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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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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, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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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 .

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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10 Comments

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 .

how can I put this blog as my reference(APA style) in bibliography part?

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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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Ever felt like a hamster on a research wheel fast, spinning with a million questions but going nowhere? You've got your topic; you're brimming with curiosity, but... what next? Think of it as your roadmap, ensuring you don't end up lost in a sea of confusing data. So, forget the research rut and get your papers! This ultimate guide to "what is research design?" will have you navigating your project like a pro, uncovering answers and avoiding dead ends. Know the features of good research design, what you mean by research design, elements of research design, and more.

What is Research Design?

Before starting with the topic, do you know what is research design in research? Well, research design is the plan that shows how the study will be done. This plan covers everything from how data will be collected to how it will be analysed. A good research design has a clear question to answer, a detailed plan for gathering information, and a way to make sense of the findings. A good research design has three key ingredients:

1. A clear question: What exactly are you trying to learn? ‍

2. Data collection: How will you gather information (surveys, interviews, experiments)?

3. Analysis: How will you make sense of the data you collect?

Elements of Research Design 

Now that you know what is research design, it is important to know the elements. The elements or components of research design help to ensure that it is reliable, valid and can yield meaningful results. They also provide a guide for the research process, helping the researcher from the initial stages of formulating the research question to the final stages of interpreting the findings. 

1. Purpose Statement: This is a clear and concise statement of the research objectives and the specific goals the research aims to achieve.

2. Research Questions: These are the specific questions the research aims to answer.

3. Research Methodology: This refers to the overall approach and specific methods used to collect and analyse data.

4. Data Collection Methods: These are the specific techniques used to gather data for the research.

5. Data Analysis Techniques: These are the methods used to analyse and interpret the collected data.

6. Units of Analysis: These are the specific entities (e.g., individuals, groups, organisations) that the research focuses on.

7. Linking Data to Propositions: This involves connecting the data collected to the research questions or hypotheses.

8. Interpretation of Findings: This involves making sense of the data and drawing conclusions based on the research objectives.

9. Possible Obstacles to the Research: This involves identifying potential challenges or issues that may arise during the research process.

10. Settings for Research Study: This refers to the context or environment in which the research is conducted.

11. Time of the Research Study: This refers to the timeframe of the research, whether it’s cross-sectional (at one specific point in time) or longitudinal (over an extended period).

Characteristics of Research Design

Research design has several key characteristics that contribute to the validity, reliability, and overall success of a research study. To know the answer for what is research design, it is important to know the characteristics. These are-

1. Reliability: A reliable research design ensures that each study’s results are accurate and can be replicated. This means that if the research is conducted again under the same conditions, it should yield similar results.

2. Validity: A valid research design uses appropriate measuring tools to gauge the results according to the research objective. This ensures that the data collected and the conclusions drawn are relevant and accurately reflect the phenomenon being studied.

3. Neutrality: A neutral research design ensures that the assumptions made at the beginning of the research are free from bias. This means that the data collected throughout the research is based on these unbiased assumptions.

4. Generalizability: A good research design draws an outcome that can be applied to a large set of people and is not limited to the sample size or the research group.

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The Process of Research Design

What is research design? A good research helps you do a really good study that gives fair, trustworthy, and useful results. But it's also good to have a bit of wiggle room for changes. If you’re wondering how to conduct a research in just 5 mins , here's a breakdown and examples to work even better.

Step 1: Establish Priorities for Research Design: 

Before conducting any research study, you must address an important question: "what is research design and how to create one?" For example, if you're researching the impact of remote learning on student performance, your priority might be to establish a clear research question and objectives.

Step 2: Choose your Data Type you Need for Research

One of the best features of research design is to decide on the type of data you need for your research. For instance, if you’re studying the effects of a new drug, you might need quantitative data like clinical trial results.

There are lots of ways to answer your research questions. Think about what you want to achieve before you decide how to do your research. The first thing, do you know what is qualitative research design and what is quantitative research design? Here's a quick difference between the two:

What is Research Design in Quantitative Research?

There are 4 main types of quantitative research design- 

What are Research Design Examples?

1. Experimental Research Methods: 

Drug Efficacy Study: A pharmaceutical company wants to test the effectiveness of a new drug. They randomly assign participants to two groups: one group receives the new drug (experimental group), and the other group receives a placebo (control group). The company then measures the health outcomes of the two groups.

2. Quasi-Experimental Research Methods:

Teaching Method Evaluation: A researcher is interested in the impact of a new teaching method. A group of students are taught using the new method, while another group is taught using the traditional method. The researcher then compares the academic performance of the two groups.

3. Descriptive Research Methods:

Consumer Behavior Survey: A company wants to understand the shopping habits of their customers. They conduct a survey asking customers about their shopping frequency, preferred products, and reasons for their preferences.

4. Correlational Research Methods:

Health and Lifestyle Study: A health researcher is interested in the relationship between physical activity levels and heart disease. They collect data on the physical activity levels and heart health of a large group of people over several years. The researcher then analyses the data to see if there is a correlation between physical activity and heart disease

What is Qualitative Research Design?

Qualitative research designs are more flexible and open-ended. They're all about deeply understanding a particular situation or topic, and you have room to be imaginative and adaptable in planning your study. Below, you'll find a list of typical qualitative research designs.

Step 3: Decide your Data Collection Techniques

Now that you understand what is research design in research, you should also know the types of what are the different types of research design techniques. Choose the methods you’ll use to gather your data. If you’re surveying consumer behaviour, for example, you might use questionnaires or interviews.

Survey methods

Surveys are like questionnaires or interviews where you ask people about what they think, do, feel, or are like. They help you gather information straight from the source. So, when you're planning a research project, you can pick either questionnaires or interviews as your main way to get data. Research design is just the plan you make for how you're going to do your research, including what methods you'll use, like surveys.

Observation methods

Observational studies are a way to gather information without bothering anyone. You just watch and note down what you see, like people's actions or how they interact, without asking them directly. You can do this right then and there, jotting down stuff, or you can record videos to check out later. Depending on what you're studying, these observations can focus on describing things or counting them up.

Secondary Data

If you can't gather data yourself, you can use info already collected by other researchers, like from government surveys or past studies. You can then analyse this data to explore new questions. This can broaden your research because you might access bigger and more diverse samples. But, since you didn't collect the data yourself, you can't choose what to measure or how, which limits your conclusions.

In simple terms, research design is about how you plan to gather and analyse data to answer your research questions. If you can't collect data directly, you might use data already gathered by others, known as secondary data, to still answer your questions.

Step 4: Sort Out your Data Analysis

When you find what research design in research, just having a bunch of raw data isn't enough to answer your questions. You also need to figure out how you're going to make sense of that data. This is where research design comes in.

If you're working with quantitative research, you'll probably use statistics to analyse your data. Statistics help you understand things like how your data is spread out, what the average is, and how different groups compare. For example, you might use tests to see if there's a connection between two things or if one group is different from another.

But if you're dealing with more qualitative research, you'll need a different approach. Instead of crunching numbers, you'll be diving deep into your data, looking for patterns and meanings. You might use methods like thematic analysis or discourse analysis to make sense of it all.

Sampling Procedures

Choosing the right way to pick people for your study is important. But it's not just about that. You also need a solid plan for how you'll reach out and get those people to join in.

Here's what you need to think about:

1. How many people do you need to join to make sure your study is good?

2. What rules will you use to decide who can join and who can't?

3. How will you get in touch with them—by mail, online, phone, or meeting them in person?

4. If you're picking people randomly, it's crucial that everyone who gets chosen actually takes part. How can you make sure most of them do?

If you're not picking people randomly, how will you ensure that your study is unbiased and represents different kinds of people? 

Benefits of Research Design

After learning about what is research design and the process, it is important to know the key benefits of a well-structured research design:

1. Minimises Risk of Errors: A good research design minimises the risk of errors and reduces inaccuracy. It ensures that the study is carried out in the right direction and that all the team members are on the same page.

2. Efficient Use of Resources: It facilitates a concrete research plan for the efficient use of time and resources. It helps the researcher better complete all the tasks, even with limited resources.

3. Provides Direction: The purpose of the research design is to enable the researcher to proceed in the right direction without deviating from the tasks. It helps to identify the major and minor tasks of the study.

4. Ensures Validity and Reliability: A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. The main advantage of a good research design is that it provides accuracy, reliability, consistency, and legitimacy to the research.

5. Facilitates Problem-Solving: A researcher can easily frame the objectives of the research work based on the design of experiments (research design). A good research design helps the researcher find the best solution for the research problems.

6. Better Documentation: It helps in better documentation of the various activities while the project work is going on.

That's it! You've explored all the answers for what is research design in research? Remember, it's not just about picking a fancy method – it's about choosing the perfect tool to answer your burning questions. By carefully considering your goals and resources, you can design a research plan that gathers reliable information and helps you reach clear conclusions. 

Frequently Asked Questions

What are the 4 types of research design, what are the important concepts of research design, what are the 5 components of a research, what are different types of research, what are the 4 major elements of a research design.

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Research Design: What it is, Elements & Types

Can you imagine doing research without a plan? Probably not. When we discuss a strategy to collect, study, and evaluate data, we talk about research design. This design addresses problems and creates a consistent and logical model for data analysis. Let’s learn more about it.

What is Research Design?

Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success.

Creating a research topic explains the type of research (experimental,  survey research ,  correlational , semi-experimental, review) and its sub-type (experimental design, research problem , descriptive case-study). 

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

The research problem an organization faces will determine the design, not vice-versa. The design phase of a study determines which tools to use and how they are used.

The Process of Research Design

The research design process is a systematic and structured approach to conducting research. The process is essential to ensure that the study is valid, reliable, and produces meaningful results.

• Consider your aims and approaches: Determine the research questions and objectives, and identify the theoretical framework and methodology for the study.
• Choose a type of Research Design: Select the appropriate research design, such as experimental, correlational, survey, case study, or ethnographic, based on the research questions and objectives.
• Identify your population and sampling method: Determine the target population and sample size, and choose the sampling method, such as random , stratified random sampling , or convenience sampling.
• Choose your data collection methods: Decide on the data collection methods , such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data.
• Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations.
• Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis , content analysis, or discourse analysis, and plan how to interpret the results.

The process of research design is a critical step in conducting research. By following the steps of research design, researchers can ensure that their study is well-planned, ethical, and rigorous.

Research Design Elements

Impactful research usually creates a minimum bias in data and increases trust in the accuracy of collected data. A design that produces the slightest margin of error in experimental research is generally considered the desired outcome. The essential elements are:

• Accurate purpose statement
• Techniques to be implemented for collecting and analyzing research
• The method applied for analyzing collected details
• Type of research methodology
• Probable objections to research
• Settings for the research study
• Measurement of analysis

Characteristics of Research Design

A proper design sets your study up for success. Successful research studies provide insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main characteristics of a design. There are four key characteristics:

• Neutrality: When you set up your study, you may have to make assumptions about the data you expect to collect. The results projected in the research should be free from research bias and neutral. Understand opinions about the final evaluated scores and conclusions from multiple individuals and consider those who agree with the results.
• Reliability: With regularly conducted research, the researcher expects similar results every time. You’ll only be able to reach the desired results if your design is reliable. Your plan should indicate how to form research questions to ensure the standard of results.
• Validity: There are multiple measuring tools available. However, the only correct measuring tools are those which help a researcher in gauging results according to the objective of the research. The  questionnaire  developed from this design will then be valid.
• Generalization:  The outcome of your design should apply to a population and not just a restricted sample . A generalized method implies that your survey can be conducted on any part of a population with similar accuracy.

The above factors affect how respondents answer the research questions, so they should balance all the above characteristics in a good design. If you want, you can also learn about Selection Bias through our blog.

Research Design Types

A researcher must clearly understand the various types to select which model to implement for a study. Like the research itself, the design of your analysis can be broadly classified into quantitative and qualitative.

Qualitative research

Qualitative research determines relationships between collected data and observations based on mathematical calculations. Statistical methods can prove or disprove theories related to a naturally existing phenomenon. Researchers rely on qualitative observation research methods that conclude “why” a particular theory exists and “what” respondents have to say about it.

Quantitative research

Quantitative research is for cases where statistical conclusions to collect actionable insights are essential. Numbers provide a better perspective for making critical business decisions. Quantitative research methods are necessary for the growth of any organization. Insights drawn from complex numerical data and analysis prove to be highly effective when making decisions about the business’s future.

Qualitative Research vs Quantitative Research

Here is a chart that highlights the major differences between qualitative and quantitative research:

In summary or analysis , the step of qualitative research is more exploratory and focuses on understanding the subjective experiences of individuals, while quantitative research is more focused on objective data and statistical analysis.

You can further break down the types of research design into five categories:

1. Descriptive: In a descriptive composition, a researcher is solely interested in describing the situation or case under their research study. It is a theory-based design method created by gathering, analyzing, and presenting collected data. This allows a researcher to provide insights into the why and how of research. Descriptive design helps others better understand the need for the research. If the problem statement is not clear, you can conduct exploratory research. 

2. Experimental: Experimental research establishes a relationship between the cause and effect of a situation. It is a causal research design where one observes the impact caused by the independent variable on the dependent variable. For example, one monitors the influence of an independent variable such as a price on a dependent variable such as customer satisfaction or brand loyalty. It is an efficient research method as it contributes to solving a problem.

The independent variables are manipulated to monitor the change it has on the dependent variable. Social sciences often use it to observe human behavior by analyzing two groups. Researchers can have participants change their actions and study how the people around them react to understand social psychology better.

3. Correlational research: Correlational research  is a non-experimental research technique. It helps researchers establish a relationship between two closely connected variables. There is no assumption while evaluating a relationship between two other variables, and statistical analysis techniques calculate the relationship between them. This type of research requires two different groups.

A correlation coefficient determines the correlation between two variables whose values range between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables, and -1 means a negative relationship between the two variables. 

4. Diagnostic research: In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. 

This design has three parts of the research:

• Inception of the issue
• Diagnosis of the issue
• Solution for the issue

5. Explanatory research : Explanatory design uses a researcher’s ideas and thoughts on a subject to further explore their theories. The study explains unexplored aspects of a subject and details the research questions’ what, how, and why.

Benefits of Research Design

There are several benefits of having a well-designed research plan. Including:

• Clarity of research objectives: Research design provides a clear understanding of the research objectives and the desired outcomes.
• Increased validity and reliability: To ensure the validity and reliability of results, research design help to minimize the risk of bias and helps to control extraneous variables.
• Improved data collection: Research design helps to ensure that the proper data is collected and data is collected systematically and consistently.
• Better data analysis: Research design helps ensure that the collected data can be analyzed effectively, providing meaningful insights and conclusions.
• Improved communication: A well-designed research helps ensure the results are clean and influential within the research team and external stakeholders.
• Efficient use of resources: reducing the risk of waste and maximizing the impact of the research, research design helps to ensure that resources are used efficiently.

A well-designed research plan is essential for successful research, providing clear and meaningful insights and ensuring that resources are practical.

QuestionPro offers a comprehensive solution for researchers looking to conduct research. With its user-friendly interface, robust data collection and analysis tools, and the ability to integrate results from multiple sources, QuestionPro provides a versatile platform for designing and executing research projects.

Our robust suite of research tools provides you with all you need to derive research results. Our online survey platform includes custom point-and-click logic and advanced question types. Uncover the insights that matter the most.

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Introduction

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

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

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

General Structure and Writing Style

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

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

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

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

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

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

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

Action Research Design

Definition and Purpose

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

What do these studies tell you ?

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

What these studies don't tell you ?

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

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

Case Study Design

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

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

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

Causal Design

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

Conditions necessary for determining causality:

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

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

Cohort Design

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

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

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

Cross-Sectional Design

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

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

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

Descriptive Design

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

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

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

Experimental Design

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

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

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

Exploratory Design

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

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

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

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

Field Research Design

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

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

What these studies don't tell you

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

Historical Design

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

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

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

Longitudinal Design

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

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

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

Meta-Analysis Design

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

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

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

Mixed-Method Design

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

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

Observational Design

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

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

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

Philosophical Design

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

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

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

Sequential Design

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

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

Systematic Review

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

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

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Major Components of Research Design

The primary intent behind the research design is to help avoid the situation in which the evidence doesn’t address the primary research questions. A research design is concerned with a logical problem and not a logistical problem. Five major components of research design  are:

1. Research study’s questions 2. Study propositions 3. Unit(s) of analysis 4. Linking data to propositions 5. Interpreting a study’s findings

Major Components of Research Design in Research Methodology

The research design components  apply to all types of qualitative, deductive research, whether in the physical or social sciences.

Research study’s questions : This first component suggests the type of the question-in terms of “who,” “what,” “where,” “how,” and “why”-provides an crucial clue concerning the most relevant research method to be used. Use three stages: In the first, make use of the literature to narrow your interest to a key topic or two. In the 2nd, take a look at closely-even dissect-a few key studies on your topic of interest. Identify the questions in those few studies and whether they conclude with new questions for future research. In the last phase, examine another group of scientific studies on the same topic. They might provide support for your potential questions or even suggest means of sharpening them.

Study propositions:  Each proposition directs focus on something which needs to be examined within the scope of study. Only if you are forced to state some propositions will you move in the right direction. For example, you may think that businesses collaborate as they gain mutual benefits. This proposition, apart from highlighting a crucial theoretical issue (that other incentives for collaboration don’t exist or are unimportant), also starts to tell you where you can search for related proof (to define and determine the extent of certain advantages to each business).

Figure: Key Components

Unit of analysis: It is associated with the fundamental problem of defining what the “case” is-a problem which has affected many researchers at the beginning of case studies. Take example of clinical patients. In this situation, an individual is being studied, and the individual is the key unit of analysis. Information regarding the appropriate individual will be collected, and several such individuals  could be part of a multiple-case study. You would need study questions and propositions to help find out the appropriate information to be collected relating to this individual or individuals. Without such questions and propositions, you could be lured to cover “everything” with regards to the individual(s), which is not possible.

Linking data to propositions:  Methods of linking data to propositions are pattern matching, explanation building, time-series analysis, logic models, and cross-case synthesis. The actual analyses will demand that you merge or compute your study data as a direct reflection of your initial study propositions.

Read Also: Elements of Research Design

Interpreting a study’s findings:  A statistical analysis determines if the results of the study support the hypothesis. A number of statistical tests, for example T-tests (that determine if two groups are statistically distinct from one another), Chi-square tests (where data are compared to an anticipated outcome) and one-way analysis of variance (provides for the comparison of multiple groups), are carried out according to the type of data, number and types of variables and data categories. Statistical analysis offer some explicit criteria for interpretations. For example, by convention, social science views a p level of less than .05 to indicate that observed differences were “statistically important.” On the other hand, much case study analysis is not going to depend on the use of statistics and so focuses on other methods of thinking about such criteria.

Read Also: Definition of Research Design

In this article, I have discussed about the major components of research design in research methodology . A research design should include the above listed five components. A research design must suggest what data should be gathered, its propositions, units of analysis, it must tell you what is to be done after the data have been gathered.

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The Importance of Research Design: A Comprehensive Guide

Research design plays a crucial role in conducting scientific studies and gaining meaningful insights. A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. This comprehensive guide will provide an in-depth understanding of research design, its key components, different types, and its role in scientific inquiry. Furthermore, it will discuss the necessary steps in developing a research design and highlight some of the challenges that researchers commonly face.

Table of Contents

Understanding research design.

Research design refers to the overall plan or strategy that outlines how a study is conducted. It serves as a blueprint for researchers, guiding them in their investigation, and helps ensure that the study objectives are met. Understanding research design is essential for researchers to effectively gather and analyze data to answer research questions.

When embarking on a research study, researchers must carefully consider the design they will use. The design determines the structure of the study, including the research questions, data collection methods, and analysis techniques. It provides clarity on how the study will be conducted and helps researchers determine the best approach to achieve their research objectives. A well-designed study increases the chances of obtaining valid and reliable results.

Definition and Purpose of Research Design

Research design is the framework that outlines the structure of a study, including the research questions, data collection methods, and analysis techniques. It provides a systematic approach to conducting research and ensures that all aspects of the study are carefully planned and executed.

The purpose of research design is to provide a clear roadmap for researchers to follow. It helps them define the research questions they want to answer and identify the variables they will study. By clearly defining the purpose of the study, researchers can ensure that their research design aligns with their objectives.

Key Components of Research Design

A research design consists of several key components that influence the study’s validity and reliability. These components include the research questions, variables and operational definitions, sampling techniques, data collection methods, and statistical analysis procedures.

The research questions are the foundation of any study. They guide the entire research process and help researchers focus their efforts. By formulating clear and concise research questions, researchers can ensure that their study addresses the specific issues they want to investigate.

Variables and operational definitions are also crucial components of research design. Variables are the concepts or phenomena that researchers want to measure or study. Operational definitions provide a clear and specific description of how these variables will be measured or observed. By clearly defining variables and their operational definitions, researchers can ensure that their study is consistent and replicable.

Sampling techniques play a vital role in research design as well. Researchers must carefully select the participants or samples they will study to ensure that their findings are generalizable to the larger population. Different sampling techniques, such as random sampling or purposive sampling, can be used depending on the research objectives and constraints.

Data collection methods are another important component of research design. Researchers must decide how they will collect data, whether through surveys, interviews, observations, or experiments. The choice of data collection method depends on the research questions and the type of data needed to answer them.

Finally, statistical analysis procedures are used to analyze the collected data and draw meaningful conclusions. Researchers must determine the appropriate statistical tests or techniques to use based on the nature of their data and research questions. The choice of statistical analysis procedures ensures that the data is analyzed accurately and that the results are valid and reliable.

Types of Research Design

Research design encompasses various types that researchers can choose depending on their research goals and the nature of the phenomenon being studied. Understanding the different types of research design is essential for researchers to select the most appropriate approach for their study.

When embarking on a research project, researchers must carefully consider the design they will employ. The design chosen will shape the entire study, from the data collection process to the analysis and interpretation of results. Let’s explore some of the most common types of research design in more detail.

Experimental Design

Experimental design involves manipulating one or more variables to observe their effect on the dependent variable. This type of design allows researchers to establish cause-and-effect relationships between variables by controlling for extraneous factors. Experimental design often relies on random assignment and control groups to minimize biases.

Imagine a group of researchers interested in studying the effects of a new teaching method on student performance. They could randomly assign students to two groups: one group would receive instruction using the new teaching method, while the other group would receive instruction using the traditional method. By comparing the performance of the two groups, the researchers can determine whether the new teaching method has a significant impact on student learning.

Experimental design provides a strong foundation for making causal claims, as it allows researchers to control for confounding variables and isolate the effects of the independent variable. However, it may not always be feasible or ethical to manipulate variables, leading researchers to explore alternative designs.

Free 44-page Experimental Design Guide

For Beginners and Intermediates

• Introduction to experimental methods
• Respondent management with groups and populations
• How to set up stimulus selection and arrangement

Non-Experimental Design

Non-experimental design is used when it is not feasible or ethical to manipulate variables. This design relies on naturally occurring variations in data and focuses on observing and describing relationships between variables. Non-experimental design can be useful for exploratory research or when studying phenomena that cannot be controlled, such as human behavior.

For instance, researchers interested in studying the relationship between socioeconomic status and health outcomes may collect data from a large sample of individuals and analyze the existing differences. By examining the data, they can determine whether there is a correlation between socioeconomic status and health, without manipulating any variables.

Non-experimental design allows researchers to study real-world phenomena in their natural setting, providing valuable insights into complex social, psychological, and economic processes. However, it is important to note that non-experimental designs cannot establish causality, as there may be other variables at play that influence the observed relationships.

Quasi-Experimental Design

Quasi-experimental design resembles experimental design but lacks the element of random assignment. In situations where random assignment is not possible or practical, researchers can utilize quasi-experimental designs to gather data and make inferences. However, caution must be exercised when drawing causal conclusions from quasi-experimental studies.

Consider a scenario where researchers are interested in studying the effects of a new drug on patient recovery time. They cannot randomly assign patients to receive the drug or a placebo due to ethical considerations. Instead, they can compare the recovery times of patients who voluntarily choose to take the drug with those who do not. While this design allows for data collection and analysis, it is important to acknowledge that other factors, such as patient motivation or severity of illness, may influence the observed outcomes.

Quasi-experimental designs are valuable when experimental designs are not feasible or ethical. They provide an opportunity to explore relationships and gather data in real-world contexts. However, researchers must be cautious when interpreting the results, as causal claims may be limited due to the lack of random assignment.

By understanding the different types of research design, researchers can make informed decisions about the most appropriate approach for their study. Each design offers unique advantages and limitations, and the choice depends on the research question, available resources, and ethical considerations. Regardless of the design chosen, rigorous methodology and careful data analysis are crucial for producing reliable and valid research findings.

The Role of Research Design in Scientific Inquiry

A well-designed research study enhances the validity and reliability of the findings. Research design plays a crucial role in ensuring the scientific rigor of a study and facilitates the replication of studies by other researchers. Understanding the role of research design in scientific inquiry is vital for researchers to conduct impactful and robust research.

Ensuring Validity and Reliability

Research design plays a critical role in ensuring the validity and reliability of the study’s findings. Validity refers to the degree to which the study measures what it intends to measure, while reliability pertains to the consistency and stability of the results. Through careful consideration of the research design, researchers can minimize potential biases and increase the accuracy of their measurements.

Facilitating Replication of Studies

A robust research design allows for the replication of studies by other researchers. Replication plays a vital role in the scientific process as it helps confirm the validity and generalizability of research findings. By clearly documenting the research design, researchers enable others to reproduce the study and validate the results, thereby contributing to the cumulative knowledge in a field.

Steps in Developing a Research Design

Developing a research design involves a systematic process that includes several important steps. Researchers need to carefully consider each step to ensure that their study is well-designed and capable of addressing their research questions effectively.

Identifying Research Questions

The first step in developing a research design is to identify and define the research questions or hypotheses. Researchers need to clearly articulate what they aim to investigate and what specific information they want to gather. Clear research questions provide guidance for the subsequent steps in the research design process.

Selecting Appropriate Design Type

Once the research questions are identified, researchers need to select the most appropriate type of research design. The choice of design depends on various factors, including the research goals, the nature of the research questions, and the available resources. Careful consideration of these factors is crucial to ensure that the chosen design aligns with the study objectives.

Determining Data Collection Methods

After selecting the research design, researchers need to determine the most suitable data collection methods. Depending on the research questions and the type of data required, researchers can utilize a range of methods, such as surveys, interviews, observations, or experiments. The chosen methods should align with the research objectives and allow for the collection of high-quality data.

One of the most important considerations when designing a study in human behavior research is participant recruitment. We have written a comprehensive guide on best practices and pitfalls to be aware of when recruiting participants, which can be read here.

Enhancing Research Design with iMotions and Biosensors

Introduction to enhanced research design.

In the realm of scientific studies, especially within human cognitive-behavioral research, the deployment of advanced technologies such as iMotions software and biosensors has revolutionized research design. This chapter delves into how these technologies can be integrated into various research designs, improving the depth, accuracy, and reliability of scientific inquiries.

Integrating iMotions in Research Design

Imotions software: a key to multimodal data integration.

The iMotions platform stands as a pivotal tool in modern research design. It’s designed to integrate data from a plethora of biosensors, providing a comprehensive analysis of human behavior. This software facilitates the synchronizing of physiological, cognitive, and emotional responses with external stimuli, thus enriching the understanding of human behavior in various contexts.

Biosensors: Gateways to Deeper Insights

Biosensors, including eye trackers, EEG, GSR, ECG, and facial expression analysis tools, provide nuanced insights into the subconscious and conscious aspects of human behavior. These tools help researchers in capturing data that is often unattainable through traditional data collection methods like surveys and interviews.

Application in Different Research Designs

• Eye Tracking : In experimental designs, where the impact of visual stimuli is crucial, eye trackers can reveal how subjects interact with these stimuli, thereby offering insights into cognitive processes and attention.
• EEG : EEG biosensors allow researchers to monitor brain activity in response to controlled experimental manipulations, offering a window into cognitive and emotional responses.

• Facial Expression Analysis : In observational studies, analyzing facial expressions can provide objective data on emotional responses in natural settings, complementing subjective self-reports.
• GSR/EDA : These tools measure physiological arousal in real-life scenarios, giving researchers insights into emotional states without the need for intrusive measures.
• EMG : In studies where direct manipulation isn’t feasible, EMG can indicate subtle responses to stimuli, which might be overlooked in traditional observational methods.
• ECG/PPG : These sensors can be used to understand the impact of various interventions on physiological states such as stress or relaxation.

Streamlining Research Design with iMotions

The iMotions platform offers a streamlined process for integrating various biosensors into a research design. Researchers can easily design experiments, collect multimodal data, and analyze results in a unified interface. This reduces the complexity often associated with handling multiple streams of data and ensures a cohesive and comprehensive research approach.

Integrating iMotions software and biosensors into research design opens new horizons for scientific inquiry. This technology enhances the depth and breadth of data collection, paving the way for more nuanced and comprehensive findings.

Whether in experimental, non-experimental, or quasi-experimental designs, iMotions and biosensors offer invaluable tools for researchers aiming to uncover the intricate layers of human behavior and cognitive processes. The future of research design is undeniably intertwined with the advancements in these technologies, leading to more robust, reliable, and insightful scientific discoveries.

Challenges in Research Design

Research design can present several challenges that researchers need to overcome to conduct reliable and valid studies. Being aware of these challenges is essential for researchers to address them effectively and ensure the integrity of their research.

Ethical Considerations

Research design must adhere to ethical guidelines and principles to protect the rights and well-being of participants. Researchers need to obtain informed consent, ensure participant confidentiality, and minimize potential harm or discomfort. Ethical considerations should be carefully integrated into the research design to promote ethical research practices.

Practical Limitations

Researchers often face practical limitations that may impact the design and execution of their studies. Limited resources, time constraints, access to participants or data, and logistical challenges can pose obstacles during the research process. Researchers need to navigate these limitations and make thoughtful choices to ensure the feasibility and quality of their research.

Research design is a vital aspect of conducting scientific studies. It provides a structured framework for researchers to answer their research questions and obtain reliable and valid results. By understanding the different types of research design and following the necessary steps in developing a research design, researchers can enhance the rigor and impact of their studies.

However, researchers must also be mindful of the challenges they may encounter, such as ethical considerations and practical limitations, and take appropriate measures to address them. Ultimately, a well-designed research study contributes to the advancement of knowledge and promotes evidence-based decision-making in various fields.

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10 Essential Components of Research Design And Solid Methodology

Sep 12, 2023 | Research FAQs

How to Craft a Research Study with a Robust Methodology: A Guide

Embarking on a research journey is a fascinating endeavour that requires a solid roadmap to ensure meaningful and accurate results. Central to this roadmap is the intricate interplay between research design and research methodology. These terms might seem interchangeable, but they encompass distinct aspects of the research process.

Research design encompasses the blueprint that guides the overall structure and organisation of your study, while research methodology involves the techniques, procedures, and tools you use to collect and analyse data. In this guide, we will delve into the essential components of creating a research study with a robust methodology that stands up to scrutiny and advances knowledge in your chosen field.

The 10 Essential Components of Creating a Research Study

#1  defining research design and research methodology.

• Research Design : At its core, research design refers to the plan that outlines how you will conduct your study, from the formulation of research questions or hypotheses to data collection methods and analysis techniques. It’s the architecture that shapes the overall trajectory of your research.
• Research Methodology : Research methodology, on the other hand, comprises the techniques and tools you employ to gather and interpret data. It involves decisions about sampling, data collection instruments, data analysis methods, and even ethical considerations. Methodology breathes life into the design, enabling you to address your research questions effectively.

Research Design serves as the strategic blueprint that outlines the systematic approach you intend to take in your study. It’s not merely a series of steps, but a comprehensive plan that encompasses every facet of your research, from the fundamental research questions or hypotheses you’ll explore, down to the precise data collection methods and analytical techniques that will be employed. Think of research design as the architect’s rendering of the entire research process, guiding your journey with a clear roadmap.

In contrast, Research Methodology is the toolbox that equips you with the instruments to carry out the plan delineated by your research design. These methodologies involve a vast array of decisions – from selecting the most appropriate sampling techniques to choosing data collection instruments that perfectly align with the nature of your research questions. Furthermore, it extends to the methods you will employ to analyse the collected data and, importantly, how ethical considerations will be woven into every step. Essentially, research methodology is the engine that drives your research design, infusing it with life and enabling you to extract meaningful insights from the data.

#2 The Crucial Relationship Between Research Questions and Design

The research design hinges on well-defined research questions. Clarity in your research questions helps you choose the appropriate design to address them, whether exploratory, descriptive, causal-comparative, or experimental.

Imagine research design as a tailored suit and research questions as its measurements. A well-fitted suit starts with accurate measurements, just as a well-crafted research design begins with well-structured research questions. The clarity and precision of your research questions directly influence the design you select. A research question that aims to understand patterns might lead you to a descriptive design, while a question exploring causal relationships could guide you toward an experimental design. Thus, the foundational connection between research questions and design choices cannot be overstated.

#3 Types of Research Designs

• Descriptive Designs : Ideal for capturing the “what” of a phenomenon, descriptive designs involve observing and describing behaviours, attributes, or characteristics. They are often preliminary and offer insights for more in-depth studies.
• Experimental Designs : These designs allow researchers to establish cause-and-effect relationships by manipulating variables. They involve control groups, experimental groups, and random assignment.
• Correlational Designs : These designs examine the relationships between variables, helping researchers understand how changes in one variable might relate to changes in another.
• Longitudinal Designs : Suitable for studying changes over time, longitudinal designs involve observing the same participants at multiple points, revealing developmental trends.

Descriptive Designs : Descriptive research acts as a reconnaissance mission, capturing the essence of a phenomenon. These designs excel at answering “what” questions, laying the groundwork for further in-depth studies. For example, a descriptive study might explore the characteristics of a newly discovered species, providing the basic data required for more extensive research.

Experimental Designs : Experimental designs are akin to scientific laboratories, allowing researchers to manipulate variables to establish cause-and-effect relationships. By employing control and experimental groups, researchers can isolate the influence of specific factors, providing robust evidence for causal claims. These designs are particularly useful in fields like medicine, where testing the efficacy of new treatments is paramount.

Correlational Designs : These designs delve into relationships between variables, revealing how changes in one variable correspond to changes in another. For instance, a correlational study might uncover a strong connection between sleep patterns and academic performance, shedding light on potential influences.

Longitudinal Designs : Longitudinal designs are like time-lapse photography, capturing changes in individuals or groups over an extended period. By observing the same participants at various points, researchers gain insights into developmental trends, making them essential for studying human growth, societal changes, or educational trajectories.

# 4 Sampling Techniques and Considerations

Using the right sample is pivotal for research validity. Techniques like random sampling, stratified sampling, and convenience sampling impact the generalisability of findings. Considerations like sample size and representativeness influence the strength of your conclusions.

Selecting the right sample is like choosing the ingredients for a recipe – it determines the flavour of your study. Random sampling is like drawing ingredients blindly, ensuring a representative mix. Stratified sampling is akin to selecting specific categories of ingredients to ensure a balanced dish, while convenience sampling is comparable to using the ingredients at hand.

The size of your sample impacts the quality of your dish, with larger samples offering more robust conclusions. Just as a well-cooked dish pleases the palate, a well-selected sample pleases the academic appetite.

#5  Data Collection Instruments

Questionnaires, interviews, surveys, observations, and existing records are common data collection tools. The choice depends on your research questions, design, and available resources. Validity and reliability are essential in ensuring that your instruments measure what they intend to and yield consistent results.

Picture data collection instruments as a diverse ensemble of tools in a researcher’s toolkit. The choice of instrument depends on the research design, questions, and resources at hand. Questionnaires act as surveys, gathering structured information efficiently. Interviews are like in-depth conversations, unearthing nuanced insights. Observations become windows into behaviour, revealing hidden patterns. Existing records act as historical archives, offering glimpses into the past. Regardless of the instrument, their validity and reliability are akin to a chef’s precision – crucial for a satisfying outcome.

# 6 Data Analysis Methods

Quantitative studies often involve statistical analysis techniques like regression, ANOVA, and chi-square tests. Qualitative studies use methods like thematic analysis, content analysis, and grounded theory to extract meaningful patterns from textual or visual data.

In the research kitchen, data analysis methods are the secret recipes that transform raw ingredients into delectable dishes. Quantitative studies employ statistical techniques like regression, ANOVA, and chi-square tests, transforming numbers into meaningful insights.

Qualitative studies, however, are like the art of pairing flavours, using thematic analysis, content analysis, and grounded theory to extract the essence from textual or visual data. Just as a culinary masterpiece marries ingredients in perfect harmony, data analysis methods bring coherence to the research findings.

#7 Ethical Considerations

Ethical research demands safeguarding participants’ rights, obtaining informed consent, and ensuring confidentiality. Adhering to ethical guidelines builds trust and credibility in your study.

Ethics in research is like the dining etiquette at a grand banquet – it ensures that participants are treated with respect and integrity. Safeguarding their rights is the equivalent of offering them a seat at the table. Obtaining informed consent is akin to explaining the menu before serving a dish, ensuring participants are aware of what’s involved. Confidentiality acts as the vault that protects their privacy, building trust and integrity in the research process. Adhering to ethical guidelines is as crucial as adhering to table manners – it cultivates an environment of professionalism and mutual respect.

# 8 Validity and Reliability

Validity assesses whether your study measures what it intends to, while reliability gauges the consistency of your measurements. Triangulation, member checks, and inter-rater reliability enhance the robustness of your study.

In research, validity and reliability are like the sturdy foundation of a well-built structure. Validity ensures that your study measures what it intends to, just as a sturdy pillar supports the weight it’s designed for. Reliability is the consistency that underpins your measurements – like the uniformity of bricks in a wall. Just as an architect cross-checks plans, triangulation and member checks enhance the robustness of your research. Inter-rater reliability acts as the seal of approval, ensuring that multiple perspectives align harmoniously.

#9 Pilot Studies

Conducting a pilot study helps identify potential issues with your research design, methodology, or instruments before the main study. It’s a crucial step in refining your approach.

A pilot study is akin to a dress rehearsal before a grand performance. Imagine your research as a stage production – a pilot study is the practice run that unveils potential glitches, ensuring that the main event goes off without a hitch. Like trying on costumes and testing lighting cues, a pilot study uncovers issues with design, methodology, and instruments before the curtains rise. This prelude to the main research endeavour can be likened to fine-tuning an orchestra before the concert – a crucial step in refining your approach and ensuring the spotlight shines on your findings.

Key Takeaways

• Research design is the blueprint that guides your study, while research methodolog y involves the techniques for data collection and analysis.
• Research design is closely linked to well-defined research questions, shaping the direction of the study.
• Types of research designs include descriptive, experimental, correlational, and longitudinal.
• Sampling techniques impact the representativeness of your findings, and data collection instruments should be valid and reliable.
• Quantitative studies use statistical analysis, while qualitative studies employ techniques like thematic analysis.
• Ethical considerations are paramount, and maintaining validity and reliability enhances study credibility.
• Pilot studies help identify and rectify issues before the main study.
• Research is iterative and involves refining your approach based on insights.

#1 0 Iterative Nature of Research

Research is seldom linear; it often involves refining, revisiting, and adapting your approach based on emerging insights and challenges.

Picture research as a sculptor moulding clay – it’s an iterative process that requires shaping and reshaping until the desired form emerges. Just as an artist revisits their canvas, research often demands revisiting and refining your approach. As you uncover insights, challenges, and unexpected discoveries, adapting your strategy becomes essential. Embracing this iterative nature is akin to sailing with the wind – it allows you to navigate uncharted waters and reach new horizons of understanding.

Keep in mind that research design and methodology work in harmony, guiding your journey while equipping you with the tools to explore uncharted territories. Clarity in your research questions sets the tone, with design choices echoing the path you chart. From choosing data collection tools to adhering to ethical considerations, every step contributes to the symphony of a meticulously crafted research study.

Contributing To Advancements in Your Field

A robust research study hinges on the symbiotic relationship between research design and research methodology. The design serves as the guiding framework, while the methodology equips you with the tools to explore your research questions rigorously. The journey from formulating clear research questions to selecting appropriate designs, sampling methods, data collection instruments, and analysis techniques demands meticulous planning, ethical considerations, and a commitment to validity and reliability.

Embrace the iterative nature of research, welcoming the opportunity to refine and adapt your approach as you uncover new dimensions of understanding within your chosen field. Remember, a well-crafted research study not only contributes to academia but also shapes the larger discourse in your area of interest.

Useful Resources

• Research Methods Knowledge Base – An extensive repository of information on research methods and design.
• Way With Words – Professional academic research transcription services that enhance the accuracy and clarity of your research interviews and recordings.

Engagement Questions

• How do you envision the relationship between research design and research methodology in optimising the quality of your study?
• Can you identify a real-world scenario where choosing the right research design significantly impacted the study’s outcomes?
• In what ways can a well-thought-out pilot study contribute to the success of your main research endeavour?

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Language: English | German

How to Construct a Mixed Methods Research Design

Wie man ein mixed methods-forschungs-design konstruiert, judith schoonenboom.

1 Institut für Bildungswissenschaft, Universität Wien, Sensengasse 3a, 1090 Wien, Austria

R. Burke Johnson

2 Department of Professional Studies, University of South Alabama, UCOM 3700, 36688-0002 Mobile, AL USA

This article provides researchers with knowledge of how to design a high quality mixed methods research study. To design a mixed study, researchers must understand and carefully consider each of the dimensions of mixed methods design, and always keep an eye on the issue of validity. We explain the seven major design dimensions: purpose, theoretical drive, timing (simultaneity and dependency), point of integration, typological versus interactive design approaches, planned versus emergent design, and design complexity. There also are multiple secondary dimensions that need to be considered during the design process. We explain ten secondary dimensions of design to be considered for each research study. We also provide two case studies showing how the mixed designs were constructed.

Zusammenfassung

Der Beitrag gibt einen Überblick darüber, wie das Forschungsdesign bei Mixed Methods-Studien angelegt sein sollte. Um ein Mixed Methods-Forschungsdesign aufzustellen, müssen Forschende sorgfältig alle Dimensionen von Methodenkombinationen abwägen und von Anfang an auf die Güte und damit verbundene etwaige Probleme achten. Wir erklären und diskutieren die für Forschungsdesigns relevanten sieben Dimensionen von Methodenkombinationen: Untersuchungsziel, Rolle von Theorie im Forschungsprozess, Timing (Simultanität und Abhängigkeit), Schnittstellen, an denen Integration stattfindet, systematische vs. interaktive Design-Ansätze, geplante vs. emergente Designs und Komplexität des Designs. Es gibt außerdem zahlreiche sekundäre Dimensionen, die bei der Aufstellung des Forschungsdesigns berücksichtigt werden müssen, von denen wir zehn erklären. Der Beitrag schließt mit zwei Fallbeispielen ab, anhand derer konkret gezeigt wird, wie Mixed Methods-Forschungsdesigns aufgestellt werden können.

What is a mixed methods design?

This article addresses the process of selecting and constructing mixed methods research (MMR) designs. The word “design” has at least two distinct meanings in mixed methods research (Maxwell 2013 ). One meaning focuses on the process of design; in this meaning, design is often used as a verb. Someone can be engaged in designing a study (in German: “eine Studie konzipieren” or “eine Studie designen”). Another meaning is that of a product, namely the result of designing. The result of designing as a verb is a mixed methods design as a noun (in German: “das Forschungsdesign” or “Design”), as it has, for example, been described in a journal article. In mixed methods design, both meanings are relevant. To obtain a strong design as a product, one needs to carefully consider a number of rules for designing as an activity. Obeying these rules is not a guarantee of a strong design, but it does contribute to it. A mixed methods design is characterized by the combination of at least one qualitative and one quantitative research component. For the purpose of this article, we use the following definition of mixed methods research (Johnson et al. 2007 , p. 123):

Mixed methods research is the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e. g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration.

Mixed methods research (“Mixed Methods” or “MM”) is the sibling of multimethod research (“Methodenkombination”) in which either solely multiple qualitative approaches or solely multiple quantitative approaches are combined.

In a commonly used mixed methods notation system (Morse 1991 ), the components are indicated as qual and quan (or QUAL and QUAN to emphasize primacy), respectively, for qualitative and quantitative research. As discussed below, plus (+) signs refer to concurrent implementation of components (“gleichzeitige Durchführung der Teilstudien” or “paralleles Mixed Methods-Design”) and arrows (→) refer to sequential implementation (“Sequenzielle Durchführung der Teilstudien” or “sequenzielles Mixed Methods-Design”) of components. Note that each research tradition receives an equal number of letters (four) in its abbreviation for equity. In this article, this notation system is used in some depth.

A mixed methods design as a product has several primary characteristics that should be considered during the design process. As shown in Table  1 , the following primary design “dimensions” are emphasized in this article: purpose of mixing, theoretical drive, timing, point of integration, typological use, and degree of complexity. These characteristics are discussed below. We also provide some secondary dimensions to consider when constructing a mixed methods design (Johnson and Christensen 2017 ).

List of Primary and Secondary Design Dimensions

On the basis of these dimensions, mixed methods designs can be classified into a mixed methods typology or taxonomy. In the mixed methods literature, various typologies of mixed methods designs have been proposed (for an overview see Creswell and Plano Clark 2011 , p. 69–72).

The overall goal of mixed methods research, of combining qualitative and quantitative research components, is to expand and strengthen a study’s conclusions and, therefore, contribute to the published literature. In all studies, the use of mixed methods should contribute to answering one’s research questions.

Ultimately, mixed methods research is about heightened knowledge and validity. The design as a product should be of sufficient quality to achieve multiple validities legitimation (Johnson and Christensen 2017 ; Onwuegbuzie and Johnson 2006 ), which refers to the mixed methods research study meeting the relevant combination or set of quantitative, qualitative, and mixed methods validities in each research study.

Given this goal of answering the research question(s) with validity, a researcher can nevertheless have various reasons or purposes for wanting to strengthen the research study and its conclusions. Following is the first design dimension for one to consider when designing a study: Given the research question(s), what is the purpose of the mixed methods study?

A popular classification of purposes of mixed methods research was first introduced in 1989 by Greene, Caracelli, and Graham, based on an analysis of published mixed methods studies. This classification is still in use (Greene 2007 ). Greene et al. ( 1989 , p. 259) distinguished the following five purposes for mixing in mixed methods research:

1.  Triangulation seeks convergence, corroboration, correspondence of results from different methods; 2.  Complementarity seeks elaboration, enhancement, illustration, clarification of the results from one method with the results from the other method; 3.  Development seeks to use the results from one method to help develop or inform the other method, where development is broadly construed to include sampling and implementation, as well as measurement decisions; 4.  Initiation seeks the discovery of paradox and contradiction, new perspectives of frameworks, the recasting of questions or results from one method with questions or results from the other method; 5.  Expansion seeks to extend the breadth and range of inquiry by using different methods for different inquiry components.

In the past 28 years, this classification has been supplemented by several others. On the basis of a review of the reasons for combining qualitative and quantitative research mentioned by the authors of mixed methods studies, Bryman ( 2006 ) formulated a list of more concrete rationales for performing mixed methods research (see Appendix). Bryman’s classification breaks down Greene et al.’s ( 1989 ) categories into several aspects, and he adds a number of additional aspects, such as the following:

(a)  Credibility – refers to suggestions that employing both approaches enhances the integrity of findings. (b)  Context – refers to cases in which the combination is justified in terms of qualitative research providing contextual understanding coupled with either generalizable, externally valid findings or broad relationships among variables uncovered through a survey. (c)  Illustration – refers to the use of qualitative data to illustrate quantitative findings, often referred to as putting “meat on the bones” of “dry” quantitative findings. (d)  Utility or improving the usefulness of findings – refers to a suggestion, which is more likely to be prominent among articles with an applied focus, that combining the two approaches will be more useful to practitioners and others. (e)  Confirm and discover – this entails using qualitative data to generate hypotheses and using quantitative research to test them within a single project. (f)  Diversity of views – this includes two slightly different rationales – namely, combining researchers’ and participants’ perspectives through quantitative and qualitative research respectively, and uncovering relationships between variables through quantitative research while also revealing meanings among research participants through qualitative research. (Bryman, p. 106)

Views can be diverse (f) in various ways. Some examples of mixed methods design that include a diversity of views are:

• Iteratively/sequentially connecting local/idiographic knowledge with national/general/nomothetic knowledge;
• Learning from different perspectives on teams and in the field and literature;
• Achieving multiple participation, social justice, and action;
• Determining what works for whom and the relevance/importance of context;
• Producing interdisciplinary substantive theory, including/comparing multiple perspectives and data regarding a phenomenon;
• Juxtaposition-dialogue/comparison-synthesis;
• Breaking down binaries/dualisms (some of both);
• Explaining interaction between/among natural and human systems;
• Explaining complexity.

The number of possible purposes for mixing is very large and is increasing; hence, it is not possible to provide an exhaustive list. Greene et al.’s ( 1989 ) purposes, Bryman’s ( 2006 ) rationales, and our examples of a diversity of views were formulated as classifications on the basis of examination of many existing research studies. They indicate how the qualitative and quantitative research components of a study relate to each other. These purposes can be used post hoc to classify research or a priori in the design of a new study. When designing a mixed methods study, it is sometimes helpful to list the purpose in the title of the study design.

The key point of this section is for the researcher to begin a study with at least one research question and then carefully consider what the purposes for mixing are. One can use mixed methods to examine different aspects of a single research question, or one can use separate but related qualitative and quantitative research questions. In all cases, the mixing of methods, methodologies, and/or paradigms will help answer the research questions and make improvements over a more basic study design. Fuller and richer information will be obtained in the mixed methods study.

Theoretical drive

In addition to a mixing purpose, a mixed methods research study might have an overall “theoretical drive” (Morse and Niehaus 2009 ). When designing a mixed methods study, it is occasionally helpful to list the theoretical drive in the title of the study design. An investigation, in Morse and Niehaus’s ( 2009 ) view, is focused primarily on either exploration-and-description or on testing-and-prediction. In the first case, the theoretical drive is called “inductive” or “qualitative”; in the second case, it is called “deductive” or “quantitative”. In the case of mixed methods, the component that corresponds to the theoretical drive is referred to as the “core” component (“Kernkomponente”), and the other component is called the “supplemental” component (“ergänzende Komponente”). In Morse’s notation system, the core component is written in capitals and the supplemental component is written in lowercase letters. For example, in a QUAL → quan design, more weight is attached to the data coming from the core qualitative component. Due to the decisive character of the core component, the core component must be able to stand on its own, and should be implemented rigorously. The supplemental component does not have to stand on its own.

Although this distinction is useful in some circumstances, we do not advise to apply it to every mixed methods design. First, Morse and Niehaus contend that the supplemental component can be done “less rigorously” but do not explain which aspects of rigor can be dropped. In addition, the idea of decreased rigor is in conflict with one key theme of the present article, namely that mixed methods designs should always meet the criterion of multiple validities legitimation (Onwuegbuzie and Johnson 2006 ).

The idea of theoretical drive as explicated by Morse and Niehaus has been criticized. For example, we view a theoretical drive as a feature not of a whole study, but of a research question, or, more precisely, of an interpretation of a research question. For example, if one study includes multiple research questions, it might include several theoretical drives (Schoonenboom 2016 ).

Another criticism of Morse and Niehaus’ conceptualization of theoretical drive is that it does not allow for equal-status mixed methods research (“Mixed Methods Forschung, bei der qualitative und quantitative Methoden die gleiche Bedeutung haben” or “gleichrangige Mixed Methods-Designs”), in which both the qualitative and quantitative component are of equal value and weight; this same criticism applies to Morgan’s ( 2014 ) set of designs. We agree with Greene ( 2015 ) that mixed methods research can be integrated at the levels of method, methodology, and paradigm. In this view, equal-status mixed methods research designs are possible, and they result when both the qualitative and the quantitative components, approaches, and thinking are of equal value, they take control over the research process in alternation, they are in constant interaction, and the outcomes they produce are integrated during and at the end of the research process. Therefore, equal-status mixed methods research (that we often advocate) is also called “interactive mixed methods research”.

Mixed methods research can have three different drives, as formulated by Johnson et al. ( 2007 , p. 123):

Qualitative dominant [or qualitatively driven] mixed methods research is the type of mixed research in which one relies on a qualitative, constructivist-poststructuralist-critical view of the research process, while concurrently recognizing that the addition of quantitative data and approaches are likely to benefit most research projects. Quantitative dominant [or quantitatively driven] mixed methods research is the type of mixed research in which one relies on a quantitative, postpositivist view of the research process, while concurrently recognizing that the addition of qualitative data and approaches are likely to benefit most research projects. (p. 124) The area around the center of the [qualitative-quantitative] continuum, equal status , is the home for the person that self-identifies as a mixed methods researcher. This researcher takes as his or her starting point the logic and philosophy of mixed methods research. These mixed methods researchers are likely to believe that qualitative and quantitative data and approaches will add insights as one considers most, if not all, research questions.

We leave it to the reader to decide if he or she desires to conduct a qualitatively driven study, a quantitatively driven study, or an equal-status/“interactive” study. According to the philosophies of pragmatism (Johnson and Onwuegbuzie 2004 ) and dialectical pluralism (Johnson 2017 ), interactive mixed methods research is very much a possibility. By successfully conducting an equal-status study, the pragmatist researcher shows that paradigms can be mixed or combined, and that the incompatibility thesis does not always apply to research practice. Equal status research is most easily conducted when a research team is composed of qualitative, quantitative, and mixed researchers, interacts continually, and conducts a study to address one superordinate goal.

Timing: simultaneity and dependence

Another important distinction when designing a mixed methods study relates to the timing of the two (or more) components. When designing a mixed methods study, it is usually helpful to include the word “concurrent” (“parallel”) or “sequential” (“sequenziell”) in the title of the study design; a complex design can be partially concurrent and partially sequential. Timing has two aspects: simultaneity and dependence (Guest 2013 ).

Simultaneity (“Simultanität”) forms the basis of the distinction between concurrent and sequential designs. In a  sequential design , the quantitative component precedes the qualitative component, or vice versa. In a  concurrent design , both components are executed (almost) simultaneously. In the notation of Morse ( 1991 ), concurrence is indicated by a “+” between components (e. g., QUAL + quan), while sequentiality is indicated with a “→” (QUAL → quan). Note that the use of capital letters for one component and lower case letters for another component in the same design suggest that one component is primary and the other is secondary or supplemental.

Some designs are sequential by nature. For example, in a  conversion design, qualitative categories and themes might be first obtained by collection and analysis of qualitative data, and then subsequently quantitized (Teddlie and Tashakkori 2009 ). Likewise, with Greene et al.’s ( 1989 ) initiation purpose, the initiation strand follows the unexpected results that it is supposed to explain. In other cases, the researcher has a choice. It is possible, e. g., to collect interview data and survey data of one inquiry simultaneously; in that case, the research activities would be concurrent. It is also possible to conduct the interviews after the survey data have been collected (or vice versa); in that case, research activities are performed sequentially. Similarly, a study with the purpose of expansion can be designed in which data on an effect and the intervention process are collected simultaneously, or they can be collected sequentially.

A second aspect of timing is dependence (“Abhängigkeit”) . We call two research components dependent if the implementation of the second component depends on the results of data analysis in the first component. Two research components are independent , if their implementation does not depend on the results of data analysis in the other component. Often, a researcher has a choice to perform data analysis independently or not. A researcher could analyze interview data and questionnaire data of one inquiry independently; in that case, the research activities would be independent. It is also possible to let the interview questions depend upon the outcomes of the analysis of the questionnaire data (or vice versa); in that case, research activities are performed dependently. Similarly, the empirical outcome/effect and process in a study with the purpose of expansion might be investigated independently, or the process study might take the effect/outcome as given (dependent).

In the mixed methods literature, the distinction between sequential and concurrent usually refers to the combination of concurrent/independent and sequential/dependent, and to the combination of data collection and data analysis. It is said that in a concurrent design, the data collection and data analysis of both components occurs (almost) simultaneously and independently, while in a sequential design, the data collection and data analysis of one component take place after the data collection and data analysis of the other component and depends on the outcomes of the other component.

In our opinion, simultaneity and dependence are two separate dimensions. Simultaneity indicates whether data collection is done concurrent or sequentially. Dependence indicates whether the implementation of one component depends upon the results of data analysis of the other component. As we will see in the example case studies, a concurrent design could include dependent data analysis, and a sequential design could include independent data analysis. It is conceivable that one simultaneously conducts interviews and collects questionnaire data (concurrent), while allowing the analysis focus of the interviews to depend on what emerges from the survey data (dependence).

Dependent research activities include a redirection of subsequent research inquiry. Using the outcomes of the first research component, the researcher decides what to do in the second component. Depending on the outcomes of the first research component, the researcher will do something else in the second component. If this is so, the research activities involved are said to be sequential-dependent, and any component preceded by another component should appropriately build on the previous component (see sequential validity legitimation ; Johnson and Christensen 2017 ; Onwuegbuzie and Johnson 2006 ).

It is under the purposive discretion of the researcher to determine whether a concurrent-dependent design, a concurrent-independent design, a sequential-dependent design, or a sequential-dependent design is needed to answer a particular research question or set of research questions in a given situation.

Point of integration

Each true mixed methods study has at least one “point of integration” – called the “point of interface” by Morse and Niehaus ( 2009 ) and Guest ( 2013 ) –, at which the qualitative and quantitative components are brought together. Having one or more points of integration is the distinguishing feature of a design based on multiple components. It is at this point that the components are “mixed”, hence the label “mixed methods designs”. The term “mixing”, however, is misleading, as the components are not simply mixed, but have to be integrated very carefully.

Determining where the point of integration will be, and how the results will be integrated, is an important, if not the most important, decision in the design of mixed methods research. Morse and Niehaus ( 2009 ) identify two possible points of integration: the results point of integration and the analytical point of integration.

Most commonly, integration takes place in the results point of integration . At some point in writing down the results of the first component, the results of the second component are added and integrated. A  joint display (listing the qualitative and quantitative findings and an integrative statement) might be used to facilitate this process.

In the case of an analytical point of integration , a first analytical stage of a qualitative component is followed by a second analytical stage, in which the topics identified in the first analytical stage are quantitized. The results of the qualitative component ultimately, and before writing down the results of the analytical phase as a whole, become quantitative; qualitizing also is a possible strategy, which would be the converse of this.

Other authors assume more than two possible points of integration. Teddlie and Tashakkori ( 2009 ) distinguish four different stages of an investigation: the conceptualization stage, the methodological experimental stage (data collection), the analytical experimental stage (data analysis), and the inferential stage. According to these authors, in all four stages, mixing is possible, and thus all four stages are potential points or integration.

However, the four possible points of integration used by Teddlie and Tashakkori ( 2009 ) are still too coarse to distinguish some types of mixing. Mixing in the experiential stage can take many different forms, for example the use of cognitive interviews to improve a questionnaire (tool development), or selecting people for an interview on the basis of the results of a questionnaire (sampling). Extending the definition by Guest ( 2013 ), we define the point of integration as “any point in a study where two or more research components are mixed or connected in some way”. Then, the point of integration in the two examples of this paragraph can be defined more accurately as “instrument development”, and “development of the sample”.

It is at the point of integration that qualitative and quantitative components are integrated. Some primary ways that the components can be connected to each other are as follows:

(1) merging the two data sets, (2) connecting from the analysis of one set of data to the collection of a second set of data, (3) embedding of one form of data within a larger design or procedure, and (4) using a framework (theoretical or program) to bind together the data sets (Creswell and Plano Clark 2011 , p. 76).

More generally, one can consider mixing at any or all of the following research components: purposes, research questions, theoretical drive, methods, methodology, paradigm, data, analysis, and results. One can also include mixing views of different researchers, participants, or stakeholders. The creativity of the mixed methods researcher designing a study is extensive.

Substantively, it can be useful to think of integration or mixing as comparing and bringing together two (or more) components on the basis of one or more of the purposes set out in the first section of this article. For example, it is possible to use qualitative data to illustrate a quantitative effect, or to determine whether the qualitative and the quantitative component yield convergent results ( triangulation ). An integrated result could also consist of a combination of a quantitatively established effect and a qualitative description of the underlying process . In the case of development, integration consists of an adjustment of an, often quantitative, for example, instrument or model or interpretation, based on qualitative assessments by members of the target group.

A special case is the integration of divergent results. The power of mixed methods research is its ability to deal with diversity and divergence. In the literature, we find two kinds of strategies for dealing with divergent results. A first set of strategies takes the detected divergence as the starting point for further analysis, with the aim to resolve the divergence. One possibility is to carry out further research (Cook 1985 ; Greene and Hall 2010 ). Further research is not always necessary. One can also look for a more comprehensive theory, which is able to account for both the results of the first component and the deviating results of the second component. This is a form of abduction (Erzberger and Prein 1997 ).

A fruitful starting point in trying to resolve divergence through abduction is to determine which component has resulted in a finding that is somehow expected, logical, and/or in line with existing research. The results of this research component, called the “sense” (“Lesart”), are subsequently compared to the results of the other component, called the “anti-sense” (“alternative Lesart”), which are considered dissonant, unexpected, and/or contrary to what had been found in the literature. The aim is to develop an overall explanation that fits both the sense and the anti-sense (Bazeley and Kemp 2012 ; Mendlinger and Cwikel 2008 ). Finally, a reanalysis of the data can sometimes lead to resolving divergence (Creswell and Plano Clark 2011 ).

Alternatively, one can question the existence of the encountered divergence. In this regard, Mathison ( 1988 ) recommends determining whether deviating results shown by the data can be explained by knowledge about the research and/or knowledge of the social world. Differences between results from different data sources could also be the result of properties of the methods involved, rather than reflect differences in reality (Yanchar and Williams 2006 ). In general, the conclusions of the individual components can be subjected to an inference quality audit (Teddlie and Tashakkori 2009 ), in which the researcher investigates the strength of each of the divergent conclusions. We recommend that researchers first determine whether there is “real” divergence, according to the strategies mentioned in the last paragraph. Next, an attempt can be made to resolve cases of “true” divergence, using one or more of the methods mentioned in this paragraph.

Design typology utilization

As already mentioned in Sect. 1, mixed methods designs can be classified into a mixed methods typology or taxonomy. A typology serves several purposes, including the following: guiding practice, legitimizing the field, generating new possibilities, and serving as a useful pedagogical tool (Teddlie and Tashakkori 2009 ). Note, however, that not all types of typologies are equally suitable for all purposes. For generating new possibilities, one will need a more exhaustive typology, while a useful pedagogical tool might be better served by a non-exhaustive overview of the most common mixed methods designs. Although some of the current MM design typologies include more designs than others, none of the current typologies is fully exhaustive. When designing a mixed methods study, it is often useful to borrow its name from an existing typology, or to construct a superior and nuanced clear name when your design is based on a modification of one or more of the designs.

Various typologies of mixed methods designs have been proposed. Creswell and Plano Clark’s ( 2011 ) typology of some “commonly used designs” includes six “major mixed methods designs”. Our summary of these designs runs as follows:

• Convergent parallel design (“paralleles Design”) (the quantitative and qualitative strands of the research are performed independently, and their results are brought together in the overall interpretation),
• Explanatory sequential design (“explanatives Design”) (a first phase of quantitative data collection and analysis is followed by the collection of qualitative data, which are used to explain the initial quantitative results),
• Exploratory sequential design (“exploratives Design”) (a first phase of qualitative data collection and analysis is followed by the collection of quantitative data to test or generalize the initial qualitative results),
• Embedded design (“Einbettungs-Design”) (in a traditional qualitative or quantitative design, a strand of the other type is added to enhance the overall design),
• Transformative design (“politisch-transformatives Design”) (a transformative theoretical framework, e. g. feminism or critical race theory, shapes the interaction, priority, timing and mixing of the qualitative and quantitative strand),
• Multiphase design (“Mehrphasen-Design”) (more than two phases or both sequential and concurrent strands are combined over a period of time within a program of study addressing an overall program objective).

Most of their designs presuppose a specific juxtaposition of the qualitative and quantitative component. Note that the last design is a complex type that is required in many mixed methods studies.

The following are our adapted definitions of Teddlie and Tashakkori’s ( 2009 ) five sets of mixed methods research designs (adapted from Teddlie and Tashakkori 2009 , p. 151):

• Parallel mixed designs (“paralleles Mixed-Methods-Design”) – In these designs, one has two or more parallel quantitative and qualitative strands, either with some minimal time lapse or simultaneously; the strand results are integrated into meta-inferences after separate analysis are conducted; related QUAN and QUAL research questions are answered or aspects of the same mixed research question is addressed.
• Sequential mixed designs (“sequenzielles Mixed-Methods-Design”) – In these designs, QUAL and QUAN strands occur across chronological phases, and the procedures/questions from the later strand emerge/depend/build on on the previous strand; the research questions are interrelated and sometimes evolve during the study.
• Conversion mixed designs (“Transfer-Design” or “Konversionsdesign”) – In these parallel designs, mixing occurs when one type of data is transformed to the other type and then analyzed, and the additional findings are added to the results; this design answers related aspects of the same research question,
• Multilevel mixed designs (“Mehrebenen-Mixed-Methods-Design”) – In these parallel or sequential designs, mixing occurs across multiple levels of analysis, as QUAN and QUAL data are analyzed and integrated to answer related aspects of the same research question or related questions.
• Fully integrated mixed designs (“voll integriertes Mixed-Methods-Design”) – In these designs, mixing occurs in an interactive manner at all stages of the study. At each stage, one approach affects the formulation of the other, and multiple types of implementation processes can occur. For example, rather than including integration only at the findings/results stage, or only across phases in a sequential design, mixing might occur at the conceptualization stage, the methodological stage, the analysis stage, and the inferential stage.

We recommend adding to Teddlie and Tashakkori’s typology a sixth design type, specifically, a  “hybrid” design type to include complex combinations of two or more of the other design types. We expect that many published MM designs will fall into the hybrid design type.

Morse and Niehaus ( 2009 ) listed eight mixed methods designs in their book (and suggested that authors create more complex combinations when needed). Our shorthand labels and descriptions (adapted from Morse and Niehaus 2009 , p. 25) run as follows:

• QUAL + quan (inductive-simultaneous design where, the core component is qualitative and the supplemental component is quantitative)
• QUAL → quan (inductive-sequential design, where the core component is qualitative and the supplemental component is quantitative)
• QUAN + qual (deductive-simultaneous design where, the core component is quantitative and the supplemental component is qualitative)
• QUAN → qual (deductive-sequential design, where the core component is quantitative and the supplemental component is qualitative)
• QUAL + qual (inductive-simultaneous design, where both components are qualitative; this is a multimethod design rather than a mixed methods design)
• QUAL → qual (inductive-sequential design, where both components are qualitative; this is a multimethod design rather than a mixed methods design)
• QUAN + quan (deductive-simultaneous design, where both components are quantitative; this is a multimethod design rather than a mixed methods design)
• QUAN → quan (deductive-sequential design, where both components are quantitative; this is a multimethod design rather than a mixed methods design).

Notice that Morse and Niehaus ( 2009 ) included four mixed methods designs (the first four designs shown above) and four multimethod designs (the second set of four designs shown above) in their typology. The reader can, therefore, see that the design notation also works quite well for multimethod research designs. Notably absent from Morse and Niehaus’s book are equal-status or interactive designs. In addition, they assume that the core component should always be performed either concurrent with or before the supplemental component.

Johnson, Christensen, and Onwuegbuzie constructed a set of mixed methods designs without these limitations. The resulting mixed methods design matrix (see Johnson and Christensen 2017 , p. 478) contains nine designs, which we can label as follows (adapted from Johnson and Christensen 2017 , p. 478):

• QUAL + QUAN (equal-status concurrent design),
• QUAL + quan (qualitatively driven concurrent design),
• QUAN + qual (quantitatively driven concurrent design),
• QUAL → QUAN (equal-status sequential design),
• QUAN → QUAL (equal-status sequential design),
• QUAL → quan (qualitatively driven sequential design),
• qual → QUAN (quantitatively driven sequential design),
• QUAN → qual (quantitatively driven sequential design), and
• quan → QUAL (qualitatively driven sequential design).

The above set of nine designs assumed only one qualitative and one quantitative component. However, this simplistic assumption can be relaxed in practice, allowing the reader to construct more complex designs. The Morse notation system is very powerful. For example, here is a three-stage equal-status concurrent-sequential design:

The key point here is that the Morse notation provides researchers with a powerful language for depicting and communicating the design constructed for a specific research study.

When designing a mixed methods study, it is sometimes helpful to include the mixing purpose (or characteristic on one of the other dimensions shown in Table  1 ) in the title of the study design (e. g., an explanatory sequential MM design, an exploratory-confirmatory MM design, a developmental MM design). Much more important, however, than a design name is for the author to provide an accurate description of what was done in the research study, so the reader will know exactly how the study was conducted. A design classification label can never replace such a description.

The common complexity of mixed methods design poses a problem to the above typologies of mixed methods research. The typologies were designed to classify whole mixed methods studies, and they are basically based on a classification of simple designs. In practice, many/most designs are complex. Complex designs are sometimes labeled “complex design”, “multiphase design”, “fully integrated design”, “hybrid design” and the like. Because complex designs occur very often in practice, the above typologies are not able to classify a large part of existing mixed methods research any further than by labeling them “complex”, which in itself is not very informative about the particular design. This problem does not fully apply to Morse’s notation system, which can be used to symbolize some more complex designs.

Something similar applies to the classification of the purposes of mixed methods research. The classifications of purposes mentioned in the “Purpose”-section, again, are basically meant for the classification of whole mixed methods studies. In practice, however, one single study often serves more than one purpose (Schoonenboom et al. 2017 ). The more purposes that are included in one study, the more difficult it becomes to select a design on the basis of the purpose of the investigation, as advised by Greene ( 2007 ). Of all purposes involved, then, which one should be the primary basis for the design? Or should the design be based upon all purposes included? And if so, how? For more information on how to articulate design complexity based on multiple purposes of mixing, see Schoonenboom et al. ( 2017 ).

It should be clear to the reader that, although much progress has been made in the area of mixed methods design typologies, the problem remains in developing a single typology that is effective in comprehensively listing a set of designs for mixed methods research. This is why we emphasize in this article the importance of learning to build on simple designs and construct one’s own design for one’s research questions. This will often result in a combination or “hybrid” design that goes beyond basic designs found in typologies, and a methodology section that provides much more information than a design name.

Typological versus interactive approaches to design

In the introduction, we made a distinction between design as a product and design as a process. Related to this, two different approaches to design can be distinguished: typological/taxonomic approaches (“systematische Ansätze”), such as those in the previous section, and interactive approaches (“interaktive Ansätze”) (the latter were called “dynamic” approaches by Creswell and Plano Clark 2011 ). Whereas typological/taxonomic approaches view designs as a sort of mold, in which the inquiry can be fit, interactive approaches (Maxwell 2013 ) view design as a process, in which a certain design-as-a-product might be the outcome of the process, but not its input.

The most frequently mentioned interactive approach to mixed methods research is the approach by Maxwell and Loomis ( 2003 ). Maxwell and Loomis distinguish the following components of a design: goals, conceptual framework, research question, methods, and validity. They argue convincingly that the most important task of the researcher is to deliver as the end product of the design process a design in which these five components fit together properly. During the design process, the researcher works alternately on the individual components, and as a result, their initial fit, if it existed, tends to get lost. The researcher should therefore regularly check during the research and continuing design process whether the components still fit together, and, if not, should adapt one or the other component to restore the fit between them. In an interactive approach, unlike the typological approach, design is viewed as an interactive process in which the components are continually compared during the research study to each other and adapted to each other.

Typological and interactive approaches to mixed methods research have been presented as mutually exclusive alternatives. In our view, however, they are not mutually exclusive. The interactive approach of Maxwell is a very powerful tool for conducting research, yet this approach is not specific to mixed methods research. Maxwell’s interactive approach emphasizes that the researcher should keep and monitor a close fit between the five components of research design. However, it does not indicate how one should combine qualitative and quantitative subcomponents within one of Maxwell’s five components (e. g., how one should combine a qualitative and a quantitative method, or a qualitative and a quantitative research question). Essential elements of the design process, such as timing and the point of integration are not covered by Maxwell’s approach. This is not a shortcoming of Maxwell’s approach, but it indicates that to support the design of mixed methods research, more is needed than Maxwell’s model currently has to offer.

Some authors state that design typologies are particularly useful for beginning researchers and interactive approaches are suited for experienced researchers (Creswell and Plano Clark 2011 ). However, like an experienced researcher, a research novice needs to align the components of his or her design properly with each other, and, like a beginning researcher, an advanced researcher should indicate how qualitative and quantitative components are combined with each other. This makes an interactive approach desirable, also for beginning researchers.

We see two merits of the typological/taxonomic approach . We agree with Greene ( 2007 ), who states that the value of the typological approach mainly lies in the different dimensions of mixed methods that result from its classifications. In this article, the primary dimensions include purpose, theoretical drive, timing, point of integration, typological vs. interactive approaches, planned vs. emergent designs, and complexity (also see secondary dimensions in Table  1 ). Unfortunately, all of these dimensions are not reflected in any single design typology reviewed here. A second merit of the typological approach is the provision of common mixed methods research designs, of common ways in which qualitative and quantitative research can be combined, as is done for example in the major designs of Creswell and Plano Clark ( 2011 ). Contrary to other authors, however, we do not consider these designs as a feature of a whole study, but rather, in line with Guest ( 2013 ), as a feature of one part of a design in which one qualitative and one quantitative component are combined. Although one study could have only one purpose, one point of integration, et cetera, we believe that combining “designs” is the rule and not the exception. Therefore, complex designs need to be constructed and modified as needed, and during the writing phase the design should be described in detail and perhaps given a creative and descriptive name.

Planned versus emergent designs

A mixed methods design can be thought out in advance, but can also arise during the course of the conduct of the study; the latter is called an “emergent” design (Creswell and Plano Clark 2011 ). Emergent designs arise, for example, when the researcher discovers during the study that one of the components is inadequate (Morse and Niehaus 2009 ). Addition of a component of the other type can sometimes remedy such an inadequacy. Some designs contain an emergent component by their nature. Initiation, for example, is the further exploration of unexpected outcomes. Unexpected outcomes are by definition not foreseen, and therefore cannot be included in the design in advance.

The question arises whether researchers should plan all these decisions beforehand, or whether they can make them during, and depending on the course of, the research process. The answer to this question is twofold. On the one hand, a researcher should decide beforehand which research components to include in the design, such that the conclusion that will be drawn will be robust. On the other hand, developments during research execution will sometimes prompt the researcher to decide to add additional components. In general, the advice is to be prepared for the unexpected. When one is able to plan for emergence, one should not refrain from doing so.

Dimension of complexity

Next, mixed methods designs are characterized by their complexity. In the literature, simple and complex designs are distinguished in various ways. A common distinction is between simple investigations with a single point of integration versus complex investigations with multiple points of integration (Guest 2013 ). When designing a mixed methods study, it can be useful to mention in the title whether the design of the study is simple or complex. The primary message of this section is as follows: It is the responsibility of the researcher to create more complex designs when needed to answer his or her research question(s) .

Teddlie and Tashakkori’s ( 2009 ) multilevel mixed designs and fully integrated mixed designs are both complex designs, but for different reasons. A multilevel mixed design is more complex ontologically, because it involves multiple levels of reality. For example, data might be collected both at the levels of schools and students, neighborhood and households, companies and employees, communities and inhabitants, or medical practices and patients (Yin 2013 ). Integration of these data does not only involve the integration of qualitative and quantitative data, but also the integration of data originating from different sources and existing at different levels. Little if any published research has discussed the possible ways of integrating data obtained in a multilevel mixed design (see Schoonenboom 2016 ). This is an area in need of additional research.

The fully-integrated mixed design is more complex because it contains multiple points of integration. As formulated by Teddlie and Tashakkori ( 2009 , p. 151):

In these designs, mixing occurs in an interactive manner at all stages of the study. At each stage, one approach affects the formulation of the other, and multiple types of implementation processes can occur.

Complexity, then, not only depends on the number of components, but also on the extent to which they depend on each other (e. g., “one approach affects the formulation of the other”).

Many of our design dimensions ultimately refer to different ways in which the qualitative and quantitative research components are interdependent. Different purposes of mixing ultimately differ in the way one component relates to, and depends upon, the other component. For example, these purposes include dependencies, such as “x illustrates y” and “x explains y”. Dependencies in the implementation of x and y occur to the extent that the design of y depends on the results of x (sequentiality). The theoretical drive creates dependencies, because the supplemental component y is performed and interpreted within the context and the theoretical drive of core component x. As a general rule in designing mixed methods research, one should examine and plan carefully the ways in which and the extent to which the various components depend on each other.

The dependence among components, which may or may not be present, has been summarized by Greene ( 2007 ). It is seen in the distinction between component designs (“Komponenten-Designs”), in which the components are independent of each other, and integrated designs (“integrierte Designs”), in which the components are interdependent. Of these two design categories, integrated designs are the more complex designs.

Secondary design considerations

The primary design dimensions explained above have been the focus of this article. There are a number of secondary considerations for researchers to also think about when they design their studies (Johnson and Christensen 2017 ). Now we list some secondary design issues and questions that should be thoughtfully considered during the construction of a strong mixed methods research design.

• Phenomenon: Will the study be addressing (a) the same part or different parts of one phenomenon? (b) different phenomena?, or (c) the phenomenon/phenomena from different perspectives? Is the phenomenon (a) expected to be unique (e. g., historical event, particular group)?, (b) something expected to be part of a more regular and predictable phenomenon, or (c) a complex mixture of these?
• Social scientific theory: Will the study generate a new substantive theory, test an already constructed theory, or achieve both in a sequential arrangement? Or is the researcher not interested in substantive theory based on empirical data?
• Ideological drive: Will the study have an explicitly articulated ideological drive (e. g., feminism, critical race paradigm, transformative paradigm)?
• Combination of sampling methods: What specific quantitative sampling method(s) will be used? What specific qualitative sampling methods(s) will be used? How will these be combined or related?
• Degree to which the research participants will be similar or different: For example, participants or stakeholders with known differences of perspective would provide participants that are quite different.
• Degree to which the researchers on the research team will be similar or different: For example, an experiment conducted by one researcher would be high on similarity, but the use of a heterogeneous and participatory research team would include many differences.
• Implementation setting: Will the phenomenon be studied naturalistically, experimentally, or through a combination of these?
• Degree to which the methods similar or different: For example, a structured interview and questionnaire are fairly similar but administration of a standardized test and participant observation in the field are quite different.
• Validity criteria and strategies: What validity criteria and strategies will be used to address the defensibility of the study and the conclusions that will be drawn from it (see Chapter 11 in Johnson and Christensen 2017 )?
• Full study: Will there be essentially one research study or more than one? How will the research report be structured?

Two case studies

The above design dimensions are now illustrated by examples. A nice collection of examples of mixed methods studies can be found in Hesse-Biber ( 2010 ), from which the following examples are taken. The description of the first case example is shown in Box 1.

Box 1

Summary of Roth ( 2006 ), research regarding the gender-wage gap within Wall Street securities firms. Adapted from Hesse-Biber ( 2010 , pp. 457–458)

Louise Marie Roth’s research, Selling Women Short: Gender and Money on Wall Street ( 2006 ), tackles gender inequality in the workplace. She was interested in understanding the gender-wage gap among highly performing Wall Street MBAs, who on the surface appeared to have the same “human capital” qualifications and were placed in high-ranking Wall Street securities firms as their first jobs. In addition, Roth wanted to understand the “structural factors” within the workplace setting that may contribute to the gender-wage gap and its persistence over time. […] Roth conducted semistructured interviews, nesting quantitative closed-ended questions into primarily qualitative in-depth interviews […] In analyzing the quantitative data from her sample, she statistically considered all those factors that might legitimately account for gendered differences such as number of hours worked, any human capital differences, and so on. Her analysis of the quantitative data revealed the presence of a significant gender gap in wages that remained unexplained after controlling for any legitimate factors that might otherwise make a difference. […] Quantitative findings showed the extent of the wage gap while providing numerical understanding of the disparity but did not provide her with an understanding of the specific processes within the workplace that might have contributed to the gender gap in wages. […] Her respondents’ lived experiences over time revealed the hidden inner structures of the workplace that consist of discriminatory organizational practices with regard to decision making in performance evaluations that are tightly tied to wage increases and promotion.

This example nicely illustrates the distinction we made between simultaneity and dependency. On the two aspects of the timing dimension, this study was a concurrent-dependent design answering a set of related research questions. The data collection in this example was conducted simultaneously, and was thus concurrent – the quantitative closed-ended questions were embedded into the qualitative in-depth interviews. In contrast, the analysis was dependent, as explained in the next paragraph.

One of the purposes of this study was explanation: The qualitative data were used to understand the processes underlying the quantitative outcomes. It is therefore an explanatory design, and might be labelled an “explanatory concurrent design”. Conceptually, explanatory designs are often dependent: The qualitative component is used to explain and clarify the outcomes of the quantitative component. In that sense, the qualitative analysis in the case study took the outcomes of the quantitative component (“the existence of the gender-wage gap” and “numerical understanding of the disparity”), and aimed at providing an explanation for that result of the quantitative data analysis , by relating it to the contextual circumstances in which the quantitative outcomes were produced. This purpose of mixing in the example corresponds to Bryman’s ( 2006 ) “contextual understanding”. On the other primary dimensions, (a) the design was ongoing over a three-year period but was not emergent, (b) the point of integration was results, and (c) the design was not complex with respect to the point of integration, as it had only one point of integration. Yet, it was complex in the sense of involving multiple levels; both the level of the individual and the organization were included. According to the approach of Johnson and Christensen ( 2017 ), this was a QUAL + quan design (that was qualitatively driven, explanatory, and concurrent). If we give this study design a name, perhaps it should focus on what was done in the study: “explaining an effect from the process by which it is produced”. Having said this, the name “explanatory concurrent design” could also be used.

The description of the second case example is shown in Box 2.

Box 2

Summary of McMahon’s ( 2007 ) explorative study of the meaning, role, and salience of rape myths within the subculture of college student athletes. Adapted from Hesse-Biber ( 2010 , pp. 461–462)

Sarah McMahon ( 2007 ) wanted to explore the subculture of college student athletes and specifically the meaning, role, and salience of rape myths within that culture. […] While she was looking for confirmation between the quantitative ([structured] survey) and qualitative (focus groups and individual interviews) findings, she entered this study skeptical of whether or not her quantitative and qualitative findings would mesh with one another. McMahon […] first administered a survey [instrument] to 205 sophomore and junior student athletes at one Northeast public university. […] The quantitative data revealed a very low acceptance of rape myths among this student population but revealed a higher acceptance of violence among men and individuals who did not know a survivor of sexual assault. In the second qualitative (QUAL) phase, “focus groups were conducted as semi-structured interviews” and facilitated by someone of the same gender as the participants (p. 360). […] She followed this up with a third qualitative component (QUAL), individual interviews, which were conducted to elaborate on themes discovered in the focus groups and determine any differences in students’ responses between situations (i. e., group setting vs. individual). The interview guide was designed specifically to address focus group topics that needed “more in-depth exploration” or clarification (p. 361). The qualitative findings from the focus groups and individual qualitative interviews revealed “subtle yet pervasive rape myths” that fell into four major themes: “the misunderstanding of consent, the belief in ‘accidental’ and fabricated rape, the contention that some women provoke rape, and the invulnerability of female athletes” (p. 363). She found that the survey’s finding of a “low acceptance of rape myths … was contradicted by the findings of the focus groups and individual interviews, which indicated the presence of subtle rape myths” (p. 362).

On the timing dimension, this is an example of a sequential-independent design. It is sequential, because the qualitative focus groups were conducted after the survey was administered. The analysis of the quantitative and qualitative data was independent: Both were analyzed independently, to see whether they yielded the same results (which they did not). This purpose, therefore, was triangulation. On the other primary dimensions, (a) the design was planned, (b) the point of integration was results, and (c) the design was not complex as it had only one point of integration, and involved only the level of the individual. The author called this a “sequential explanatory” design. We doubt, however, whether this is the most appropriate label, because the qualitative component did not provide an explanation for quantitative results that were taken as given. On the contrary, the qualitative results contradicted the quantitative results. Thus, a “sequential-independent” design, or a “sequential-triangulation” design or a “sequential-comparative” design would probably be a better name.

Notice further that the second case study had the same point of integration as the first case study. The two components were brought together in the results. Thus, although the case studies are very dissimilar in many respects, this does not become visible in their point of integration. It can therefore be helpful to determine whether their point of extension is different. A  point of extension is the point in the research process at which the second (or later) component comes into play. In the first case study, two related, but different research questions were answered, namely the quantitative question “How large is the gender-wage gap among highly performing Wall Street MBAs after controlling for any legitimate factors that might otherwise make a difference?”, and the qualitative research question “How do structural factors within the workplace setting contribute to the gender-wage gap and its persistence over time?” This case study contains one qualitative research question and one quantitative research question. Therefore, the point of extension is the research question. In the second case study, both components answered the same research question. They differed in their data collection (and subsequently in their data analysis): qualitative focus groups and individual interviews versus a quantitative questionnaire. In this case study, the point of extension was data collection. Thus, the point of extension can be used to distinguish between the two case studies.

Summary and conclusions

The purpose of this article is to help researchers to understand how to design a mixed methods research study. Perhaps the simplest approach is to design is to look at a single book and select one from the few designs included in that book. We believe that is only useful as a starting point. Here we have shown that one often needs to construct a research design to fit one’s unique research situation and questions.

First, we showed that there are there are many purposes for which qualitative and quantitative methods, methodologies, and paradigms can be mixed. This must be determined in interaction with the research questions. Inclusion of a purpose in the design name can sometimes provide readers with useful information about the study design, as in, e. g., an “explanatory sequential design” or an “exploratory-confirmatory design”.

The second dimension is theoretical drive in the sense that Morse and Niehaus ( 2009 ) use this term. That is, will the study have an inductive or a deductive drive, or, we added, a combination of these. Related to this idea is whether one will conduct a qualitatively driven, a quantitatively driven, or an equal-status mixed methods study. This language is sometimes included in the design name to communicate this characteristic of the study design (e. g., a “quantitatively driven sequential mixed methods design”).

The third dimension is timing , which has two aspects: simultaneity and dependence. Simultaneity refers to whether the components are to be implemented concurrently, sequentially, or a combination of these in a multiphase design. Simultaneity is commonly used in the naming of a mixed methods design because it communicates key information. The second aspect of timing, dependence , refers to whether a later component depends on the results of an earlier component, e. g., Did phase two specifically build on phase one in the research study? The fourth design dimension is the point of integration, which is where the qualitative and quantitative components are brought together and integrated. This is an essential dimension, but it usually does not need to be incorporated into the design name.

The fifth design dimension is that of typological vs. interactive design approaches . That is, will one select a design from a typology or use a more interactive approach to construct one’s own design? There are many typologies of designs currently in the literature. Our recommendation is that readers examine multiple design typologies to better understand the design process in mixed methods research and to understand what designs have been identified as popular in the field. However, when a design that would follow from one’s research questions is not available, the researcher can and should (a) combine designs into new designs or (b) simply construct a new and unique design. One can go a long way in depicting a complex design with Morse’s ( 1991 ) notation when used to its full potential. We also recommend that researchers understand the process approach to design from Maxwell and Loomis ( 2003 ), and realize that research design is a process and it needs, oftentimes, to be flexible and interactive.

The sixth design dimension or consideration is whether a design will be fully specified during the planning of the research study or if the design (or part of the design) will be allowed to emerge during the research process, or a combination of these. The seventh design dimension is called complexity . One sort of complexity mentioned was multilevel designs, but there are many complexities that can enter designs. The key point is that good research often requires the use of complex designs to answer one’s research questions. This is not something to avoid. It is the responsibility of the researcher to learn how to construct and describe and name mixed methods research designs. Always remember that designs should follow from one’s research questions and purposes, rather than questions and purposes following from a few currently named designs.

In addition to the six primary design dimensions or considerations, we provided a set of additional or secondary dimensions/considerations or questions to ask when constructing a mixed methods study design. Our purpose throughout this article has been to show what factors must be considered to design a high quality mixed methods research study. The more one knows and thinks about the primary and secondary dimensions of mixed methods design the better equipped one will be to pursue mixed methods research.

Acknowledgments

Open access funding provided by University of Vienna.

Biographies

1965, Dr., Professor of Empirical Pedagogy at University of Vienna, Austria. Research Areas: Mixed Methods Design, Philosophy of Mixed Methods Research, Innovation in Higher Education, Design and Evaluation of Intervention Studies, Educational Technology. Publications: Mixed methods in early childhood education. In: M. Fleer & B. v. Oers (Eds.), International handbook on early childhood education (Vol. 1). Dordrecht, The Netherlands: Springer 2017; The multilevel mixed intact group analysis: A mixed method to seek, detect, describe and explain differences between intact groups. Journal of Mixed Methods Research 10, 2016; The realist survey: How respondents’ voices can be used to test and revise correlational models. Journal of Mixed Methods Research 2015. Advance online publication.

1957, PhD, Professor of Professional Studies at University of South Alabama, Mobile, Alabama USA. Research Areas: Methods of Social Research, Program Evaluation, Quantitative, Qualitative and Mixed Methods, Philosophy of Social Science. Publications: Research methods, design and analysis. Boston, MA 2014 (with L. Christensen and L. Turner); Educational research: Quantitative, qualitative and mixed approaches. Los Angeles, CA 2017 (with L. Christensen); The Oxford handbook of multimethod and mixed methods research inquiry. New York, NY 2015 (with S. Hesse-Biber).

Bryman’s ( 2006 ) scheme of rationales for combining quantitative and qualitative research 1

• Triangulation or greater validity – refers to the traditional view that quantitative and qualitative research might be combined to triangulate findings in order that they may be mutually corroborated. If the term was used as a synonym for integrating quantitative and qualitative research, it was not coded as triangulation.
• Offset – refers to the suggestion that the research methods associated with both quantitative and qualitative research have their own strengths and weaknesses so that combining them allows the researcher to offset their weaknesses to draw on the strengths of both.
• Completeness – refers to the notion that the researcher can bring together a more comprehensive account of the area of enquiry in which he or she is interested if both quantitative and qualitative research are employed.
• Process – quantitative research provides an account of structures in social life but qualitative research provides sense of process.
• Different research questions – this is the argument that quantitative and qualitative research can each answer different research questions but this item was coded only if authors explicitly stated that they were doing this.
• Explanation – one is used to help explain findings generated by the other.
• Unexpected results – refers to the suggestion that quantitative and qualitative research can be fruitfully combined when one generates surprising results that can be understood by employing the other.
• Instrument development – refers to contexts in which qualitative research is employed to develop questionnaire and scale items – for example, so that better wording or more comprehensive closed answers can be generated.
• Sampling – refers to situations in which one approach is used to facilitate the sampling of respondents or cases.
• Credibility – refer s to suggestions that employing both approaches enhances the integrity of findings.
• Context – refers to cases in which the combination is rationalized in terms of qualitative research providing contextual understanding coupled with either generalizable, externally valid findings or broad relationships among variables uncovered through a survey.
• Illustration – refers to the use of qualitative data to illustrate quantitative findings, often referred to as putting “meat on the bones” of “dry” quantitative findings.
• Utility or improving the usefulness of findings – refers to a suggestion, which is more likely to be prominent among articles with an applied focus, that combining the two approaches will be more useful to practitioners and others.
• Confirm and discover – this entails using qualitative data to generate hypotheses and using quantitative research to test them within a single project.
• Diversity of views – this includes two slightly different rationales – namely, combining researchers’ and participants’ perspectives through quantitative and qualitative research respectively, and uncovering relationships between variables through quantitative research while also revealing meanings among research participants through qualitative research.
• Enhancement or building upon quantitative/qualitative findings – this entails a reference to making more of or augmenting either quantitative or qualitative findings by gathering data using a qualitative or quantitative research approach.
• Other/unclear.
• Not stated.

1 Reprinted with permission from “Integrating quantitative and qualitative research: How is it done?” by Alan Bryman ( 2006 ), Qualitative Research, 6, pp. 105–107.

Contributor Information

Judith Schoonenboom, Email: [email protected] .

R. Burke Johnson, Email: ude.amabalahtuos@nosnhojb .

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3.4: Components of a Research Project

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Learning Objectives

• Describe useful strategies to employ when searching for literature.
• Describe why sociologists review prior literature and how they organize their literature reviews.
• Identify the main sections contained in scholarly journal articles.
• Identify and describe the major components researchers need to plan for when designing a research project.
• Describe the importance of a research proposal.

In this section, we’ll examine the most typical components that make up a research proposal and research project, bringing in a few additional components to those we have already discussed. Keep in mind that our purpose at this stage is simply to provide a general overview of research design. The specifics of each of the following components will vary from project to project. Further, the stage of a project at which each of these components comes into play may vary.

Searching for Literature

Familiarizing yourself with research that has already been conducted on your topic is one of the first stages of conducting a research project and is crucial for coming up with a good research design. But where to start? How to start? As you search for literature, you may have to be fairly broad in your search for articles.

I’m guessing you may feel you’ve heard enough about electronic gadget addiction in this chapter, so let’s consider a different example here. On my campus, much to the chagrin of a group of student smokers, smoking was recently banned. These students were so upset by the idea that they would no longer be allowed to smoke on university grounds that they staged several smoke-outs during which they gathered in populated areas around campus and enjoyed a puff or two together.

A student in my research methods class wanted to understand what motivated this group of students to engage in activism centered around what she perceived to be, in this age of smoke-free facilities, a relatively deviant act. Were the protesters otherwise politically active? How much effort and coordination had it taken to organize the smoke-outs? The student researcher began her research by attempting to familiarize herself with the literature on her topic. Yet her search in Sociological Abstracts for “college student activist smoke-outs,” yielded no results. Concluding there was no prior research on her topic, she informed me that she would need an alternative assignment to the annotated bibliography I required since there was no literature for her to review. How do you suppose I responded to this news? What went wrong with this student’s search for literature?

In her first attempt, the student had been too narrow in her search for articles. But did that mean she was off the hook for completing the annotated bibliography assignment? Absolutely not. Instead, she went back to Sociological Abstracts and searched again using different combinations of search terms. Rather than searching for “college student activist smoke-outs” she tried, among other sets of terms, “college student activism.” This time her search yielded a great many articles. Of course, they were not focused on prosmoking activist efforts, but they were focused on her population of interest, college students, and on her broad topic of interest, activism. I suggested that reading articles on college student activism might give her some idea about what other researchers have found in terms of what motivates college students to become involved in activist efforts. I also suggested she could play around with her search terms and look for research on activism centered on other sorts of activities that are perceived by some as deviant, such as marijuana use or veganism. In other words, she needed to be broader in her search for articles.

While this student found success by broadening her search for articles, her reading of those articles needed to be narrower than her search. Once she identified a set of articles to review by searching broadly, it was time to remind herself of her specific research focus: college student activist smoke-outs. Keeping in mind her particular research interest while reviewing the literature gave her the chance to think about how the theories and findings covered in prior studies might or might not apply to her particular point of focus. For example, theories on what motivates activists to get involved might tell her something about the likely reasons the students she planned to study got involved. At the same time, those theories might not cover all the particulars of student participation in smoke-outs. Thinking about the different theories then gave the student the opportunity to focus her research plans and even to develop a few hypotheses about what she thought she was likely to find.

Reviewing the Literature

Developing an annotated bibliography is often one of the early steps that researchers take as they begin to familiarize themselves with prior research on their topic. A second step involves a literature review in which a researcher positions his or her work within the context of prior scholarly work in the area. A literature review addresses the following matters: What sorts of questions have other scholars asked about this topic? What do we already know about this topic? What questions remain? As the researcher answers these questions, he or she synthesizes what is contained in the literature, possibly organizing prior findings around themes that are relevant to his or her particular research focus.

I once advised an undergraduate student who conducted a research project on speciesism, the belief that some species are superior to or have more value and rights than others. Her research question was “Why and how do humans construct divisions between themselves and animals?” This student organized her review of literature around the two parts of her research question: the why and the how. In the “why” section of her literature review, she described prior research that addressed questions of why humans are sometimes speciesist. She organized subsections around the three most common answers that were presented in the scholarly literature. She used the same structure in the “how” section of her literature review, arranging subsections around the answers posed in previous literature about how humans construct divisions between themselves and animals. This organizational scheme helped readers understand what we already know about the topic and what theories we rely on to help make sense of the topic. In addition, by also highlighting what we still don’t know, it helped the student set the stage for her own empirical research on the topic

The preceding discussion about how to organize a review of scholarly literature assumes that we all know how to read scholarly literature. Yes, yes, I understand that you must know how to read. But reading scholarly articles can be a bit more challenging than reading a textbook. Here are a few pointers about how to do it successfully. First, it is important to understand the various sections that are typically contained in scholarly journals’ reports of empirical research. One of the most important and easiest to spot sections of a journal article is its abstract , the short paragraph at the beginning of an article that summarizes the author’s research question, methods used to answer the question, and key findings. The abstract may also give you some idea about the theoretical proclivities of the author. As a result, reading the abstract gives you both a framework for understanding the rest of the article and the punch line. It tells you what the author(s) found and whether the article is relevant to your area of inquiry.

After the abstract, most journal articles will contain the following sections (although exact section names are likely to vary): introduction, literature review, methodology, findings, and discussion. Of course, there will also be a list of references cited, lists of references cited are a useful source for finding additional literature in an area. and there may be a few tables, figures, or appendices at the end of the article as well. While you should get into the habit of familiarizing yourself with articles you wish to cite in their entirety , there are strategic ways to read journal articles that can make them a little easier to digest. Once you have read the abstract and determined that this is an article you’d like to read in full, read through the discussion section at the end of the article next. Because your own review of literature is likely to emphasize findings from previous literature, you should make sure that you have a clear idea about what those findings are. Reading an article’s discussion section helps you understand what the author views as the study’s major findings and how the author perceives those findings to relate to other research.

As you read through the rest of the article, think about the elements of research design that we have covered. What approach does the researcher take? Is the research exploratory, descriptive, or explanatory? Is it inductive or deductive? Idiographic or nomothetic? What claims does the author make about causality? What are the author’s units of analysis and observation? Use what you have learned about the promise and potential pitfalls associated with each of these research elements to help you responsibly read and understand the articles you review. Future chapters of this text will address other elements of journal articles, including choices about measurement, sampling, and research method. As you learn about these additional items, you will increasingly gain more knowledge that you can apply as you read and critique the scholarly literature in your area of inquiry.

Additional Important Components

Thinking about the overarching goals of your research project and finding and reviewing the existing literature on your topic are two of the initial steps you’ll take when designing a research project. Forming a clear research question, is another crucial step. There are a number of other important research design components you’ll need to consider, and we will discuss those here.

At the same time that you work to identify a clear research question, you will probably also think about the overarching goals of your research project. Will it be exploratory, descriptive, or explanatory? Will your approach be idiographic or nomothetic, inductive or deductive? How you design your project might also be determined in part by whether you aim for your research to have some direct application or if your goal is to contribute more generally to sociological knowledge about your topic. Next, think about what your units of analysis and units of observation will be. These will help you identify the key concepts you will study. Once you have identified those concepts, you’ll need to decide how to define them, and how you’ll know that you’re observing them when it comes time to collect your data. Defining your concepts, and knowing them when you see them, has to do with conceptualization and operationalization, the focus of a later chapter. Of course, you also need to know what approach you will take to collect your data. Thus identifying your research method is another important part of research design. You also need to think about who your research participants will be and what larger group(s) they may represent. These topics will be the focus of a later chapter too. Last, but certainly not least, you should consider any potential ethical concerns that could arise during the course of your research project. These concerns might come up during your data collection, but they might also arise when you get to the point of analyzing or sharing your research results.

Decisions about the various research components do not necessarily occur in sequential order. In fact, you may have to think about potential ethical concerns even before zeroing in on a specific research question. Similarly, the goal of being able to make generalizations about your population of interest could shape the decisions you make about your method of data collection. Putting it all together, the following list shows some of the major components you’ll need to consider as you design your research project:

• Research question
• Literature review
• Research strategy (idiographic or nomothetic, inductive or deductive)
• Research goals (basic or applied)
• Units of analysis and units of observation
• Key concepts (conceptualization and operationalization)
• Method of data collection
• Research participants (sample and population)
• Ethical concerns

Research Proposal

At the stage before actually starting the research it is often a good idea to write a research proposal detailing all of the decisions made in the preceding stages of the research process and the rationale behind each decision. This multi-part proposal should address what research questions you wish to study and why, the prior literature, theories you wish to employ along with hypotheses to be tested (if you are doing deductive research, how measurement will be done, what research method to be employed and why, and desired sampling strategy (or who the subjects are.  Also, do not forget to include a budget.  Funding agencies typically require such a proposal in order to select the best proposals for funding. Even if funding is not sought for a research project, a proposal may serve as a useful vehicle for seeking feedback from other researchers and identifying potential problems with the research project (e.g., whether some important constructs were missing from the study) before starting data collection. This initial feedback is invaluable because it is often too late to correct critical problems after data is collected in a research study.

KEY TAKEAWAYS

• When identifying and reading relevant literature, be broad in your search for articles, but be narrower in your reading of articles.
• Writing an annotated bibliography can be a helpful first step to familiarize yourself with prior research in your area of interest.
• Literature reviews summarize and synthesize prior research.
• Literature reviews are typically organized around substantive ideas that are relevant to one’s research question rather than around individual studies or article authors.
• When designing a research project, be sure to think about, plan for, and identify a research question, a review of literature, a research strategy, research goals, units of analysis and units of observation, key concepts, method(s) of data collection, population and sample, and potential ethical concerns.
• A research proposal is also important to consider.
• Find and read a complete journal article that addresses a topic that is of interest to you. In four to eight sentences, summarize the author’s research question, theoretical framing, methods used, and major findings. Reread the article, and see how close you were in reporting these key elements. What did you understand and remember best? What did you leave out? What reading strategies may have helped you better recall relevant details from the article?
• Using the example of students’ electronic gadget addictions, design a hypothetical research project by identifying a plan for each of the nine components of research design that are presented in this section.

What is Research Design? Features, Components

• Post last modified: 13 August 2023
• Reading time: 15 mins read
• Post category: Research Methodology

What is Research Design?

Research design refers to the overall strategy or plan that a researcher outlines to conduct a study and gather relevant data to address a research question or test a hypothesis. It serves as a blueprint for the entire research process, providing a structure and guidance for the collection, analysis, and interpretation of data.

In the field of research, the major purpose of research is to find a solution for a given research problem. The researcher can find a solution to a research problem by ensuring that he/she uses an appropriate research design.

Table of Content

• 1 What is Research Design?
• 2 Concept of Research Design
• 3 Need and Features of Research Design
• 4.1 Neutrality
• 4.2 Reliability
• 4.3 Performance
• 4.4 General practice
• 4.5 Qualitative
• 4.6 Quantitative
• 5.1 Research questions
• 5.2 Course suggestions
• 5.3 Unit analysis
• 5.4 Data linking and propositions
• 5.5 Interpretation of findings from the study

The chances of success of a research project depend on how the researcher has taken care to develop a research design that is in line with the research problem. A research design is created or developed when the researcher prepares a plan, structure and strategy for conducting research.

Research design is the base over which a researcher builds his research. A good research design provides vital information to a researcher with respect to a research topic, data type, data sources and techniques of data collection used in the research. In this chapter, you will study about the concept of research design, its need, features, components, etc.

Next, the chapter will describe the types of research design, research design framework, and types of errors affecting research design. Towards the end, you will study about the meaning of experiments and types of experiments.

Concept of Research Design

The research design refers to the framework of research methods and techniques selected by a researcher. The design chosen by the researchers allows them to use appropriate methods to study and plan their studies effectively and in the future. The descriptive research method focuses primarily on defining the nature of a class of people, without focusing on the “why” of something happening.

In other words, it “explains” the topic of research, without covering why “it” happens. Let us study in detail about the concept of research design, its requirements, features or characteristics, designing research framework its related case studies and observations.

Cross-sectional and longitudinal studies, casual research and errors arising while designing the research which are related to improper selection of respondents. This is a framework for determining the research methods and techniques to be used. This design enables researchers to set the research methods that are most relevant to the subject.

The design of the research topic describes the type of research (testing, research, integration, experimentation, review) and its sub-type (test design, research problem, descriptive case study). Research design can also be considered as the blueprint for collection, measurement and analysis of data.

The type of research problem the organisation is facing will determine the structure of the research and not the other way around. The study design phase determines which tools to use and how to use them. Impact studies often create less bias in the data and increase confidence in the accuracy of the data collected. A design that produces a small error limit in test studies is usually considered to be the desired result.

In research, the important things are:

• A specific statement of intent
• Strategies used to collect and analyse data
• Type of research methodology
• Potential objections to research
• Research study settings
• Analysis rating

Need and Features of Research Design

Much of what we do in our daily lives is based on understanding, what we have learned from others, or what we have learned through personal experience or observation. Sometimes, there are conflicting ideas about what is good or what works in a particular situation.

In addition, what works in one situation or situation may be ineffective or even harmful in another, or it may be combined with other measures. Psychological techniques ignore the impact of external factors that can influence what is seen. Even in health care settings, there are gaps in knowledge, ideas about how something can work better and ideas for improvement.

Since health professionals cannot afford to be risky, research is needed. For clinical trials, this is also a legal requirement that pharmaceutical companies cannot obtain marketing authorisation (i.e., permission to sell their new drugs) until they are approved by the relevant authorities.

Another advantage of doing research is that in most studies, the findings can be statistically recorded and statistically evaluated to determine if the findings are significant (meaning how much they can be called with a certain degree of certainty that they are not just a risk factor).

With limited studies, results can usually be performed in a broader population (for example, in people with dementia, caregivers, GPs, or generalised individuals, depending on the study group). This is because steps would be taken to ensure that the group of participants in the study, represented other people in that category, as far as possible.

The advantage of many quality studies is that they allow for a thorough investigation of a particular aspect of the human experience. They give people the opportunity to express in their own words how they feel, what they think, and how they make sense of the world around them.

In some cases, the results may be passed on to others as conditions. However, the advantage of quality studies is that it provides rich, logical and insightful information on the complexity of human experience with all the contradictions, differences and idiosyncrasies. Others discuss topics that have not been researched before and maybe facing issues that are controversial, critical, or illegal.Some courses also work to give voice to vulnerable or small groups

Features of Research Design

Proper research design makes your study a success. Effective research provides accurate and impartial information. You will need to create a survey that meets all the key design features. Key features of a good research design are:

When planning your study, you may need to think about the details you are going to collect. The results shown in the study should be fair and impartial. Understand the ideas about the last scores tested and the conclusions from most people and consider those who agree with the results obtained.

Reliability

With regular research, the researcher involved expects the same results regularly. Research design should be developed in a way that good research questions are developed and quality results are ensured. You will only be able to access the expected results if your design is reliable.

Performance

There are many measuring tools available. However, the only valid measurement tools are those that assist the researcher in measuring results according to the research purpose. The list of questions created from this project will be valid.

General practice

The effect of your design should apply to people and not just to the restricted sample. A comprehensive design means that your survey can be done on any part of the people with the same accuracy. The above factors affect the way respondents respond to research questions and therefore all of the above factors should be balanced in good design. The researcher must have a clear understanding of the different types of study design in order to choose which model to use in the study.

Qualitative

Quality research helps in understanding the problem and to develop hypothesis. Researchers rely on high-quality research methods that conclude “why” a certain idea exists and what “responders” say.

Quantitative

A quantitative study is one of the situations in which statistical conclusions are arrived at on the basis of collected data. Numbers provide a better idea of how to make critical business decisions. Research is needed for the growth of any organisation. The information taken from the data and the analysis of the hard data is very effective in making decisions related to the future of the business.

Components of Research Design

The main purpose behind the design of the study is to help avoid a situation where the evidence does not address the main research questions. The research design is about a logical problem and not a planning problem.

The five main components of a research design are:

Research questions

Course suggestions.

• Units of analysis
• Linking data to propositions
• Interpretation of the findings of the study

The components of research design apply to all types of standardised, extra-terrestrial research, whether physical or social sciences.

This first item raises the type of question – about “who,” “what,” “where,” “how,” and “why” – provides important clues as to the proper research methodology used. Use three paragraphs: First, use the books to reduce your interest in one or two topics. In 2nd paragraph, take a closer look — or cut — a few key lessons from your favorite topic. Find questions in those few studies and conclude with new questions for future research. In the 3rd paragraph, check out another science group on the same topic. They may offer support for your potential questions or suggest ways to sharpen it.

Each suggestion directs the focus to something needed to be tested within the study. Only if you are forced to give some suggestions will you go the right way. For example, you would think that businesses are cooperating as they receive the same benefits. This suggestion, in addition to highlighting an important theoretical issue (that some corporate incentives do not exist or do not matter), also begins to tell you where to look for related evidence (defining and determining the magnitude of specific benefits in each business).

Unit analysis

It is associated with the basic problem of defining what “case” is – a problem that has affected many researchers at the beginning of the study. Take the example of medical patients. In this case, the person is being studied, and that person is an important unit of analysis.

Information about the right person will be collected, and few such people can be part of a multidisciplinary investigation. You will need study questions and suggestions to help you find the right information to collect about this person or people. Without such questions and suggestions, you may be tempted to cover “everything” about the person (s), which is not possible.

Data linking and propositions

Data linking methods and propositions such as pattern, definition structure, time series analysis, logic models and cross-case synthesis. The actual analysis will require you to compile or calculate your study data as a direct indication of your initial study suggestions.

Interpretation of findings from the study

Statistical analysis determines whether the research results support the hypothesis. Several statistical tests, for example, T-tests (determining whether two groups are statistically different from each other), Chi-square tests (where data is compared with the expected result), and oneway analysis of variance (provides multiple group comparisons), are performed by data type, number, and types of variables and data categories.

Statistical analysis provides some clear ways to translate. For example, according to the agreement, social science looks at a level below -55 to show that perceived differences are “statistically significant.” On the other hand, the analysis of many cases will not depend on the use of statistics and therefore focuses on alternative approaches to these approaches.

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• Vegetables , fruits , whole grains , healthy oils , and healthy proteins like nuts , beans , fish , and chicken should make it into the shopping cart every week, along with a little yogurt or other dairy foods  if desired.
• The Healthy Eating Pyramid also addresses other aspects of a healthy lifestyle— exercise , weight control , vitamin D , and multivitamin supplements , and moderation in alcohol  for people who drink—so it’s a useful tool for health professionals and health educators.

Translating nutrition advice into a colorful pyramid is great way to illustrate what foods make up a healthy diet. The shape immediately suggests that some foods are good and should be eaten often, and that others aren’t so good and should be eaten only occasionally. The layers represent major food groups that contribute to the total diet. The problem with the US government’s original Food Guide Pyramid, released in 1992, was that it conveyed the wrong dietary advice. And MyPyramid, its 2005 replacement, was vague and confusing.

MyPyramid, unveiled in 2005, was essentially the Food Guide Pyramid turned on its side, without any explanatory text. Six swaths of color swept from the apex of MyPyramid to the base: orange for grains, green for vegetables, red for fruits, a teeny band of yellow for oils, blue for milk, and purple for meat and beans. The widths suggested how much food a person should choose from each group. A band of stairs running up the side of the Pyramid, with a little stick figure chugging up it, served as a reminder of the importance of physical activity.

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Research on the intelligent design system for automotive panel die based on geometry and knowledge driven

• Original Article
• Published: 22 May 2024

Cite this article

• Chuipin Kong 1 ,
• Jialin Lei 1 &
• XiongHui Zhou 1  

Automotive panel die design is a significant issue in vehicle development, playing a key role in refining quality and efficiency in the entire vehicle evolution process. It also contributes to reducing production costs and meeting the demands of the fashionable and dynamic vehicle market. This article delves into the intricacies of panel die design, focusing on essential issues such as complex three-dimensional (3D) curve and surface processing. A solution for intelligent die design based on geometry and knowledge-driven approaches is proposed. By thoroughly exploring the forming process of panel parts and incorporating die design expertise, a structured design knowledge base is established, which consolidates extensive experience and professional technology in this field. A curve offset algorithm based on progressive iteration is advocated. By adopting a dynamic error sampling algorithm that considers offset distance, the accuracy of the offset curve is ensured. In addition, the method of adjusting the offset direction is adopted to avoid the problem of overlapping offset curves and prevent distortion. Subsequently, a smooth surface is created based on the trimming curve and its offset curve to obtain a parameterized modeling of key non-standard components such as trimming tools. Furthermore, a solution strategy is proposed to handle these dynamic changes and constraints associated with parameter values, ranges, and accuracy requirements of standard components by developing a standard component computer-aided design (CAD) library, which can be seamlessly integrated into various CAD platforms through cloud-based deployment. By integrating die design knowledge, computer-aided geometric design (CAGD) theory, intelligent algorithms, and standard component libraries, a geometric and knowledge-based intelligent design system for automotive panel dies was successfully opened up. After example verification, the efficiency and quality of die design can be upgraded to a large extent.

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Acknowledgements

We express our thanks to Renping Luo, Huping Sun, Yan Wang, and Jing Gao from General Motors’ stamping department for their great supports during the research.

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Kong, C., Lei, J. & Zhou, X. Research on the intelligent design system for automotive panel die based on geometry and knowledge driven. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-13728-z

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Published : 22 May 2024

DOI : https://doi.org/10.1007/s00170-024-13728-z

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Research Insight: G2 creates opportunities for diversified development of Vietnamese chip industry, but challenges remain

Credit: AFP

While the US-China technology rivalry and the resultant G2 framework have prompted the semiconductor industry to diversify production risks, Vietnam is emerging as an attractive location for global semiconductor players to serve the purpose, ranking....

While the US-China technology rivalry and the resultant G2 framework have prompted the semiconductor industry to diversify production risks, Vietnam is emerging as an attractive location for global semiconductor players to serve the purpose, ranking closely behind Singapore and Malaysia among ASEAN countries. Nevertheless, there exist challenges that must be overcome to fully realize its potential.

DIGITIMES Research finds Vietnam's semiconductor industry concentrated in and around Hanoi and Ho Chi Minh City, with distinct regional specializations. The northern region focuses on memory testing and assembly, serving South Korean consumer electronics supply chains there to meet local market demand. The south boasts more logic IC design and testing firms, but with smaller design houses and a "one large, many small" structure in testing and assembly companies, primarily oriented towards exports.

Driven by the electronics industry's growing need to diversify risks, Vietnam's semiconductor sector is experiencing an expansion. New entrants are joining the market while existing players are increasing their investments in both northern and southern cities.

In northern Vietnam, for instance, US-based Amkor Technology saw its new packaging and testing plant in Bac Ninh province become operational in late 2023. This, coupled with the existing dominance of memory testing and assembly, suggests a future with a broader range of semiconductor products and a larger export share.

Southern Vietnam is also experiencing a similar growth trend fueled by the G2 framework. In late 2023, Intel announced investments in expanding its chips packaging facility in Ho Chi Minh City, and the Dutch packaging equipment maker BE Semiconductor Industries also disclosed plans to set up operations in the city. While historically focused on IC design, the South is also showing signs of diversification, paving the way for a more comprehensive industry structure in the future.

Based on observations by DIGITIMES Research, the G2 framework does create an opportunity for Vietnam to attract international semiconductor investments, but challenges persist in both north and south.

In the north, limited power supply capacity hinders growth, and the region's historical focus as a political center translates to fewer university departments fostering semiconductor talent compared to the south.

Conversely, the south boasts a rich cluster of IC design houses, but most remain small-scale R&D centers, suggesting local demand is still limited. To further develop the southern industry and achieve scale, fostering local market demand through government policies alongside nurturing a robust industry ecosystem will be crucial.

Furthermore, increasingly aggressive semiconductor self-sufficiency efforts in Europe, the US, and Japan cast doubt on whether future global demand will be enough to sustain Vietnam's semiconductor exports and drive its manufacturing towards economies of scale.

Credit: DIGITIMES

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