how to do good research design

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 24 June 2024, from https://www.scribbr.co.uk/research-methods/research-design/

Is this article helpful?

Shona McCombes

• How it works

How to Write a Research Design – Guide with Examples

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

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

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

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

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

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

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

Step 1: Establish Priorities for Research Design

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

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

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

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

For example:

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

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

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

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

Step 2: Data Type you Need for Research

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

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

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

Need help with a thesis chapter?

• Hire an expert from ResearchProspect today!
• Statistical analysis, research methodology, discussion of the results or conclusion – our experts can help you no matter how complex the requirements are.

Step 3: Data Collection Techniques

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

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

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

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

Research Methods

Step 4: Procedure of Data Analysis

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

Quantitative Data Analysis

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

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

Qualitative Data Analysis

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

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

Step 5: Write your Research Proposal

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

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

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

How to Write a Research Design – Conclusion

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

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

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

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

Frequently Asked Questions

What is research design.

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

How to write a research design?

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

How to write the design section of a research paper?

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

How to write a research design in methodology?

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

You May Also Like

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

This article is a step-by-step guide to how to write statement of a problem in research. The research problem will be half-solved by defining it correctly.

To help students organise their dissertation proposal paper correctly, we have put together detailed guidelines on how to structure a dissertation proposal.

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

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…

Need a helping hand?

Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst... there’s more!

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 ...

You Might Also Like:

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?

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Privacy Policy

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Research Methods – Types, Examples and Guide

Research Paper Introduction – Writing Guide and...

Research Paper Title – Writing Guide and Example

Tables in Research Paper – Types, Creating Guide...

Figures in Research Paper – Examples and Guide

Assignment – Types, Examples and Writing Guide

Leave a comment x.

Save my name, email, and website in this browser for the next time I comment.

What is Research Design? Characteristics, Types, Process, & Examples

Link Copied

Share on Facebook

Share on Twitter

Share on LinkedIn

Your search has come to an end!

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.

Got a hang of research, now book yout student accommodation with one click!

Book through amber today!

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.

Your ideal student home & a flight ticket awaits

Follow us on :

Related Posts

Masters at Cambridge University: Top Courses, Eligibility, Fee

25 Best Undergraduate Business Schools in the World

Top 10 Universities in France | Fees | Rankings | Acceptance Rate

Planning to Study Abroad ?

Your ideal student accommodation is a few steps away! Please fill in your details below so we can find you a new home!

We have got your response

amber © 2024. All rights reserved.

4.8/5 on Trustpilot

Rated as "Excellent" • 4800+ Reviews by students

Rated as "Excellent" • 4800+ Reviews by Students

What Is a Research Design? | Definition, Types & Guide

Introduction

Parts of a research design, types of research methodology in qualitative research, narrative research designs, phenomenological research designs, grounded theory research designs.

• Ethnographic research designs

Case study research design

Important reminders when designing a research study.

A research design in qualitative research is a critical framework that guides the methodological approach to studying complex social phenomena. Qualitative research designs determine how data is collected, analyzed, and interpreted, ensuring that the research captures participants' nuanced and subjective perspectives. Research designs also recognize ethical considerations and involve informed consent, ensuring confidentiality, and handling sensitive topics with the utmost respect and care. These considerations are crucial in qualitative research and other contexts where participants may share personal or sensitive information. A research design should convey coherence as it is essential for producing high-quality qualitative research, often following a recursive and evolving process.

Theoretical concepts and research question

The first step in creating a research design is identifying the main theoretical concepts. To identify these concepts, a researcher should ask which theoretical keywords are implicit in the investigation. The next step is to develop a research question using these theoretical concepts. This can be done by identifying the relationship of interest among the concepts that catch the focus of the investigation. The question should address aspects of the topic that need more knowledge, shed light on new information, and specify which aspects should be prioritized before others. This step is essential in identifying which participants to include or which data collection methods to use. Research questions also put into practice the conceptual framework and make the initial theoretical concepts more explicit. Once the research question has been established, the main objectives of the research can be specified. For example, these objectives may involve identifying shared experiences around a phenomenon or evaluating perceptions of a new treatment.

Methodology

After identifying the theoretical concepts, research question, and objectives, the next step is to determine the methodology that will be implemented. This is the lifeline of a research design and should be coherent with the objectives and questions of the study. The methodology will determine how data is collected, analyzed, and presented. Popular qualitative research methodologies include case studies, ethnography , grounded theory , phenomenology, and narrative research . Each methodology is tailored to specific research questions and facilitates the collection of rich, detailed data. For example, a narrative approach may focus on only one individual and their story, while phenomenology seeks to understand participants' lived common experiences. Qualitative research designs differ significantly from quantitative research, which often involves experimental research, correlational designs, or variance analysis to test hypotheses about relationships between two variables, a dependent variable and an independent variable while controlling for confounding variables.

Literature review

After the methodology is identified, conducting a thorough literature review is integral to the research design. This review identifies gaps in knowledge, positioning the new study within the larger academic dialogue and underlining its contribution and relevance. Meta-analysis, a form of secondary research, can be particularly useful in synthesizing findings from multiple studies to provide a clear picture of the research landscape.

Data collection

The sampling method in qualitative research is designed to delve deeply into specific phenomena rather than to generalize findings across a broader population. The data collection methods—whether interviews, focus groups, observations, or document analysis—should align with the chosen methodology, ethical considerations, and other factors such as sample size. In some cases, repeated measures may be collected to observe changes over time.

Data analysis

Analysis in qualitative research typically involves methods such as coding and thematic analysis to distill patterns from the collected data. This process delineates how the research results will be systematically derived from the data. It is recommended that the researcher ensures that the final interpretations are coherent with the observations and analyses, making clear connections between the data and the conclusions drawn. Reporting should be narrative-rich, offering a comprehensive view of the context and findings.

Overall, a coherent qualitative research design that incorporates these elements facilitates a study that not only adds theoretical and practical value to the field but also adheres to high quality. This methodological thoroughness is essential for achieving significant, insightful findings. Examples of well-executed research designs can be valuable references for other researchers conducting qualitative or quantitative investigations. An effective research design is critical for producing robust and impactful research outcomes.

Each qualitative research design is unique, diverse, and meticulously tailored to answer specific research questions, meet distinct objectives, and explore the unique nature of the phenomenon under investigation. The methodology is the wider framework that a research design follows. Each methodology in a research design consists of methods, tools, or techniques that compile data and analyze it following a specific approach.

The methods enable researchers to collect data effectively across individuals, different groups, or observations, ensuring they are aligned with the research design. The following list includes the most commonly used methodologies employed in qualitative research designs, highlighting how they serve different purposes and utilize distinct methods to gather and analyze data.

The narrative approach in research focuses on the collection and detailed examination of life stories, personal experiences, or narratives to gain insights into individuals' lives as told from their perspectives. It involves constructing a cohesive story out of the diverse experiences shared by participants, often using chronological accounts. It seeks to understand human experience and social phenomena through the form and content of the stories. These can include spontaneous narrations such as memoirs or diaries from participants or diaries solicited by the researcher. Narration helps construct the identity of an individual or a group and can rationalize, persuade, argue, entertain, confront, or make sense of an event or tragedy. To conduct a narrative investigation, it is recommended that researchers follow these steps:

Identify if the research question fits the narrative approach. Its methods are best employed when a researcher wants to learn about the lifestyle and life experience of a single participant or a small number of individuals.

Select the best-suited participants for the research design and spend time compiling their stories using different methods such as observations, diaries, interviewing their family members, or compiling related secondary sources.

Compile the information related to the stories. Narrative researchers collect data based on participants' stories concerning their personal experiences, for example about their workplace or homes, their racial or ethnic culture, and the historical context in which the stories occur.

Analyze the participant stories and "restore" them within a coherent framework. This involves collecting the stories, analyzing them based on key elements such as time, place, plot, and scene, and then rewriting them in a chronological sequence (Ollerenshaw & Creswell, 2000). The framework may also include elements such as a predicament, conflict, or struggle; a protagonist; and a sequence with implicit causality, where the predicament is somehow resolved (Carter, 1993).

Collaborate with participants by actively involving them in the research. Both the researcher and the participant negotiate the meaning of their stories, adding a credibility check to the analysis (Creswell & Miller, 2000).

A narrative investigation includes collecting a large amount of data from the participants and the researcher needs to understand the context of the individual's life. A keen eye is needed to collect particular stories that capture the individual experiences. Active collaboration with the participant is necessary, and researchers need to discuss and reflect on their own beliefs and backgrounds. Multiple questions could arise in the collection, analysis, and storytelling of individual stories that need to be addressed, such as: Whose story is it? Who can tell it? Who can change it? Which version is compelling? What happens when narratives compete? In a community, what do the stories do among them? (Pinnegar & Daynes, 2006).

Make the most of your data with ATLAS.ti

Powerful tools in an intuitive interface, ready for you with a free trial today.

A research design based on phenomenology aims to understand the essence of the lived experiences of a group of people regarding a particular concept or phenomenon. Researchers gather deep insights from individuals who have experienced the phenomenon, striving to describe "what" they experienced and "how" they experienced it. This approach to a research design typically involves detailed interviews and aims to reach a deep existential understanding. The purpose is to reduce individual experiences to a description of the universal essence or understanding the phenomenon's nature (van Manen, 1990). In phenomenology, the following steps are usually followed:

Identify a phenomenon of interest . For example, the phenomenon might be anger, professionalism in the workplace, or what it means to be a fighter.

Recognize and specify the philosophical assumptions of phenomenology , for example, one could reflect on the nature of objective reality and individual experiences.

Collect data from individuals who have experienced the phenomenon . This typically involves conducting in-depth interviews, including multiple sessions with each participant. Additionally, other forms of data may be collected using several methods, such as observations, diaries, art, poetry, music, recorded conversations, written responses, or other secondary sources.

Ask participants two general questions that encompass the phenomenon and how the participant experienced it (Moustakas, 1994). For example, what have you experienced in this phenomenon? And what contexts or situations have typically influenced your experiences within the phenomenon? Other open-ended questions may also be asked, but these two questions particularly focus on collecting research data that will lead to a textural description and a structural description of the experiences, and ultimately provide an understanding of the common experiences of the participants.

Review data from the questions posed to participants . It is recommended that researchers review the answers and highlight "significant statements," phrases, or quotes that explain how participants experienced the phenomenon. The researcher can then develop meaningful clusters from these significant statements into patterns or key elements shared across participants.

Write a textual description of what the participants experienced based on the answers and themes of the two main questions. The answers are also used to write about the characteristics and describe the context that influenced the way the participants experienced the phenomenon, called imaginative variation or structural description. Researchers should also write about their own experiences and context or situations that influenced them.

Write a composite description from the structural and textural description that presents the "essence" of the phenomenon, called the essential and invariant structure.

A phenomenological approach to a research design includes the strict and careful selection of participants in the study where bracketing personal experiences can be difficult to implement. The researcher decides how and in which way their knowledge will be introduced. It also involves some understanding and identification of the broader philosophical assumptions.

Grounded theory is used in a research design when the goal is to inductively develop a theory "grounded" in data that has been systematically gathered and analyzed. Starting from the data collection, researchers identify characteristics, patterns, themes, and relationships, gradually forming a theoretical framework that explains relevant processes, actions, or interactions grounded in the observed reality. A grounded theory study goes beyond descriptions and its objective is to generate a theory, an abstract analytical scheme of a process. Developing a theory doesn't come "out of nothing" but it is constructed and based on clear data collection. We suggest the following steps to follow a grounded theory approach in a research design:

Determine if grounded theory is the best for your research problem . Grounded theory is a good design when a theory is not already available to explain a process.

Develop questions that aim to understand how individuals experienced or enacted the process (e.g., What was the process? How did it unfold?). Data collection and analysis occur in tandem, so that researchers can ask more detailed questions that shape further analysis, such as: What was the focal point of the process (central phenomenon)? What influenced or caused this phenomenon to occur (causal conditions)? What strategies were employed during the process? What effect did it have (consequences)?

Gather relevant data about the topic in question . Data gathering involves questions that are usually asked in interviews, although other forms of data can also be collected, such as observations, documents, and audio-visual materials from different groups.

Carry out the analysis in stages . Grounded theory analysis begins with open coding, where the researcher forms codes that inductively emerge from the data (rather than preconceived categories). Researchers can thus identify specific properties and dimensions relevant to their research question.

Assemble the data in new ways and proceed to axial coding . Axial coding involves using a coding paradigm or logic diagram, such as a visual model, to systematically analyze the data. Begin by identifying a central phenomenon, which is the main category or focus of the research problem. Next, explore the causal conditions, which are the categories of factors that influence the phenomenon. Specify the strategies, which are the actions or interactions associated with the phenomenon. Then, identify the context and intervening conditions—both narrow and broad factors that affect the strategies. Finally, delineate the consequences, which are the outcomes or results of employing the strategies.

Use selective coding to construct a "storyline" that links the categories together. Alternatively, the researcher may formulate propositions or theory-driven questions that specify predicted relationships among these categories.

Develop and visually present a matrix that clarifies the social, historical, and economic conditions influencing the central phenomenon. This optional step encourages viewing the model from the narrowest to the broadest perspective.

Write a substantive-level theory that is closely related to a specific problem or population. This step is optional but provides a focused theoretical framework that can later be tested with quantitative data to explore its generalizability to a broader sample.

Allow theory to emerge through the memo-writing process, where ideas about the theory evolve continuously throughout the stages of open, axial, and selective coding.

The researcher should initially set aside any preconceived theoretical ideas to allow for the emergence of analytical and substantive theories. This is a systematic research approach, particularly when following the methodological steps outlined by Strauss and Corbin (1990). For those seeking more flexibility in their research process, the approach suggested by Charmaz (2006) might be preferable.

One of the challenges when using this method in a research design is determining when categories are sufficiently saturated and when the theory is detailed enough. To achieve saturation, discriminant sampling may be employed, where additional information is gathered from individuals similar to those initially interviewed to verify the applicability of the theory to these new participants. Ultimately, its goal is to develop a theory that comprehensively describes the central phenomenon, causal conditions, strategies, context, and consequences.

Ethnographic research design

An ethnographic approach in research design involves the extended observation and data collection of a group or community. The researcher immerses themselves in the setting, often living within the community for long periods. During this time, they collect data by observing and recording behaviours, conversations, and rituals to understand the group's social dynamics and cultural norms. We suggest following these steps for ethnographic methods in a research design:

Assess whether ethnography is the best approach for the research design and questions. It's suitable if the goal is to describe how a cultural group functions and to delve into their beliefs, language, behaviours, and issues like power, resistance, and domination, particularly if there is limited literature due to the group’s marginal status or unfamiliarity to mainstream society.

Identify and select a cultural group for your research design. Choose one that has a long history together, forming distinct languages, behaviours, and attitudes. This group often might be marginalized within society.

Choose cultural themes or issues to examine within the group. Analyze interactions in everyday settings to identify pervasive patterns such as life cycles, events, and overarching cultural themes. Culture is inferred from the group members' words, actions, and the tension between their actual and expected behaviours, as well as the artifacts they use.

Conduct fieldwork to gather detailed information about the group’s living and working environments. Visit the site, respect the daily lives of the members, and collect a diverse range of materials, considering ethical aspects such as respect and reciprocity.

Compile and analyze cultural data to develop a set of descriptive and thematic insights. Begin with a detailed description of the group based on observations of specific events or activities over time. Then, conduct a thematic analysis to identify patterns or themes that illustrate how the group functions and lives. The final output should be a comprehensive cultural portrait that integrates both the participants (emic) and the researcher’s (etic) perspectives, potentially advocating for the group’s needs or suggesting societal changes to better accommodate them.

Researchers engaging in ethnography need a solid understanding of cultural anthropology and the dynamics of sociocultural systems, which are commonly explored in ethnographic research. The data collection phase is notably extensive, requiring prolonged periods in the field. Ethnographers often employ a literary, quasi-narrative style in their narratives, which can pose challenges for those accustomed to more conventional social science writing methods.

Another potential issue is the risk of researchers "going native," where they become overly assimilated into the community under study, potentially jeopardizing the objectivity and completion of their research. It's crucial for researchers to be aware of their impact on the communities and environments they are studying.

The case study approach in a research design focuses on a detailed examination of a single case or a small number of cases. Cases can be individuals, groups, organizations, or events. Case studies are particularly useful for research designs that aim to understand complex issues in real-life contexts. The aim is to provide a thorough description and contextual analysis of the cases under investigation. We suggest following these steps in a case study design:

Assess if a case study approach suits your research questions . This approach works well when you have distinct cases with defined boundaries and aim to deeply understand these cases or compare multiple cases.

Choose your case or cases. These could involve individuals, groups, programs, events, or activities. Decide whether an individual or collective, multi-site or single-site case study is most appropriate, focusing on specific cases or themes (Stake, 1995; Yin, 2003).

Gather data extensively from diverse sources . Collect information through archival records, interviews, direct and participant observations, and physical artifacts (Yin, 2003).

Analyze the data holistically or in focused segments . Provide a comprehensive overview of the entire case or concentrate on specific aspects. Start with a detailed description including the history of the case and its chronological events then narrow down to key themes. The aim is to delve into the case's complexity rather than generalize findings.

Interpret and report the significance of the case in the final phase . Explain what insights were gained, whether about the subject of the case in an instrumental study or an unusual situation in an intrinsic study (Lincoln & Guba, 1985).

The investigator must carefully select the case or cases to study, recognizing that multiple potential cases could illustrate a chosen topic or issue. This selection process involves deciding whether to focus on a single case for deeper analysis or multiple cases, which may provide broader insights but less depth per case. Each choice requires a well-justified rationale for the selected cases. Researchers face the challenge of defining the boundaries of a case, such as its temporal scope and the events and processes involved. This decision in a research design is crucial as it affects the depth and value of the information presented in the study, and therefore should be planned to ensure a comprehensive portrayal of the case.

Qualitative and quantitative research designs are distinct in their approach to data collection and data analysis. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research prioritizes understanding the depth and richness of human experiences, behaviours, and interactions.

Qualitative methods in a research design have to have internal coherence, meaning that all elements of the research project—research question, data collection, data analysis, findings, and theory—are well-aligned and consistent with each other. This coherence in the research study is especially crucial in inductive qualitative research, where the research process often follows a recursive and evolving path. Ensuring that each component of the research design fits seamlessly with the others enhances the clarity and impact of the study, making the research findings more robust and compelling. Whether it is a descriptive research design, explanatory research design, diagnostic research design, or correlational research design coherence is an important element in both qualitative and quantitative research.

Finally, a good research design ensures that the research is conducted ethically and considers the well-being and rights of participants when managing collected data. The research design guides researchers in providing a clear rationale for their methodologies, which is crucial for justifying the research objectives to the scientific community. A thorough research design also contributes to the body of knowledge, enabling researchers to build upon past research studies and explore new dimensions within their fields. At the core of the design, there is a clear articulation of the research objectives. These objectives should be aligned with the underlying concepts being investigated, offering a concise method to answer the research questions and guiding the direction of the study with proper qualitative methods.

Carter, K. (1993). The place of a story in the study of teaching and teacher education. Educational Researcher, 22(1), 5-12, 18.

Charmaz, K. (2006). Constructing grounded theory. London: Sage.

Creswell, J. W., & Miller, D. L. (2000). Determining validity in qualitative inquiry. Theory Into Practice, 39(3), 124-130.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.

Ollerenshaw, J. A., & Creswell, J. W. (2000, April). Data analysis in narrative research: A comparison of two “restoring” approaches. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage.

Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park, CA: Sage.

van Manen, M. (1990). Researching lived experience: Human science for an action sensitive pedagogy. Ontario, Canada: University of Western Ontario.

Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage

Whatever your research objectives, make it happen with ATLAS.ti!

Download a free trial today.

Educational resources and simple solutions for your research journey

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

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

Table of Contents

What is research design?  

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

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

Research design elements  

Research design elements include the following:  

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

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

Characteristics of research design  

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

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

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

Different types of research design  

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

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

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

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

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

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

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

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

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

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

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

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

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

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

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

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

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

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

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

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

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

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

Example : Comparing school dropout levels and possible bullying events.  

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

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

Benefits of research design  

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

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

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

Frequently Asked Questions (FAQ) on Research Design

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

Broadly speaking there are two basic types of research design –

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

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

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

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

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

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

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

Researcher.Life is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Researcher.Life All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 21+ years of experience in academia, Researcher.Life All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $17 a month !    

Related Posts

Take Top AI Tools for Researchers for a Spin with the Editage All Access 7-Day Pass!

Thesis Defense: How to Ace this Crucial Step

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

5 Research design

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

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

Key attributes of a research design

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

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

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

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

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

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

Improving internal and external validity

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

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

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

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

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

Popular research designs

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

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

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

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

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

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

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

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

Selecting research designs

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

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

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

Share This Book

The Four Types of Research Design — Everything You Need to Know

Updated: December 11, 2023

Published: January 18, 2023

When you conduct research, you need to have a clear idea of what you want to achieve and how to accomplish it. A good research design enables you to collect accurate and reliable data to draw valid conclusions.

In this blog post, we'll outline the key features of the four common types of research design with real-life examples from UnderArmor, Carmex, and more. Then, you can easily choose the right approach for your project.

Table of Contents

What is research design?

The four types of research design, research design examples.

Research design is the process of planning and executing a study to answer specific questions. This process allows you to test hypotheses in the business or scientific fields.

Research design involves choosing the right methodology, selecting the most appropriate data collection methods, and devising a plan (or framework) for analyzing the data. In short, a good research design helps us to structure our research.

Marketers use different types of research design when conducting research .

There are four common types of research design — descriptive, correlational, experimental, and diagnostic designs. Let’s take a look at each in more detail.

Researchers use different designs to accomplish different research objectives. Here, we'll discuss how to choose the right type, the benefits of each, and use cases.

Research can also be classified as quantitative or qualitative at a higher level. Some experiments exhibit both qualitative and quantitative characteristics.

Free Market Research Kit

5 Research and Planning Templates + a Free Guide on How to Use Them in Your Market Research

• SWOT Analysis Template
• Survey Template
• Focus Group Template

Download Free

All fields are required.

You're all set!

Click this link to access this resource at any time.

Experimental

An experimental design is used when the researcher wants to examine how variables interact with each other. The researcher manipulates one variable (the independent variable) and observes the effect on another variable (the dependent variable).

In other words, the researcher wants to test a causal relationship between two or more variables.

In marketing, an example of experimental research would be comparing the effects of a television commercial versus an online advertisement conducted in a controlled environment (e.g. a lab). The objective of the research is to test which advertisement gets more attention among people of different age groups, gender, etc.

Another example is a study of the effect of music on productivity. A researcher assigns participants to one of two groups — those who listen to music while working and those who don't — and measure their productivity.

The main benefit of an experimental design is that it allows the researcher to draw causal relationships between variables.

One limitation: This research requires a great deal of control over the environment and participants, making it difficult to replicate in the real world. In addition, it’s quite costly.

Best for: Testing a cause-and-effect relationship (i.e., the effect of an independent variable on a dependent variable).

Correlational

A correlational design examines the relationship between two or more variables without intervening in the process.

Correlational design allows the analyst to observe natural relationships between variables. This results in data being more reflective of real-world situations.

For example, marketers can use correlational design to examine the relationship between brand loyalty and customer satisfaction. In particular, the researcher would look for patterns or trends in the data to see if there is a relationship between these two entities.

Similarly, you can study the relationship between physical activity and mental health. The analyst here would ask participants to complete surveys about their physical activity levels and mental health status. Data would show how the two variables are related.

Best for: Understanding the extent to which two or more variables are associated with each other in the real world.

Descriptive

Descriptive research refers to a systematic process of observing and describing what a subject does without influencing them.

Methods include surveys, interviews, case studies, and observations. Descriptive research aims to gather an in-depth understanding of a phenomenon and answers when/what/where.

SaaS companies use descriptive design to understand how customers interact with specific features. Findings can be used to spot patterns and roadblocks.

For instance, product managers can use screen recordings by Hotjar to observe in-app user behavior. This way, the team can precisely understand what is happening at a certain stage of the user journey and act accordingly.

Brand24, a social listening tool, tripled its sign-up conversion rate from 2.56% to 7.42%, thanks to locating friction points in the sign-up form through screen recordings.

Carma Laboratories worked with research company MMR to measure customers’ reactions to the lip-care company’s packaging and product . The goal was to find the cause of low sales for a recently launched line extension in Europe.

The team moderated a live, online focus group. Participants were shown w product samples, while AI and NLP natural language processing identified key themes in customer feedback.

This helped uncover key reasons for poor performance and guided changes in packaging.

We carry a range of biosensors from the top hardware producers. All compatible with iMotions

iMotions for Higher Education

Imotions for business.

What is the Observer Effect? 

Morten Pedersen

Neuroaesthetics: Decoding the Brain’s Love for Art and Beauty

Consumer Insights

News & Events

• iMotions Lab
• iMotions Online
• Eye Tracking
• Eye Tracking Screen Based
• Eye Tracking VR
• Eye Tracking Glasses
• Eye Tracking Webcam
• FEA (Facial Expression Analysis)
• Voice Analysis
• EDA/GSR (Electrodermal Activity)
• EEG (Electroencephalography)
• ECG (Electrocardiography)
• EMG (Electromyography)
• Respiration
• iMotions Lab: New features
• iMotions Lab: Developers
• EEG sensors
• Sensory and Perceptual
• Consumer Inights
• Human Factors R&D
• Work Environments, Training and Safety
• Customer Stories
• Published Research Papers
• Document Library
• Customer Support Program
• Help Center
• Release Notes
• Contact Support
• Partnerships
• Mission Statement
• Ownership and Structure
• Executive Management
• Job Opportunities

Publications

• Newsletter Sign Up

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.

Last edited

About the author

See what is next in human behavior research

Follow our newsletter to get the latest insights and events send to your inbox.

Related Posts

Unlocking the Potential of VR Eye Trackers: How They Work and Their Applications

Exploring Mobile Eye Trackers: How Eye Tracking Glasses Work and Their Applications

Understanding Screen-Based Eye Trackers: How They Work and Their Applications

Product guides.

Can you use HTC VIVE Pro Eye for eye tracking research?

You might also like these.

How Does Environmental Ergonomics Affect Behavior?

Human Factors and UX

Can You Build Your Own Eye Tracking Glasses?

Case Stories

Explore Blog Categories

Best Practice

Collaboration, product news, research fundamentals, research insights, 🍪 use of cookies.

We are committed to protecting your privacy and only use cookies to improve the user experience.

Chose which third-party services that you will allow to drop cookies. You can always change your cookie settings via the Cookie Settings link in the footer of the website. For more information read our Privacy Policy.

• gtag This tag is from Google and is used to associate user actions with Google Ad campaigns to measure their effectiveness. Enabling this will load the gtag and allow for the website to share information with Google. This service is essential and can not be disabled.
• Livechat Livechat provides you with direct access to the experts in our office. The service tracks visitors to the website but does not store any information unless consent is given. This service is essential and can not be disabled.
• Pardot Collects information such as the IP address, browser type, and referring URL. This information is used to create reports on website traffic and track the effectiveness of marketing campaigns.
• Third-party iFrames Allows you to see thirdparty iFrames.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Grad Med Educ
• v.8(1); 2016 Feb

Research Design Considerations

Associated data.

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

The Challenge

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

What Is Known

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

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

Key Differences in Qualitative and Quantitative Approaches

Use of theory.

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

Use of Logic

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

Purpose of Results

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

Establishing Rigor

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

View of Objectivity

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

Sampling Strategies

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

How You Can Start TODAY

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

How You Can Start TODAY—An Example

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

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

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

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

What You Can Do LONG TERM

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

Supplementary Material

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

Research Methods Guide: Research Design & Method

• Introduction
• Survey Research
• Interview Research
• Data Analysis
• Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

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

Which research method should I choose ?

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

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

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

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

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

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

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

Prevent plagiarism. Run a free check.

Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bevans, R. (2023, June 21). Guide to Experimental Design | Overview, 5 steps & Examples. Scribbr. Retrieved June 28, 2024, from https://www.scribbr.com/methodology/experimental-design/

Is this article helpful?

Rebecca Bevans

Other students also liked, random assignment in experiments | introduction & examples, quasi-experimental design | definition, types & examples, how to write a lab report, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

15 Steps to Good Research

• Define and articulate a research question (formulate a research hypothesis). How to Write a Thesis Statement (Indiana University)
• Identify possible sources of information in many types and formats. Georgetown University Library's Research & Course Guides
• Judge the scope of the project.
• Reevaluate the research question based on the nature and extent of information available and the parameters of the research project.
• Select the most appropriate investigative methods (surveys, interviews, experiments) and research tools (periodical indexes, databases, websites).
• Plan the research project. Writing Anxiety (UNC-Chapel Hill) Strategies for Academic Writing (SUNY Empire State College)
• Retrieve information using a variety of methods (draw on a repertoire of skills).
• Refine the search strategy as necessary.
• Write and organize useful notes and keep track of sources. Taking Notes from Research Reading (University of Toronto) Use a citation manager: Zotero or Refworks
• Evaluate sources using appropriate criteria. Evaluating Internet Sources
• Synthesize, analyze and integrate information sources and prior knowledge. Georgetown University Writing Center
• Revise hypothesis as necessary.
• Use information effectively for a specific purpose.
• Understand such issues as plagiarism, ownership of information (implications of copyright to some extent), and costs of information. Georgetown University Honor Council Copyright Basics (Purdue University) How to Recognize Plagiarism: Tutorials and Tests from Indiana University
• Cite properly and give credit for sources of ideas. MLA Bibliographic Form (7th edition, 2009) MLA Bibliographic Form (8th edition, 2016) Turabian Bibliographic Form: Footnote/Endnote Turabian Bibliographic Form: Parenthetical Reference Use a citation manager: Zotero or Refworks

Adapted from the Association of Colleges and Research Libraries "Objectives for Information Literacy Instruction" , which are more complete and include outcomes. See also the broader "Information Literacy Competency Standards for Higher Education."

• Basics of Research Process
• Methodology
• Research Design: Definition, Types, Characteristics & Study Examples
• Speech Topics
• Basics of Essay Writing
• Essay Topics
• Other Essays
• Main Academic Essays
• Research Paper Topics
• Basics of Research Paper Writing
• Miscellaneous
• Chicago/ Turabian
• Data & Statistics
• Admission Writing Tips
• Admission Advice
• Other Guides
• Student Life
• Studying Tips
• Understanding Plagiarism
• Academic Writing Tips
• Basics of Dissertation & Thesis Writing

• Essay Guides
• Research Paper Guides
• Formatting Guides
• Admission Guides
• Dissertation & Thesis Guides

Research Design: Definition, Types, Characteristics & Study Examples

Table of contents

Use our free Readability checker

A research design is the blueprint for any study. It's the plan that outlines how the research will be carried out. A study design usually includes the methods of data collection, the type of data to be gathered, and how it will be analyzed. Research designs help ensure the study is reliable, valid, and can answer the research question.

Behind every groundbreaking discovery and innovation lies a well-designed research. Whether you're investigating a new technology or exploring a social phenomenon, a solid research design is key to achieving reliable results. But what exactly does it means, and how do you create an effective one? Stay with our paper writers and find out:

• Detailed definition
• Types of research study designs
• How to write a research design
• Useful examples.

Whether you're a seasoned researcher or just getting started, understanding the core principles will help you conduct better studies and make more meaningful contributions.

What Is a Research Design: Definition

Research design is an overall study plan outlining a specific approach to investigating a research question . It covers particular methods and strategies for collecting, measuring and analyzing data. Students  are required to build a study design either as an individual task or as a separate chapter in a research paper , thesis or dissertation .

Before designing a research project, you need to consider a series aspects of your future study:

• Research aims What research objectives do you want to accomplish with your study? What approach will you take to get there? Will you use a quantitative, qualitative, or mixed methods approach?
• Type of data Will you gather new data (primary research), or rely on existing data (secondary research) to answer your research question?
• Sampling methods How will you pick participants? What criteria will you use to ensure your sample is representative of the population?
• Data collection methods What tools or instruments will you use to gather data (e.g., conducting a survey , interview, or observation)?
• Measurement  What metrics will you use to capture and quantify data?
• Data analysis  What statistical or qualitative techniques will you use to make sense of your findings?

By using a well-designed research plan, you can make sure your findings are solid and can be generalized to a larger group.

Research design example

You are going to investigate the effectiveness of a mindfulness-based intervention for reducing stress and anxiety among college students. You decide to organize an experiment to explore the impact. Participants should be randomly assigned to either an intervention group or a control group. You need to conduct pre- and post-intervention using self-report measures of stress and anxiety.

What Makes a Good Study Design? 

To design a research study that works, you need to carefully think things through. Make sure your strategy is tailored to your research topic and watch out for potential biases. Your procedures should be flexible enough to accommodate changes that may arise during the course of research. 

A good research design should be:

• Clear and methodologically sound
• Feasible and realistic
• Knowledge-driven.

By following these guidelines, you'll set yourself up for success and be able to produce reliable results.

Research Study Design Structure

A structured research design provides a clear and organized plan for carrying out a study. It helps researchers to stay on track and ensure that the study stays within the bounds of acceptable time, resources, and funding.

A typical design includes 5 main components:

• Research question(s): Central research topic(s) or issue(s).
• Sampling strategy: Method for selecting participants or subjects.
• Data collection techniques: Tools or instruments for retrieving data.
• Data analysis approaches: Techniques for interpreting and scrutinizing assembled data.
• Ethical considerations: Principles for protecting human subjects (e.g., obtaining a written consent, ensuring confidentiality guarantees).

Research Design Essential Characteristics

Creating a research design warrants a firm foundation for your exploration. The cost of making a mistake is too high. This is not something scholars can afford, especially if financial resources or a considerable amount of time is invested. Choose the wrong strategy, and you risk undermining your whole study and wasting resources. 

To avoid any unpleasant surprises, make sure your study conforms to the key characteristics. Here are some core features of research designs:

• Reliability   Reliability is stability of your measures or instruments over time. A reliable research design is one that can be reproduced in the same way and deliver consistent outcomes. It should also nurture accurate representations of actual conditions and guarantee data quality.
• Validity For a study to be valid , it must measure what it claims to measure. This means that methodological approaches should be carefully considered and aligned to the main research question(s).
• Generalizability Generalizability means that your insights can be practiced outside of the scope of a study. When making inferences, researchers must take into account determinants such as sample size, sampling technique, and context.
• Neutrality A study model should be free from personal or cognitive biases to ensure an impartial investigation of a research topic. Steer clear of highlighting any particular group or achievement.

Key Concepts in Research Design

Now let’s discuss the fundamental principles that underpin study designs in research. This will help you develop a strong framework and make sure all the puzzles fit together.

Primary concepts

Types of Approaches to Research Design

Study frameworks can fall into 2 major categories depending on the approach to compiling data you opt for. The 2 main types of study designs in research are qualitative and quantitative research. Both approaches have their unique strengths and weaknesses, and can be utilized based on the nature of information you are dealing with. 

Quantitative Research  

Quantitative study is focused on establishing empirical relationships between variables and collecting numerical data. It involves using statistics, surveys, and experiments to measure the effects of certain phenomena. This research design type looks at hard evidence and provides measurements that can be analyzed using statistical techniques. 

Qualitative Research 

Qualitative approach is used to examine the behavior, attitudes, and perceptions of individuals in a given environment. This type of study design relies on unstructured data retrieved through interviews, open-ended questions and observational methods. 

If you need your study done yesterday, leave StudyCrumb a “ write my research paper for me ” notice and have your project completed by experts.

Types of Research Designs & Examples

Choosing a research design may be tough especially for the first-timers. One of the great ways to get started is to pick the right design that will best fit your objectives. There are 4 different types of research designs you can opt for to carry out your investigation:

• Experimental
• Correlational
• Descriptive
• Diagnostic/explanatory.

Below we will go through each type and offer you examples of study designs to assist you with selection.

1. Experimental

In experimental research design , scientists manipulate one or more independent variables and control other factors in order to observe their effect on a dependent variable. This type of research design is used for experiments where the goal is to determine a causal relationship. 

Its core characteristics include:

• Randomization
• Manipulation
• Replication.

A pharmaceutical company wants to test a new drug to investigate its effectiveness in treating a specific medical condition. Researchers would randomly assign participants to either a control group (receiving a placebo) or an experimental group (receiving the new drug). They would rigorously control all variables (e.g, age, medical history) and manipulate them to get reliable results.

2. Correlational

Correlational study is used to examine the existing relationships between variables. In this type of design, you don’t need to manipulate other variables. Here, researchers just focus on observing and measuring the naturally occurring relationship.

Correlational studies encompass such features: 

• Data collection from natural settings
• No intervention by the researcher
• Observation over time.

A research team wants to examine the relationship between academic performance and extracurricular activities. They would observe students' performance in courses and measure how much time they spend engaging in extracurricular activities.

3. Descriptive 

Descriptive research design is all about describing a particular population or phenomenon without any interruption. This study design is especially helpful when we're not sure about something and want to understand it better.

Descriptive studies are characterized by such features:

• Random and convenience sampling
• Observation
• No intervention.

A psychologist wants to understand how parents' behavior affects their child's self-concept. They would observe the interaction between children and their parents in a natural setting. Gathered information will help her get an overview of this situation and recognize some patterns.

4. Diagnostic

Diagnostic or explanatory research is used to determine the cause of an existing problem or a chronic symptom. Unlike other types of design, here scientists try to understand why something is happening. 

Among essential hallmarks of explanatory studies are: 

• Testing hypotheses and theories
• Examining existing data
• Comparative analysis.

A public health specialist wants to identify the cause of an outbreak of water-borne disease in a certain area. They would inspect water samples and records to compare them with similar outbreaks in other areas. This will help to uncover reasons behind this accident.

How to Design a Research Study: Step-by-Step Process

When designing your research don't just jump into it. It's important to take the time and do things right in order to attain accurate findings. Follow these simple steps on how to design a study to get the most out of your project.

1. Determine Your Aims 

The first step in the research design process is figuring out what you want to achieve. This involves identifying your research question, goals and specific objectives you want to accomplish. Think whether you want to explore a specific issue or develop a new theory? Setting your aims from the get-go will help you stay focused and ensure that your study is driven by purpose. 

Once  you are clear with your goals, you need to decide on the main approach. Will you use qualitative or quantitative methods? Or perhaps a mixture of both?

2. Select a Type of Research Design

Choosing a suitable design requires considering multiple factors, such as your research question, data collection methods, and resources. There are various research design types, each with its own advantages and limitations. Think about the kind of data that would be most useful to address your questions. Ultimately, a well-devised strategy should help you gather accurate data to achieve your objectives.

3. Define Your Population and Sampling Methods

To design a research project, it is essential to establish your target population and parameters for selecting participants. First, identify a cohort of individuals who share common characteristics and possess relevant experiences. 

For instance, if you are researching the impact of social media on mental health, your population could be young adults aged 18-25 who use social media frequently.

With your population in mind, you can now choose an optimal sampling method. Sampling is basically the process of narrowing down your target group to only those individuals who will participate in your study. At this point, you need to decide on whether you want to randomly choose the participants (probability sampling) or set out any selection criteria (non-probability sampling). 

To examine the influence of social media on mental well-being, we will divide a whole population into smaller subgroups using stratified random sampling . Then, we will randomly pick participants from each subcategory to make sure that findings are also true for a broader group of young adults.

4. Decide on Your Data Collection Methods

When devising your study, it is also important to consider how you will retrieve data.  Depending on the type of design you are using, you may deploy diverse methods. Below you can see various data collection techniques suited for different research designs. 

Data collection methods in various studies

Additionally, if you plan on integrating existing data sources like medical records or publicly available datasets, you want to mention this as well. 

5. Arrange Your Data Collection Process

Your data collection process should also be meticulously thought out. This stage involves scheduling interviews, arranging questionnaires and preparing all the necessary tools for collecting information from participants. Detail how long your study will take and what procedures will be followed for recording and analyzing the data. 

State which variables will be studied and what measures or scales will be used when assessing each variable.

Measures and scales 

Measures and scales are tools used to quantify variables in research. A measure is any method used to collect data on a variable, while a scale is a set of items or questions used to measure a particular construct or concept. Different types of scales include nominal, ordinal, interval, or ratio , each of which has distinct properties

Operationalization 

When working with abstract information that needs to be quantified, researchers often operationalize the variable by defining it in concrete terms that can be measured or observed. This allows the abstract concept to be studied systematically and rigorously. 

Operationalization in study design example

If studying the concept of happiness, researchers might operationalize it by using a scale that measures positive affect or life satisfaction. This allows us to quantify happiness and inspect its relationship with other variables, such as income or social support.

Remember that research design should be flexible enough to adjust for any unforeseen developments. Even with rigorous preparation, you may still face unexpected challenges during your project. That’s why you need to work out contingency plans when designing research.

6. Choose Data Analysis Techniques

It’s impossible to design research without mentioning how you are going to scrutinize data. To select a proper method, take into account the type of data you are dealing with and how many variables you need to analyze. 

Qualitative data may require thematic analysis or content analysis.

Quantitative data, on the other hand, could be processed with more sophisticated statistical analysis approaches such as regression analysis, factor analysis or descriptive statistics.

Finally, don’t forget about ethical considerations. Opt for those methods that minimize harm to participants and protect their rights.

Research Design Checklist

Having a checklist in front of you will help you design your research flawlessly.

• checkbox I clearly defined my research question and its significance.
• checkbox I considered crucial factors such as the nature of my study, type of required data and available resources to choose a suitable design.
• checkbox A sample size is sufficient to provide statistically significant results.
• checkbox My data collection methods are reliable and valid.
• checkbox Analysis methods are appropriate for the type of data I will be gathering.
• checkbox My research design protects the rights and privacy of my participants.
• checkbox I created a realistic timeline for research, including deadlines for data collection, analysis, and write-up.
• checkbox I considered funding sources and potential limitations.

Bottom Line on Research Design & Study Types

Designing a research project involves making countless decisions that can affect the quality of your work. By planning out each step and selecting the best methods for data collection and analysis, you can ensure that your project is conducted professionally.

We hope this article has helped you to better understand the research design process. If you have any questions or comments, ping us in the comments section below.

Entrust us your study and we will find the best research paper writer to complete your project. Count on a unique work and fast turnaround.

FAQ About Research Study Designs

1. what is a study design.

Study design, or else called research design, is the overall plan for a project, including its purpose, methodology, data collection and analysis techniques. A good design ensures that your project is conducted in an organized and ethical manner. It also provides clear guidelines for replicating or extending a study in the future.

2. What is the purpose of a research design?

The purpose of a research design is to provide a structure and framework for your project. By outlining your methodology, data collection techniques, and analysis methods in advance, you can ensure that your project will be conducted effectively.

3. What is the importance of research designs?

Research designs are critical to the success of any research project for several reasons. Specifically, study designs grant:

• Clear direction for all stages of a study
• Validity and reliability of findings
• Roadmap for replication or further extension
• Accurate results by controlling for potential bias
• Comparison between studies by providing consistent guidelines.

By following an established plan, researchers can be sure that their projects are organized, ethical, and reliable.

4. What are the 4 types of study designs?

There are generally 4 types of study designs commonly used in research:

• Experimental studies: investigate cause-and-effect relationships by manipulating the independent variable.
• Correlational studies: examine relationships between 2 or more variables without intruding them.
• Descriptive studies: describe the characteristics of a population or phenomenon without making any inferences about cause and effect.
• Explanatory studies: intended to explain causal relationships.

Joe Eckel is an expert on Dissertations writing. He makes sure that each student gets precious insights on composing A-grade academic writing.

You may also like

For more advanced studies, you can even combine several types. Mixed-methods research may come in handy when exploring complex phenomena that cannot be adequately captured by one method alone.

• How It Works
• PhD thesis writing
• Master thesis writing
• Bachelor thesis writing
• Dissertation writing service
• Dissertation abstract writing
• Thesis proposal writing
• Thesis editing service
• Thesis proofreading service
• Thesis formatting service
• Coursework writing service
• Research paper writing service
• Architecture thesis writing
• Computer science thesis writing
• Engineering thesis writing
• History thesis writing
• MBA thesis writing
• Nursing dissertation writing
• Psychology dissertation writing
• Sociology thesis writing
• Statistics dissertation writing
• Buy dissertation online
• Write my dissertation
• Cheap thesis
• Cheap dissertation
• Custom dissertation
• Dissertation help
• Pay for thesis
• Pay for dissertation
• Senior thesis
• Write my thesis

How To Write A Research Design Like A Pro

The overall strategy that a researcher chooses to address all the different parts of their study in a logical and clear manner is known as a research design.

So, what is research design in research paper? A research design is a general plan explaining what one looks to do so as to answer the research question. Generally, it is a detailed outline of how research or an investigation will take place including; how data will be collected, which tools will be employed and how they will be used, and the ways through with the data will be analyzed.

It lays out the method you use to collect, measure, and analyze information. It states that you do this logically and coherently to ensure that you thoroughly address the research problem with which you are dealing. There are numerous types of research design including:

Action Study Research Design Case Study Research Design Casual Research Design Cohort Research Design Cross-Sectional Research Design Correlational Research Design Descriptive Research Design Experimental Research Design Exploratory Research Design Historical Research Design Longitudinal Research Design Observational Research Design Philosophical Research Design Qualitative Research Design Quantitative Research Design Sequential Research Design

The research paper design you choose depends on the research problem. You should analyze the problem carefully and consider it from numerous perspectives. You may consider using a mixed methods research design which is a combination of any two designs listed above. But you must choose a type of research design that is strong and will make your project progress smoothly.

Example Of A Nursing Research Design

To assess the links between professional satisfaction, job satisfaction, and contributing factors using a quantitative approach, an appropriate method is to gain use questionnaires or surveys that provide numerical data from the sample. To achieve an appropriate sample, a sampling plan should be developed. In this case, the population of concern will be identified. This will be nursing staff members, possibly across a wide range of departments to gain a better insight into the links overall. A stratified sampling method would be appropriate here to ensure that the sample is made up of sub-populations that are in line with the sub-populations of the total: the strata should include gender, number of years in nursing, department, and any other factors that could be confounding variables. This will ensure that the sample is representative of the population of interest. In a population of 1000 nurses, a confidence interval of five, and a confidence level of 99%, the sample size needed is 400. Inclusion criteria will include: nursing staff working at the hospital, ability to speak the language that the survey is administered in, and those that have given informed consent. Exclusion criteria will be visiting nursing staff, staff who are not nurses, and those that do not hold relevant nursing criteria.

Sampling Plan: Qualitative

A more appropriate methodology for qualitative approaches is to use interviews or focus groups. This means that the sample size can be much smaller, often as low as ten. In this case, the sampling plan will have the same population of concern, but a different approach to sampling can be used. It may be more appropriate to use quota, self-selection sampling here, as nursing staff need to be willing to give up some time to respond. This has drawbacks, including self-selection bias, but it would be unethical to force nursing staff to participate in the project, especially considering interviews can take one hour or more. The inclusion and exclusion criteria are as above.

How to Write a Research Design Proposal

For most research problems, you will have to make some tradeoffs. One design can be strong in some areas and weak in other areas. This is the reason many students choose to select more than one design to gather all the information accurately and effectively they need to address the problem. This is one of the first things you should know about how to write research design and methods section.

• Consider Your Practicalities and Priorities

What do we mean when we say you need to think about practicalities and priorities? Another thing to know about how to write design and methodology of the research is asking several questions before settling on one or two methodologies. You will not have the time or resources to conduct tests using several research designs, so you need to write down and answer precisely what your priorities are and the practical nature of your study.

A good place to start is at the library where you have access to other academic studies in your field. You can find similar studies and look at published samples that have been approved by experts in the field. You can also get a sense of the number of resources you have available. Pre-planning is a great way of making sure your project stays on track.

• Determine the Kind of Information You Need

The next to know about how to write a qualitative research design is figuring out the kind of information or data you need to answer the research problem. There are two places where you get this: through primary and secondary data. In your research study, you get original data through experiments, interviews, and surveys. This is information you analyze and incorporate into your research finding.

Your study will also incorporate information gathered by someone else in previous studies. This type of data is available in libraries and online databases where you can look at national statistics, official records, and publications from academic and government sources.

• Identify How You Are Going to Collect Information

Once you know the kind of information you need to gather (qualitative and quantitative) you need to decide where, when, and how you will gather it. How to write a research design requires you to describe your research methods. This means putting in detail the materials, procedures, tools, and techniques you will use and apply. You also need to point out the criteria you will use to choose your participants and sources. (For example, how many participants will you need to fill out services to get a good method to sample).

• Decide How You Are Going to Analyze the Information

Another thing you need to know about how to write a research design relates to the way you are going to analyze the information you collect. The process of analysis is the last step you need to develop your research design. Numerous computer applications will sort through information and retrieve what you need to answer the research problem (For example, Access and Excel). Identify the ones you will use and state this in the research design.

• Draft Your Research Design as You Would Other Sections

Now you can start writing the first draft. You should approach this like you would other academic assignments. Use a draft that lists all the sub-sections you need to address in the research design. Be clear and concise. The research design should not include your opinions. It must show the reader an exact description of the way you conducted your study.

• Revise Your Research Design After Some Time Away

Hindsight is one of the best things that can come from separating yourself from your assignment for a few days. We recommend students remove themselves completely from their work to get a mental break. The distance will help them rethink their writing and make changes that improve the overall quality of an assignment.

The trick is to do stay away a few days instead of just a few hours. The time away from any piece of writing will allow for more self-evaluation that is objective. Many students will find ways to remove, add, or rearrange words, phrases, sentences, and paragraphs that make their assignments stronger.

• Edit and Proofread Your Research Design for Perfection

These two activities are not interchangeable. Editing focuses on deep issues like correcting sentence constructions and word choices. A thorough editing session will improve things like clarity, readability, and tone. Proofreading focuses on details like grammar, punctuation, and misspellings. It will also look at page numbering, formatting, alignment, and visual elements.

Both activities are important stages of the writing process. A great editor will begin his or her work during the revising process. A great proofread will also begin his or her work during the editing process. While they may overlap you should always treat them as two separate tasks and designate enough time to do each without distraction.

• Have a Colleague Review Your Work for Feedback

Having a colleague or peer review your work is an important step to the academic writing process. A person or a group of people that understands your field and the high standard of researching and writing that comes with putting together a great research paper can valuable toward your success at the collegiate and graduate levels. Even if you can only show your work to one person for a few hours, his or her feedback can help you make changes to improve the overall quality of your research design.

Here are some questions you should consider before asking someone to review your work:

Do they understand the research subject and/or topic? Do they know the professor or panel that will grade your work? Have they submitted research studies in the past? Do they have great to excellent grades when it comes to research? Are they committed to providing you constructive criticism and feedback?

What to Do If You Can’t Do the Research Design

You may not have enough time to create this section, especially when you have a short deadline. On these occasions, it is a good idea to find a template for a research design paper. You can find templates online or can refer to published research papers in academic journals. The formats are standard so as long as you apply your words to a template that matches your design approach.

If you need more information or assistance learning about how to write a research design section, our customer support team can point you to more resources or put you in contact with one of our academic writing and editing experts. Each expert has earned either a bachelor’s or master’s degree and specialize in specific disciplines. You can rest assured that you will be assigned someone that knows your field inside and outside and can give you the writing research design and methodology help that you need to excel academically.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Comment * Error message

Name * Error message

Email * Error message

Save my name, email, and website in this browser for the next time I comment.

As Putin continues killing civilians, bombing kindergartens, and threatening WWIII, Ukraine fights for the world's peaceful future.

Ukraine Live Updates

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

How to Make a “Good” Presentation “Great”

• Guy Kawasaki

Remember: Less is more.

A strong presentation is so much more than information pasted onto a series of slides with fancy backgrounds. Whether you’re pitching an idea, reporting market research, or sharing something else, a great presentation can give you a competitive advantage, and be a powerful tool when aiming to persuade, educate, or inspire others. Here are some unique elements that make a presentation stand out.

• Fonts: Sans Serif fonts such as Helvetica or Arial are preferred for their clean lines, which make them easy to digest at various sizes and distances. Limit the number of font styles to two: one for headings and another for body text, to avoid visual confusion or distractions.
• Colors: Colors can evoke emotions and highlight critical points, but their overuse can lead to a cluttered and confusing presentation. A limited palette of two to three main colors, complemented by a simple background, can help you draw attention to key elements without overwhelming the audience.
• Pictures: Pictures can communicate complex ideas quickly and memorably but choosing the right images is key. Images or pictures should be big (perhaps 20-25% of the page), bold, and have a clear purpose that complements the slide’s text.
• Layout: Don’t overcrowd your slides with too much information. When in doubt, adhere to the principle of simplicity, and aim for a clean and uncluttered layout with plenty of white space around text and images. Think phrases and bullets, not sentences.

As an intern or early career professional, chances are that you’ll be tasked with making or giving a presentation in the near future. Whether you’re pitching an idea, reporting market research, or sharing something else, a great presentation can give you a competitive advantage, and be a powerful tool when aiming to persuade, educate, or inspire others.

• Guy Kawasaki is the chief evangelist at Canva and was the former chief evangelist at Apple. Guy is the author of 16 books including Think Remarkable : 9 Paths to Transform Your Life and Make a Difference.

Partner Center