what type of research design is quantitative

Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

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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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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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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.

Quantitative Research Design: Four Common Ways to Collect Your Data Efficiently

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.

In doing your research on whatever goals you have in mind, you make a plan to reach those goals. You spell out the specific items that you want to pursue in your research objectives.

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.

You need to engage the architect’s help to draw what you have in mind (your concept), estimate the cost to build it, and list the steps to follow to bring that plan into reality. The architect comes up with a blueprint of the house, detailing the size and quantity of reinforced steel bars, the floor plan, dimensions of the house, and aesthetics.

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.

To build your dream house, you will need to have a good plan–your design.

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.

Now, here are the four quantitative research designs.

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.

Quantitative research design examples are given for each of the four quantitative research designs in the next section.

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

Descriptive research design.

A willingness to pay (WTP) study aimed to determine the vehicle owner’s knowledge about air quality and attitude towards the government’s regulation of requiring emission testing every time the car’s registration is renewed. This investigation will provide information that will show how knowledgeable the respondents are about air quality and reveal patterns of behavior towards the government’s measures to control carbon emissions. It explores the drivers’ willingness to pay for vehicle maintenance costs.

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.

The study by Amindrad et al. (2013) on the Relationship Between Awareness, Knowledge and Attitudes Towards Environmental Education Among Secondary School Students in Malaysia exemplifies this research design.

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?

The following video explains this research design further with two examples.

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

Note that those listed are not mutually exclusive research designs. We can combine them.

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.

That wraps it up.

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.

Aminrad, Z., Zakariya, S. Z. B. S., Hadi, A. S., & Sakari, M. (2013). Relationship between awareness, knowledge and attitudes towards environmental education among secondary school students in Malaysia. World Applied Sciences Journal, 22(9), 1326-1333.

© 2020 October 15 P. A. Regoniel, updated 27 November 2021

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About the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

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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.

Quantitative and Qualitative Research

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• Quantitative vs Qualitative
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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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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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2 Types of Quantitative Research Designs

There are three main groups of Research Designs that will be explored in this chapter.

• Experimental
• Quasi-experimental
• Non-experimental

When reviewing each design, the purpose and key features of the design, advantages and disadvantages, and the most commonly used designs within the category will be reviewed.

1. Experimental Design 

Purpose:  Evaluate outcomes in terms of efficacy and/or cost effectiveness

Experimental design features include: 

• Randomization of subjects to groups
• Manipulation of independent variable (e.g., an intervention or treatment)
• ​​Control – the use of a control group and control measures (for controlling extraneous variables )​

Advantages:   

• Most appropriate for testing cause-and-effect relationships (e.g., generalizability is most likely)
• Provides the highest level of evidence (e.g., level II) for single studies

Disadvantages: 

• Attrition especially control group participants or with ‘before-after’ experimental designs
• Feasibility and logistics may be an issue is certain settings (e.g., long-term care homes)

Caution: Not all research questions are amenable to experimental manipulation or randomization

Most Commonly Used Experimental Designs

• True experimental (pre- post-test ) design (also referred to as Randomized Control Trials or RCTs ):

Figure 3. True experimental design (pre-post-test).

• After-only (post-test only) design :

Figure 4. After-only (post-test only) design.

• Solomon four-group design

This design is similar to the true experimental design but has an additional two groups, for a total of four groups. Two groups are experimental, while two groups are control. These “extra” groups do not receive the pre-test, allowing the researchers to evaluate the effect of the pretest on the post-test in the first two groups.

2. Quasi-Experimental Design

Purpose: Similar to experimental design, but used when not all the features of an experimental design can be met:

• Manipulation of the independent variable (e.g., an intervention or treatment)
• Experimental and control groups may not be randomly assigned (no randomization)
• There may or may not be a control group

Advantages: 

• Feasibility and logistics are enhanced, particularly in clinical settings
• Offers some degree of generalizability (e.g., applicable to population of interest)
• May be more adaptable in real-world practice environments

Disadvantages:   

• Generally weaker than experimental designs because groups may not be equal with respect to extraneous variable due to the lack of randomization
• As a result, cause-and-effect relationships are difficult to claim

Options for Quasi-experimental Designs include :

• Non-equivalent control group design 

Figure 5. Classical Quasi-Experimental Design. Adapted from Knowledge for Health

• After-only control group design

Figure 6. Post-Test Only Quasi-Experimental Design. Adapted from Knowledge for Health.

• Time-series design Important note: The time series design is considered quasi-experimental because subjects serve as their ‘own controls’ (same group of people, compared before and after the intervention for changes over time). 

Figure 7. Time-series design. Adapted from Knowledge for Health

• One group pre-test-post-design design In this design there is no control group. The one group, considered the experimental group, is tested pre and post the intervention. The design is still considered quasi-experimental as there is manipulation of the intervention.

3. Non-experimental

Purpose: When the problem to be solved or examined is not amenable to experimentation; used when the researcher wants to:

• Study a phenomenon at one point in time or over a period of time
• Study (and measure) variables as they naturally occur
• Test relationships and differences among variables
• Used when the knowledge base on a phenomenon of interest is limited or when the research question is broad or exploratory in nature
• Appropriate for forecasting or making predictions
• Useful when the features of an experiment (e.g., randomization, control, and manipulation) are not appropriate or possible (e.g., ethical issues)
• Inability to claim cause-and-effect relationships

Options for Non-experimental Designs include:

• Survey studies: descriptive, exploratory, comparative
• Relationship or difference studies: Correlational, developmental
• Cross-sectional studies
• Longitudinal or Prospective studies

Figure 8. Longitudinal or Prospective studies. Adapted from University of Minnesota, Driven for Discover Libraries .

• Retrospective ( Ex Post Facto ) studies

Additional terms to consider when reading research

Learners may find it difficult when reading research to identify the Research Design used. Please consult the table below for more information on terms frequently used in research.

This refers to how the sample is selected. When randomization is used each participant from the desired population has an equal chance of being assigned to the experimental or control group.

These are variable that may interfere with the independent and dependent variables. Also called mediating variables.

The loss of participants from the study.

An Introduction to Quantitative Research Design for Students in Health Sciences Copyright © 2024 by Amy Hallaran and Julie Gaudet is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Aspects of Quantative (Empirical) Research

♦   Statement of purpose—what was studied and why.

  ♦   Description of the methodology (experimental group, control group, variables, test conditions, test subjects, etc.).

  ♦   Results (usually numeric in form presented in tables or graphs, often with statistical analysis).

♦   Conclusions drawn from the results.

  ♦   Footnotes, a bibliography, author credentials.

Hint: the abstract (summary) of an article is the first place to check for most of the above features.  The abstract appears both in the database you search and at the top of the actual article.

Types of Quantitative Research

There are four (4) main types of quantitative designs: descriptive, correlational, quasi-experimental, and experimental.

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Types of Qualitative Research

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

Introduction

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

• Ethnographic research designs

Case study research design

Important reminders when designing a research study.

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

Theoretical concepts and research question

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

Methodology

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

Literature review

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

Data collection

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

Data analysis

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Ethnographic research design

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

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

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Experimental Research Design

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

Quasi-experimental Research Design

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

Case Study Research Design

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

Longitudinal Research Design

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

Structure of Research Design

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

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

Example of Research Design

An Example of Research Design could be:

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

Research design:

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

How to Write Research Design

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

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

When to Write Research Design

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

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

Purpose of Research Design

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

Some of the key purposes of research design include:

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

Applications of Research Design

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

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

Advantages of Research Design

Here are some advantages of research design:

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

Research Design Vs Research Methodology

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Data Analysis in Research: Types & Methods

Data analysis is a crucial step in the research process, transforming raw data into meaningful insights that drive informed decisions and advance knowledge. This article explores the various types and methods of data analysis in research, providing a comprehensive guide for researchers across disciplines.

Data Analysis in Research

Overview of Data analysis in research

Data analysis in research is the systematic use of statistical and analytical tools to describe, summarize, and draw conclusions from datasets. This process involves organizing, analyzing, modeling, and transforming data to identify trends, establish connections, and inform decision-making. The main goals include describing data through visualization and statistics, making inferences about a broader population, predicting future events using historical data, and providing data-driven recommendations. The stages of data analysis involve collecting relevant data, preprocessing to clean and format it, conducting exploratory data analysis to identify patterns, building and testing models, interpreting results, and effectively reporting findings.

• Main Goals : Describe data, make inferences, predict future events, and provide data-driven recommendations.
• Stages of Data Analysis : Data collection, preprocessing, exploratory data analysis, model building and testing, interpretation, and reporting.

Types of Data Analysis

1. descriptive analysis.

Descriptive analysis focuses on summarizing and describing the features of a dataset. It provides a snapshot of the data, highlighting central tendencies, dispersion, and overall patterns.

• Central Tendency Measures : Mean, median, and mode are used to identify the central point of the dataset.
• Dispersion Measures : Range, variance, and standard deviation help in understanding the spread of the data.
• Frequency Distribution : This shows how often each value in a dataset occurs.

2. Inferential Analysis

Inferential analysis allows researchers to make predictions or inferences about a population based on a sample of data. It is used to test hypotheses and determine the relationships between variables.

• Hypothesis Testing : Techniques like t-tests, chi-square tests, and ANOVA are used to test assumptions about a population.
• Regression Analysis : This method examines the relationship between dependent and independent variables.
• Confidence Intervals : These provide a range of values within which the true population parameter is expected to lie.

3. Exploratory Data Analysis (EDA)

EDA is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It helps in discovering patterns, spotting anomalies, and checking assumptions with the help of graphical representations.

• Visual Techniques : Histograms, box plots, scatter plots, and bar charts are commonly used in EDA.
• Summary Statistics : Basic statistical measures are used to describe the dataset.

4. Predictive Analysis

Predictive analysis uses statistical techniques and machine learning algorithms to predict future outcomes based on historical data.

• Machine Learning Models : Algorithms like linear regression, decision trees, and neural networks are employed to make predictions.
• Time Series Analysis : This method analyzes data points collected or recorded at specific time intervals to forecast future trends.

5. Causal Analysis

Causal analysis aims to identify cause-and-effect relationships between variables. It helps in understanding the impact of one variable on another.

• Experiments : Controlled experiments are designed to test the causality.
• Quasi-Experimental Designs : These are used when controlled experiments are not feasible.

6. Mechanistic Analysis

Mechanistic analysis seeks to understand the underlying mechanisms or processes that drive observed phenomena. It is common in fields like biology and engineering.

Methods of Data Analysis

1. quantitative methods.

Quantitative methods involve numerical data and statistical analysis to uncover patterns, relationships, and trends.

• Statistical Analysis : Includes various statistical tests and measures.
• Mathematical Modeling : Uses mathematical equations to represent relationships among variables.
• Simulation : Computer-based models simulate real-world processes to predict outcomes.

2. Qualitative Methods

Qualitative methods focus on non-numerical data, such as text, images, and audio, to understand concepts, opinions, or experiences.

• Content Analysis : Systematic coding and categorizing of textual information.
• Thematic Analysis : Identifying themes and patterns within qualitative data.
• Narrative Analysis : Examining the stories or accounts shared by participants.

3. Mixed Methods

Mixed methods combine both quantitative and qualitative approaches to provide a more comprehensive analysis.

• Sequential Explanatory Design : Quantitative data is collected and analyzed first, followed by qualitative data to explain the quantitative results.
• Concurrent Triangulation Design : Both qualitative and quantitative data are collected simultaneously but analyzed separately to compare results.

4. Data Mining

Data mining involves exploring large datasets to discover patterns and relationships.

• Clustering : Grouping data points with similar characteristics.
• Association Rule Learning : Identifying interesting relations between variables in large databases.
• Classification : Assigning items to predefined categories based on their attributes.

5. Big Data Analytics

Big data analytics involves analyzing vast amounts of data to uncover hidden patterns, correlations, and other insights.

• Hadoop and Spark : Frameworks for processing and analyzing large datasets.
• NoSQL Databases : Designed to handle unstructured data.
• Machine Learning Algorithms : Used to analyze and predict complex patterns in big data.

Applications and Case Studies

Numerous fields and industries use data analysis methods, which provide insightful information and facilitate data-driven decision-making. The following case studies demonstrate the effectiveness of data analysis in research:

Medical Care:

• Predicting Patient Readmissions: By using data analysis to create predictive models, healthcare facilities may better identify patients who are at high risk of readmission and implement focused interventions to enhance patient care.
• Disease Outbreak Analysis: Researchers can monitor and forecast disease outbreaks by examining both historical and current data. This information aids public health authorities in putting preventative and control measures in place.
• Fraud Detection: To safeguard clients and lessen financial losses, financial institutions use data analysis tools to identify fraudulent transactions and activities.
• investing Strategies: By using data analysis, quantitative investing models that detect trends in stock prices may be created, assisting investors in optimizing their portfolios and making well-informed choices.
• Customer Segmentation: Businesses may divide up their client base into discrete groups using data analysis, which makes it possible to launch focused marketing efforts and provide individualized services.
• Social Media Analytics: By tracking brand sentiment, identifying influencers, and understanding consumer preferences, marketers may develop more successful marketing strategies by analyzing social media data.
• Predicting Student Performance: By using data analysis tools, educators may identify at-risk children and forecast their performance. This allows them to give individualized learning plans and timely interventions.
• Education Policy Analysis: Data may be used by researchers to assess the efficacy of policies, initiatives, and programs in education, offering insights for evidence-based decision-making.

Social Science Fields:

• Opinion mining in politics: By examining public opinion data from news stories and social media platforms, academics and policymakers may get insight into prevailing political opinions and better understand how the public feels about certain topics or candidates.
• Crime Analysis: Researchers may spot trends, anticipate high-risk locations, and help law enforcement use resources wisely in order to deter and lessen crime by studying crime data.

Data analysis is a crucial step in the research process because it enables companies and researchers to glean insightful information from data. By using diverse analytical methodologies and approaches, scholars may reveal latent patterns, arrive at well-informed conclusions, and tackle intricate research inquiries. Numerous statistical, machine learning, and visualization approaches are among the many data analysis tools available, offering a comprehensive toolbox for addressing a broad variety of research problems.

Data Analysis in Research FAQs:

What are the main phases in the process of analyzing data.

In general, the steps involved in data analysis include gathering data, preparing it, doing exploratory data analysis, constructing and testing models, interpreting the results, and reporting the results. Every stage is essential to guaranteeing the analysis’s efficacy and correctness.

What are the differences between the examination of qualitative and quantitative data?

In order to comprehend and analyze non-numerical data, such text, pictures, or observations, qualitative data analysis often employs content analysis, grounded theory, or ethnography. Comparatively, quantitative data analysis works with numerical data and makes use of statistical methods to identify, deduce, and forecast trends in the data.

What are a few popular statistical methods for analyzing data?

In data analysis, predictive modeling, inferential statistics, and descriptive statistics are often used. While inferential statistics establish assumptions and draw inferences about a wider population, descriptive statistics highlight the fundamental characteristics of the data. To predict unknown values or future events, predictive modeling is used.

In what ways might data analysis methods be used in the healthcare industry?

In the healthcare industry, data analysis may be used to optimize treatment regimens, monitor disease outbreaks, forecast patient readmissions, and enhance patient care. It is also essential for medication development, clinical research, and the creation of healthcare policies.

What difficulties may one encounter while analyzing data?

Answer: Typical problems with data quality include missing values, outliers, and biased samples, all of which may affect how accurate the analysis is. Furthermore, it might be computationally demanding to analyze big and complicated datasets, necessitating certain tools and knowledge. It’s also critical to handle ethical issues, such as data security and privacy.

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Differential attainment in assessment of postgraduate surgical trainees: a scoping review

• Rebecca L. Jones 1 , 2 ,
• Suwimol Prusmetikul 1 , 3 &
• Sarah Whitehorn 1  

BMC Medical Education volume  24 , Article number:  597 ( 2024 ) Cite this article

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Introduction

Solving disparities in assessments is crucial to a successful surgical training programme. The first step in levelling these inequalities is recognising in what contexts they occur, and what protected characteristics are potentially implicated.

This scoping review was based on Arksey & O’Malley’s guiding principles. OVID and Embase were used to identify articles, which were then screened by three reviewers.

From an initial 358 articles, 53 reported on the presence of differential attainment in postgraduate surgical assessments. The majority were quantitative studies (77.4%), using retrospective designs. 11.3% were qualitative. Differential attainment affects a varied range of protected characteristics. The characteristics most likely to be investigated were gender (85%), ethnicity (37%) and socioeconomic background (7.5%). Evidence of inequalities are present in many types of assessment, including: academic achievements, assessments of progression in training, workplace-based assessments, logs of surgical experience and tests of technical skills.

Attainment gaps have been demonstrated in many types of assessment, including supposedly “objective” written assessments and at revalidation. Further research is necessary to delineate the most effective methods to eliminate bias in higher surgical training. Surgical curriculum providers should be informed by the available literature on inequalities in surgical training, as well as other neighbouring specialties such as medicine or general practice, when designing assessments and considering how to mitigate for potential causes of differential attainment.

Peer Review reports

Diversity in the surgical workforce has been a hot topic for the last 10 years, increasing in traction following the BlackLivesMatter movement in 2016 [ 1 ]. In the UK this culminated in publication of the Kennedy report in 2021 [ 2 ]. Before this the focus was principally on gender imbalance in surgery, with the 2010 Surgical Workforce report only reporting gender percentages by speciality, with no comment on racial profile, sexuality distribution, disability occurrence, or socioeconomic background [ 3 ].

Gender is not the only protected characteristic deserving of equity in surgery; many groups find themselves at a disadvantage during postgraduate surgical examinations [ 4 ] and at revalidation [ 5 ]. This phenomenon is termed ‘differential attainment’ (DA), in which disparities in educational outcomes, progression rates, or achievements between groups with protected characteristics occur [ 4 ]. This may be due to the assessors’ subconscious bias, or a deficit in training and education before assessment.

One of the four pillars of medical ethics is “justice”, emphasising that healthcare should be provided in a fair, equitable, and ethical manner, benefiting all individuals and promoting the well-being of society as a whole. This applies not only to our patients but also to our colleagues; training should be provided in a fair, equitable, and ethical manner, benefiting all. By applying the principle of justice to surgical trainees, we can create an environment that is supportive, inclusive, and conducive to professional growth and well-being.

A diverse consultant body is crucial for providing high-quality healthcare to a diverse patient population. It has been shown that patients are happier when cared for by a doctor with the same ethnic background [ 6 ]. Takeshita et al. [ 6 ] proposed this is due to a greater likelihood of mutual understanding of cultural values, beliefs, and preferences and is therefore more likely to cultivate a trusting relationship, leading to accurate diagnosis, treatment adherence and improved patient understanding. As such, ensuring that all trainees are justly educated and assessed throughout their training may contribute to improving patient care by diversifying the consultant body.

Surgery is well known to have its own specific culture, language, and social rules which are unique even within the world of medicine [ 7 , 8 ]. Through training, graduates develop into surgeons, distinct from other physicians and practitioners [ 9 ]. As such, research conducted in other medical domains is not automatically applicable to surgery, and behavioural interventions focused on reducing or eliminating bias in training need to be tailored specifically to surgical settings.

Consequently, it’s important that the surgical community asks the questions:

Does DA exist in postgraduate surgical training, and to what extent?

Why does DA occur?

What groups or assessments are under-researched?

How can we apply this knowledge, or acquire new knowledge, to provide equity for trainees?

The following scoping review hopes to provide the surgical community with robust answers for future of surgical training.

Aims and research question

The aim of this scoping review is to understand the breadth of research about the presence of DA in postgraduate surgical education and to determine themes pertaining to causes of inequalities. A scoping review was chosen to provide a means to map the available literature, including published peer-reviewed primary research and grey literature.

Following the methodological framework set out by Arksey and O’Malley [ 10 ], our research was intended to characterise the literature addressing DA in HST, including Ophthalmology, Obstetrics & Gynaecology (O&G). We included literature from English-language speaking countries, including the UK and USA.

Search strategy

We used search terms tailored to our target population characteristics (e.g., gender, ethnicity), concept (i.e., DA) and context (i.e., assessment in postgraduate surgical education). Medline and Embase were searched with the assistance of a research librarian, with addition of synonyms. This was conducted in May 2023, and was exported to Microsoft Excel for further review. The reference lists of included articles were also searched to find any relevant data sources that had yet to be considered. In addition, to identify grey literature, a search was performed for the term “differential attainment” and “disparity” on the relevant stakeholders’ websites (See supplemental Table 1 for full listing). Stakeholders were included on the basis of their involvement in governance or training of surgical trainees.

Study selection

To start we excluded conference abstracts that were subsequently published as full papers to avoid duplications ( n  = 337). After an initial screen by title to exclude obviously irrelevant articles, articles were filtered to meet our inclusion and exclusion criteria (Table  1 ). The remaining articles ( n  = 47) were then reviewed in their entirety, with the addition of five reports found in grey literature. Following the screening process, 45 studies were recruited for scoping review (Fig.  1 ).

Charting the data

The extracted data included literature title, authors, year of publication, country of study, study design, population characteristic, case number, context, type of assessment, research question and main findings (Appendix 1). Extraction was performed initially by a single author and then subsequently by a second author to ensure thorough review. Group discussion was conducted in case of any disagreements. As charting occurred, papers were discovered within reference lists of included studies which were eligible for inclusion; these were assimilated into the data charting table and included in the data extraction ( n  = 8).

Collating, summarizing and reporting the results

The included studies were not formally assessed in their quality or risk of bias, consistent with a scoping review approach [ 10 ]. However, group discussion was conducted during charting to aid argumentation and identify themes and trends.

We conducted a descriptive numerical summary to describe the characteristics of included studies. Then thematic analysis was implemented to examine key details and organise the attainment quality and population characteristics based on their description. The coding of themes was an iterative process and involved discussion between authors, to identify and refine codes to group into themes.

We categorised the main themes as gender, ethnicity, country of graduation, individual and family background in education, socioeconomic background, age, and disability. The number of articles in each theme is demonstrated in Table  2 . Data was reviewed and organised into subtopics based on assessment types included: academic achievement (e.g., MRCS, FRCS), assessments for progression (e.g., ARCP), workplace-based assessment (e.g., EPA, feedback), surgical experience (e.g., case volume), and technical skills (e.g., visuo-spatial tasks).

PRISMA flow diagram

44 articles defined the number of included participants (89,399 participants in total; range of participants across individual studies 16–34,755). Two articles reported the number of included studies for their meta-analysis (18 and 63 included articles respectively). Two reports from grey literature did not define the number of participants they included in their analysis. The characteristics of the included articles are displayed in Table  2 .

Growth in published literature on differential attainment over the past 40 years

Academic achievement

In the American Board of Surgery Certifying Exam (ABSCE), Maker [ 11 ] found there to be no significant differences in terms of gender when comparing those who passed on their first attempt and those who did not in general surgery training, a finding supported by Ong et al. [ 12 ]. Pico et al. [ 13 ] reported that in Orthopaedic training, Orthopaedic In-Training Examination (OITE) and American Board of Orthopaedic Surgery (ABOS) Part 1 scores were similar between genders, but that female trainees took more attempts in order to pass. In the UK, two studies reported significantly lower Membership of the Royal College of Surgeons (MRCS) pass rates for female trainees compared to males [ 4 , 14 ]. However, Robinson et al. [ 15 ] presented no significant gender differences in MRCS success rates. A study assessing Fellowship of the Royal College of Surgeons (FRCS) examination results found no significant gender disparities in pass rates [ 16 ]. In MRCOG examination, no significant gender differences were found in Part 1 scores, but women had higher pass rates and scores in Part 2 [ 17 ].

Assessment for Progression

ARCP is the annual process of revalidation that UK doctors must perform to progress through training. A satisfactory progress outcome (“outcome 1”) allows trainees to advance through to the next training year, whereas non-satisfactory outcomes (“2–5”) suggest inadequate progress and recommends solutions, such as further time in training or being released from the training programme. Two studies reported that women received 60% more non-satisfactory outcomes than men [ 16 , 18 ]. In contrast, in O&G men had higher non-satisfactory ARCP outcomes without explicit reasons for this given [ 19 ].

Regarding Milestone evaluations based from the US Accreditation Council for Graduate Medical Education (ACGME), Anderson et al. [ 20 ] reported men had higher ratings of knowledge of diseases at postgraduate year 5 (PGY-5), while women had lower mean score achievements. This was similar to another study finding that men and women had similar competencies at PGY-1 to 3, and that it was only at PGY-5 that women were evaluated lower than men [ 21 ]. However, Kwasny et al. [ 22 ] found no difference in trainers’ ratings between genders, but women self-rated themselves lower. Salles et al. [ 23 ] demonstrated significant improvement in scoring in women following a value-affirmation intervention, while this intervention did not affect men.

Workplace-based Assessment

Galvin et al. [ 24 ] reported better evaluation scores from nurses for PGY-2 male trainees, while females received fewer positive and more negative comments. Gerull et al. [ 25 ] demonstrated men received compliments with superlatives or standout words, whereas women were more likely to receive compliments with mitigating phrases (e.g., excellent vs. quite competent).

Hayward et al. [ 26 ] investigated assessment of attributes of clinical performance (ethics, judgement, technical skills, knowledge and interpersonal skills) and found similar scoring between genders.

Several authors have studied autonomy given to trainees in theatre [ 27 , 28 , 29 , 30 , 31 ]. Two groups found no difference in level of granted autonomy between genders but that women rated lower perceived autonomy on self-evaluation [ 27 , 28 ]. Other studies found that assessors consistently gave female trainees lower autonomy ratings, but only in one paper was this replicated in lower performance scores [ 29 , 30 , 31 ].

Padilla et al. [ 32 ] reported no difference in entrustable professional activity assessment (EPA) levels between genders, yet women rated themselves much lower, which they regarded as evidence of imposter syndrome amongst female trainees. Cooney et al. [ 33 ] found that male trainers scored EPAs for women significantly lower than men, while female trainers rated both genders similarly. Conversely, Roshan et al. [ 34 ] found that male assessors were more positive in feedback comments to female trainees than male trainees, whereas they also found that comments from female assessors were comparable for each gender.

Surgical Experience

Gong et al. [ 35 ] found significantly fewer cataract operations were performed by women in ophthalmology residency programmes, which they suggested could be due to trainers being more likely to give cases to male trainees. Female trainees also participated in fewer robotic colorectal procedures, with less operative time on the robotic console afforded [ 36 ]. Similarly, a systematic review highlighted female trainees in various specialties performed fewer cases per week and potentially had limited access to training facilities [ 37 ]. Eruchalu et al. [ 38 ] found that female trainees performed fewer cases, that is, until gender parity was reached, after which case logs were equivalent.

Technical skills

Antonoff et al. [ 39 ] found higher scores for men in coronary anastomosis skills, with women receiving more “fail” assessments. Dill-Macky et al. [ 40 ] analysed laparoscopic skill assessment using blinded videos of trainees and unblinded assessments. While there was no difference in blinded scores between genders, when comparing blinded and unblinded scores individually, assessors were less likely to agree on the scores of women compared to men. However, another study about laparoscopic skills by Skjold-Ødegaard et al. [ 41 ] reported higher performance scores in female residents, particularly when rated by women. The lowest score was shown in male trainees rated by men. While some studies showed disparities in assessment, several studies reported no difference in technical skill assessments (arthroscopic, knot tying, and suturing skills) between genders [ 42 , 43 , 44 , 45 , 46 ].

Several studies investigated trainees’ abilities to complete isolated tasks associated with surgical skills. In laparoscopic tasks, men were initially more skilful in peg transfer and intracorporeal knot tying than women. Following training, the performance was not different between genders [ 47 ]. A study on microsurgical skills reported better initial visual-spatial and perceptual ability in men, while women had better fine motor psychomotor ability. However, these differences were not significant, and all trainees improved significantly after training [ 48 ]. A study by Milam et al. [ 49 ] revealed men performed better in mental rotation tasks and women outperformed in working memory. They hypothesised that female trainees would experience stereotype threat, fear of being reduced to a stereotype, which would impair their performance. They found no evidence of stereotype threat influencing female performance, disproving their hypothesis, a finding supported by Myers et al. [ 50 ].

Ethnicity and country of graduation

Most papers reported ethnicity and country of graduation concurrently, for example grouping trainees as White UK graduates (WUKG), Black and minority ethnicity UK graduates (BME UKG), and international medical graduates (IMG). Therefore, these areas will be addressed together in the following section.

When assessing the likelihood of passing American Board of Surgery (ABS) examinations on first attempt, Yeo et al. [ 51 ] found that White trainees were more likely than non-White. They found that the influence of ethnicity was more significant in the end-of-training certifying exam than in the start-of-training qualifying exam. This finding was corroborated in a study of both the OITE and ABOS certifying exam, suggesting widening inequalities during training [ 52 ].

Two UK-based studies reported significantly higher MRCS pass rates in White trainees compared to BMEs [ 4 , 14 ]. BMEs were less likely to pass MRCS Part A and B, though this was not true for Part A when variations in socioeconomic background were corrected for [ 14 ]. However, Robinson et al. [ 53 ] found no difference in MRCS pass rates based on ethnicity. Another study by Robinson et al. [ 15 ] demonstrated similar pass rates between WUKGs and BME UKGs, but IMGs had significantly lower pass rates than all UKGs. The FRCS pass rates of WUKGs, BME UKGs and IMGs were 76.9%, 52.9%, and 53.9%, respectively, though these percentages were not statistically significantly different [ 16 ].

There was no difference in MRCOG results based on ethnicity, but higher success rates were found in UKGs [ 19 ]. In FRCOphth, WUKGs had a pass rate of 70%, higher than other groups of trainees, with a pass rate of only 45% for White IMGs [ 52 ].

By gathering data from training programmes reporting little to no DA due to ethnicity, Roe et al. [ 54 ] were able to provide a list of factors they felt were protective against DA, such as having supportive supervisors and developing peer networks.

Assessment for progression

RCOphth [ 55 ] found higher rates of satisfactory ARCP outcomes for WUKGs compared to BME UKGs, followed by IMGs. RCOG [ 19 ] discovered higher rates of non-satisfactory ARCP outcomes from non-UK graduates, particularly amongst BMEs and those from the European Economic Area (EEA). Tiffin et al. [ 56 ] considered the difference in experience between UK graduates and UK nationals whose primary medical qualification was gained outside of the UK, and found that the latter were more likely to receive a non-satisfactory ARCP outcome, even when compared to non-UK nationals.

Woolf et al. [ 57 ] explored reasons behind DA by conducting interview studies with trainees. They investigated trainees’ perceptions of fairness in evaluation and found that trainees felt relationships developed with colleagues who gave feedback could affect ARCP results, and might be challenging for BME UKGs and IMGs who have less in common with their trainers.

Workplace-based assessment

Brooks et al. [ 58 ] surveyed the prevalence of microaggressions against Black orthopaedic surgeons during assessment and found 87% of participants experienced some level of racial discrimination during workplace-based performance feedback. Black women reported having more racially focused and devaluing statements from their seniors than men.

Surgical experience

Eruchalu et al. [ 38 ] found that white trainees performed more major surgical cases and more cases as a supervisor than did their BME counterparts.

Dill-Macky et al. [ 40 ] reported no significant difference in laparoscopic surgery assessments between ethnicities.

Individual and family background in education

Two studies [ 4 , 16 ] concentrated on educational background, considering factors such as parental occupation and attendance of a fee-paying school. MRCS part A pass rate was significantly higher for trainees for whom Medicine was their first Degree, those with university-educated parents, higher POLAR (Participation In Local Areas classification group) quintile, and those from fee-paying schools. Higher part B pass rate was associated with graduating from non-Graduate Entry Medicine programmes and parents with managerial or professional occupations [ 4 ]. Trainees with higher degrees were associated with an almost fivefold increase in FRCS success and seven times more scientific publications than their counterparts [ 16 ].

Socioeconomic background

Two studies used Index of Multiple Deprivation quintile, the official measure of relative deprivation in England based on geographical areas for grading socioeconomic level. The area was defined at the time of medical school application. Deprivation quintiles (DQ) were calculated, ranging from DQ1 (most deprived) to DQ5 (least deprived) [ 4 , 14 ].

Trainees with history of less deprivation were associated with higher MRCS part A pass rate. More success in part B was associated with history of no requirement for income support and less deprived areas [ 4 ]. Trainees from DQ1 and DQ2 had lower pass rates and higher number of attempts to pass [ 14 ]. A general trend of better outcomes in examination was found from O&G trainees in less deprived quintiles [ 19 ].

Trainees from DQ1 and DQ2 received significantly more non-satisfactory ARCP outcomes (24.4%) than DQ4 and DQ5 (14.2%) [ 14 ].

Trainees who graduated at age less than 29 years old were more likely to pass MRCS than their counterparts [ 4 ].

Authors [ 18 , 56 ] found that older trainees received more non-satisfactory ARCP outcomes. Likewise, there was higher percentage of non-satisfactory ARCP outcomes in O&G trainees aged over 45 compared with those aged 25–29 regardless of gender [ 19 ].

Trainees with disability had significantly lower pass rates in MRCS part A compared to candidates without disability. However, the difference was not significant for part B [ 59 ].

What have we learnt from the literature?

It is heartening to note the recent increase in interest in DA (27 studies in the last 4 years, compared to 26 in the preceding 40) (Fig.  2 ). The vast majority (77%) of studies are quantitative, based in the US or UK (89%), focus on gender (85%) and relate to clinical assessments (51%) rather than examination results. Therefore, the surgical community has invested primarily in researching the experience of women in the USA and UK.

Interestingly, a report by RCOG [ 19 ] showed that men were more likely to receive non-satisfactory ARCP outcomes than women, and a study by Rushd et al. [ 17 ] found that women were more likely to pass part 2 of MRCOG than men. This may be because within O&G men are the “out-group” (a social group or category characterised by marginalisation or exclusion by the dominant cultural group) as 75% of O&G trainees are female [ 60 ].

This contrasts with other specialities in which men are the in-group and women are seen to underperform. Outside of O&G, in comparison to men, women are less likely to pass MRCS [ 4 , 14 ], receive satisfactory ARCP outcome [ 16 , 18 ], or receive positive feedback [ 24 ], whilst not performing the same number of procedures as men [ 34 , 35 ]. This often leads to poor self-confidence in women [ 32 ], which can then worsen performance [ 21 ].

It proves difficult to comment on DA for many groups due to a lack of evidence. The current research suggests that being older, having a disability, graduate entry to medicine, low parental education, and living in a lower socioeconomic area at the time of entering medical school are all associated with lower MRCS pass rates. Being older and having a lower socioeconomic background are also associated with non-satisfactory ARCP outcomes, slowing progression through training.

These characteristics may provide a compounding negative effect – for example having a previous degree will automatically make a trainee older, and living in a lower socioeconomic area makes it more likely their parents will have a non-professional job and not hold a higher degree. When multiple protected characteristics interact to produce a compounded negative effect for a person, it is often referred to as “intersectional discrimination” or “intersectionality” [ 61 ]. This is a concept which remains underrepresented in the current literature.

The literature is not yet in agreement over the presence of DA due to ethnicity. There are many studies that report perceived discrimination, however the data for exam and clinical assessment outcomes is equivocal. This may be due to the fluctuating nature of in-groups and out-groups, and multiple intersecting characteristics. Despite this, the lived experience of BME surgeons should not be ignored and requires further investigation.

What are the gaps in the literature?

The overwhelming majority of literature exploring DA addresses issues of gender, ethnicity or country of medical qualification. Whilst bias related to these characteristics is crucial to recognise, studies into other protected characteristics are few and far between. The only paper on disability reported striking differences in attainment between disabled and non-disabled registrars [ 59 ]. There has also been increased awareness about neurodiversity amongst doctors and yet an exploration into the experience of neurodiverse surgeons and their progress through training has yet to be published [ 62 ].

The implications of being LGBTQ + in surgical training have not been recognised nor formally addressed in the literature. Promisingly, the experiences of LGBTQ + medical students have been recognised at an undergraduate level, so one can hope that this will be translated into postgraduate education [ 63 , 64 ]. While this is deeply entwined with experiences of gender discrimination, it is an important characteristic that the surgical community would benefit from addressing, along with disability. To a lesser extent, the effect of socioeconomic background and age have also been overlooked.

Characterising trainees for the purpose of research

Ethnicity is deeply personal, self-defined, and may change over time as personal identity evolves, and therefore arbitrarily grouping diverse ethnic backgrounds is unlikely to capture an accurate representation of experiences. There are levels of discrimination even within minority groups; colourism in India means dark-skinned Indians will experience more discrimination than light-skinned Indians, even from those within in their own ethnic group [ 65 ]. Therefore, although the studies included in the scoping review accepted self-definitions of ethnicity, this is likely not enough to fully capture the nuances of bias and discrimination present in society. For example, Ellis et al. [ 4 ] grouped participants as “White”, “Mixed”, “Asian”, “Black” and “Other”, however they could have also assigned a skin tone value such as the NIS Skin Colour Scale [ 66 ], thus providing more detail.

Ethnicity is more than genetic heritage; it is also cultural expression. The experience of an IMG in UK postgraduate training will differ from that of a UKG, an Indian UKG who grew up in India, and an Indian UKG who grew up in the UK. These are important distinctions which are noted in the literature (e.g. by Woolf et al., 2016 [ 57 ]) however some do not distinguish between ethnicity and graduate status [ 15 ] and none delve into an individual’s cultural expression (e.g., clothing choice) and how this affects the perception of their assessors.

Reasons for DA

Despite the recognition of inequalities in all specialties of surgery, there is a paucity of data explicitly addressing why DA occurs. Reasons behind the phenomenon must be explored to enable change and eliminate biases. Qualitative research is more attuned to capturing the complexities of DA through observation or interview-based studies. Currently most published data is quantitative, and relies on performance metrics to demonstrate the presence of DA while ignoring the causes. Promisingly, there are a gradually increasing number of qualitative, predominantly interview-based, studies (Fig.  2 ).

To create a map of DA in all its guises, an analysis of the themes reported to be contributory to its development is helpful. In our review of the literature, four themes have been identified:

Training culture

In higher surgical training, for there to be equality in outcomes, there needs to be equity in opportunities. Ellis et al. [ 4 ] recognised that variation in training experiences, such as accessibility of supportive peers and senior role models, can have implications on attainment. Trainees would benefit from targeted support at times of transition, such as induction or at examinations, and it may be that currently the needs of certain groups are being met before others, reinforcing differential attainment [ 4 ].

Experience of assessment

Most literature in DA relates to the presence (or lack of) an attainment gap in assessments, such as ARCP or MRCS. It is assumed that these assessments of trainee development are objective and free of bias, and indeed several authors have described a lack of bias in these high-stakes examinations (e.g., Ong et al., 2019 [ 12 ]; Robinson et al., 2019 [ 53 ]). However, in some populations, such as disabled trainees, there are differences in attainment [ 59 ]. This is demonstrated despite legislation requiring professional bodies to make reasonable adjustments to examinations for disabled candidates, such as additional time, text formatting amendments, or wheelchair-accessible venues [ 67 ]. Therefore it would be beneficial to investigate the implementation of these adjustments across higher surgical examinations and identify any deficits.

Social networks

Relationships between colleagues may influence DA in multiple ways. Several studies identified that a lack of a relatable and inspiring mentor may explain why female or BME doctors fail to excel in surgery [ 4 , 55 ]. Certain groups may receive preferential treatment due to their perceived familiarity to seniors [ 35 ]. Robinson et al. [ 15 ] recognised that peer-to-peer relationships were also implicated in professional development, and the lack thereof could lead to poor learning outcomes. Therefore, a non-discriminatory culture and inclusion of trainees within the social network of training is posited as beneficial.

Personal characteristics

Finally, personal factors directly related to protected characteristics have been suggested as a cause of DA. For example, IMGs may perform worse in examinations due to language barriers, and those from disadvantaged backgrounds may have less opportunity to attend expensive courses [ 14 , 16 ]. Although it is impossible to exclude these innate deficits from training, we may mitigate their influence by recognising their presence and providing solutions.

The causes of DA may also be grouped into three levels, as described by Regan de Bere et al. [ 68 ]: macro (the implications of high-level policy), meso (focusing on institutional or working environments) and micro (the influence of individual factors). This can intersect with the four themes identified above, as training culture can be enshrined at both an institutional and individual level, influencing decisions that relate to opportunities for trainees, or at a macro level, such as in the decisions made on nationwide recruitment processes. These three levels can be used to more deeply explore each of the four themes to enrich the discovery of causes of DA.

Discussions outside of surgery

Authors in General Practice (e.g., Unwin et al., 2019 [ 69 ]; Pattinson et al., 2019 [ 70 ]), postgraduate medical training (e.g., Andrews, Chartash, and Hay, 2021 [ 71 ]), and undergraduate medical education (e.g., Yeates et al., 2017 [ 72 ]; Woolf et al., 2013 [ 73 ]) have published more extensively in the aetiology of DA. A study by Hope et al. [ 74 ] evaluating the bias present in MRCP exams used differential item functioning to identify individual questions which demonstrated an attainment gap between male and female and Caucasian and non-Caucasian medical trainees. Conclusions drawn about MRCP Part 1 examinations may be generalisable to MRCS Part A or FRCOphth Part 1: they are all multiple-choice examinations testing applied basic science and usually taken within the first few years of postgraduate training. Therefore it is advisable that differential item functioning should also be applied to these examinations. However, it is possible that findings in some subspecialities may not be generalisable to others, as training environments can vary profoundly. The RCOphth [ 55 ] reported that in 2021, 53% of ophthalmic trainees identified as male, whereas in Orthopaedics 85% identified as male, suggesting different training environments [ 5 ]. It is useful to identify commonalities of DA between surgical specialties and in the wider scope of medical training.

Limitations of our paper

Firstly, whilst aiming to provide a review focussed on the experience of surgical trainees, four papers contained data about either non-surgical trainees or medical students. It is difficult to draw out the surgeons from this data and therefore it is possible that there are issues with generalisability. Furthermore, we did not consider the background of each paper’s authors, as their own lived experience of attainment gap could form the lens through which they commented on surgical education, colouring their interpretation. Despite intending to include as many protected characteristics as possible, inevitably there will be lived experiences missed. Lastly, the experience of surgical trainees outside of the English-speaking world were omitted. No studies were found that originated outside of Europe or North America and therefore the presence or characteristics of DA outside of this area cannot be assumed.

Experiences of inequality in surgical assessment are prevalent in all surgical subspecialities. In order to further investigate DA, researchers should ensure all protected characteristics are considered - and how these interact - to gain insight into intersectionality. Given the paucity of current evidence, particular focus should be given to the implications of disability, and specifically neurodiversity, in progress through training as they are yet to be explored in depth. In defining protected characteristics, future authors should be explicit and should avoid generalisation of cultural backgrounds to allow authentic appreciation of attainment gap. Few authors have considered the driving forces between bias in assessment and DA, and therefore qualitative studies should be prioritised to uncover causes for and protective factors against DA. Once these influences have been identified, educational designers can develop new assessment methods that ensure equity across surgical trainees.

Data availability

All data provided during this study are included in the supplementary information files.

Abbreviations

Accreditation Council for Graduate Medical Education

American Board of Orthopaedic Surgery

American Board of Surgery

American Board of Surgery Certifying Exam

Annual Review of Competence Progression

Black, Asian, and Minority Ethnicity

Council on Resident Education in Obstetrics and Gynecology

Differential Attainment

Deprivation Quintile

European Economic Area

Entrustable Professional Activities

Fellowship of The Royal College of Ophthalmologists

Fellow of the Royal College of Surgeons

General Medical Council

Higher Surgical Training

International Medical Graduate

In-Training Evaluation Report

Member of the Royal College of Obstetricians and Gynaecologists

Member of the Royal College of Physicians

Member of the Royal College of Surgeons

Obstetrics and Gynaecology

Orthopaedic In-Training Examination

Participation In Local Areas

Postgraduate Year

The Royal College of Ophthalmologists

The Royal College of Obstetricians and Gynaecologists

The Royal College of Surgeons of England

United Kingdom Graduate

White United Kingdom Graduate

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RJ, SP and SW conceived the study. RJ carried out the search. RJ, SP and SW reviewed and appraised articles. RJ, SP and SW extracted data and synthesized results from articles. RJ, SP and SW prepared the original draft of the manuscript. RJ and SP prepared Figs. 1 and 2. All authors reviewed and edited the manuscript and agreed to the final version.

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Jones, R.L., Prusmetikul, S. & Whitehorn, S. Differential attainment in assessment of postgraduate surgical trainees: a scoping review. BMC Med Educ 24 , 597 (2024). https://doi.org/10.1186/s12909-024-05580-2

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Data from: Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design

The type 2 modified augmented design (MAD2) is an efficient unreplicated experimental design used for evaluating large numbers of lines in plant breeding and for assessing genetic variation in a population. Statistical methods and data adjustment for soil heterogeneity have been previously described for this design. In the absence of replicated test genotypes in MAD2, their total variance cannot be partitioned into genetic and error components as required to estimate heritability and genetic correlation of quantitative traits, the two conventional genetic parameters used for breeding selection. We propose a method of estimating the error variance of unreplicated genotypes that uses replicated controls, and then of estimating the genetic parameters. Using the Delta method, we also derived formulas for estimating the sampling variances of the genetic parameters. Computer simulations indicated that the proposed method for estimating genetic parameters and their sampling variances was feasible and the reliability of the estimates was positively associated with the level of heritability of the trait. A case study of estimating the genetic parameters of three quantitative traits, iodine value, oil content, and linolenic acid content, in a biparental recombinant inbred line population of flax with 243 individuals, was conducted using our statistical models. A joint analysis of data over multiple years and sites was suggested for genetic parameter estimation. A pipeline module using SAS and Perl was developed to facilitate data analysis and appended to the previously developed MAD data analysis pipeline ( http://probes.pw.usda.gov/bioinformatics_tools/MADPipeline/index.html ).

Resource Title: Table S1. The raw phenotypic data of a population with 243 RILs derived from a cross between ‘CDC Bethune’ and ‘Macbeth’ (BM) for the case study..

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Pengembangan Media Beberan Jual Beli untuk Melatih Kecakapan Literasi Numerasi Siswa Tunarungu

• Zakiyah Zakiyah Sekolah Luar Biasa Negeri Seduri Mojokerto,Indonesia
• Lailin Mustiana Sekolah Dasar Negeri KALEN Mojokerto Jawa Timur, Indonesia

This study aims to determine the characteristics of the sale and purchase disclosure media to be developed, to test the feasibility of the buying and selling disclosure media, and to test the effectiveness of the use of buying and selling disclosure media on the results of students' numeracy literacy skills. This type of research is Research and development (R & D). The product of development in this research is the media of buying and selling information, namely the development of buying and selling statements to train numeracy literacy skills. The research design using the ADDIE approach was developed by Dick and Carry. To design a learning system with 5 development phases including: Analysis, Design, Development or Production, Implementation or Delivery and Evaluations with qualitative and quantitative data analysis. The development of buying and selling information media was carried out at SLBN Seduri Mojokerto in August-September in the odd semester of the 2021/2022 academic year. The data sources for this research were 4th grade deaf students. The results of expert validation in this development research obtained a validity percentage of 82, 3, the development of the media for selling and selling, the student response value was obtained at 78, the results of the analysis obtained good results. So it can be concluded that the results of the media disclosure of buying and selling are effectively used .

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