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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Shona McCombes

Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst... there’s more!

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

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10 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

how to cite this page

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

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

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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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

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

Table of Contents

What is research design?  

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

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

Research design elements  

Research design elements include the following:  

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

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

Characteristics of research design  

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

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

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

Different types of research design  

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

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

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

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

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

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

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

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

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

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

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

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

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

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

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

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

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

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

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

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

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

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

Example : Comparing school dropout levels and possible bullying events.  

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

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

Benefits of research design  

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

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

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

Frequently Asked Questions (FAQ) on Research Design

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

Broadly speaking there are two basic types of research design –

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

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

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

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

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

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

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

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What is the quantitative research design? Why is the research design needed? What are the four main types of quantitative research designs used by researchers?

This article illustrates research design using an analogy, explains why a research design is needed, describes four main types of research designs, and gives examples of each research design’s application.

An Analogy of Research Design

Researching to reach a predetermined goal is like building a house. To avoid costly rebuilding, it would be a good idea to make a plan first and consider all the requirements to produce one that appeals to your taste.

If your house comprises not only one floor but two, or even three, and you want the house to be sturdy, that could last decades or generations; you will need to engage a structural engineer. He makes sure that the home maintains its integrity and can handle the loads and forces they encounter through time.

And, of course, the electrical connections require the expertise of an electrical engineer. He plans how the electrical circuits are arranged in the entire house to make it convenient for you to access electricity.

Why is a Research Design Needed?

As pointed out earlier, the main reasons for coming up with a research design relate to efficiency and effectiveness. If you have a good research design, you will save time, energy, and cost in doing your research. You have a plan to get the data that you want to answer the research objectives.

Thus, before conducting research, you already have in mind what to expect. And of course, you will know how much that would cost you. If you cannot afford it, then you revise your plan.

Defining the Research Objectives

However, your research design or plan cannot be carried out if you don’t have a clear idea about what you want. The architect cannot design a project based on a simple directive to make a house plan. The outcome may not be to your liking, and you will just be wasting your money and his time. It will be a hit-and-miss approach.

Thus, you will need to define your research objectives based on your topic of interest. What do you want to achieve in your research? Will you be dealing with people, animals, plants, or things?

Will you manipulate some variables? Will you compare different groups? Would you want to know which  variable  causes an effect on other variables? Or will you describe what is there?

It all boils down to  what you want . Be very clear if you’re going to describe things, correlate them, find out if one causes the other, or put up an experiment to test if manipulating one variable can effect a change to another variable.

The Four Main Types of Quantitative Research Design

Experts classify quantitative research design into four types. These are descriptive, correlational, causal-comparative, and experimental research.

The four quantitative research designs are distinguished from each other in Figure 1. Please note that as you go from left to right; the approach becomes more manipulative. The descriptive research design studies the existing situation, whereas the researcher manipulates variables at the other end, using the experimental method.

Examples of the Application of the Different Research Design on the Same Subject

Descriptive research design, correlational research design.

The same study on air quality may be conducted as in Example 1, but this time, the respondent’s awareness about air quality is correlated with their attitude towards emission testing.

Causal-Comparative Research Design

Still, on the air quality study, you might want to know what causes the respondents to behave positively or negatively towards emission testing. Does attitude have something to do with a person’s educational background? Or perhaps, their capacity to pay for emission testing?

Experimental Research Design

Using still the air quality study, you might now want to test if two groups of drivers behaved differently when one group was required to attend a seminar on air pollution, and the other group was not required to attend.

The two groups’ members were randomly assigned, and all other variables were kept constant, meaning the respondents have similar characteristics where only attendance at the seminar is the difference.

You are interested in finding the difference between a person’s attitude towards emission testing. And what discriminates them from the other is that one group attended a seminar on air pollution while the other group did not.

Final Notes

For example, you can undertake a study that uses both a descriptive and a correlational research design. Hence, you describe this approach in your methodology as a descriptive-correlational research design.

There are still other types of research designs out there. What is important here is that you are clear about what you want to investigate.

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How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On October 3, 2023

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

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

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

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

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

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

Step 1: Establish Priorities for Research Design

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

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

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

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

For example:

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

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

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

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

Step 2: Data Type you Need for Research

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

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

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

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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.

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3.2 Quantitative Research Designs

Quantitive research study designs can be broadly classified into two main groups (observational and experimental) depending on if an intervention is assigned. If an intervention is assigned, then an experimental study design will be considered; however, if no intervention is planned or assigned, then an observational study will be conducted. 3 These broad classes are further subdivided into specific study designs, as shown in Figure 3.1. In practice, quantitative studies usually begin simply as descriptive studies, which could subsequently be progressed to more complex analytic studies and then to experimental studies where appropriate.

Observational studies

Observational studies are research designs that involve observing and measuring the characteristics of a sample or population without intervening, altering or manipulating any variables (Figure 3.1). 3 Observational studies can be further subdivided into descriptive and analytic studies. 3

Descriptive observational studies

Descriptive studies are research designs that describe or measure the characteristics of a specific population or phenomenon. These characteristics include descriptions related to the phenomenon under investigation, the people involved, the place, and the time. 4 These study designs are typically non-experimental and do not involve manipulating variables; rather, they rely on the collection and analysis of numerical data to draw conclusions. Examples of descriptive studies include case reports, case series, ecological studies and cross-sectional (prevalence studies). 2 These are discussed below

• Case Reports and Case series

Case reports and case series are both types of descriptive studies in research. A case report is a detailed account of the medical history, diagnosis, treatment, and outcome of a single patient. 5 On the other hand, case series is a collection of cases with similar clinical features. 5 Case series are frequently used to explain the natural history of a disease, the clinical characteristics, and the health outcomes for a group of patients who underwent a certain treatment. Case series typically involve a larger number of patients than case reports. 5 Both case reports and case series are used to illustrate unusual or atypical features found in patients in practice. 5 In a typical, real-world clinical situation, they are both used to describe the clinical characteristics and outcomes of individual patients or a group of patients with a particular condition. These studies have the potential to generate new research questions and ideas. 5 However, there are drawbacks to both case reports and case series, such as the absence of control groups and the potential for bias. Yet, they can be useful sources of clinical data, particularly when researching uncommon or recently discovered illnesses. 5 An example of a case report is the study by van Tulleken, Tipton and Haper, 2018 which showed that open-water swimming was used as a treatment for major depressive disorder for a 24-year-old female patient. 6 Weekly open (cold) water swimming was trialled, leading to an immediate improvement in mood following each swim. A sustained and gradual reduction in symptoms of depression, and consequently a reduction in, and cessation of, medication was observed. 6 An example of a case series is the article by Chen et al , 2020  which described the epidemiology and clinical characteristics of COVID-19 infection among 12 confirmed cases in Jilin Province, China. 7

• Ecological studies

Ecological studies examine the relationship between exposure and outcome at the population level. Unlike other epidemiological studies focusing on individual-level data, ecological studies use aggregate data to investigate the relationship between exposure and outcome of interest. 8 In ecological studies, data on prevalence and the degree of exposure to a given risk factor within a population are typically collected and analysed to see if exposure and results are related. 8 Ecological studies shed light on the total burden of disease or health-related events within a population and assist in the identification of potential risk factors that might increase the incidence of disease/event. However,  these studies cannot prove causation or take into account characteristics at the individual level that can influence the connection between exposure and result. This implies that ecological findings cannot be interpreted and extrapolated to individuals. 9 For example, the association between urbanisation and Type 2 Diabetes was investigated at the country level, and the role of intermediate variables (physical inactivity, sugar consumption and obesity) was examined. One of the key findings of the study showed that in high-income countries (HIC), physical inactivity and obesity were the main determinants of T2D prevalence. 10 However, it will be wrong to infer that people who are physically inactive and obese in HIC have a higher risk of T2D.

• Cross-sectional Descriptive (Prevalence) studies

A cross-sectional study is an observational study in which the researcher collects data on a group of participants at a single point in time. 11 The goal is to describe the characteristics of the group or to explore relationships between variables. Cross-sectional studies can be either descriptive or analytical (Figure 3.2). 11 Descriptive cross-sectional studies are also known as prevalence studies measuring the proportions of health events or conditions in a given population. 11 Although analytical cross-sectional studies also measure prevalence, however, the relationship between the outcomes and other variables, such as risk factors, is also assessed. 12 The main strength of cross-sectional studies is that they are quick and cost-effective. However, they cannot establish causality and may be vulnerable to bias and confounding ( these concepts will be discussed further later in this chapter under “avoiding error in quantitative research) .  An example of a cross-sectional study is the study by Kim et al., 2020 which examined burnout and job stress among physical and occupational therapists in various Korean hospital settings. 13 Findings of the study showed that burnout and work-related stress differed significantly based on several factors, with hospital size, gender, and age as the main contributory factors. The more vulnerable group consisted of female therapists in their 20s at small- or medium-sized hospitals with lower scores for quality of life. 13

Analytical Observational studies

Analytical observational studies aim to establish an association between exposure and outcome and identify causes of disease (causal relationship). 14 Analytical observational studies include analytical cross-sectional ( discussed above ), case-control and cohort studies. 14 This research method could be prospective(cohort study) or retrospective (case-control study), depending on the direction of the enquiry. 14

• Case-control studies

A case-control study is a retrospective study in which the researcher compares a group of individuals with a specific outcome (cases) to a group of individuals without that outcome (controls) to identify factors associated with the outcome. 15 As shown in Figure 3.3 below, the cases and controls are recruited and asked questions retrospectively (going back in time) about possible risk factors for the outcome under investigation.  A case-control study is relatively efficient in terms of time, money and effort, suited for rare diseases or outcomes with a long latent period, and can examine multiple risk factors. 15 For example, before the cause of lung cancer, was established, a case-control study was conducted by British researchers Richard Doll and Bradford Hill in 1950. 16 Subjects with lung cancer were compared with those who did not have lung cancer, and details about their smoking habits were obtained. 16 The findings from this initial study showed that cancer patients were more frequent and heavy smokers. 16 Over the years, more evidence has been generated implicating tobacco as a significant cause of lung cancer. 17, 18 Case-control studies are, therefore, useful for examining rare outcomes and can be conducted more quickly and with fewer resources than other study designs. Nonetheless, it should be noted that case-control studies are susceptible to bias in selecting cases and controls and may not be representative of the overall population. 15

• Cohort Study

Cohort studies are longitudinal studies in which the researcher follows a group of individuals who share a common characteristic (e.g., age, occupation) over time to monitor the occurrence of a particular health outcome. 19 The study begins with the selection of a group of individuals who are initially free of the disease or health outcome of interest (the “cohort”). The cohort is then divided into two or more groups based on their level of exposure (for example, those who have been exposed to a certain risk factor and those who have not). 19 Participants are then followed up, and their health outcomes are tracked over time. The incidence of the health outcome is compared between exposed and non-exposed groups, and the relationship between exposure and the outcome is quantified using statistical methods. 19 Cohort studies can be prospective or retrospective (Figure 3.4). 20 In a prospective cohort study, the researchers plan the study so that participants are enrolled at the start of the study and followed over time. 20, 21 In a retrospective cohort study, data on exposure and outcome are collected from existing records or databases. The researchers go back in time (via available records) to find a cohort that was initially healthy and “at risk” and assess each participant’s exposure status at the start of the observation period. 20, 21 Cohort studies provide an understanding of disease risk factors based on findings in thousands of individuals over many years and are the foundation of epidemiological research. 19 They are useful for investigating the natural history of a disease, identifying risk factors for a disease, providing strong evidence for causality and estimating the incidence of a disease or health outcome in a population. However, they can be expensive and time-consuming to conduct. 15 An example of a cohort study is the study by Watts et al, 2015 which investigated whether the communication and language skills of children who have a history of stuttering are different from children who do not have a history of stuttering at ages 2–5 years. 22 The findings revealed that children with a history of stuttering, as a group, demonstrated higher scores on early communication and language measures compared to their fluent peers. According to the authors, clinicians can be reassured by the finding that, on average, children who stutter have early communication and language skills that meet developmental expectations. 22

Experimental Study Designs (Interventional studies)

Experimental studies involve manipulating one or more variables in order to measure their effects on one or more outcomes. 23 In this type of study, the researcher assigns individuals to two or more groups that receive or do not receive the intervention. Well-designed and conducted interventional studies are used to establish cause-and-effect relationships between variables. 23  Experimental studies can be broadly classified into two – randomised controlled trials and non-randomised controlled trials. 23 These study designs are discussed below:

• Randomised Controlled Trial

Randomised controlled trials (RCTs) are experimental studies in which participants are randomly assigned to the intervention or control arm of the study. 23 The experimental group receives the intervention, while the control group does not (Figure 3.5). RCTs involve random allocation (not by choice of the participants or investigators) of participants to a control or intervention group (Figure 3.5). 24   Randomization or random allocation minimises bias and offers a rigorous method to analyse cause-and-effect links between an intervention and outcome. 24 Randomization balances participant characteristics (both observed and unobserved) between the groups. 24 This is so that any differences in results can be attributed to the research intervention. 24 The most basic form of randomisation is allocating treatment by tossing a coin. Other methods include using statistical software to generate random number tables and assigning participants by simple randomisation or allocating them sequentially using numbered opaque envelopes containing treatment information. 25 This is why RCTs are often considered the gold standard in research methodology. 24 While RCTs are effective in establishing causality, they are not without limitations. RCTs are expensive to conduct and time-consuming. In addition, ethical considerations may limit the types of interventions that can be tested in RCTs. They may also not be appropriate for rare events or diseases and may not always reflect real-world situations, limiting their application in clinical practice. 24   An example of a randomised controlled trial is the study by Shebib et al., 2019 which investigated the effect of a 12-week digital care program (DCP) on improving lower-back pain. The treatment group (DCP) received the 12-week DCP, consisting of sensor-guided exercise therapy, education, cognitive behavioural therapy, team and individual behavioural coaching, activity tracking, and symptom tracking – all administered remotely via an app. 26 While the control group received three digital education articles only. The findings of the study showed that the DCP resulted in improved health outcomes compared to treatment-as-usual and has the potential to scale personalised evidence-based non-invasive treatment for patients with lower-back pain. 26

• Non-randomised controlled design (Quasi-experimental)

Non-randomised controlled trial (non-RCT) designs are used where randomisation is impossible or difficult to achieve. This type of study design requires allocation of the exposure/intervention by the researcher. 23 In some clinical settings, it is impossible to randomise or blind participants. In such cases, non-randomised designs are employed. 27 Examples include pre-posttest design (with or without controls) and interrupted time series. 27, 28 For the pre-posttest design that involves a control group, participants (subjects) are allocated to intervention or control groups (without randomisation) by the researcher. 28 On the other hand, it could be a single pre-posttest design study where all subjects are assessed at baseline, the intervention is given, and the subjects are re-assessed post-intervention. 28 An example of this type of study was reported by Lamont and Brunero (2018 ), who examined the effect of a workplace violence training program for generalist nurses in the acute hospital setting. The authors found a statistically significant increase in behaviour intention scores and overall confidence in coping with patient aggression post-test. 29 Another type of non-RCT study is the interrupted time series (ITS) in which data are gathered before and after intervention at various evenly spaced time points (such as weekly, monthly, or yearly). 30 Thus, it is crucial to take note of the precise moment an intervention occurred. The primary goal of an interrupted time series is to determine whether the data pattern observed post-intervention differs from that noted prior. 30 Several ITS were conducted to investigate the effectiveness of the different prevention strategies (such as lockdown and border closure) used during the COVID pandemic. 31, 32 Although non-RCT may be more feasible to RCTs, they are more prone to bias than RCTs due to the lack of randomisation and may not be able to control for all the variables that might affect the outcome. 23

Hierarchy of Evidence

While each study design has its unique characteristics and strengths, they are not without weaknesses (as already discussed) that impact the accuracy of the results and research evidence they provide. The hierarchy of evidence is a framework used to rank the evidence provided by different study designs in research evaluating healthcare interventions with respect to the strength of the presented results (i.e., validity and reliability of the findings). 33 Study designs can be ranked in terms of their ability to provide valid evidence on the effectiveness (intervention achieves the intended outcomes), appropriateness (impact of the intervention from the perspective of its recipient) and feasibility (intervention is implementable) of the research results they provide. 33 As shown in Figure 3.6, meta-analyses, systematic reviews, and RCTs provide stronger best-practice evidence and scientific base for clinical practice than descriptive studies as well as case reports and case series. Nonetheless, it is important to note that the research question/ hypothesis determines the study design, and not all questions can be answered using an interventional design. In addition, there are other factors that need to be considered when choosing a study design, such as funding, time constraints, and ethical considerations, and these factors are discussed in detail in chapter 6.

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Methodology

• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

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

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Types Of Quantitative Research Designs And Methods

Quantitative research design uses a variety of empirical methods to assess a phenomenon. The most common method is the experiment,…

Quantitative research design uses a variety of empirical methods to assess a phenomenon. The most common method is the experiment, but there are other types of quantitative research as well, such as correlation studies and case studies.

In contrast with qualitative research, which relies on subjective interpretations and extensive explorations, the various types of quantitative methods use objective analysis to reveal patterns and relations among data points that often have a numerical value. Quantitative research provides a mathematical summary of the results.

Let’s look at quantitative research design, the types of quantitative research methods and their respective strengths and weaknesses.

Types Of Quantitative Research

Components of quantitative research design.

If a researcher is studying a single variable, time, space, or another construct, they’re engaged in qualitative research. However, if that variable is a collection of quantitative data points—such as the number of employees that use a workplace break room compared to the number of employees who use other break rooms—the researcher is engaged in quantitative research.

Here are some methods commonly used in quantitative research design:

1. Experiment

The experiment is perhaps the most common way for quantitative researchers to gather data. In this method, researchers manipulate one variable at a time, while they hold all other variables constant. If a researcher wishes to determine which type of computer mouse is easier for employees to use, they must ensure the employees are experienced with computers, comfortable with their chairs or desks and have no issues with their eyesight. Common methods for this type of research include randomized experiments, non-randomized experiments, clinical trials and field studies.

2. Correlation

Correlation studies come in many forms, from simple correlation diagrams to the analysis of multiple variables. For instance, a researcher examining rates of depression among veterinarians could look at associations between self-perceived social status, salary and depression.

3. Cohort Studies

Cohort studies provide a way to measure the extent of change over a period of time. This type of research can lead to results that are both objective and subjective, depending on the type of study employed. For instance, a cohort study examining police officer salaries could determine what salary a police officer should make in an area. However, this same study could also delve into the subjective question of whether police officers are fairly paid compared to other professions.

Research design is a critical factor in the success of a study.

While there are many types of quantitative research methods that can be employed, the basic parts of all research designs are the same. Here are the principal components:

At the heart of every research project is a well-framed and considered question. Having a clear objective is the most important part of quantitative research design. Some examples of research questions could be:

• Which type of coffee brewing method extracts the most flavor?
• Which books are contributing most to a publisher’s profit?
• Which newspaper is the most widely read in a city?

In quantitative research design, researchers may explore the relationship between variables in a correlation study, or it could mean determining what variables are best in an experiment.

Once the aim is in place, the actual data collection method must be chosen. This will depend on the data needed to answer the research question. Some options are:

• Participant observations
• Experimental data

As long as the data is expressed numerically, it is quantitative data.

The selection process used to choose participants is a critical component of all types of quantitative research designs. Researchers need a well-defined population. This group can be as small as two people, but it could also be thousands of people as well.

Data Analysis

Once the data is collated, a researcher must decide how to analyze it. Some options at their disposal include:

• Descriptive analysis
• Content analysis
• Statistical tests

Once again, it depends on the research question and the goals of the study.

Presentation

This is sometimes referred to as dissemination. How will the research findings be shared with the world? Common choices are:

• Presentations
• Website articles and blogs

A quantitative researcher’s greatest contribution is that their work can be replicated. Because quantitative research relies on numbers, the results of the study can be exactly duplicated by other researchers.

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Explore Harappa Diaries to learn more about topics such as What is Qualitative Research , Types Of Qualitative Research Methods , Quantitative Vs Qualitative Research and How To Apply Starbursting Technique to upgrade your knowledge and skills.

Research Design

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From broad assumptions to comprehensive methods of data collection, analysis, and interpretation, research plans and procedures involve various decisions and approaches which are essential in order to carefully study a specific topic. That’s why researchers should use the suitable procedures of inquiry or research designs and certain research methods of data collection, analysis, and interpretation. However, what is a research design? In this post, we will explain the main purpose of research designs, different types of research designs, steps on how to effectively write a systematic research design, the research design format and research design examples.

Research Design Definition

Research design is a crucial element when conducting a research work. Along with research approaches and research methods, research designs represent a clear perspective about research. So, these components demonstrate information in a successive way: from extensive constructions of research to the narrow procedures of methods. 

What Is a Research Design?

A research design is a type of inquiry within wide-ranging approaches in the research field such as qualitative, quantitative and mixed methods approaches. It significantly provides a certain direction for procedures in a specific research study. Also known as strategies of inquiry, there are numerous research designs accessible to many researchers that significantly guide them towards advanced data analysis and assist them in examining complex models. 

Research Design Examples

1. Experimental Design

• Example : A pharmaceutical company tests a new drug by giving it to one group and a placebo to another under controlled conditions to observe the effects on illness recovery rates.

2. Quasi-Experimental Design

• Example : A school implements a new teaching method in some classes but not others and compares the academic performance of students across these classes to assess the method’s effectiveness.

3. Cross-Sectional Design

• Example : A market research company surveys 1,000 smartphone users at one point in time to determine consumer preferences for mobile phone brands.

4. Longitudinal Study

• Example : A university research project tracks the same group of students from enrollment through graduation to study changes in their academic performance and social behaviors over the years.

5. Case Study

• Example : A business analyst conducts a detailed study on a single company that successfully pivoted its business model during a financial downturn, to understand the strategies and factors that led to its recovery.

6. Comparative Study

• Example : A researcher compares the healthcare systems of two countries to evaluate the impact of policy differences on patient outcomes.

7. Correlational Study

• Example : A psychologist studies the relationship between social media usage and self-esteem by measuring both variables among a group of teenagers.

8. Ethnography

• Example : An anthropologist lives within a remote tribe for a year to observe and report on their cultural practices and social interactions.

9. Phenomenology

• Example : A study focuses on a group of survivors from a natural disaster, exploring their personal experiences and emotional responses to understand their coping mechanisms.

10. Grounded Theory

• Example : Researchers collect data from various startups to develop a theory about the key factors that contribute to entrepreneurial success in the tech industry.

11. Content Analysis

• Example : A media studies student analyzes the portrayal of gender roles in a decade’s worth of TV commercials to track changing societal attitudes.

12. Action Research

• Example : A community development organization collaborates with residents to identify and address urgent neighborhood problems, using feedback to guide project adjustments.

13. Narrative Research

• Example : A historian interviews WWII veterans to compile their war experiences into a book that explores personal narratives from the conflict.

14. Survey Research

• Example : A non-profit organization conducts a nationwide survey to gather data on public opinion regarding climate change.

15. Experimental Auction

• Example : An economist uses an experimental auction to determine how much consumers are willing to pay for organic versus non-organic produce.

16. Simulation

• Example : Engineers use computer simulations to predict the impacts of earthquake stress on building structures.

17. Field Experiment

• Example : A biologist observes behavioral changes in wildlife introduced to a newly established nature reserve compared to those in an undisturbed control area.

18. Meta-Analysis

• Example : A medical researcher combines data from several studies on drug efficacy to provide stronger evidence of its benefits and side effects.

19. Cohort Study

• Example : Public health officials follow a cohort of smokers over 20 years to study the long-term health outcomes compared to non-smokers.

20. Archival Research

• Example : A scholar accesses old political documents and speeches to analyze patterns of rhetoric used by leaders during critical historical events.

Main Purpose of Research Designs

The main purpose of research designs is to guide you in terms of analyzing various complex models and articulating new procedures for conducting any types of research fields like in social science research. Medical researchers, field researchers, academic researchers, scientific researchers, academic  researchers and other kinds of researchers use research designs to properly conduct their research projects as they consciously structure their research work in order to answer the key research questions which guide the overall research study and the appropriate hypothesis. Additionally, a research design provides essential information about the parts of the research study methods like data collection, instrumentation selection, participant recruitment and analysis.

Types of Research Designs

Case study research design.

As an in-depth study of a specific research issue, a case study research design is commonly used to narrow down a very far-reaching field of research into one or a few easily researchable examples. It is a beneficial type of research design  for testing whether a certain theory and model really applies to phenomena in the real world. So, it means that researchers  who are using a case study design can implement a variety of research methodologies and depend on multiple collections of sources to examine a research problem.

Descriptive Research Design

A descriptive research design is a type of research design that assists in providing answers to the key questions of what, when, who, where, and how related with a  specific research problem. However, it does not conclusively ensure answers to why questions. Being used to acquire important details about the current status of the phenomena, this research design clearly describes what exists based on the variables or conditions in a particular situation. So, this means researchers use this research design to observe a certain subject matter in a completely natural and constant natural environment. Additionally, it acts as a pre-cursor towards more quantitative research designs.

Causal Research Design

Researchers use a type of research design called causal design to measure what kind of impact a certain change will have on current norms and assumptions.  It is used to narrow down the cause and effect relationship easily by ensuring that both variables are not influenced by any force other than each other. A causal research design is used to maintain accuracy in the variables and determine the exact impact that a particular variable has on another variable. Applying this research design also explores the connection between two matters. 

Correlational Research Design

When it comes to setting up the statistical pattern between two clearly interconnected variables, researchers use a type of research design called correlational research design as it refers to a non-experimental method in research work that conducts studies on the relationships between two variables by utilizing statistical analysis. This is a fundamental research design in order to test specific relationships between categorical or quantitative variables without the manipulation of an independent variable. Simply, correlational research aims at observing and measuring historical patterns between two variables. 

Cross-Sectional Research Design

A cross-sectional research design is used by researchers to collect data only once and examine a certain population at a single point in time by having a slice or cross-section of a particular group and variables being documented for each participant. Researchers and other investigators measure the outcome and the exposures in the participants of the research study at similar time. The participants in a cross-sectional research study are simply chosen according to the exclusion and inclusion criteria being established for the study. Also, this type of research design is important for carrying out population-based surveys and assessing the prevalence of certain matters like diseases in clinic-based samples. 

Diagnostic Research Design

Composed of major research phases such as problem inception, problem diagnosis and problem solution, a diagnostic research design is a type of research design used by researchers to make a clear evaluation of a certain problem or phenomenon’s cause. If the researchers need to fully understand the factors and other essential aspects that are generating concerns and issues inside the company or organization in detail, they should use a diagnostic research design. Carrying out a diagnostic research design allows them to know exactly the time when the issue appears, the underlying cause of the issues, potential influences of the issue which lead to its worsening, and the effective solutions for the issue. 

Factorial Research Design

Researchers use a factorial research design to investigate the major effects of two or more individual  independent variables in a simultaneous way, and to allow them to recognize interactions among variables. When the effects of one variable differ based on the levels of another variable, an interaction is made and these interactions can only be recognized when the variables are combined and investigated. If you need to yield valid conclusions over a wide array of experimental conditions, use a factorial research design to estimate the effects of a factor based on various levels of the other factors.

Historical Research Design

A historical research design is a type of research design that provides a fundamental context for understanding our modern society while informing global concepts like foregin policy development. Researchers use this research design to guide them when it comes to analyzing the past events, developing new concepts, examining the previous information or events to test their validity, and formulating logical decisions that impact our society, economy, and culture. Typically, they collect, verify and synthesize evidence from the past to build facts that defend or refute a hypothesis. Thus, a historical research design involves the comprehensive study and analysis of data about past events, developments and other experiences. 

Action Research Design

In order to promote iterative learning, comprehensive evaluation and improvement, many researchers and other professionals use action research design especially teachers, professors and other key individuals working in schools or in the education sector. With this design, they can collect sufficient information about current programs and outcomes so that they are able to analyze the collected information, develop a cohesive plan to improve it, collect changes after a new plan is carried out, and produce conclusions based on the improvements. So, professionals who use an action research design focus on operational or technical, collaboration, critical reflection, and transformative change of their own process of taking action and conducting research. 

Legal Research Design

A legal research design is commonly used by researchers working in the legal sector as they carefully identify and retrieve information which are crucial to support in their legal decision-making process. Legal researchers develop a research plan, consult primary and secondary sources, expand and update primary law and analyze and organize results. There are two types of legal research: doctrinal or non-empirical research and non-doctrinal or empirical methods. 

Longitudinal Research Design

Use a longitudinal research design if you need to investigate similar individuals repeatedly so that you can determine any changes that might happen over a period of time. Researchers apply this type of research design in order to observe and gather adequate data on a number of variables without trying to affect those variables. Most generally used in economics, epidemiology and medicine, longitudinal research design is also used in social sciences and other scientific fields. It is also the opposite of a cross-sectional research design. Implementing this design can help researchers to follow their subjects in real time and allow repeated observations of the same individual over time.

Marketing Research Design

In marketing research design, business professionals such as project managers, content marketing specialists, sales and marketing experts and brand managers use marketing research questionnaires to collect information and clearly understand the intended audience or target market of a business firm or an organization. This type of research design will significantly assist them in developing industry and market analysis and designing worthwhile products, enhancing user experience, and designing an effective marketing strategy that fully engages quality leads and elevates conversion rates.

Narrative Research Design

If you need to focus on studying a specific person, you may use a narrative research design which refers to writing narratives about the experiences of individuals, telling a life experience, and explaining the meaning of the individual’s experience. Several types of narrative research design are analysis of narrative projects, collecting background information from narrative interview report , interviews and re-storying, oral history and journals and storytelling, and letter writing. To conduct narrative research, researchers need to code narrative blocks, group and read by live event, create nested story structure codes, examine the structure of the story, make comparisons and tell the main idea of the narrative research.

Experimental Research Design

As a blueprint of the research procedure, an experimental research design is used by researchers to allow them to manage and control over all aspects that may influence the outcome of an experiment. Performing a research work with this type of design helps researchers to determine or predict what may happen. Often used where there exists a time priority in a cause and effect relationship, an experimental research design is also applied when there is a consistency in a cause and effect relationship, and if there is a great magnitude of correlation. Plus, it enables researchers to provide the highest level of evidence for single studies.

Observational Research Design

In several cases where the researchers have no control over the experiment being conducted, they use an observational research design to draw a conclusion after making a comparison of subjects against a control group. With this type of research design, you can gather a depth of information about a specific behavior, show interrelationships among multidimensional aspects of group interactions, and generalize your results to real life situations. If you need to discover what kind of variables may be crucial before utilizing other research methods, use an observational research design.

Exploratory Research Design

An exploratory research design is a type of research design which is integral when it comes to investigating a specific and unclear research issue. Researchers use this research design to have an in-depth understanding of a research problem and its context prior to the further development and execution of the research process. So, an exploratory research design acts as a groundwork to facilitate research work while it manages other research concerns which have not been sufficiently investigated in the last years.  

Retrospective Research Design

When the outcome of interest has already taken place at the period the research study is started, researchers use a type of research design called retrospective research design which enables them to formulate ideas about potential associations and thoroughly examine possible relationships without causal statements. It is a very feasible research design in terms of scope, resources, and time. However, it cannot yield causal effects due to the absence of random assignment and random selection. Still, researchers can use this design because it is less expensive to conduct and can be used immediately.

Cohort Research Design

If you need to conduct a study over a time period which involves members of a population that the subject originated from, and united by some similarity, you must use a cohort research design as it guides you in analyzing the statistical occurrence within a specialized subgroup which is united by similar characteristics linked to the research problem. Researchers are able to measure possible causes prior to the result having taken place and show that these causes preceded the result. Also, it can provide clear insight into effects over time and is linked to a wide range of diverse cultural, economic, social, and political changes. 

Meta-Analysis Research Design

Considered as an evidence-based resource with confirmatory data analysis, a meta-analysis research design is used by researchers to create statistical significance with studies that have conflicting outcomes, to generate a more appropriate estimate of effect magnitude, to bring a more in-depth analysis of risks, safety data and advantages, and to analyze subgroups with individual members that are not significant statistically. Researchers systematically integrate essential qualitative and quantitative study data from various selected research studies to draw out a single conclusion that provides greater statistical effect.

Quantitative Research Design

A quantitative research design is a type of research design used by researchers to explore and investigate how many people act, feel, think or feel in a specific manner. As the major research design in the social sciences and other fields, it is generally aimed at developing strategies, and techniques with the use of numeric patterns or a range of numeric data. Social scientists, communication researchers and other professionals bring knowledge and set up a clear understanding about certain matters in the social environment and other fields. Simply, this type of research design depends on data that are being observed or measured.

Qualitative Research Design

When it comes to understanding various concepts, experiences or opinions, researchers use a qualitative research design through a collection and in-depth analysis of non-numerical data like a, text or video. Also, they use this type of research design to collect comprehensive insights into a problem or form new ideas for their research study. Generally used in the humanities and social sciences like anthropology, education, health sciences and others, qualitative research design is used to clearly understand people’s experiences and focus on meaningful data interpretation. 

Mixed Method Research Design

A mixed methods research design is a type of research design when the researchers and other professionals collect, analyze, and mix both quantitative and qualitative research and methods in a single study so that they can easily understand a certain research problem. To execute this design properly, you need to understand both quantitative and qualitative research. Some major types of mixed method research design are triangulation design, embedded design, and explanatory design. 

Research Design Writing

Looking at the long list of types of research designs in this post may be overwhelming for you. It is possible to get lost from these details because these classifications are made up from various disciplines with highlighted diverse elements of research designs and many other aspects in research. Your research questions might lead you to try creating a theory and then selecting the right research design for your study. What research study would you use in that case? How will you outline your research design? 

Research Design Elements

Hypotheses and objectives.

• Hypotheses are testable predictions about the relationships between variables.
• Objectives define the purpose of the study and what the research aims to achieve.
• Independent variables are manipulated to observe their effect on dependent variables.
• Dependent variables are the outcomes measured in the experiment.
• Control variables are kept constant to ensure that any changes in the dependent variable are due to the independent variable.
• Population and Sample : The population is the entire set of individuals relevant to the research question, while the sample is a subset of the population that is studied.
• Sampling Methods : Methods like random sampling, stratified sampling, or convenience sampling dictate how participants are chosen from the population.

Data Collection Methods

• Qualitative methods such as interviews, observations, and focus groups gather non-numerical data.
• Quantitative methods such as surveys, experiments, and secondary data analysis gather numerical data.

Study Design Types

• Descriptive studies describe characteristics of the population or phenomena being studied.
• Analytical studies investigate the relationships between variables.
• Experimental designs manipulate variables to determine cause-and-effect relationships, often using control and experimental groups.

Data Analysis Techniques

• Statistical Analysis : Techniques vary depending on the nature of the data and may include descriptive statistics, inferential statistics, regression analysis, etc.
• Qualitative Analysis : Methods like thematic analysis or content analysis are used to interpret textual data.

Ethics and Reliability

• Ethical Considerations : Ensuring the confidentiality, consent, and welfare of participants.
• Reliability and Validity : Strategies to ensure that the study can be replicated and that the results truly represent what they are supposed to measure.

Research Design in Research Methodology

Research design in research methodology refers to the blueprint or framework that guides how a research project is conducted, aiming to ensure the validity and reliability of the findings. It encompasses the overall strategy and methods chosen to integrate the different components of the study in a coherent and logical manner, effectively addressing the research questions. Research design outlines the procedures for collecting, measuring, and analyzing data. It is pivotal in determining the type of evidence gathered and how it is interpreted. Types of research design include experimental, correlational, descriptive, and qualitative designs, each suited to different kinds of research questions and objectives, influencing how researchers select participants, define variables, and structure the overall study. This design process is crucial for aligning the methodology with the study’s goals, thereby enhancing the robustness and integrity of the results.

Research Design in Qualitative Research

Research design in qualitative research involves structuring the approach to explore complex phenomena by focusing on the meanings, concepts, characteristics, and descriptions of the subject matter. Unlike quantitative research, which seeks to quantify variables, qualitative research design is more flexible and adaptive, often evolving as the study progresses. It typically includes methods such as interviews, focus groups, observations, and content analysis, which allow for a deep, narrative understanding of participants’ experiences and social contexts. This type of design is oriented towards understanding “how” and “why” things happen, aiming to provide insights into human behavior, social processes, and cultural phenomena. The design in qualitative research is crucial for ensuring depth, richness, and relevance in the data collected, allowing researchers to capture the complexities of the phenomena in question. This approach requires a thoughtful integration of various elements like the research questions, the nature of the participants, the settings, and the researcher’s philosophical standpoint, all of which influence the data collection and analysis procedures.

How to Write a Research Design

Once the researchers formulate their research questions, they need to work on designing their overall research work and research investigation reports while using research designs appropriate for their respective work. When should you use a survey? Conduct experiments or perform participant observation? Need to combine several research designs? Structuring a well-coordinated research design will guide you in developing the right methods for your research goals. Here are some steps that you need to follow while writing a suitable research design for your research project:

1. Think about your specific aims and research approach.

First of all, have a clear understanding of what your research project will investigate. This will help you to properly think about what you really want to accomplish in your study.

2. Select a type of research design

There are wide-ranging types of research designs that you can select based on your research goals and objectives. Each research design gives you a framework for the overall structure of your research work.

3. Define your intended audience and sampling method

Make sure that you fully define who or what your research study will aim on, and what specific sampling method that you will use when you select your participants or subjects. Some examples of sampling methods are probability sampling and non-probability sampling.

4. Select your data collection methods

In order to effectively measure variables and gather sufficient information, you must select the one data collection method or several data collection methods like survey methods to enable you in acquiring original knowledge and comprehensive insights into your research problem. 

5. Develop a cohesive plan for your data collection methods

Next, you need to develop a systematic plan for your data collection methods so that you can accurately define your variables and make sure that you have credible and trustworthy measurements.

6. Choose the suitable data analysis strategies for your study

Lastly, you need to determine what specific data analysis strategies you will use in your research study. Read some research papers related to your research study so that you can choose the suitable data analysis strategies. 

Characteristics of Research Design

Research design is fundamental in conducting a reliable and valid study. Here are the key characteristics that define a strong research even further

• Research designs are tailored to address specific research questions or hypotheses. The design guides the methodology to ensure that the data collected is appropriate and sufficient to answer the research questions effectively.

Rigorous and Methodical

• A well-designed study follows a systematic, structured approach to ensure the integrity and quality of the research. This includes detailed planning of procedures like data collection and analysis to minimize errors and biases.

Feasibility

• The chosen design must be practical and manageable within the given resources and time constraints. It should also consider ethical issues, ensuring that the study can be conducted without undue risk to participants.

Flexibility

• While research designs must be structured, they should also allow for adjustments as new insights and conditions arise during the study, provided these changes do not compromise the study’s objectives.

Replicability

• A robust research design can be replicated by other researchers, which helps in validating the findings through repeated studies in similar or varying contexts.

Specificity

• Research designs should be specific enough to clearly define the population, variables, methods of data collection, and methods of analysis. This clarity is crucial for the validity and reliability of the study.
• Research designs often include mechanisms to control for variables that could influence the outcomes. In experimental designs, for example, this could mean controlling the environment or randomizing subjects to different groups to ensure that the results are due to the intervention and not other factors.

Validity and Reliability

• Ensuring the research measures what it intends to measure (validity) and can produce consistent results under consistent conditions (reliability) are critical aspects of research design.
• All research designs must incorporate ethical considerations to protect participants from harm, ensure confidentiality, and promote integrity in the research process.

Resource Efficient

• Effective research designs make optimal use of available resources, including time, money, and personnel, to achieve the research objectives without unnecessary expenditure.

Research Design Format

Research Goals and Purpose Statement: While formulating your research question, set your specific research goals and purpose while highlighting your priorities for your research design. Every research study has diverse priorities that’s why you need to clarify your exact aims and purpose in your research study.

Research Data Type: Indicate what specific type of research data essential for your research study. Consider your research questions and hypotheses so that you can choose the right research data type. Some examples of research data types are primary data, secondary data, qualitative data, and quantitative data.

Data Collection Methods: Determine the research data collection method that you will use in your study so that you are able to address your research problem. Research methods such as procedures, materials, tools, and techniques are commonly used for research studies. 

Data Analysis Procedure: Select the proper data analysis procedure for the design of your research study. You can use a quantitative data analysis or qualitative data analysis based on your needs and preferences.

Benefits of Research Design

A well-crafted research design is crucial for the success of any scientific study. It provides a structured approach to investigate research questions and ensures that the findings are valid and applicable. Here are the key benefits:

Enhances Validity

• Internal Validity : Good research design controls for confounding variables, ensuring that the observed effects are due to the independent variables.
• External Validity : It allows findings to be generalized to other settings or populations, enhancing the broader applicability of the research.

Increases Reliability

• Consistency : A structured design helps ensure that the study can be reliably reproduced under similar conditions, which is fundamental for building trust in the findings.
• Accuracy : Precision in the design helps in minimizing errors and biases, providing more accurate results.

Facilitates Data Collection

• Efficiency : Efficient design reduces the resources (time, cost, effort) required to conduct the study.
• Appropriateness : It ensures that the chosen methods and techniques are suitable for the research question and objectives, thereby optimizing data collection.

Supports Objective Analysis

• Reduces Bias : A good design minimizes the researcher’s biases by using blinded assessments, randomized allocations, etc.
• Statistical Power : Proper design increases the likelihood that the study will detect any true effects of the variables being tested, thereby preventing false negatives.

Enhances Ethical Integrity

• Protects Participants : Ensures that the research adheres to ethical standards, protecting participants’ rights and well-being.
• Moral Responsibility : Promotes transparency and accountability in research, fostering trust among participants and the public.

Improves Decision Making

• Informed Decisions : The findings from a well-designed study provide robust evidence that can inform policy-making, clinical practices, and other decision-making processes.
• Problem Solving : Helps identify effective interventions and solutions by clearly demonstrating what works, what doesn’t, and under what conditions.

Guides Future Research

• Foundation for Further Studies : Establishes a solid basis for future research, indicating potential new areas to explore or methodological improvements to consider.
• Contributes to Theory : Helps in building or testing theoretical frameworks, contributing to the overall knowledge and understanding of a particular discipline.

What is research design?

Research design is a structured framework that guides the collection and analysis of data for a research project.

Why is research data design important?

Effective research design ensures accurate, reliable data collection and analysis, leading to valid conclusions.

What are the types of research designs?

Common types include experimental, correlational, and observational research designs.

How does research design affect reliability?

A well-structured research design enhances the reliability of the findings by minimizing biases and errors.

What is the difference between qualitative and quantitative research designs?

Qualitative research designs explore phenomena in-depth, while quantitative designs quantify data and often involve statistical analysis.

How do you choose a research design?

Choose based on the research question, objectives, and the nature of the data required.

What is a case study in research design?

A case yet study involves an in-depth investigation of a single subject or entity to uncover unique insights.

How does a cohort study design work?

A cohort study design follows a group sharing a common characteristic over time to assess outcomes.

What is the significance of a cross-sectional study design?

Cross-sectional studies analyze data from a population at a specific point in time to identify patterns and correlations.

How can a research design be ethical?

Ensure informed consent, confidentiality, and transparency to uphold the ethical standards of research.

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

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

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Experimental Research Design

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

Quasi-experimental Research Design

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

Case Study Research Design

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

Longitudinal Research Design

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

Structure of Research Design

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

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

Example of Research Design

An Example of Research Design could be:

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

Research design:

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

How to Write Research Design

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

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

When to Write Research Design

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

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

Purpose of Research Design

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

Some of the key purposes of research design include:

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

Applications of Research Design

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

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

Advantages of Research Design

Here are some advantages of research design:

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

Research Design Vs Research Methodology

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Your ultimate guide to quantitative research.

12 min read You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

What is quantitative research?

Quantitative is the research method of collecting quantitative data – this is data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed.

Quantitative research deals with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data .

Quantitative data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

To collect numerical data, surveys are often employed as one of the main research methods to source first-hand information in primary research . Quantitative research can also come from third-party research studies .

Quantitative research is widely used in the realms of social sciences, such as biology, chemistry, psychology, economics, sociology, and marketing .

Research teams collect data that is significant to proving or disproving a hypothesis research question – known as the research objective. When they collect quantitative data, researchers will aim to use a sample size that is representative of the total population of the target market they’re interested in.

Then the data collected will be manually or automatically stored and compared for insights.

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Quantitative vs qualitative research

While the quantitative research definition focuses on numerical data, qualitative research is defined as data that supplies non-numerical information.

Quantitative research focuses on the thoughts, feelings, and values of a participant , to understand why people act in the way they do . They result in data types like quotes, symbols, images, and written testimonials.

These data types tell researchers subjective information, which can help us assign people into categories, such as a participant’s religion, gender , social class, political alignment, likely favored products to buy, or their preferred training learning style.

For this reason, qualitative research is often used in social research, as this gives a window into the behavior and actions of people.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative methods. If you want to explore ideas, thoughts, and meanings, use qualitative methods.

However, quantitative and qualitative research methods are both recommended when you’re looking to understand a point in time, while also finding out the reason behind the facts.

Quantitative research data collection methods

Quantitative research methods can use structured research instruments like:

• Surveys : A survey is a simple-to-create and easy-to-distribute research method , which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Quantitative questions tend to be closed questions that ask for a numerical result, based on a range of options, or a yes/no answer that can be tallied quickly.

• Face-to-face or phone interviews: Interviews are a great way to connect with participants , though they require time from the research team to set up and conduct.

Researchers may also have issues connecting with participants in different geographical regions . The researcher uses a set of predefined close-ended questions, which ask for yes/no or numerical values.

• Polls: Polls can be a shorter version of surveys , used to get a ‘flavor’ of what the current situation is with participants. Online polls can be shared easily, though polls are best used with simple questions that request a range or a yes/no answer.

Quantitative data is the opposite of qualitative research, another dominant framework for research in the social sciences, explored further below.

Quantitative data types

Quantitative research methods often deliver the following data types:

• Test Scores
• Percent of training course completed
• Performance score out of 100
• Number of support calls active
• Customer Net Promoter Score (NPS)

When gathering numerical data, the emphasis is on how specific the data is, and whether they can provide an indication of what ‘is’ at the time of collection. Pre-existing statistical data can tell us what ‘was’ for the date and time range that it represented

Quantitative research design methods (with examples)

Quantitative research has a number of quantitative research designs you can choose from:

Descriptive

This design type describes the state of a data type is telling researchers, in its native environment. There won’t normally be a clearly defined research question to start with. Instead, data analysis will suggest a conclusion , which can become the hypothesis to investigate further.

Examples of descriptive quantitative design include:

• A description of child’s Christmas gifts they received that year
• A description of what businesses sell the most of during Black Friday
• A description of a product issue being experienced by a customer

Correlational

This design type looks at two or more data types, the relationship between them, and the extent that they differ or align. This does not look at the causal links deeper – instead statistical analysis looks at the variables in a natural environment.

Examples of correlational quantitative design include:

• The relationship between a child’s Christmas gifts and their perceived happiness level
• The relationship between a business’ sales during Black Friday and the total revenue generated over the year
• The relationship between a customer’s product issue and the reputation of the product

Causal-Comparative/Quasi-Experimental

This design type looks at two or more data types and tries to explain any relationship and differences between them, using a cause-effect analysis. The research is carried out in a near-natural environment, where information is gathered from two groups – a naturally occurring group that matches the original natural environment, and one that is not naturally present.

This allows for causal links to be made, though they might not be correct, as other variables may have an impact on results.

Examples of causal-comparative/quasi-experimental quantitative design include:

• The effect of children’s Christmas gifts on happiness
• The effect of Black Friday sales figures on the productivity of company yearly sales
• The effect of product issues on the public perception of a product

Experimental Research

This design type looks to make a controlled environment in which two or more variables are observed to understand the exact cause and effect they have. This becomes a quantitative research study, where data types are manipulated to assess the effect they have. The participants are not naturally occurring groups, as the setting is no longer natural. A quantitative research study can help pinpoint the exact conditions in which variables impact one another.

Examples of experimental quantitative design include:

• The effect of children’s Christmas gifts on a child’s dopamine (happiness) levels
• The effect of Black Friday sales on the success of the company
• The effect of product issues on the perceived reliability of the product

Quantitative research methods need to be carefully considered, as your data collection of a data type can be used to different effects. For example, statistics can be descriptive or correlational (or inferential). Descriptive statistics help us to summarize our data, while inferential statistics help infer conclusions about significant differences.

Advantages of quantitative research

• Easy to do : Doing quantitative research is more straightforward, as the results come in numerical format, which can be more easily interpreted.
• Less interpretation : Due to the factual nature of the results, you will be able to accept or reject your hypothesis based on the numerical data collected.
• Less bias : There are higher levels of control that can be applied to the research, so bias can be reduced , making your data more reliable and precise.

Disadvantages of quantitative research

• Can’t understand reasons: Quantitative research doesn’t always tell you the full story, meaning you won’t understand the context – or the why, of the data you see, why do you see the results you have uncovered?
• Useful for simpler situations: Quantitative research on its own is not great when dealing with complex issues. In these cases, quantitative research may not be enough.

How to use quantitative research to your business’s advantage

Quantitative research methods may help in areas such as:

• Identifying which advert or landing page performs better
• Identifying how satisfied your customers are
• How many customers are likely to recommend you
• Tracking how your brand ranks in awareness and customer purchase intent
• Learn what consumers are likely to buy from your brand.

6 steps to conducting good quantitative research

Businesses can benefit from quantitative research by using it to evaluate the impact of data types. There are several steps to this:

• Define your problem or interest area : What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis : Ask yourself what could be the causes for the situation with those data types.
• Plan your quantitative research : Use structured research instruments like surveys or polls to ask questions that test your hypothesis.
• Data Collection : Collect quantitative data and understand what your data types are telling you. Using data collected on different types over long time periods can give you information on patterns.
• Data analysis : Does your information support your hypothesis? (You may need to redo the research with other variables to see if the results improve)
• Effectively present data : Communicate the results in a clear and concise way to help other people understand the findings.

How Qualtrics products can enhance & simplify the quantitative research process

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting quantitative research. From survey creation and data collection to statistical analysis and data reporting, it can help all your internal teams gain insights from your numerical data.

Quantitative methods are catered to your business through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of quantitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Stats IQ™ and Driver IQ™ make analyzing numerical data easy and simple. Choose to highlight key findings based on variables or highlight statistically insignificant findings. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualizations quickly:

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Reference Examples

More than 100 reference examples and their corresponding in-text citations are presented in the seventh edition Publication Manual . Examples of the most common works that writers cite are provided on this page; additional examples are available in the Publication Manual .

To find the reference example you need, first select a category (e.g., periodicals) and then choose the appropriate type of work (e.g., journal article ) and follow the relevant example.

When selecting a category, use the webpages and websites category only when a work does not fit better within another category. For example, a report from a government website would use the reports category, whereas a page on a government website that is not a report or other work would use the webpages and websites category.

Also note that print and electronic references are largely the same. For example, to cite both print books and ebooks, use the books and reference works category and then choose the appropriate type of work (i.e., book ) and follow the relevant example (e.g., whole authored book ).

Examples on these pages illustrate the details of reference formats. We make every attempt to show examples that are in keeping with APA Style’s guiding principles of inclusivity and bias-free language. These examples are presented out of context only to demonstrate formatting issues (e.g., which elements to italicize, where punctuation is needed, placement of parentheses). References, including these examples, are not inherently endorsements for the ideas or content of the works themselves. An author may cite a work to support a statement or an idea, to critique that work, or for many other reasons. For more examples, see our sample papers .

Reference examples are covered in the seventh edition APA Style manuals in the Publication Manual Chapter 10 and the Concise Guide Chapter 10

Related handouts

• Common Reference Examples Guide (PDF, 147KB)
• Reference Quick Guide (PDF, 225KB)

Textual Works

Textual works are covered in Sections 10.1–10.8 of the Publication Manual . The most common categories and examples are presented here. For the reviews of other works category, see Section 10.7.

• Journal Article References
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Data and Assessments

Data sets are covered in Section 10.9 of the Publication Manual . For the software and tests categories, see Sections 10.10 and 10.11.

• Data Set References
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Audiovisual Media

Audiovisual media are covered in Sections 10.12–10.14 of the Publication Manual . The most common examples are presented together here. In the manual, these examples and more are separated into categories for audiovisual, audio, and visual media.

• Artwork References
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• Online Course or MOOC References
• Podcast References
• PowerPoint Slide or Lecture Note References
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• Transcript of an Audiovisual Work References
• YouTube Video References

Online Media

Online media are covered in Sections 10.15 and 10.16 of the Publication Manual . Please note that blog posts are part of the periodicals category.

• Facebook References
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• LinkedIn References
• Online Forum (e.g., Reddit) References
• TikTok References
• X References
• Webpage on a Website References
• Clinical Practice References
• Open Educational Resource References
• Whole Website References