perspectives and inflexibility in values and beliefs. The subscale has demonstrated good reliability among community adults with current or a history of mental health treatment [ 156 ].

Social problems

By using the social problems list, derived from the Improving Access to Psychological Therapies (IAPT) program [ 157 ], social problems (e.g., financial problems, housing problem, and unemployment) are assessed in direct discussion with the patient.

Social support

The Multidimensional Scale of Perceived Social Support (MSPSS; [ 158 ]) is assessed to investigate perceived support from three sources: significant others, family, and friends. The MSPSS contains 12 items which can be rated on a seven point Likert scale. Psychometric properties of the MSPSS are satisfactory among psychiatric outpatients and BPD patients [ 159 , 160 ]. In addition to the MSPSS, the research assistant rates the patient’s social network taking into account the size of the network and potential pathogenic influences.

Stigma of immutability

BPD has been associated to stigma of immutability [ 161 ]. We have developed five 7-point Likert scale items assessing the extent to which participants believe that BPD is resistant to treatment.

The Traumatic Experience Checklist (TEC; [ 162 ]) is used to assess traumatic experiences, including emotional abuse, emotional neglect, sexual abuse, sexual harassment, physical abuse, and threat to life/ bizarre punishment/ intense pain. The TEC includes 30 descriptions of various traumatic experiences. The TEC has demonstrated favorable psychometric properties in Dutch psychiatric patients [ 162 ].

Verbal intelligence

The Dutch version of the National Adult Reading Test (DART; [ 163 ]) is used as a proxy for verbal intelligence. The DART is a reading test including 50 irregularly spelled words. Based on previous research, the DART yields an adequate estimation of verbal intelligence and has shown adequate psychometric properties across a variety of populations [ 164 ].

Other patient characteristics, collected using a self-report questionnaire

In addition to the questionnaires, participants fill out several questions developed by the authors about the willingness and ability to engage in a therapeutic relationship, perceived suitability of DBT and ST (treatment preference), and the absence or presence of an attachment figure in the past.

Other patient characteristics, collected using a questionnaire filled out by clinicians (intake questionnaire)

Clinicians responsible for the intake assessment will fill out the nine-item intake questionnaire for each participant, including questions about the willingness and ability to engage in a therapeutic relationship, the willingness and ability to examine the link between childhood history and present problems, high vs. low level borderline personality organization [ 165 ], the request for help, the degree to which a syndrome disorder might interfere with treatment response, and perceived suitability of DBT and ST. These questions have been formulated by the authors.

Both treatments include non-specific (attachment and therapeutic alliance), BPD-treatment-generic (therapeutic environment characterized by genuineness, safety, and equality), and BPD-treatment-specific (ST: beliefs and schema modes; DBT: emotion regulation and skills use) mechanisms of change. The proposed mediators are repeatedly measured: at baseline, except for measures requiring information about the therapy (i.e., therapeutic environment, therapeutic alliance, and attachment styles with respect to the most important therapist and group members), and every six months after start of the treatment phase. In addition, a selection of the proposed mediators (i.e., selection of schema modes, skills use, beliefs, and therapeutic environment) are also collected every three months after start of the treatment phase, during the first two years.

The Experience in Close Relationships-Relationship Structures Questionnaire (ECR-RS; [ 166 ]) is a brief version of the Experience in Close Relationships-Revised (ECR-R; [ 167 ]). The ECR-RS measures attachment patterns in different relational domains, such as relationships with parents and friends. The ECR-RS can also be adapted to measure a person’s general attachment style. In this study, three versions of the ECR-RS are used, measuring general attachment style and attachment styles with respect to two targets (i.e., most important therapist and group members). The ECR-RS contains nine items, assessing two attachment dimensions: attachment-related anxiety and avoidance. The items can be rated on a seven point Likert scale. The ECR-RS has shown adequate psychometric properties in a large web-based sample ( N  > 21.000), comparable to the ECR-R [ 166 ]. As experience with the treatment is required in order to be able to complete the questions about the most important therapist and group members, these questions will be filled out three weeks after start of the treatment phase.

Idiosyncratic dysfunctional beliefs were elicited with a semi-structured interview at baseline. Three to five idiosyncratic dysfunctional beliefs related to the self (e.g., “I am worthless”), others (e.g., “People always reject me”), and emotions (e.g., “Expressing emotions is a sign of weakness”) are formulated. Participants rate the degree to which they believe in each statement on a 100 mm VAS at baseline and at every subsequent assessment. This procedure has been used in previous research (e.g., [ 168 , 169 ]). The VAS has found to be useful for assessing variations in intensity of beliefs in patients with a personality disorder [ 169 ]. In addition to the idiosyncratic dysfunctional beliefs, participants rate the credibility of one functional belief (“I consider myself a good person”) on a 100 mm VAS.

Emotion regulation

Emotion regulation is assessed by the Difficulties in Emotion Regulation Scale Short Form (DERS-SF; [ 170 ]), a brief version of the widely used DERS [ 171 ]. The DERS-SF measures non-acceptance of emotional responses, difficulties engaging in goal-directed behavior, impulse control difficulties, limited access to emotion regulation strategies, lack of emotional clarity, and lack of emotional awareness. The awareness subscale is excluded based on recommendations of among others Hallion et al. [ 172 ] and Bardeen et al. [ 173 ]. Lack of emotional awareness is assessed by the Awareness subscale of the Difficulties in Emotion Regulation Scale 18 (DERS-18; [ 174 ]). The DERS-SF, without the awareness subscale, consists of 15 items. The Awareness subscale of the DERS-18 is measured by three items. All items can be rated on a five point Likert scale. Both questionnaires have demonstrated good psychometric properties among outpatients [ 172 ].

Schema mode ratings

The Schema Mode Inventory (SMI; [ 175 ]) measures the extent to which 16 different (dysfunctional as well as functional) schema modes are endorsed. The SMI consists of 143 items that are scored on a six point Likert scale. Previous research using a sample of non-patients and patients with a syndrome disorder and/or personality disorder has demonstrated acceptable psychometric properties [ 176 ]. The five maladaptive schema modes that are central to BPD (i.e., vulnerable child, angry child, impulsive child, detached protector, and punitive parent; [ 101 ]) and one functional schema mode (i.e., healthy adult) are assessed every three months during the first two years.

The 59-item Dialectical Behavior Therapy-Ways of Coping Checklist (DBT-WCCL; [ 177 ]) is an adaptation of the Revised Ways of Coping Checklist (RWCCL; [ 178 ]). The DBT-WCCL measures DBT skills use and maladaptive coping skills use over the previous month. All items are assessed using a four point Likert scale. The DBT-WCCL has shown adequate to excellent reliability and validity among BPD patients [ 177 ].

Therapeutic alliance

The therapeutic alliance is measured with the Working Alliance Inventory-Short (WAI-S; [ 179 , 180 ]). The WAI-S consists of three subscales (agreement on goals, agreement on tasks, and bond between patient and therapist), each consisting of four items which can be scored on a five point Likert scale. Observed psychometric properties of the WAI-S were satisfactory in a patient sample [ 179 , 181 ]. Since experience with the treatment is required in order to be able to complete the WAI-S, the WAI-S will be filled out three weeks after start of the treatment phase.

Therapeutic environment

Key characteristics of a promoting therapeutic environment (i.e., genuineness, safety, and equality) are assessed by 13 items formulated by ST experts (A. Arntz and O. Brand-de Wilde) and rated on a 100 mm VAS. The items measure the extent to which the participant feels a) the individual therapist, group therapists, and group members are genuine with him/her; b) he or she can tell the individual therapist and group therapists everything; c) safe in the individual and group therapy; d) safe to show vulnerability and express negative feelings in the individual and group therapy; e) the individual and group therapists take personal responsibility for their mistakes; and f) the individual and group therapists see him/her as equal. Since experience with the treatment is required in order to be able to complete this questionnaire, this questionnaire will not be assessed at baseline.

Primary outcome

Bpd severity.

The primary outcome measure is the change in severity and frequency of the DSM-5 BPD manifestations between baseline until three-year follow-up, assessed with the total score of the Borderline Personality Disorder Severity Index version 5 (BPDSI-5; [ 182 , 183 ]). The BPDSI-5 is a semi-structured interview consisting of 70 items rating the nine DSM-5 BPD criteria over the prior three months. All items are rated on a 11-point Likert scale (0 = never to 10 = daily), except for the subscale Identity Disturbance which is rated on a 5-point Likert Scale (0 = absent to 4 = dominant, clear, and well-defined) and multiplied by 2.5. The total score consists of the sum of the nine criteria scores and ranges from 0 to 90. The scores on the BPDSI-5 subscales provide information on the severity of each of the nine criteria. The BPDSI-5 is a modified version of the BPDSI-IV [ 182 , 183 ] in which a few questions have been slightly reworded and exact frequency scores have been added in addition to the Likert scale. The BPDSI-IV has proven to be a reliable and valid measure among non-patients and (BPD) patients [ 182 , 183 ]. Previous research has shown that a cut-off score of 15 differentiates between BPD patients and controls [ 183 ]. In addition, a score of 20 distinguishes BPD patients from non-BPD patients [ 183 , 184 , 185 ].

Secondary outcome measures

As accumulating evidence suggests that BPD severity and level of functioning are only loosely associated, attention will be paid to outcomes in different areas, including symptoms, functioning, and well-being. The outcome measures are administered at baseline and every six months after start of the treatment phase. In addition, patients’ ratings of experienced burden due to BPD manifestations and well-being are collected every three months after start of the treatment phase, during the first two years.

Costs, including healthcare costs, patient and family costs, and costs outside the healthcare sector, are measured using a retrospective cost interview especially designed for BPD patients [ 80 ]. Healthcare costs include visits to general practitioners, hospitals, crisis centers, psychologists and psychiatrists, use of medication, social work, paramedical care, and alternative treatments. Patient and family costs include informal care (i.e., care provided by the patient’s family, friends, or neighbors) and out of pocket costs (e.g., drugs, alcohol, excessive spending). Costs in other sectors include productivity losses from unpaid work (study and voluntary work) and paid work. Since it is difficult to distinguish between BPD-related costs and costs due to other psychological disorders [ 17 ], only a distinction will be made between costs due to psychological disorders and costs due to somatic diseases. The cost interview will be conducted by trained research assistants using a recall period of six months (baseline assessment), the number of weeks since randomization (assessment six months after start of the treatment phase), or the number of weeks since the previous assessment (assessments 12, 18, and 24 months after start of the treatment phase and both follow-up assessments).

Dutch guidelines [ 186 , 187 ] will be used to determine total costs. Healthcare costs will be calculated by volumes of resource use multiplied by their corresponding unit costs, derived from Hakkaart-van Roijen et al. [ 186 ]. Prescribed medication costs will be determined based on national reference prices. Informal care costs will be computed by multiplying the number of hours the patient receives informal care by shadow prices [ 186 ]. Shadow prices will also be used to value lost productivity in study and voluntary work. Productivity losses from paid work will be valued according to the Human Capital Approach [ 188 ]. Out of pocket costs, such as alcohol and excessive spending, will be directly retrieved from the cost interview or, in case of over-the-counter medication, from the Dutch Pharmacotherapeutic Compass [ 186 ].

Demographics

General patient characteristics (e.g., age, ethnicity, marital status, educational level, employment status) will be collected using a semi-structured demographic interview. During this interview, additional patient characteristics such as treatment history, request for help, medication use, substance use, and duration of BPD manifestations will be recorded. For an overview of all characteristics, see Additional file 4 .

Experienced burden due to BPD

Patient’s self-reported experienced burden of BPD manifestations are measured using the Ultrashort BPD Checklist, a shortened version of the validated BPD Checklist [ 189 ]. The Ultrashort BPD Checklist consists of nine to 11 5-point Likert scale items (the number of items will be based on the upcoming validation study), each related to a specific DSM-5 BPD criterion. Based on an initial psychometric evaluation, the Ultrashort BPD Checklist showed good to excellent psychometric properties in a sample with BPD and cluster C patients, patients with a syndrome disorder, and non-patients, similar to the BPD Checklist [ 189 ].

General psychopathological symptoms

The Brief Symptom Inventory (BSI; [ 190 , 191 ]) is a self-report instrument measuring general psychiatric symptoms at the time of assessment. The BSI is a short version of the Symptom-Check-List (SCL-90-R) and contains 53 items assessing nine symptom dimensions: somatization, obsession-compulsion, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism. All items are assessed using a five point Likert scale. Previous research in Dutch community and patient samples has demonstrated good reliability and validity [ 191 , 192 ].

Global functioning and impairment

Global functioning and impairment is assessed by the 36-item World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) interview version [ 193 ]. The WHODAS 2.0 is a general measure to assess disability in six major life domains (cognition, mobility, self-care, getting along, life activities, and participation). For each item, participants have to report how much difficulty they experienced in the last 30 days. The six domain scores and overall functioning score have shown good psychometric properties in a general population sample as well as a patient sample [ 193 ].

Quality of life

Generic quality of life is assessed using the 5-level EuroQol 5D version (EQ-5D-5L; [ 194 ]). The questionnaire measures five health state dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression). Each dimension is divided into five severity levels: no problem, slight problems, moderate problems, severe problems, and extreme problems. The profiles from the five health state dimensions are assigned a value based on the Dutch social tariffs to generate health utilities [ 195 ]. These utilities will be used to calculate Quality Adjusted Life Years (QALYs) by multiplying the change in utility values between assessments by the length of the period between assessments. In addition to the five health state dimensions, the EQ-5D-5L contains a VAS item which records the patient’s self-reported health status ranging from 0 (worst health you can imagine) to 100 (best health you can imagine). The EQ-5D-5L has shown to be a reliable and valid measure among different patient groups in different countries [ 196 ].

As a complement to the EQ-5D-5L, the Mental Health Quality of Life seven-dimensional Questionnaire (MHQoL-7D; [ 197 ]) will be administered. The MHQoL-7D is a recently developed instrument to assess quality of life specifically in people with mental health problems. The MHQoL-7D consists of seven quality of life domains (self-image, independence, mood, relationships, daily activities, physical health, and hope) and a VAS item which records the patient’s self-reported psychological well-being. A study into the psychometric properties of the MHQoL-7D is currently running. The MHQoL-7D will only be included in the analysis if it is demonstrated to be a psychometrically sound instrument and Dutch social tariffs are available.

Insomnia complaints are assessed by the Insomnia Severity Index (ISI; [ 198 ]). The ISI contains seven items that are scored on a five point Likert scale. The ISI has shown to be a valid measure in community and insomnia patient samples [ 198 ], although the reliability was questionable in some studies (e.g., [ 199 , 200 ]). In addition to insomnia, the number of nights with nightmares and the total number of nightmares in the week prior to the assessment are measured using the Nightmare Frequency Questionnaire (NFQ; [ 201 ]). Based on previous research among posttraumatic stress disorder (PTSD) patients, the NFQ appears reliable for measuring nightmare frequency [ 201 ].

Well-being is measured using a single item measuring happiness [ 202 ] and the Outcome Rating Scale (ORS; [ 203 ]). The single item measures general happiness in the months prior to the assessment on a seven point Likert scale. Reliability and validity were good among undergraduates [ 202 ], and sensitivity to change was excellent in a BPD sample [ 184 ]. The ORS consists of four VAS items assessing four areas of functioning: individual (personal well-being), interpersonal (family and close relationships), social (work and/or school functioning), and overall (general sense of well-being). We slightly adapted the third dimension of the ORS by excluding friendships, because of its overlap with the second dimension (interpersonal functioning). Hafkenscheid et al. [ 204 ] reported adequate psychometric properties of the ORS is a Dutch outpatient sample.

Statistical analyses

The statistical analyses for the (cost-)effectiveness, mechanisms of change and treatment selection studies are under development. For example, according to Cohen et al. [ 48 ], the treatment selection field is still in its developmental stage and statistical methods are constantly evolving. Recently, great efforts have been made by several authors (e.g., [ 205 , 206 ]) to select the optimal prediction model by comparing different variable selection techniques. Considering the ongoing advances in methodological approaches, the statistical analyses described below should be considered as examples of appropriate analytic methods. We will determine the optimal methods at the time of the analyses. An update of the protocol will be published, including the selected statistical methods, before start of the data-analyses. The statistical analyses will be performed according to the intention-to-treat (ITT) principle (i.e., including all patients that have been randomized and received at least one treatment session). In addition to the primary analysis based on the ITT principle, a completers analysis will be conducted by excluding patients who dropped out prematurely (i.e., termination of the treatment before planned end, without patient and therapist agreeing that enough improvement has been reached to justify the termination) or deviated from the protocol (e.g., sought other psychological treatment in addition to the study treatment). No interim analyses are planned.

A two-step approach will be applied to determine the optimal treatment for a particular patient by identifying patient characteristics that predict (differential) treatment response. First, we will examine which of the candidate predictors (see Additional file 4 for an overview) predict (differential) treatment response. Many different variable selection approaches can be used to identify which of the candidate predictors contribute to the prediction of treatment outcome, for example elastic net regularization [ 207 ], Bayesian additive regression trees [ 208 ], or a combination between different variables selection procedures [ 48 ]. Second, individual treatment recommendations are generated based on a prediction model including the variables that predict (differential) treatment response. For each patient, the most beneficial treatment will be identified by using the prediction model to estimate the predicted outcomes for both treatments including the difference in predicted outcomes.

Our primary analysis will focus on individual treatment recommendations based on change in BPD manifestations and will therefore reveal the advantage in symptom relief that may be gained if patients are allocated to their predicted optimal treatment compared to their predicted non-optimal treatment. To investigate the advantage that may be gained in other outcomes, such as functioning and cost-effectiveness, generalization analyses will be performed by testing the performance of the prediction model for these outcomes.

It is hypothesized that the treatments exert a remedial effect on the frequency and severity of BPD manifestations by their impact on the BPD-treatment-specific (ST: beliefs and schema modes; DBT: emotion regulation and skills use), BPD-treatment-generic (therapeutic environment characterized by genuineness, safety, and equality), and non-specific (attachment and therapeutic alliance) mechanisms of change. Since potential mediators and outcome will be assessed multiple times, temporal patterns of change can be studied by performing mediation analysis for longitudinal data, for example multilevel autoregressive mediation analysis [ 209 ] or multilevel structural equation modeling [ 210 ]. By using advanced statistical models, the hierarchical structure of the data (repeated measures nested within patients, who in turn are nested within centers) can be taken into account and possible concurrent and temporal relations between mediators and outcome can be investigated.

Clinical effectiveness

Change in the outcome measures and the relative effectiveness of the two treatment conditions will be analyzed using mixed regression so that all available data are used, and taking into account the dependencies among observations nested within individuals nested within centers. Potential center effects are modeled by including a random effect which enables generalization of results outside the trial and maximizes statistical power [ 211 ]. Since group sessions in both treatments are offered in a semi-open format, patients will start with group treatment at different time points. One can imagine that patients starting treatment at the same time point are more interdependent compared to patients starting treatment at different time points. Therefore, we will take into account, if needed, the interdependency of patients. The underlying distributions of the mixed regression models will be determined based on the variable type (i.e., scale or nominal) and the distribution of residuals (e.g., normal, gamma, negative binomial).

Cost-effectiveness

The cost-effectiveness evaluation will be performed from a societal perspective and includes a cost-effectiveness analysis (CEA) and cost-utility analysis (CUA). The primary clinical outcome for the CEA will be the severity of the BPD manifestations and for the CUA utility scores will be derived from the quality of life instrument(s), both with a time horizon of 12 months after the end of treatment. The net benefit will be used to express cost-effectiveness. For each patient, the net benefit will be calculated by subtracting the costs incurred by the patient from the amount that the society is willing to pay for the health benefit [ 212 ]. The development of the net benefit over time and differences between the treatments will be modeled using multilevel modeling in which the hierarchical structure of the data and potential missing values are taken into account. The best fitting model to describe the development over time and the appropriate distribution of the net benefit data (e.g., gamma distribution, log-normal distribution) will be based on the data. Cost-effectiveness acceptability curves (CEACs) will be drawn showing the probability that one treatment is more cost-effective compared to the other treatment, given the observed data, for a range of willingness-to-pay values. Sensitivity analyses will be performed to address the uncertainties in methodology and assumptions and to test for the robustness of findings.

Additional substudies

Several secondary studies will be conducted, including, but not limited to, the investigation of the heterogeneity of BPD and substance abuse among BPD patients, a qualitative study into the perspectives of patients and therapists, and psychometric evaluations. First, BPD is characterized by considerable heterogeneity [ 165 , 213 ]. Over the past years, researchers have attempted to identify BPD subtypes based on different indicator variables (e.g., DSM-5 criteria, interpersonal characteristics, temperament) and different statistical strategies (e.g., exploratory factor analysis, Q-factor analysis, finite mixture modeling) [ 214 ]. The BPD subtypes that emerged differed substantially between studies. According to Hallquist and Pilkonis [ 214 ], advances in classifying BPD subtypes can be made by using a theoretical model as guidance, for example Kernberg’s theory [ 215 ]. Therefore, a substudy into the heterogeneity of BPD will be performed based on theoretically justified indicators and state-of-the-art statistical methods.

A second substudy will focus on the co-occurrence of substance abuse and BPD. Research suggests that patients with BPD and substance abuse have more severe problems, including higher rates of suicide attempts, more treatment noncompliance, and increased risk of violence, compared to BPD patients without substance abuse (e.g., [ 216 , 217 , 218 ]). However, few trials have assessed the effectiveness of treatments for BPD patients with substance abuse. In addition, research into the effect of BPD treatment on substance abuse is also limited [ 219 ]. Third, qualitative research will be conducted to explore the perspectives of patients and therapists in key areas, including predictors, mechanisms of change, the treatments, and the implementation of the results in clinical practice. Finally, psychometric evaluations of several Dutch questionnaires (e.g., Dialectical Behavior Therapy-Ways of Coping Checklist, Ultrashort BPD Checklist) will be performed.

This article described the study protocol of a multicenter RCT focusing on the (differential) treatment effectiveness of DBT and ST for patients with BPD. The primary aim of the study is to improve treatment outcome of DBT and ST for BPD patients by optimizing treatment selection through identifying patient characteristics that specify which patients will benefit most from which treatment. In addition, we aim to elucidate the change mechanisms of DBT and ST, which is crucial for improving treatments and, in turn, treatment response [ 51 , 52 , 220 ]. Finally, the comparative effectiveness and cost-effectiveness of DBT and ST will be compared.

This trial provides a unique opportunity to gain more insight into one of the main questions dominating the psychotherapy research agenda: “What works for whom and why?”. Although DBT and ST share some important characteristics, different interventions related to different assumed core deficits in BPD are provided [ 58 ]. As each treatment provides a different therapeutic milieu and focuses on different goals and tasks, a particular treatment may be a better fit with some patients compared to others [ 45 ]. In this study, patient characteristics of (differential) treatment response will be identified and individual treatment recommendations (DBT or ST) will be generated. In addition, for each patient, an estimate will be provided of the potential advantage in symptom relief that might be gained in case the patient was allocated to his or her indicated treatment. Moreover, the potential advantage in other outcomes, for example functioning and cost-effectiveness, will also be estimated. Knowing which treatment is most cost-effective for whom may lead to more efficient allocation of healthcare resources, which is important, as the current healthcare system is characterized by constraints in resources (e.g., people, time, budget; [ 221 ]). However, before a treatment selection procedure can be implemented in clinical practice, replication and external validation of the prediction model is needed. Subsequently, a prospective study in which the patient and clinician collaborate in selecting the optimal treatment (i.e., shared decision making; [ 222 ]), guided by treatment recommendations based on the prediction model, should be conducted to evaluate the advantage of a treatment selection procedure. By using a state-of-the-art approach, the results of the current study can serve as the starting point for future studies into personalized medicine among BPD patients, and is therefore of great importance.

In addition, this trial provides insight into the comparative (cost-)effectiveness of DBT and ST. Although the effectiveness of both treatments has been established, DBT and ST have not been directly compared. Therefore, and because outcome measures differ substantially between studies on the effectiveness of DBT or ST, hypotheses concerning the differential effectiveness can hardly be formulated. According to the “Dodo Bird effect” [ 223 , 224 ], all evidence-based psychotherapies are equally effective, suggesting that DBT and ST will produce equivalent outcomes. However, a meta-analysis into the comparative effectiveness of evidence-based treatments for personality disorders demonstrated that some treatments may be more effective than others [ 225 ]. In addition, Fassbinder et al. [ 226 ] hypothesized that ST may be more effective than DBT in reducing psychiatric comorbidity and improving quality of life, while DBT may lead to a better and faster reduction in self-harming and suicidal behaviors. Moreover, although not assessed in direct comparison with ST, the meta-analysis of Storebø et al. [ 25 ] into psychological treatments for BPD indicated that DBT may be especially effective for BPD-severity, self-harm, and psychosocial functioning. They also pointed out that more research into the effects of BPD-tailored treatments, including head-to-head comparisons, is needed. By focusing on an array of outcomes, this study will extend our knowledge on the potential differential effects of DBT and ST.

This study has several strengths. First, this RCT is quite inclusive in terms of patient characteristics, and as such designed to reflect clinical practice to enhance ecological validity. Second, this trial is conducted by a research group including researchers with balanced allegiance to either ST or DBT and an independent researcher (i.e., C.J.M. Wibbelink), to prevent the potential effect of research allegiance on treatment outcomes [ 227 ]. Third, we adopt a broad view on treatment response by including outcome measures reflecting different areas of recovery (e.g., BPD symptoms, functioning, well-being). Focusing on outcomes beyond symptom reduction is in line with patients’ view on recovery [ 76 , 77 ]. In addition, it follows a multi-method assessment approach, as the outcome measures include both self-report questionnaires and semi-structured interviews. Fourth, we include a large amount and broad range of patient characteristics potentially predictive of (differential) treatment response across DBT and ST. Finally, the presumed mediators and outcomes will be frequently measured on multiple time points during the treatments and mediation analyses will be performed by using state-of-the-art statistical analysis methods [ 228 ]. This allows us to establish concurrent as well as temporal relationships between the mediators and outcomes [ 228 ]. However, according to Lemmens et al. [ 229 ], understanding psychotherapeutic change may be too challenging, even in optimal research designs. Psychotherapy consists of a complex interplay of multiple mechanisms on different levels. Finding that a construct (e.g., therapeutic alliance) mediates treatment outcome does not explain how changes in this construct lead to changes in the outcome as it could involve several processes (e.g., cognitions, behaviors, emotions, neural systems) [ 63 ]. It is therefore highly questionable if these complex processes can be assessed by relatively simple mediational models. As such, this is one of the potential limitations of the current study.

This study has several other limitations that should be considered when evaluating the results. First, as power is conventionally set a 80% [ 84 , 230 , 231 , 232 ], we used a minimum criterion of 80% power for the power analyses. However, this means that we accept a 20% chance of a false negative result. Second, since DBT and ST are both evidence-based treatments for BPD, differential effects in treatment outcome may be small or non-existing. To demonstrate equivalence or small effects between treatments, a very large sample size is needed. The sample size of the current study is not large enough (i.e., does not have ≥80% power) to reliably detect a small differential treatment effect. However, the comparison of treatments is not the main aim of the study. In addition, according to Luedtke et al. [ 233 ], a sample size of at least 300 patients per condition is required to have sufficient power for applying multivariable prediction models. Nonetheless, they also noticed that a smaller sample size might be justified if studies are designed to develop prediction models that can be tested in future studies. Moreover, the results of this study can contribute to building a database including trials on BPD that can be analyzed with meta-analytic techniques.

Second, this study does not include a no treatment control group, which might affect internal validity. When improvements are found in both treatments, but no significant differences between the treatments, the absence of a control group implies that it cannot be ruled out that non-specific factors such as attention or time (maturation) caused the improvements. However, including a control group receiving no treatment would clearly be unethical (e.g., patients are at risk of suicide). For similar reasons, it is not possible to standardize medication use and crisis management sessions. Any additional treatment or medication use will be monitored and included in the analyses.

Third, one of the treatment elements of DBT is out of office hours between-session (telephone) consultation by the individual therapist. The targets of telephone consultation include, among others, reducing self-harm and suicidal behavior and teaching patients how to apply learned skills in everyday life in order to encourage skills generalization [ 19 ]. In the current study, some centers provide 24/7 access to telephone consultation by the patient’s individual therapist, while the other centers provide telephone consultation within the limitations of the individual therapists, or within working hours. In case of emergency, the standard emergency procedures of each center will be followed. Although outside of office hours availability is considered to be an essential element of DBT by some authors [ 113 , 234 ], the link between telephone consultation and outcome in DBT has not been evaluated [ 95 , 235 ]. There is some preliminary support for the importance of telephone consultation [ 236 ]. However, studies into the effectiveness of DBT that did not apply 24-h telephone consultation by the individual therapist have found positive outcomes (e.g., [ 235 , 237 ]). Van den Bosch and Sinnaeve [ 238 ] studied treatment programs of 25 DBT teams in the Netherlands. They found that only 36% of the DBT teams applied telephone consultation according to the guidelines of DBT. It can therefore be concluded that the current study is a good reflection of clinical practice, which enhances generalizability of our findings. Notwithstanding, we will monitor between-session (telephone) consultation within centers and examine potential effects.

Fourth, in this study, a component of treatment integrity (treatment adherence) will be assessed, which is, surprisingly, not standard procedure in trials investigating BPD treatments [ 25 ]. However, treatment integrity also constitutes of treatment differentiation and therapist competence [ 110 ]. Treatment adherence and treatment differentiation are closely related, in contrast to treatment adherence and therapist competence [ 239 ]. Treatment adherence represents a quantitative aspect of treatment integrity (i.e., how frequently a therapist utilizes prescribed techniques and procedures and avoids proscribed techniques and procedures), while competence represents a qualitative aspect (i.e., how well prescribed techniques and procedures are implemented) [ 109 ]. Adherence does not necessary presuppose competence; even with adequate adherence, therapists may deliver the treatment in an incompetent manner. The absence of competence ratings may threaten the validity of our results [ 109 ]. Moreover, treatment adherence will be assessed by trained master psychology students, whereas for DBT, adherence ratings by reliably trained therapists are considered the gold standard [ 240 , 241 ]. However, students will receive a training from experienced therapists.

Fifth, it is a subject of some debate whether the EQ-5D is a valid instrument to measure quality of life in BPD patients, which can affect the economic evaluation [ 81 ]. According to Brazier [ 242 ], the EQ-5D might not measure what matters to patients with psychiatric disorders. In addition, in a cost-effectiveness study among BPD patients, van Asselt et al. [ 79 ] found contradictory results on the incremental risk ratios when recovery was based on the EQ-5D compared to the BPDSI-IV. In contrast, adequate responsiveness of the EQ-5D has been found in a BPD sample [ 243 ]. In addition, Soeteman et al. [ 244 ] concluded that the EQ-5D is sensitive to changes in the health status of patients with cluster B personality disorders. In addition to the EQ-5D, quality of life will be assessed by a recently developed instrument specially developed for patients with mental health problems (MHQoL-7D; [ 197 ]). The validation of this instrument is currently in progress, but preliminary results are promising [ 197 ]. Another point for consideration is that conclusions with regard to the most cost-effective treatment choice can be affected by the amount the society is willing to pay for an additional unit of effectiveness (i.e., willingness-to-pay threshold). Soeteman et al. [ 244 ] concluded that outpatient psychotherapy for cluster B personality disorder patients is the optimal treatment choice in case society is not willing to pay more than €12.274; otherwise, day hospital psychotherapy was the optimal treatment choice. To date, there is no consensus about reasonable willingness-to-pay thresholds, although guidelines have been proposed by the Dutch healthcare authority [ 245 ]. We will therefore calculate the probability of each treatment being cost-effective for different willingness-to-pay values. As a result, the optimal treatment choice can be different for different willingness-to-pay values.

Finally, this study is conducted during the COVID-19 pandemic. The COVID-19 pandemic has significant disrupted effects on society and is related to increased burden of mental health among individuals with mental disorders [ 246 , 247 ]. Moreover, some authors suggest that patients with severe psychopathology, including BPD, may be especially at risk for symptom deterioration [ 247 , 248 ]. In addition, some patients will temporarily receive treatment via videoconferencing in case face-to-face treatment is restricted in mental healthcare centers. Research on the effectiveness of online individual psychotherapy has found positive effects for several mental health disorders, including PTSD [ 249 ], anxiety disorders [ 250 ], and depression [ 251 ]. However, research on the effectiveness of online group psychotherapy is scarce [ 95 , 252 ]. Consequently, we will control for a potential effect of the COVID-19 pandemic in the analyses.

Specialized evidence-based treatments have been developed and evaluated for BPD, including DBT and ST. However, BPD patients vary widely in their response to treatment, and poor response to one treatment does not imply poor response to another treatment. The selection of the optimal treatment for a particular patient is a daily task of the clinician, but very scant evidence is available to guide these decisions. This study will extend our knowledge on one of the main issues in psychotherapy research; understanding for whom a treatment works and how. As such, this study helps pave the way for an evidence-based personalized medicine for patients with BPD.

Trial status

Recruitment has started in January 2019 and is still ongoing. The estimated completion date of the recruitment is September 2021. Protocol version 07 is currently active.

Availability of data and materials

Not applicable.

Abbreviations

Autism Spectrum Quotient-10 items

Brief Experiential Avoidance Questionnaire

Borderline Personality Disorder

Borderline Personality Severity Index, fifth edition

Brief Symptom Inventory

Computerized Adaptive Test of Personality Disorder-Static Form

Cost-effectiveness analysis

Cost-effectiveness acceptability curves

Cost-utility analysis

Dutch Adult Reading Test

Dialectical Behavior Therapy

Dialectical Behavior Therapy-Ways of Coping Checklist

Difficulties in Emotion Regulation Scale 18

Difficulties in Emotion Regulation Scale Short Form

Diagnostic and Statistical Manual of Mental Disorders

Experience in Close Relationships-Revised

Experiences in Close Relationships-Relationship Structures questionnaire

5-level EuroQol 5D version

Frost Multidimensional Perfectionism Scale-Brief

General Psychiatric Management

Improving Access to Psychological Therapies

Locus of Control scale

Insomnia Severity Index

Intention-to-treat

Level of Personality Functioning Scale-Brief Form 2.0

Medical Ethics Committee of the Academic Medical Center

Mental Health Quality of Life seven-dimensional Questionnaire

Minnesota Multiphasic Personality Inventory-2 Restructured Form

Multidimensional Scale of Perceived Social Support

Nightmare Frequency Questionnaire

Outcome Rating Scale

Positive Mental Health scale

Posttraumatic Stress Disorder

Quality Adjusted Life Years

Randomized Clinical Trial

8-item Reflective Functioning Questionnaire

Structured Clinical Interview for DSM-5

Structured Clinical Interview for DSM-5 Disorders Clinician Version

Structural Clinical Interview for DSM-5 Personality Disorders

Structural Clinical Interview for DSM-5 Syndrome Disorders

Severity Indices of Personality Problems

Structured Clinical Interview for DSM-5 Screening Personality Questionnaire

Structured Clinical Interview for DSM-5 Syndroomstoornissen Vragenlijst

Schema Mode Inventory

Standard Protocol Items: Recommendations for Interventional Trials

Self-Reflection and Insight Scale

Schema Therapy

Traumatic Experience Checklist

Transference Focused Psychotherapy

Ten-Item Personality Inventory

Treatment Motivation Scales for Forensic Outpatient Treatment

Gordon Test of Visual Imagery Control

University of Rhode Island Change Assessment

Visual Analogue Scale

Working Alliance Inventory-Short

World Health Organization Disability Assessment Schedule 2.0

American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5®). 5th ed. Arlington: American Psychiatric Publishing; 2013.

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Acknowledgements

We would like to thank all patients, therapists, supervisors, research assistants, and students for their involvement in the study. We also thank our advisory board for sharing their knowledge and support, and their critical review of the study design. Finally, we are thankful to Herman Vinckers and Lindy Boyette who helped with the translation of the questionnaires.

This study received funding from Stichting Achmea Gezondheidszorg, CZ Fonds, and Stichting Volksbond Rotterdam. The funding bodies had no role in the design of the study and will not be involved in the collection, analysis, and interpretation of the data, nor in writing the manuscripts. The grant was subjected to a peer review process.

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CJMW: wrote the manuscript; involved in the implementation and coordination of the data collection; involved in the design of the study. AA: principal investigator; initial conception and design of the study. JHK: principal investigator; initial conception and design of the study. RPPPG: statistical counseling. RS: involved in the design of the study. MB, OMCB, ECPD, SGA, CJ, AMK, LK, MP, AS, FIS: responsible for the recruitment of participants and data collection in their mental healthcare center. All authors read, contributed and approved the final manuscript.

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Additional file 1..

SPIRIT 2013 checklist.

Additional file 2.

Informed consent form (Appendix A) and additional informed consent form videoconferencing (Appendix B).

Additional file 3.

Overview of syndrome disorders assessed with the SCID-5-S.

Additional file 4.

Candidate predictors based on clinicians’ appraisals (Table 1) and candidate predictors based on the literature and suggestions of a patient representative of the Borderline Foundation of the Netherlands (Table 2).

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Wibbelink, C.J.M., Arntz, A., Grasman, R.P.P.P. et al. Towards optimal treatment selection for borderline personality disorder patients (BOOTS): a study protocol for a multicenter randomized clinical trial comparing schema therapy and dialectical behavior therapy. BMC Psychiatry 22 , 89 (2022). https://doi.org/10.1186/s12888-021-03670-9

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Optimization of cassava peel ash concrete using central composite design method

• Uzoma Ibe Iro 1 ,
• George Uwadiegwu Alaneme   ORCID: orcid.org/0000-0003-4863-7628 1 , 2 ,
• Imoh Christopher Attah 3 ,
• Nakkeeran Ganasen 4 ,
• Stellamaris Chinenye Duru 5 &
• Bamidele Charles Olaiya 2  

Scientific Reports volume  14 , Article number:  7901 ( 2024 ) Cite this article

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• Engineering
• Materials science

Cassava peel ash (CPA) is an abundant agricultural byproduct that has shown promise as an additional cementitious material in concrete manufacturing. This research study aims to optimize the incorporation of CPA in concrete blends using the central composite design (CCD) methodology to determine the most effective combination of ingredients for maximizing concrete performance. The investigation involves a physicochemical analysis of CPA to assess its pozzolanic characteristics. Laboratory experiments are then conducted to assess the compressive and flexural strengths of concrete mixtures formulated with varying proportions of CPA, cement, and aggregates. The results show that a mix ratio of 0.2:0.0875:0.3625:0.4625 for cement, CPA, fine, and coarse aggregates, respectively, yields a maximum compressive strength of 28.51 MPa. Additionally, a maximum flexural strength of 10.36 MPa is achieved with a mix ratio of 0.2:0.0875:0.3625:0.525. The experimental data were used to develop quadratic predictive models, followed by statistical analyses. The culmination of the research resulted in the identification of an optimal concrete blend that significantly enhances both compressive and flexural strength. To ensure the reliability of the model, rigorous validation was conducted using student’s t-test, revealing a strong correlation between laboratory findings and simulated values, with computed p-values of 0.9987 and 0.9912 for compressive and flexural strength responses, respectively. This study underscores the potential for enhancing concrete properties and reducing waste through the effective utilization of CPA in the construction sector.

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Introduction

Concrete stands as one of the extensively utilized construction materials worldwide; however, its manufacturing contributes considerably to environmental consequences because of the substantial energy consumption and carbon emissions linked to cement production 1 , 2 . In response to these environmental considerations and to advocate for sustainable construction methods, researchers are progressively investigating alternative materials and mix formulations. One such material of interest is cassava peel ash (CPA), a waste product generated from cassava processing 3 . Cassava (Manihot esculenta) is a vital crop in many tropical countries, and its processing generates substantial amounts of waste, primarily in the form of cassava peels. Improper disposal of cassava peels can lead to environmental pollution and health hazards 4 . However, recent research has shown that these waste cassava peels can be effectively converted into ash, known as cassava peel ash (CPA), and utilized as a supplementary material in concrete production. CPA contains pozzolanic properties, similar to other supplementary cementitious materials like fly ash or silica fume. Pozzolanic substances have the capability to interact with calcium hydroxide in cement, resulting in the formation of supplementary cementitious compounds, ultimately enhancing the properties of concrete 4 , 5 . However, the optimization of CPA's incorporation into concrete mixes is essential to ensure the desired performance characteristics are achieved 6 .

The incorporation of CPA in concrete has gained attention due to its pozzolanic characteristics, which can contribute to enhanced strength, durability, and reduced environmental impact. The pozzolanic nature of cassava peel ash indicates its capacity to undergo a reaction with calcium hydroxide in the presence of moisture, resulting in the formation of extra cementitious compounds 7 . This chemical process contributes to the reinforcement of strength and improvement of the durability of concrete. Moreover, CPA have found a wide acceptance in their utilization in civil engineering materials like concrete and soil re-engineering. In recent times utilization of this agro waste derivatives as supplementary cementitious material has been practiced to enhance concrete’s mechanical properties when used in cement replacement strategy 8 , 9 . Several research investigations have explored the possibilities of incorporating CPA in concrete applications. Ogunbode et al. 10 explores the mechanical and microstructure properties of concrete composites made using CPA and kenaf bio-fibers. The study likely investigates the potential for incorporating these sustainable materials into concrete mixtures. Also, in research carried out by Olubunmi et al. 11 , they investigate the use of cassava peel ash and wood ash as partial cement replacements in concrete. Various replacement percentages were tested, with 5%, 10%, and 15% replacements meeting plain concrete strength specifications. Higher percentages, such as 20% and 25%, were unsuitable for structural concrete. The study suggests that incorporating these materials into concrete production can help reduce environmental pollution.

Optimization of cassava peel ash (CPA) concrete using the Central Composite Design (CCD) method is an innovative approach that aims to improve the properties and performance of concrete by incorporating cassava peel ash as a supplementary cementitious material 12 , 13 . Statistical analysis of the experimental data enables researchers to model the relationship between the variables and the response using regression techniques and response surface methodology 14 . This allows for the identification of significant factors, evaluation of their individual and interactive effects, and determination of the optimal parameter values that maximize the desired response 15 . The optimization of CPA concrete using the CCD method offers several advantages, including reduced time and cost compared to traditional trial-and-error approaches. It enables researchers to efficiently explore a wide range of parameters and their interactions, leading to improved understanding and control over the properties of CPA concrete 16 , 17 . By identifying the optimal combination of variables, it is possible to enhance the performance, sustainability, and economic viability of concrete structures. It also provides a systematic and data-driven approach to guide the selection and proportioning of materials, ultimately leading to improved concrete performance and sustainability 18 .

Lately, many researchers have employed unconventional techniques to assess concrete performance concerning the interplay of mix ingredients 19 , 20 , 21 . These methods encompass statistical, computational, and analytical approaches. Hassan et al. 22 evaluate the use of micro and nano palm oil fuel ash (POFA) as supplementary cementitious materials in high-strength blended concrete. The research aims to optimize the concrete mix proportions using Central Composite Design and Response Surface Methodology. The experimental results validate mathematical models, indicating a close agreement between predictions and data. The study suggests an optimal mix with 10% micro POFA and 1.50–2.85% nano POFA, meeting optimization criteria for fresh and hardened concrete properties. Moreover, Ali et al. 23 investigate the use of pumice stone (PS) as a replacement for natural coarse aggregates in concrete. Various percentages of PS are used in the mix, and response surface methodology (RSM) is employed for experimentation. The study suggests that up to 30% of PS can be replaced in lightweight aggregate concrete, resulting in compressive strength greater than 15 MPa, split tensile strength at 7–12% of CS, and flexural strength at 9–11% of CS. The proposed quadratic model is highly relevant, with a coefficient of determination (R 2 ) above 99% for all responses. Also, Ali et al . 24 researched on the utilization of waste foundry sand (WFS) as a partial replacement for fine aggregate in concrete mixtures and assess its impact on fresh concrete performance and mechanical properties. WFS ratios were adjusted using Design-Expert software's Central Composite Design (CCD) tool in Response Surface Methodology (RSM). Results showed highest mechanical properties at 20% WFS replacement and 56 days curing, with compressive strength of 29.37 MPa, splitting tensile strength of 3.828 MPa, and flexural strength of 8.0 MPa. However, upto 30% replacement, fresh qualities of substitutes were akin to the control mix.

Furthermore, the optimization of cassava peel ash concrete using the Central Composite Design method is a valuable research approach that allows for the systematic exploration and optimization of various variables to enhance the properties and performance of concrete and provides a systematic and data-driven approach to improve the properties and performance of concrete 13 , 25 . Utilizing this method, researchers can maximize the utilization of cassava peel ash, a waste material, while improving the performance, sustainability of concrete structures and contribute to the development of more resource-efficient construction materials 26 . The study aims to optimize parameters for concrete with CPA utilizing the CCD method. This approach facilitates a systematic exploration of various variables and their interactions to identify the optimal combination that achieves the desired properties in the concrete. By employing statistical analysis and response surface modeling, the study aims to develop a comprehensive understanding of the relationship between the variables and the response, enabling the identification of the optimal parameter values.

The outcomes of this research will provide valuable insights into the optimization of CPA concrete, enabling more efficient utilization of cassava peel ash and enhancing the sustainability and performance of concrete structures. Ultimately, this study is motivated by several factors. Firstly, it aims to promote sustainable and eco-efficient construction practices by utilizing agricultural waste in concrete production. Secondly, there is potential for economic benefits through cost savings by replacing traditional cement with CPA. Additionally, the study seeks to enhance concrete performance by systematically exploring different mixture formulations using advanced design methodologies. The utilization of technology like Design Expert software streamlines the optimization process and contributes to advancements in sustainable construction practices. Overall, the research aims to improve concrete sustainability, cost-effectiveness, and performance through the effective integration of CPA.

Materials and methods

The experimental investigation utilized Grade 53 Dangote cement, obtained from the open market for building materials in Imo State, Nigeria. Furthermore, it adheres to the standards, composition, and compliance requirements outlined in BS 12 (1978).

Water plays a crucial role as a component in the concrete mixture, influencing the mechanical, rheological, and durability properties. For the laboratory tests, we employed potable water that complies with the specifications outlined in ASTM C1602-12 (2012) for concrete applications.

In this experimental study, we employed river sand sourced from Akwa Ibom State, Nigeria, as the fine aggregate. The fine aggregate employed meets the criteria outlined in BS-EN 12,620 and ASTM C125-16 and can pass through a 2.36 mm sieve. As for the coarse aggregate, crushed granite with well-graded properties and devoid of harmful substances was employed, and adherence to BS EN12620. The coarse aggregate has a maximum size of 20 mm.

Cassava peel ash (CPA)

The cassava peel was collected from Abayi-umuokoroato village, situated in the Abayi Ancient Kingdom of Obingwa Local Government Area in Abia State, Nigeria. Subsequently, the cassava peel was subjected to sun drying. It was then incinerated in a controlled kiln at a temperature range of approximately 500 °C to 850 °C for 60 min to ensure environmental protection. The resulting burnt material was carefully gathered and sieved in the laboratory, using a 150 µm sieve size, to obtain finely divided ash material for the experiments. The picture of the cassava peel waste taken in the laboratory during the experiments along with the processed ash samples are shown in Fig.  1

Ash samples derived from cassava peel.

Design of experiment using CCD

Response Surface Methodology is a statistical method employed for experiment design to uncover relationships between variables and responses. Its primary goal is optimizing these variables to anticipate the most favorable responses 27 . CCD is a valuable technique for establishing a functional connection between the variables and responses. it incorporates a nested factorial or fractional factorial design with central points, enhanced by a set of 'star points' for curvature estimation. While the center-to-factorial point distance is ± 1 unit for each factor, the center-to-star point distance is |α|> 1. The specific value of α is determined based on design requirements and the number of factors in question, however, Face Centered Central Composite Design (FCCD) which have all the axial points are projected on the surfaces was utilized for the formulation 28 . Design Expert 13.0.5.0 Software was used for designing the experiments, mathematically modeling, statistically analyzing, and optimizing the response parameters. In essence, the Central Composite Design (CCD) includes 2n factorial experiments along with 2n axial experiments, and the experimental error is assessed using center point replicates (n c ). Therefore, a Face Centered Central Composite Design (FCCD) comprises 2n factorial runs, coded as + −1, expanded by 2n axial points like (1, 0, 0…0), (0, + −a, 0…0), …(0, 0, + −… 0), and n c center points (0, 0, 0...0). The total number of required experimental runs (N) for CCD is determined by Eq. ( 1 ) 29 .

In this context, n represents the number of variables, while n c pertains to the number of central points. For our study, which incorporated four input variables, we adopted a CCD design consisting of twelve factorial points, eight axial points, and a single repetition at the center. The arrangement of these points can be visualized in Fig.  2 . Consequently, we conducted a total of twenty-five experimental runs, considering four parameters, each varying at three levels denoted as − 1, 0, and 1 30 .

FCCD diagrammatic illustration 29 .

Formulation of mixture components ratio

In CCD, mix design refers to the process of determining the composition of the experimental mixtures that will be used in the study. The methodological approach involves selecting appropriate levels or values for the variables being studied and preparing the experimental mixtures accordingly 31 . The mix design process in central composite design involves carefully selecting variable ranges, determining design points, assigning variable levels, calculating ingredient proportions, and preparing the experimental mixtures. This allows for a systematic exploration of the variable space and helps in understanding the interactions between the factor levels and the target response(s) of interest. The collected data from the experiments can then be used for statistical analysis and optimization to ascertain the optimal mix composition that achieves the desired objectives of the study 32 , 33 . The concrete mix design parameters for this experimental study indicated target strength of 25 N/mm 2 , with a cement content of 290 kg/m 3 , coarse aggregate content of 1198.65 kg/m 3 , and fine aggregate content of 766.35 kg/m 3 which were derived from relevant literature 34 , 35 . Furthermore, taking water-cement-ratio (w/c) of 0.5, the central composite design mixture formulation obtained with the aid of design expert software for the experimental investigations showing four components’ constituents of cement, cassava peel ash (CPA), fine and coarse aggregates is shown in Tables 1 , 2 . Moreover, the experimental factor space for the four components in the mixture design and the cubic plot standard error of design were presented in Figs. 3 , 4 . The plot displayed the factor space on the x-axis, illustrating three sections (center, factorial, and axial) for the central composite design using Design Expert Software. Meanwhile, the mixture components' ratios for the 25 experimental runs were depicted on the y-axis of the plot. Additionally, it was noted that 16 out of the 25 design points are located on the factorial plane within the factor space. Among these, eight data points are positioned at both the lower and upper limits for the four mixture components 36 , 37 .

Experimental factor space.

Cube standard error of design.

Compressive strength property

The mixture components were accurately weighed and thoroughly mixed based on the specified formula. The resulting uniform concrete mixture was compacted into 150 mm × 150 mm × 150 mm cubic molds. These green concrete specimens, blended with CPA, were submerged in a curing tank filled with clean water for 28 days at normal temperature. After the curing period, they were weighed, and their compressive strength was determined following the BS EN 12,390-4 standard. The cubes underwent crushing tests using the Okhard Machine Tool’s WA-1000B digital display Universal Testing Machine, with a testing range of 0–1000kN. The cubes were positioned between two 25 mm-thick steel plates that covered the top and bottom, and force was incrementally applied until the cubes failed in compression 38 , 39 .

Flexural strength

The procedure for the flexural strength test will adhere to BS EN 12,390-5 (2009) standards, utilizing test specimens with dimensions of 400 × 100 × 100 mm. These specimens will be thoroughly batched and mixed in accordance with the specified component fractions. Subsequently, the concrete beams formed will be demolded and allowed to cure for a 28-day hydration period before undergoing the flexural test. After twenty-eight days of curing, three samples from each experimental run will be subjected to testing, and the average flexural strength will be determined. This process will be repeated for each mix proportion, testing three specimens per proportion and calculating the average flexural strength for each 40 .

Ethics and compliance statement

Authors comply with the International Union for Conservation of Nature (IUCN) Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora in this research article.

Consent to participate

All authors were highly cooperative and involved in research activities and preparation of this article.

Results discussion and analysis

Test materials characterization.

A sequence of laboratory examinations was carried out on the constituent elements to evaluate their suitability as construction materials in civil engineering. The examinations encompassed sieve analysis and specific gravity tests on aggregates and admixtures to assess particle size distribution and gradation. The results of the sieve analysis test are presented in Fig.  5 , depicting the particle size variation with a cumulative frequency distribution curve. The findings revealed that the coarse aggregate exhibited a passing sieve size of 76.2–11.6% for 10–2 mm, while the fine aggregates demonstrated a passing sieve size of 93.4–0.13 for 2 mm–75 µm 41 . Moreover, the CPA admixtures in the concrete showed a passing sieve size of 99.99–84.63% for 2 mm–75 µm. The results conform to the requirements outlined by BS 882, indicating well-graded sand and gravel particles for enhanced concrete durability performance 38 , 39 .

Particles size distribution of test ingredient.

Chemical characterization of the test cement and CPA

The chemical attributes of the examined admixtures were assessed through X-ray fluorescence (XRF). The results revealed that CPA consists of Fe 2 O 3 (6.02%), Al 2 O 3 (19.88%), and SiO 2 (55.93%), and totaling 81.83% composition, indicating a favorable pozzolanic property compliant with ASTM C618, 98 specifications 40 . Furthermore, the cement composition indicated 9.85% for CaO, 51.4% for SiO 2 , and 20.6% for Al 2 O 3 . The plentiful presence of these elemental oxides in the examined materials supports extensive cement hydration, improving the mechanical strength and longevity of the resulting environmentally friendly concrete, as depicted in Table 3 . The reaction mechanism of hydration enables the amalgamation of aluminate and silicate oxides from the admixture with hydrated calcium, leading to the formation of a more robust mass over time 41 .

Effects of CPA admixtures on the mechanical laboratory response

The effective mass values for the ingredients were determined using the ratio conversion method, ensuring precise measurements for each experimental run with a w/c of 0.5. This conversion took into account the standard concrete density of 2400 kg/m 3 and applied the relationship between volume, density, and mass 42 . The mass required to fill the cubic mold was determined by multiplying the calculated mold volume (m 3 ) by the concrete density. For each experimental run, three cube and beam samples were produced, and the average compressive strength response is provided in Tables 4 – 5 . The graphical representation of the influence of cement and CPA interactions on the compressive and flexural strength responses is presented in Fig.  6 . The contour plot illustrates a consistent rise in both compressive and flexural strength attributes of the CPA-blended concrete as the proportion of CPA replacing cement in the mixture gradually increases from 0.025 to 0.875. However, the strength responses begin to decline with further increments in the CPA ratio, particularly at 0.12 and beyond. The maximum compressive strength recorded was 28.51 MPa, achieved with a concrete mixture ratio of 0.2:0.0875:0.3625:0.4625 for cement, CPA, fine, and coarse aggregates. Conversely, the minimum compressive strength of 17.25 MPa corresponded to a mixture ratio of 0.15:0.15:0.425:0.525. Moreover, incorporating 19% cement, 2.4% CPA, 34.6% fine aggregate, and 44% coarse aggregates notably enhanced the compressive strength behavior of the green concrete. Additionally, the highest flexural strength of 10.36 MPa was achieved with a mixture ratio of 0.2:0.0875:0.3625:0.525, and the lowest flexural strength of 4.22 MPa was observed with a mixture ratio of 0.15:0.15:0.425:0.525. Furthermore, obtained results showed that proportions of 17.02% of cement, 7.45% of CPA, 30.85% of fine aggregate and 44.68 of coarse aggregate produced best performance in terms of flexural strength of the CPA-concrete 43 , 44 . Overall, the concrete's mechanical strength behavior complied with NCP-1 and BS-8110 specifications, attributed to the pozzolanic properties derived from the abundance of aluminosilicate oxides in the CPA combined with Portland cement, resulting in the formation of calcium silicate hydrate 45 , 46 .

Impact of the interaction between CPA and cement on (a) Compressive Strength and (b) Flexural Strength.

Development and validation of the model

The information obtained from the experimental procedure, which involved the application of the prescribed proportions of mixture ingredients and the corresponding responses to assess the mechanical performance of the created CPA-cement blended concrete, was utilized for constructing the model through response surface methodology. The process involves expertly choosing square root transformation with polynomial analysis type for the purpose of considering non-linearity of the datasets to generate accurate model predictions 47 . Further statistical computations were conducted on the datasets to assess their appropriateness for the intended modeling purposes, including fit statistics and analysis of variance (ANOVA). This crucial preliminary statistical analysis provides a fit summary to identify models using performance indicators such as the coefficient of determination (Rsqd.), PRESS (predicted residual sum of squares), which evaluates how well the sought-after models fit each point in the design, lack of fit tests, and sequential model sum of squares to determine the highest polynomial order with significant additional terms, as detailed in Tables 6 , 7 , 8 , 9 . The presented fit statistical outcomes indicate a preference for quadratic models, with R-sqd. values of 0.8675 and 0.9102 for compressive and flexural strength responses, respectively. From the sequential sum of squares computation results, p value of 0.0237 and 0.0014 for compressive and flexural strength responses respectively 38 , 48 .

Analysis of variance (ANOVA) result

Following the identification of a suitable polynomial model, as suggested during the fit statistical analysis, ANOVA is conducted. In this step, descriptive and statistical tests are carried out to assess the significance levels of the mixture model independent variables concerning the response parameters 49 . The computational outcomes are detailed in Table 10 for the compressive strength response, indicating a Model F-value of 4.68, signifying the significance of the model. There is only a 0.94% (p-value of 0.0094) probability that an F-value of this magnitude could occur due to random variations. Additionally, the statistical results for the flexural strength response show a Model F-value of 7.24, suggesting the significance of the model as shown in Table 11 . There is only a 0.17% (p-value of 0.0017) chance that an F-value of this magnitude could occur due to random variations 50 .

Derived coefficient estimates and model equations

In line with the experimental plan and subsequent statistical fit ANOVA computations, regression analysis enabled the prediction of each response. This analysis was conducted using Design Expert software, exploring the interaction between variables and responses. The CCD experimental design data facilitated the evaluation of mathematical prediction equations, as illustrated in Table 12 . The equations, in terms of coded factors, could be employed to make predictions regarding the response for specified levels of each factor. These predictions were formulated as a function of the factors A, B, C, and D, representing the proportion of cement, CPA, fine aggregates, and coarse aggregates, respectively 51 .

Diagnostics plots

The diagnostic statistical graphs, presented as scattered plots of residuals or model prediction errors against the predicted values, serve to assess whether further refinement of the estimation is possible. These graphs are also utilized to gauge the goodness-of-fit of the developed model using studentized residuals, confirming adherence to regression assumption conditions and identifying potential influential observations that could significantly impact the analysis results. It's noteworthy that the standard errors of the derived residuals differ unless the experimental runs' leverages in the design are identical, signifying that raw residuals belong to varying populations and are insufficient for evaluating regression assumptions 52 , 53 . However, studentized residuals are preferred as they map all normal distributions in different dimensions to a unitary distribution. Regarding the desired response variables, diagnostic statistical tests in this analysis were conducted at upper and lower intervals of ± 4.29681, encompassing predicted vs. residual, normal probability, experimental run vs. residuals, predicted vs. actual, and Box-Cox power transformation. These tests aid in detecting issues with the analysis, including outliers, as depicted in Figs. 7 – 10 . These diagnostic statistical plots provide essential criteria for selecting an appropriate power transformation law to evaluate the effects on the response variables at the current lambda of 0.5. Figures  11 – 13 illustrate the interaction effect of CPA admixture versus the concrete ingredients concerning the mechanical strength response. The patterns of compressive and flexural strength discernible from these plots aid in comprehending the parameters for optimum responses when CPA is incorporated into the concrete mixture. The results indicate that the addition of CPA led to improvements in the mechanical properties of the concrete, with the best results achieved at an 11.21% replacement of cement with CPA in the mixture 54 , 55 .

Residuals normal probability plots for the target responses.

Residuals vs. Predicted plots.

Residuals vs. Experimental Runs plots.

Box-cox plots for power transformation.

Surface Plot for OPC vs. CPA.

Surface Plot for Fine Aggregate. vs. CPA.

Surface Plot for Coarse Agg. vs. CPA.

Optimization analysis

After completing the diagnostic statistical analysis and influence graphical calculations, numerical optimization is undertaken using a desirability function. This function assesses the imposed optimization criteria on the model variables to maximize the target response parameters. To achieve this objective, the characteristics of the objective function are analytically adjusted through modifications to weight functions in accordance with the predetermined model variable criteria 56 . These adjustments consider multicollinearity conditions to enable the attainment of favorable conditions and achieve a desirability score of 1.0 within the boundary conditions of 0 ≤ d(yi) ≤ 1. The optimization component of this experimental design seeks the combination of mixture ratios in the feasible factor space, simultaneously satisfying the formulated and imposed criteria on the response parameters and corresponding factor levels 57 . The primary goal of the optimization is set to maximize the target responses, while the combination ratios of the four components are set within the in-range option to determine the optimal proportion of factor levels that yield a maximum response, as detailed in Table 13 . The optimization solution derived from the analytical procedures of the mixture experiment designs is presented in Table 14 and Fig.  14 . The obtained results reveal an optimal desirability score of 1.0 at a combination ratio of 0.222:0.083:0.306:0.406, resulting in maximized compressive and flexural strength of 29.832 MPa and 10.948 MPa, respectively 58 .

Optimization ramps.

Optimization contour plot

The contour plot serves as a crucial tool for visualizing the functional points within the feasible experimental region through iterative mixture design optimization solutions. It is a graphical representation tool for presenting 3D surfaces through contour plotting 59 . Three-dimensional surface plots provide a diagrammatic presentation of the relationships and interactions between the proportions of mixture components and the response parameters 60 , 61 . The 3D plots for the optimal solution, considering the desirability function and showing the response surface for the corresponding points in the analysis, are depicted in Fig.  15 . These graphical solutions illustrate the desirability function of all optimal solutions, adjusted according to the multi-response optimization. From the plot, it is evident that the green surface represents the lowest desirability function, occurring in the range of 0.025–0.05 and 0.15–0.125 fractions of CPA. The highest desirability function is indicated by the red-colored surface, covering the range of 0.075–0.12 fraction of CPA 62 , 63 , 64 .

3D Surface Plot for the Optimization Solutions.

Model simulation and validation

This marks the final phase of the model validation process, where we replicate a real-life scenario to provide essential guidance to designers, contractors, and operators regarding the performance of the developed quadratic model 65 , 66 . The simulation of the model aims to ensure that the validation achieved during statistical diagnostics and inference computations is applicable in real-life situations. Student’s t-test was further employed to determine the statistically significant difference between the simulated model results and the experimental or actual values 64 . A graphical plot illustrating the experimental-derived responses vs. model-simulated results is presented in Fig.  16 . The computed results, obtained with the assistance of Microsoft Excel statistical software, are detailed in Table 15 . The calculated results reveal p (T ≤ t) two-tail values of 0.9987 and 0.9912 for compressive and flexural strength responses, respectively. The statistical outcomes indicate that there is no significant difference between the actual and model-predicted results, signifying acceptable model performance 67 , 68 .

Actual vs. Model Predicted Responses.

The present investigation aimed to optimize the formulation of concrete blended with cassava peel ash (CPA) to achieve superior mechanical properties using a mixture design approach. The study focused on four key parameters: cement content, CPA content, fine aggregate content, and coarse aggregate content, with the primary objectives being to enhance compressive and flexural strength characteristics. Below are the main outcomes derived from the experimental research:

The research study optimizes a mixture consisting of four components, aiming to evaluate the mechanical strength characteristics of the resulting green concrete. The limits for the design mixture components’ ratios were established based on formulations derived from expert knowledge in relevant literature, ensuring an optimal mixture proportion conducive to maximizing strength response.

Chemical property analysis affirmed the beneficial pozzolanic characteristics of cassava peel ash (CPA) when utilized as a supplementary cementitious material (SCM). The CPA composition revealed notable percentages of Fe 2 O 3 (6.02%), Al 2 O 3 (19.88%), and SiO 2 (55.93%), summing up to 81.83%. These findings underscore the potential suitability of CPA as an effective SCM in concrete formulations, owing to its significant content of pozzolanic elements.

The experimental program utilized a face-centered central composite design for laboratory experiments, resulting in a maximum compressive strength of 28.51 MPa and a flexural strength of 10.36 MPa. Subsequently, a quadratic predictive model was developed using the laboratory data, and statistical analyses were conducted to assess the datasets. Through numerical optimization and graphical statistical computations, the optimal levels of mixture ingredients were identified, resulting in a desirability score of 1.0 at a mix ratio of 0.222:0.083:0.306:0.406. This optimal composition led to enhanced compressive and flexural strengths of 29.832 MPa and 10.948 MPa, respectively.

Adequacy tests performed on the generated model demonstrated a robust correlation between laboratory results and model-simulated values, as confirmed by the student's t-test. These findings underscore the effectiveness of the CCD method in optimizing mixture compositions to achieve desired concrete properties, thereby offering valuable insights for enhancing the mechanical performance of green concrete formulations.

Recommendation for future research

Investigation of Additional Parameters: Future studies could explore the impact of varying parameters such as water-cement ratio, curing conditions, and particle size distribution of cassava peel ash (CPA) on the mechanical properties of concrete. This comprehensive approach would provide a more nuanced understanding of the factors influencing concrete performance.

Durability Testing: Given the importance of long-term durability in concrete structures, future research could focus on evaluating the resistance of CPA-blended concrete to environmental factors such as freeze–thaw cycles, sulfate attack, and alkali-silica reaction. Conducting accelerated aging tests and field exposure studies would provide valuable insights into the durability performance of CPA concrete.

Sustainability Assessment: Further studies could assess the environmental impact of utilizing cassava peel ash as a supplementary cementitious material in concrete production. Life cycle assessments and carbon footprint analyses could be conducted to quantify the environmental benefits of incorporating CPA and compare them with traditional concrete formulations.

Optimization of Mixture Design: Continuation of research into optimizing the mixture design of CPA concrete using advanced statistical methods, such as artificial intelligence algorithms, could further enhance the mechanical properties of concrete while minimizing material usage and costs.

Field Applications and Performance Monitoring: Real-world implementation of CPA concrete in construction projects followed by systematic performance monitoring would provide valuable data on its behavior under actual loading and environmental conditions. Long-term monitoring of structures built with CPA concrete would enable the assessment of its structural integrity, durability, and sustainability in practical applications.

Data availability

All data generated or analyzed during this study are included in this published article.

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Iro, U.I., Alaneme, G.U., Attah, I.C. et al. Optimization of cassava peel ash concrete using central composite design method. Sci Rep 14 , 7901 (2024). https://doi.org/10.1038/s41598-024-58555-0

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The proposed framework’s effectiveness is underscored by its ability to recover constraints utilized in GDL, demonstrating its potential as a general-purpose framework for deep learning. GDL, which uses a group-theoretic perspective to describe neural layers, has shown promise across various applications by preserving symmetries. However, it encounters limitations when faced with complex data structures. The category theory-based approach overcomes these limitations and provides a structured methodology for implementing diverse neural network architectures.

The Centre of this research is applying category theory to understand and create neural network architectures. This approach enables the creation of neural networks that are more closely aligned with the structures of the data they process, enhancing both the efficiency and effectiveness of these models. The research highlights the universality and flexibility of category theory as a tool for neural network design, offering new insights into the integration of constraints and operations within neural network models.

In conclusion, this research introduces a groundbreaking framework based on category theory for designing neural network architectures. By bridging the gap between the specification of constraints and their implementations, the framework offers a comprehensive approach to neural network design. The application of category theory not only recovers and extends the constraints used in frameworks like GDL but also opens up new avenues for developing sophisticated neural network architectures. 

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Sana Hassan

Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.

• Sana Hassan https://www.marktechpost.com/author/sana-hassan/ Researchers at Tsinghua University Propose SPMamba: A Novel AI Architecture Rooted in State-Space Models for Enhanced Audio Clarity in Multi-Speaker Environments
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• Sana Hassan https://www.marktechpost.com/author/sana-hassan/ Researchers at Google AI Innovates Privacy-Preserving Cascade Systems for Enhanced Machine Learning Model Performance
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