what are the research study designs

Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

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

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10 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

how to cite this page

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

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

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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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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Shona McCombes

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

Introduction

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

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

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

General Structure and Writing Style

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

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

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

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

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

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

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

Action Research Design

Definition and Purpose

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

What do these studies tell you ?

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

What these studies don't tell you ?

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

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

Case Study Design

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

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

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

Causal Design

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

Conditions necessary for determining causality:

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

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

Cohort Design

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

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

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

Cross-Sectional Design

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

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

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

Descriptive Design

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

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

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

Experimental Design

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

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

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

Exploratory Design

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

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

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

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

Field Research Design

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

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

What these studies don't tell you

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

Historical Design

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

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

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

Longitudinal Design

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

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

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

Meta-Analysis Design

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

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

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

Mixed-Method Design

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

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

Observational Design

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

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

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

Philosophical Design

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

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

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

Sequential Design

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

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

Systematic Review

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

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

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Understanding Research Study Designs

Table of Contents

In order to find the best possible evidence, it helps to understand the basic designs of research studies. The following basic definitions and examples of clinical research designs follow the “ levels of evidence.”

Case Series and Case Reports

Case control studies, cohort studies, randomized controlled studies, double-blind method, meta-analyses, systematic reviews.

These consist either of collections of reports on the treatment of individual patients with the same condition, or of reports on a single patient.

• Case series/reports are used to illustrate an aspect of a condition, the treatment or the adverse reaction to treatment.
• Example : You have a patient that has a condition that you are unfamiliar with. You would search for case reports that could help you decide on a direction of treatment or to assist on a diagnosis.
• Case series/reports have no control group (one to compare outcomes), so they have no statistical validity.
• The benefits of case series/reports are that they are easy to understand and can be written up in a very short period of time.

Patients who already have a certain condition are compared with people who do not.

• Case control studies are generally designed to estimate the odds (using an odds ratio) of developing the studied condition/disease. They can determine if there is an associational relationship between condition and risk factor
• Example: A study in which colon cancer patients are asked what kinds of food they have eaten in the past and the answers are compared with a selected control group.
• Case control studies are less reliable than either randomized controlled trials or cohort studies.
• A major drawback to case control studies is that one cannot directly obtain absolute risk (i.e. incidence) of a bad outcome.
• The advantages of case control studies are they can be done quickly and are very efficient for conditions/diseases with rare outcomes.

Also called longitudinal studies, involve a case-defined population who presently have a certain exposure and/or receive a particular treatment that are followed over time and compared with another group who are not affected by the exposure under investigation.

• Cohort studies may be either prospective (i.e., exposure factors are identified at the beginning of a study and a defined population is followed into the future), or historical/retrospective (i.e., past medical records for the defined population are used to identify exposure factors).
• Cohort studies are used to establish causation of a disease or to evaluate the outcome/impact of treatment, when randomized controlled clinical trials are not possible.
• Example: One of the more well-know examples of a cohort study is the Framingham Heart Study, which followed generations of residents of Framingham, Massachusetts.
• Cohort studies are not as reliable as randomized controlled studies, since the two groups may differ in ways other than the variable under study.
• Other problems with cohort studies are that they require a large sample size, are inefficient for rare outcomes, and can take long periods of time. 

This is a study in which 1) There are two groups, one treatment group and one control group. The treatment group receives the treatment under investigation, and the control group receives either no treatment (placebo) or standard treatment. 2) Patients are randomly assigned to all groups. 

• Randomized controlled trials are considered the “gold standard” in medical research. They lend themselves best to answering questions about the effectiveness of different therapies or interventions.
• Randomization helps avoid the bias in choice of patients-to-treatment that a physician might be subject to. It also increases the probability that differences between the groups can be attributed to the treatment(s) under study.
• Having a  control group allows for a comparison of treatments – e.g., treatment A produced favorable results 56% of the time versus treatment B in which only 25% of patients had favorable results.
• There are certain types of questions on which randomized controlled studies cannot be done for ethical reasons, for instance, if patients were asked to undertake harmful experiences (like smoking) or denied any treatment beyond a placebo when there are known effective treatments.

A type of randomized controlled clinical trial/study in which neither medical staff/physician nor the patient knows which of several possible treatments/therapies the patient is receiving.

• Example : Studies of treatments that consist essentially of taking pills are very easy to do double blind – the patient takes one of two pills of identical size, shape, and color, and neither the patient nor the physician needs to know which is which.
• A double blind study is the most rigorous clinical research design because, in addition to the randomization of subjects, which reduces the risk of bias, it can eliminate or minimize the placebo effect which is a further challenge to the validity of a study.

Meta-analysis is a systematic, objective way to combine data from many studies, usually from randomized controlled clinical trials, and arrive at a pooled estimate of treatment effectiveness and statistical significance.

• Meta-analysis can also combine data from case/control and cohort studies. The advantage to merging these data is that it increases sample size and allows for analyses that would not otherwise be possible.
• They should not be confused with reviews of the literature or systematic reviews. 
• Two problems with meta-analysis are publication bias (studies showing no effect or little effect are often not published and just “filed” away) and the quality of the design of the studies from which data is pulled. This can lead to misleading results when all the data on the subject from “published” literature are summarized.

A systematic review is a comprehensive survey of a topic that takes great care to find all relevant studies of the highest level of evidence, published and unpublished, assess each study, synthesize the findings from individual studies in an unbiased, explicit and reproducible way and present a balanced and impartial summary of the findings with due consideration of any flaws in the evidence. In this way it can be used for the evaluation of either existing or new technologies and practices.   

A systematic review is more rigorous than a traditional literature review and attempts to reduce the influence of bias. In order to do this, a systematic review follows a formal process:

• Clearly formulated research question
• Published & unpublished (conferences, company reports, “file drawer reports”, etc.) literature is carefully searched for relevant research
• Identified research is assessed according to an explicit methodology
• Results of the critical assessment of the individual studies are combined
• Final results are placed in context, addressing such issues are quality of the included studies, impact of bias and the applicability of the findings
• The difference between a systematic review and a meta-analysis is that a systematic review looks at the whole picture (qualitative view), while a meta-analysis looks for the specific statistical picture (quantitative view). 

R esearch Process in the Health Sciences  (35:37 min): Overview of the scientific research process in the health sciences. Follows the seven steps: defining the problem, reviewing the literature, formulating a hypothesis, choosing a research design, collecting data, analyzing the data and interpretation and report writing. Includes a set of additional readings and library resources.

Research Study Designs in the Health Sciences  (29:36 min): An overview of research study designs used by health sciences researchers. Covers case reports/case series, case control studies, cohort studies, correlational studies, cross-sectional studies, experimental studies (including randomized control trials), systematic reviews and meta-analysis.  Additional readings and library resources are also provided.

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Study designs

This short article gives a brief guide to the different study types and a comparison of the advantages and disadvantages.

See also  Levels of Evidence  

These study designs all have similar components (as we’d expect from the PICO):

• A defined population (P) from which groups of subjects are studied
• Outcomes (O) that are measured

And for experimental and analytic observational studies:

• Interventions (I) or exposures (E) that are applied to different groups of subjects

Overview of the design tree

Figure 1 shows the tree of possible designs, branching into subgroups of study designs by whether the studies are descriptive or analytic and by whether the analytic studies are experimental or observational. The list is not completely exhaustive but covers most basics designs.

Figure: Tree of different types of studies (Q1, 2, and 3 refer to the three questions below)

> Download a PDF by Jeremy Howick about study designs

Our first distinction is whether the study is analytic or non-analytic. A  non-analytic  or  descriptive  study does not try to quantify the relationship but tries to give us a picture of what is happening in a population, e.g., the prevalence, incidence, or experience of a group. Descriptive studies include case reports, case-series, qualitative studies and surveys (cross-sectional) studies, which measure the frequency of several factors, and hence the size of the problem. They may sometimes also include analytic work (comparing factors “” see below).

An  analytic  study attempts to quantify the relationship between two factors, that is, the effect of an intervention (I) or exposure (E) on an outcome (O). To quantify the effect we will need to know the rate of outcomes in a comparison (C) group as well as the intervention or exposed group. Whether the researcher actively changes a factor or imposes uses an intervention determines whether the study is considered to be observational (passive involvement of researcher), or experimental (active involvement of researcher).

In  experimental  studies, the researcher manipulates the exposure, that is he or she allocates subjects to the intervention or exposure group. Experimental studies, or randomised controlled trials (RCTs), are similar to experiments in other areas of science. That is, subjects are allocated to two or more groups to receive an intervention or exposure and then followed up under carefully controlled conditions. Such studies controlled trials, particularly if randomised and blinded, have the potential to control for most of the biases that can occur in scientific studies but whether this actually occurs depends on the quality of the study design and implementation.

In  analytic observational  studies, the researcher simply measures the exposure or treatments of the groups. Analytical observational studies include case””control studies, cohort studies and some population (cross-sectional) studies. These studies all include matched groups of subjects and assess of associations between exposures and outcomes.

Observational studies investigate and record exposures (such as interventions or risk factors) and observe outcomes (such as disease) as they occur. Such studies may be purely descriptive or more analytical.

We should finally note that studies can incorporate several design elements. For example, a the control arm of a randomised trial may also be used as a cohort study; and the baseline measures of a cohort study may be used as a cross-sectional study.

Spotting the study design

The type of study can generally be worked at by looking at three issues (as per the Tree of design in Figure 1):

Q1. What was the aim of the study?

• To simply describe a population (PO questions) descriptive
• To quantify the relationship between factors (PICO questions) analytic.

Q2. If analytic, was the intervention randomly allocated?

• No? Observational study

For observational study the main types will then depend on the timing of the measurement of outcome, so our third question is:

Q3. When were the outcomes determined?

• Some time after the exposure or intervention? cohort study (‘prospective study’)
• At the same time as the exposure or intervention? cross sectional study or survey
• Before the exposure was determined? case-control study (‘retrospective study’ based on recall of the exposure)

Advantages and Disadvantages of the Designs

Randomised Controlled Trial

An experimental comparison study in which participants are allocated to treatment/intervention or control/placebo groups using a random mechanism (see randomisation). Best for study the effect of an intervention.

Advantages:

• unbiased distribution of confounders;
• blinding more likely;
• randomisation facilitates statistical analysis.

Disadvantages:

• expensive: time and money;
• volunteer bias;
• ethically problematic at times.

Crossover Design

A controlled trial where each study participant has both therapies, e.g, is randomised to treatment A first, at the crossover point they then start treatment B. Only relevant if the outcome is reversible with time, e.g, symptoms.

• all subjects serve as own controls and error variance is reduced thus reducing sample size needed;
• all subjects receive treatment (at least some of the time);
• statistical tests assuming randomisation can be used;
• blinding can be maintained.
• all subjects receive placebo or alternative treatment at some point;
• washout period lengthy or unknown;
• cannot be used for treatments with permanent effects

Cohort Study

Data are obtained from groups who have been exposed, or not exposed, to the new technology or factor of interest (eg from databases). No allocation of exposure is made by the researcher. Best for study the effect of predictive risk factors on an outcome.

• ethically safe;
• subjects can be matched;
• can establish timing and directionality of events;
• eligibility criteria and outcome assessments can be standardised;
• administratively easier and cheaper than RCT.
• controls may be difficult to identify;
• exposure may be linked to a hidden confounder;
• blinding is difficult;
• randomisation not present;
• for rare disease, large sample sizes or long follow-up necessary.

Case-Control Studies

Patients with a certain outcome or disease and an appropriate group of controls without the outcome or disease are selected (usually with careful consideration of appropriate choice of controls, matching, etc) and then information is obtained on whether the subjects have been exposed to the factor under investigation.

• quick and cheap;
• only feasible method for very rare disorders or those with long lag between exposure and outcome;
• fewer subjects needed than cross-sectional studies.
• reliance on recall or records to determine exposure status;
• confounders;
• selection of control groups is difficult;
• potential bias: recall, selection.

Cross-Sectional Survey

A study that examines the relationship between diseases (or other health-related characteristics) and other variables of interest as they exist in a defined population at one particular time (ie exposure and outcomes are both measured at the same time). Best for quantifying the prevalence of a disease or risk factor, and for quantifying the accuracy of a diagnostic test.

• cheap and simple;
• ethically safe.
• establishes association at most, not causality;
• recall bias susceptibility;
• confounders may be unequally distributed;
• Neyman bias;
• group sizes may be unequal.

Study designs: Part 1 - An overview and classification

Affiliations.

• 1 Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India.
• 2 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India.
• PMID: 30319950
• PMCID: PMC6176693
• DOI: 10.4103/picr.PICR_124_18

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on "study designs," we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

Keywords: Epidemiologic methods; research design; research methodology.

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Research Design: What it is, Elements & Types

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

What is Research Design?

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

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

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

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

The Process of Research Design

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

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

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

Research Design Elements

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

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

Characteristics of Research Design

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

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

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

Research Design Types

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

Qualitative research

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

Quantitative research

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

Qualitative Research vs Quantitative Research

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

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

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

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

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

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

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

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

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

This design has three parts of the research:

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

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

Benefits of Research Design

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

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

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

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

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Research Methodologies

Research design, external validity, internal validity, threats to validity.

• What are research methodologies?
• What are research methods?
• Additional Sources

According to Jenkins-Smith, et al. (2017), a research design is the set of steps you take to collect and analyze your research data.  In other words, it is the general plan to answer your research topic or question.  You can also think of it as a combination of your research methodology and your research method.  Your research design should include the following: 

• A clear research question
• Theoretical frameworks you will use to analyze your data
• Key concepts
• Your hypothesis/hypotheses
• Independent and dependent variables (if applicable)
• Strengths and weaknesses of your chosen design

There are two types of research designs:

• Experimental design: This design is like a standard science lab experiment because the researcher controls as many variables as they can and assigns research subjects to groups.  The researcher manipulates the experimental treatment and gives it to one group.  The other group receives the unmanipulated treatment (or not treatment) and the researcher examines affect of the treatment in each group (dependent variable).  This design can have more than two groups depending on your study requirements.
• Observational design: This is when the researcher has no control over the independent variable and which research participants get exposed to it.  Depending on your research topic, this is the only design you can use.  This is a more natural approach to a study because you are not controlling the experimental treatment.  You are allowing the variable to occur on its own without your interference.  Weather experiments are a great example of observational design because the researcher has no control over the weather and how it changes.

When considering your research design, you will also need to consider your study's validity and any potential threats to its validity.  There are two types of validity: external and internal validity.  Each type demonstrates a degree of accuracy and thoughtfulness in a study and they contribute to a study's reliability.  Information about external and internal validity is included below.

External validity is the degree to which you can generalize the findings of your research study.  It is determining whether or not the findings are applicable to other settings (Jenkins-Smith, 2017).  In many cases, the external validity of a study is strongly linked to the sample population.  For example, if you studied a group of twenty-five year old male Americans, you could potentially generalize your findings to all twenty-five year old American males.  External validity is also the ability for someone else to replicate your study and achieve the same results (Jenkins-Smith, 2017).  If someone replicates your exact study and gets different results, then your study may have weak external validity.

Questions to ask when assessing external validity:

• Do my conclusions apply to other studies?
• If someone were to replicate my study, would they get the same results?
• Are my findings generalizable to a certain population?

Internal validity is when a researcher can conclude a causal relationship between their independent variable and their dependent variable.  It is a way to verify the study's findings because it draws a relationship between the variables (Jenkins-Smith, 2017).  In other words, it is the actual factors that result in the study's outcome (Singh, 2007).  According to Singh (2007), internal validity can be placed into 4 subcategories:

• Face validity: This confirms the fact that the measure accurately reflects the research question.
• Content validity: This assesses the measurement technique's compatibility with other literature on the topic.  It determines how well the tool used to gather data measures the item or concept that the researcher is interested in.
• Criterion validity: This demonstrates the accuracy of a study by comparing it to a similar study.
• Construct validity: This measures the appropriateness of the conclusions drawn from a study.

According to Jenkins-Smith (2017), there are several threats that may impact the internal and external validity of a study:

Threats to External Validity

• Interaction with testing: Any testing done before the actual experiment may decrease participants' sensitivity to the actual treatment.
• Sample misrepresentation: A population sample that is unrepresentative of the entire population.
• Selection bias: Researchers may have bias towards selecting certain subjects to participate in the study who may be more or less sensitive to the experimental treatment.
• Environment: If the study was conducted in a lab setting, the findings may not be able to transfer to a more natural setting.

Threats to Internal Validity

• Unplanned events that occur during the experiment that effect the results.
• Changes to the participants during the experiment, such as fatigue, aging, etc.
• Selection bias: When research subjects are not selected randomly.
• If participants drop out of the study without completing it.
• Changing the way the data is collected or measured during the study.
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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

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

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Experimental Research Design

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

Quasi-experimental Research Design

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

Case Study Research Design

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

Longitudinal Research Design

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

Structure of Research Design

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

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

Example of Research Design

An Example of Research Design could be:

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

Research design:

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

How to Write Research Design

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

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

When to Write Research Design

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

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

Purpose of Research Design

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

Some of the key purposes of research design include:

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

Applications of Research Design

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

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

Advantages of Research Design

Here are some advantages of research design:

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

Research Design Vs Research Methodology

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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The expansion of research designs, methodologies, processes, and practices

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Researchers’ beliefs about legitimate and credible research designs, methodologies, processes, and practices have expanded in a great many ways since the 1970s, and I — who obtained my bachelor’s, master’s, and doctoral degrees in the 1970s — have observed the continual expansion over the past several decades with both awe and delight. There is so much more that we, as a larger society, can learn when we look at puzzling phenomena from multiple angles and perspectives!

Noteworthy Changes in Practical Research, 13th Edition (2024)

To capture recent trends in research methodologies and perspectives, I have made several noteworthy changes to the content of the latest edition:

• I have significantly reorganized and greatly expanded discussions of various action research and participatory designs (see Chapter 10). A separate blog post presents a brief description of action research.
• I have increased discussions of the ethics of research and possible biases that might adversely affect the quality of a research project and/or research report (see Chapters 4, 6, 7, 8, 9, 10, 11, 12, and 13).
• I have added discussions of open science practices (see Chapters 5 and 13). A separate blog post provides a short overview of what open science can entail.
• I have included several new illustrative examples of research methodologies (see Chapters 8, 9, and 10).
• With the assumption that my readers are now more technologically literate than they were even a few years ago, I have updated discussions of technology-based strategies (e.g. new software options) while cutting back on coverage of more elementary strategies (e.g. how to use word-processing software).
• I have added five new Conceptual Analysis features with which readers can self-assess their understanding of key concepts and principles in the book (see Chapters 1, 6, 11, and 12).
• At the end of each chapter, I have added a short summary that can help readers mentally revisit and review central ideas within the chapter.

Beginning with the 12th edition, the book no longer includes a chapter on historical research. (With significant advances in other research methodologies, there was simply no longer any room in the book for it.) Yet historical research methodologies are essential for students in history and related disciplines and can also be useful in other disciplines. Readers who would like to learn more about historical research can find an entire chapter on the topic in the book’s 11th edition, published in 2016.

Two changes in the latest, 13th edition of Practical Research appear on the book’s cover and title page. First, the subtitle of the book, previously Planning and Design , has changed to Design and Process . Although this change might strike my readers as a subtle one, it reflects the ways in which the book’s contents have evolved over the years to focus increasingly on the many, many simultaneous and sequential processes — not only physical processes but mental processes as well — that underlie well-designed, high-quality research endeavors.

Second, whereas Paul Leedy and I have been coauthors of the past several editions of the book, I am now listed as sole author. To explain this change, I must go back to the very first edition. In the early 1970s, as a professor at American University, Paul saw the need for a research methods book that provided specific, concrete guidance for students and other novice researchers who were working on independent research projects (e.g. projects for masters’ theses and doctoral dissertations). The result was the first edition of the book, published in 1974.

As various research methodologies continued to evolve in many academic disciplines, Paul updated the book in 2nd, 3rd, 4th, 5th, and 6th editions (with contributions by Tim Newby, Peggy Ertmer, and the editorial staff in the 6th edition).

With Paul’s eventual retirement, his editor, Kevin Davis, asked me to take over, beginning with the 7th edition. Paul’s ideas and words have always been key elements of the book, however, and thus it was quite appropriate to list us as coauthors. But as I wrote the 13th edition, the book had changed so much that it made sense to Pearson’s editorial staff that I become sole author.

About the author

Jeanne Ellis Ormrod, Author and Educator

Jeanne Ellis Ormrod  is an educational psychologist with specializations in learning, cognition, and child development. She received a PhD and an MS in educational psychology at The Pennsylvania State University and an AB in psychology from Brown University. For many years, she was a faculty member at the University of Northern Colorado, where she taught courses in learning and cognition, educational psychology, measurement, and research methods. She is currently Professor Emerita of Psychological Sciences at UNC.

Practical Research  is a do-it-yourself, how-to manual for planning and conducting research. 

The  13th Edition  includes the latest technology-based strategies and tools for research, a greater focus on the ethics of research, new examples, and expanded discussions of action research and participatory designs.

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• Published: 10 May 2024

Novice providers’ success in performing lumbar puncture: a randomized controlled phantom study between a conventional spinal needle and a novel bioimpedance needle

• Helmiina Lilja 1   na1 ,
• Maria Talvisara 1   na1 ,
• Vesa Eskola 2 , 3 ,
• Paula Heikkilä 2 , 3 ,
• Harri Sievänen 4 &
• Sauli Palmu 2 , 3  

BMC Medical Education volume  24 , Article number:  520 ( 2024 ) Cite this article

167 Accesses

Metrics details

Lumbar puncture (LP) is an important yet difficult skill in medical practice. In recent years, the number of LPs in clinical practice has steadily decreased, which reduces residents’ clinical exposure and may compromise their skills and attitude towards LP. Our study aims to assess whether the novel bioimpedance needle is of assistance to a novice provider and thus compensates for this emerging knowledge gap.

This randomized controlled study, employing a partly blinded design, involved 60 s- and third-year medical students with no prior LP experience. The students were randomly assigned to two groups consisting of 30 students each. They performed LP on an anatomical lumbar model either with the conventional spinal needle or the bioimpedance needle. Success in LP was analysed using the independent samples proportion procedure. Additionally, the usability of the needles was evaluated with pertinent questions.

With the conventional spinal needle, 40% succeeded in performing the LP procedure, whereas with the bioimpedance needle, 90% were successful ( p  < 0.001). The procedures were successful at the first attempt in 5 (16.7%) and 15 (50%) cases ( p  = 0.006), respectively. Providers found the bioimpedance needle more useful and felt more confident using it.

Conclusions

The bioimpedance needle was beneficial in training medical students since it significantly facilitated the novice provider in performing LP on a lumbar phantom. Further research is needed to show whether the observed findings translate into clinical skills and benefits in hospital settings.

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Lumbar puncture (LP) is one of the essential skills of physicians in medical practice, especially in the fields of neurology, neurosurgery, emergency medicine and pediatrics. It is one of the procedures that medical students practice in their training. LP is an important clinical procedure for diagnosing neurological infections and inflammatory diseases and excluding subarachnoid hemorrhage [ 1 ]. LP can also be used for examining the spread of cancer cells to the central nervous system in diagnosing acute lymphoblastic leukemia (ALL) and for delivering intrathecal administration of chemotherapy in patients with ALL [ 2 ]. In recent years, the number of LPs in clinical practice has steadily decreased [ 3 , 4 ]. Over the past decade, a 37% decrease in LPs was observed across US children’s hospitals [ 3 ]. Similar trends have also been observed in emergency medicine [ 4 ]. Stricter criteria in practice guidelines, changes in patient demographics, and development in medical imaging have likely contributed to this decrease. This trend presumably reduces residents’ clinical exposure and may compromise their skills and attitude towards LP.

When performed by an experienced physician, LP is a relatively safe procedure, albeit not always straightforward or free from complications [ 4 ]. The spinal needle used in LP is thin and flexible, making its insertion into the spinal canal without seeing the location of the needle tip or destination challenging. The physician performing the procedure must master the specific lumbar anatomy to avoid complications [ 5 ]. The LP technique is not the only thing that matters, but patients’ size and comfort also affect the success of the procedure [ 6 ]. Hence, a practitioner lacking adequate experience in LP should be appropriately supervised when performing the procedure [ 4 ]. Nevertheless, there are situations in which such supervision is not possible.

Little experience in performing LPs may require more attempts to obtain cerebrospinal fluid (CSF) samples [ 7 ]. Because of several attempts, blood can be introduced to CSF and result in a traumatic LP. Success at the first attempt is associated with a lower incidence of traumatic LPs [ 2 , 8 , 9 , 10 , 11 , 12 ]. A bloody CSF sample complicates the diagnostics [ 8 ]. It has also been shown that a high number of attempts increases the incidence of postdural puncture headache (PDPH), the most common complication of LP, in addition to other adverse effects [ 9 ].

Considering the possible complications and difficulties of performing LP, a concern arises regarding whether inexperienced physicians can perform LP with adequate confidence and safety. The use of a novel bioimpedance-based spinal needle system could offer a solution. This needle provides real-time feedback from the needle tip when penetrating the lumbar tissues and informs the physician when the needle tip reaches CSF with an audio-visual alarm. This information may make performing the LP procedure smoother, thus decreasing the incidence of the most common complications [ 13 ]. A bioimpedance-based spinal needle system has been recently found clinically feasible in LPs among adults, adolescents, and children, including neonates [ 2 , 14 , 15 ].

The current phantom study aimed to assess whether the novel needle technology can compensate for the lack of experience when a medical student performs LP for the first time. In particular, we compared the performance of the bioimpedance spinal needle and conventional spinal needle in terms of the overall success rate of the LP procedure, success rate at the first attempt, duration of the procedure, and number of stylet removals. We hypothesized that novice users would find the bioimpedance needle more useful in performing LPs than a conventional spinal needle. If so proven, the use of this novel device can contribute to training medical students in this important skill and facilitate situations when an inexperienced physician needs to perform LP without the supervision and guidance of an experienced physician [ 4 ].

We planned to recruit 60 medical students from Tampere University in this randomized controlled trial. Students who were studying medicine for their third year or less were considered eligible for the study. At this stage of studies, they were expected to have no clinical experience and be thus naïve in performing an LP. All students had the same baseline knowledge regarding lumbar spine anatomy.

The participants were recruited by sending an invitation e-mail to all potentially eligible medical students. The email provided information about the study. Of the 177 students who responded to the invitation, 60 students were included on a first-come-first-serve basis. The participants were rewarded with a 10€ voucher to the university campus cafeteria.

Randomization lists in blocks of six were generated for two groups (A and B) before recruitment by an independent person who was not involved in recruitment or data collection. Participants assigned to group A used a conventional spinal needle (90 mm long 22G Quincke-type needle), and those to group B used the bioimpedance needle system (IQ-Tip system with a 90 mm long IQ-Tip needle, Injeq Plc, Tampere, Finland).

The study LPs were performed on an adult-size anatomical lumbar phantom (Blue Phantom BPLP2201, CAE Healthcare, FL, USA) intended for medical training and practising. The phantom is made of a tissue-simulating elastomer material that looks and feels like human soft tissue. Skeletal structures made of hard material and a plastic tube mimicking the spinal canal are embedded in the phantom. The saline inside the tube mimics CSF and is under hydrostatic pressure. The phantom offers a relatively realistic feel in palpating the lumbar anatomy and getting haptic feedback from the advancing needle.

The study LPs were performed in February 2023 in ten different sessions, with 6 participants in each session. Two separate rooms were used to conduct the study. The participants were first admitted to a waiting room and then separately to another room where each student performed the study LP with the assigned spinal needle under supervision (HL and MT). By having these two rooms, we ensured that no information was exchanged after or during the procedure.

Before the study LPs, the participants were shown an instructional video on how to perform an LP from the widely used Finnish medical database Terveysportti [ 16 ] and a video on the operation of the bioimpedance needle [ 13 ]. The first video (duration 3 min) describes the indications, contraindications and a step-by-step instruction on how the procedure is performed. The latter is a 25- second animation showing how the bioimpedance system operates and guides the procedure. In addition, the supervisor gave each participant the following instructions before starting the study LP: When you think you have reached the subarachnoid space, remove the stylet from the needle. If you are in the correct place, the fluid will start flowing from the needle. You may redirect the needle as many times as you wish, but you are only allowed to remove the needle and do a new attempt five times. Please wait a while when you have removed the stylet because it may take a while before the fluid starts dropping. These instructions were given to all participants irrespective of the study group to standardize the information in all sessions.

After watching the videos and listening to the instructions, the participants became aware of their assigned study group. Participants were allowed five attempts, while redirections of the needle and stylet removals could be performed as many times as needed. We measured the duration of the LP procedure and collected data on the number of stylet removals, the number of attempts, and whether the LP was successful.

The duration of the procedure was defined from the point when the needle penetrated the phantom surface to either when the first drop of fluid fell from the needle, or the participant wanted to stop or had used all five attempts. There was no maximum time for completing the LP procedure. The procedure was defined as successful if the participant succeeded in obtaining a drop of fluid from the needle.

In addition, seven relevant statements to this study were chosen from the System Usability Scale (SUS) [ 17 ], which is an industry standard for evaluating the usability of various devices and systems. The seven statements, slightly modified from the original statements, are shown in Table  1 . After performing the study LP and irrespective of their success, all participants were asked to respond to the statements using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree).

Statistical analysis

For the estimation of statistical power, we assumed that the overall success rate would be 60% with the conventional needle (group A) and 90% with the bioimpedance needle (group B). Then, the sample size of 60 participants divided randomly into two equal-sized groups would be sufficient to detect a between-group at a significance level of p  < 0.05 and with 80% statistical power if such a difference truly exists.

Overall success in performing the lumbar puncture and success at the first attempt in the groups were analysed by the independent samples proportion procedure. The median number of attempts and stylet removals in the successful procedures were compared by independent samples Mann‒Whitney U test. Responses to the seven usability statements were compared by this test as well.

Statistical analyses were performed with IBM SPSS Statistics for Windows, version 29.0 (IBM Corp., Armonk, NY, USA). A p value less than 0.05 was considered statistically significant.

Sixty medical students were randomly assigned into two groups, 30 performing the LP procedure on the lumbar phantom using a conventional spinal needle and 30 using the bioimpedance needle. None of the participants had previous experience in performing an LP.

With the conventional spinal needle (group A), 12 out of 30 participants (40%) succeeded in performing the LP procedure, whereas with the bioimpedance needle (group B), 27 out of 30 participants (90%) were successful ( p  < 0.001). The procedures were successful at the first attempt in 5 (16.7%) and 15 (50%) cases ( p  = 0.006), respectively.

Figure  1 illustrates the number of attempts and stylet removals in the study groups. Regarding the success of the procedure at any attempt, the median number of attempts was 2 (range 1–5) for the conventional needle and 1 [ 1 , 2 , 3 , 4 , 5 ] for the bioimpedance needle ( p  = 0.56).

In the successful procedures, the median number of stylet removals was 4 [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ] and 1 (1–33) ( p  = 0.001), respectively. The mean duration of a successful procedure was 3:51 (SD 3:43) with the conventional needle and 1:59 (2:25) with the bioimpedance needle ( p  = 0.068).

The responses to the seven usability statements are illustrated in Fig.  2 . Regarding the statements on regular use, ease of use, need for support from an experienced user, learning to use, and cumbersomeness, the responses differed significantly between groups, consistently favouring the bioimpedance needle ( p  < 0.001). Regarding the feeling of confidence in use, the responses significantly favoured the bioimpedance needle ( p  = 0.012). Likewise, the responses significantly favoured the bioimpedance needle to less need to learn many things before its use.

Distributions of the number of attempts in successful LP procedures (left panel) with the conventional spinal needle (group A, yellow bars) and with the bioimpedance needle (group B, blue bars). Respective distributions of the number of stylet removals (right panel) in groups A and B

After performing the LP, the provider answered seven statements about the usability of the needle in question on a scale of 1 (strongly disagree) to 5 (strongly agree). Distributions of responses to every seven usability statements in group A (conventional spinal needle, yellow bars) and in group B (bioimpedance needle, blue bars) using the System Usability Scale (SUS)

The decline in the number of LPs during the last decade [ 3 , 4 ] likely weakening the practical knowledge and skills of novice physicians served as the rationale for the current study. Using a solid randomized controlled study design, we assessed whether bioimpedance-based tissue detection technology could help an inexperienced provider perform LP. Our study was conducted among early-stage medical students who had no previous experience with LPs. Following our hypothesis, we found that the use of a bioimpedance needle in simulated phantom LPs was useful to novice providers. The bioimpedance needle decreased not only the number of attempts to achieve a successful LP but also its time, in addition to the significantly lower number of stylet removals during the procedure. Furthermore, the usability of the bioimpedance needle was found to be significantly better than that of the spinal needle used currently in clinical practice.

The users of the bioimpedance needle found the novel device easy and intuitive to learn and use while feeling more confident in performing LP compared to those using the conventional needle. They also expressed their interest in using the bioimpedance needle regularly. It is recalled that the present providers were all novices without earlier experience in LP, and therefore, the observed between-group differences in performance could have been smaller with more experienced providers.

Of common bedside procedures in clinical practice, LP was recently found to be associated with the lowest baseline levels of experience and confidence among 4 th− to 6th -year medical students. However, a single seminar with standardized simulation training brought more confidence to the LP procedure among these students [ 18 ]. Other recent studies have also shown that simulation-based education can improve procedural competence and skills in performing LP [ 19 , 20 , 21 , 22 ]. In these studies, the participants had more experience than in our study, but the benefits of simulation-based learning were significant. A recent study assessing a mixed reality simulator found this approach helpful in learning of LP among residents, faculty, interns, and medical students, approximately 60% having no previous experience in LP [ 23 ]. After mixed reality training, the success rate of LP increased while the time of the procedure decreased [ 23 ], which is in line with our findings. Virtual reality-based training in LP learning has also been studied, and it might have beneficial results in the provider’s skills and confidence [ 24 , 25 ]. All these findings speak for the utility of various simulation approaches in adopting essential (new) clinical skills for LP at different stages of medical studies and careers.

Lumbar puncture is commonly considered a difficult and possibly frightening procedure to perform. In addition to the physician’s experience and skills, there are other factors that affect the success of LP, including patient size, spinal deformities, lumbar anatomy, cooperation and comfort [ 6 ]. Occasionally, a physician may have to insert the needle more than once to succeed in LP. However, repeated attempts are associated with several complications, such as PDPH and traumatic LP [ 7 , 10 , 11 , 12 , 26 , 27 , 28 ]. In our study, the median number of attempts was two for the conventional spinal needle and one for the bioimpedance needle. The low number of attempts may have also contributed to the low incidence of traumatic LP and PDPH observed in pediatric patients with leukemia, whose intrathecal therapy was administered using the bioimpedance needle [ 15 ]. Since the basic use of a bioimpedance needle is virtually similar to that of a conventional spinal needle with no need for additional devices (e.g., ultrasound imaging), it may offer a notable option for effective teaching of LP among medical students. Its real-time CSF detection ability is likely to consolidate the learning experience and increase confidence in one’s skills.

In this study, we found a significantly higher success rate and confidence in procedural skills of medical students associated with using the bioimpedance needle compared to the conventional spinal needle. Should these benefits translate into the real clinical world and manifest as a lower incidence of failed LP procedures and procedure-related complications, a higher incidence of high-quality CSF samples, a lower need for repeated procedures, a lower need for experienced and more expensive physicians to supervise, perform, or complete the LP procedure, substantial savings in the total costs of the lumbar puncture procedure are possible despite the initially higher unit cost of the bioimpedance needle system compared to conventional spinal needles. Further clinical studies on the benefits of the bioimpedance needle system in clinical LP procedures are needed to confirm these speculations.

The major strengths of the present study are the randomized controlled, partly blinded design and adequate sample size. The random assignment of participants to study groups and data analysis were performed by an independent person who was not involved in recruitment or data collection. The participants received the same instructions and information before performing their assigned LP procedure and were asked not to study LP in advance to keep the participants as naïve in performing LP as possible. Obviously, we could not control for this and have full certainty about the prior information on retrieval of the participants. However, the participants were not told before the study session which type of spinal needle they would use in their assigned LP.

During the LP sessions, there were a few technical issues concerning the lumbar phantom and bioimpedance needle. First, since the pressure inside the phantom spinal canal (plastic tube) affects the fluid flow through the needle, we attempted to keep the height of the hydrostatic saline column constant by adding new saline as needed, but slight variation in pressure may have occurred, and concerned all study LP procedures. Second, when the plastic tube and surrounding phantom material are pierced multiple times in succession, it is possible that the leakage of saline moistens the rubbery material and increases markedly its electrical conductivity despite the self-healing property of the material. Had this happened, consequent false detections may have led to unnecessary removals of the stylet in the LP procedures performed with the bioimpedance needle system. Therefore, as a precaution, the maximum number of participants at each session was limited to six to mitigate the risk of moistening of material. Third, in two cases, the bioimpedance needle system did not detect saline, although the needle tip was in the correct place, confirmed by saline flow after stylet removal. This rate of missed detections in line with clinical experience [ 2 , 15 ] and may be due to elastomer remnants stuck at the needle tip compromising the bioimpedance measurement and saline detection. However, despite the failed functionality, the mechanical performance of the bioimpedance needle as a spinel needle is maintained and LP could be performed as usual. Regarding the credibility of the present findings, the bioimpedance needle did not get any undue benefit from these technical issues compared to the conventional spinal needle.

Given that the participants were clinically inexperienced early-stage medical students, the study was conducted using an anatomical lumbar phantom, not on actual patients. Obviously, the haptic feedback from the phantom and anatomic variation in the lumbar region do not fully correspond to a real patient. On the other hand, the use of phantom takes off the pressure from a novice provider and possibly eases the procedure, not having to take thought on a patient’s comfort, anatomy, and condition. Although the LP procedure was performed for the first time without the guidance of an experienced physician, the users of the bioimpedance needle felt more confident and performed significantly better than those with the conventional spinal needle. If used for teaching purposes, the bioimpedance needle and the anatomical lumbar phantom could offer a positive experience of the LP procedure and raise confidence in one’s own skills before the first real patient encounter. Whether the present promising results of a phantom study would translate into improved performance in actual clinical work calls for further investigation.

Lumbar puncture is a widely used but demanding procedure needed for the diagnosis and treatment of several diseases. It is relatively safe when performed correctly, but due to the decreasing trend of performed LP procedures, a concern has arisen concerning novice physicians’ expertise in LP. The bioimpedance needle could offer a solution to this problem and facilitate practical training of LP among early-stage medical students. The present randomized controlled phantom study showed that providers with no previous experience in LP perceived the bioimpedance needle as more useful, became confident, and achieved significantly higher success rates both overall and at the first attempt with fewer stylet removals compared to those using a conventional spinal needle. Further research is needed to show whether the observed findings translate into clinical skills and benefits in hospital settings.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Acute lymphoblastic leukemia

Cerebrospinal fluid

• Lumbar puncture

Postdural puncture headache

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Helmiina Lilja and Maria Talvisara contributed equally to this work.

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Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland

Helmiina Lilja & Maria Talvisara

Tampere Center for Child, Adolescent and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, Tampere, 33520, Finland

Vesa Eskola, Paula Heikkilä & Sauli Palmu

Tampere University Hospital, Elämänaukio 2, Tampere, 33520, Finland

Injeq Plc, Biokatu 8, Tampere, Tampere, 33520, Finland

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H.L. and M.T.: data collection, data analysis, drafting the manuscript, editing the manuscript. V.E. and P.H.: planning the study, editing the manuscript. H.S. and S.P.: conceptualizing and planning the study, data analysis, editing the manuscript.

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Lilja, H., Talvisara, M., Eskola, V. et al. Novice providers’ success in performing lumbar puncture: a randomized controlled phantom study between a conventional spinal needle and a novel bioimpedance needle. BMC Med Educ 24 , 520 (2024). https://doi.org/10.1186/s12909-024-05505-z

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DOI : https://doi.org/10.1186/s12909-024-05505-z

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Study designs: Part 7 – Systematic reviews

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Director, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India

In this series on research study designs, we have so far looked at different types of primary research designs which attempt to answer a specific question. In this segment, we discuss systematic review, which is a study design used to summarize the results of several primary research studies. Systematic reviews often also use meta-analysis, which is a statistical tool to mathematically collate the results of various research studies to obtain a pooled estimate of treatment effect; this will be discussed in the next article.

In the previous six articles in this series on study designs, we have looked at different types of primary research study designs which are used to answer research questions. In this article, we describe the systematic review, a type of secondary research design that is used to summarize the results of prior primary research studies. Systematic reviews are considered the highest level of evidence for a particular research question.[ 1 ]

SYSTEMATIC REVIEWS

As defined in the Cochrane Handbook for Systematic Reviews of Interventions , “Systematic reviews seek to collate evidence that fits pre-specified eligibility criteria in order to answer a specific research question. They aim to minimize bias by using explicit, systematic methods documented in advance with a protocol.”[ 2 ]

NARRATIVE VERSUS SYSTEMATIC REVIEWS

Review of available data has been done since times immemorial. However, the traditional narrative reviews (“expert reviews”) do not involve a systematic search of the literature. Instead, the author of the review, usually an expert on the subject, used informal methods to identify (what he or she thinks are) the key studies on the topic. The final review thus is a summary of these “selected” studies. Since studies are chosen at will (haphazardly!) and without clearly defined criteria, such reviews preferentially include those studies that favor the author's views, leading to a potential for subjectivity or selection bias.

In contrast, systematic reviews involve a formal prespecified protocol with explicit, transparent criteria for the inclusion and exclusion of studies, thereby ensuring completeness of coverage of the available evidence, and providing a more objective, replicable, and comprehensive overview it.

META-ANALYSIS

Many systematic reviews use an additional tool, known as meta-analysis, which is a statistical technique for combining the results of multiple studies in a systematic review in a mathematically appropriate way, to create a single (pooled) and more precise estimate of treatment effect. The feasibility of performing a meta-analysis in a systematic review depends on the number of studies included in the final review and the degree of heterogeneity in the inclusion criteria as well as the results between the included studies. Meta-analysis will be discussed in detail in the next article in this series.

THE PROCESS OF A SYSTEMATIC REVIEW

The conduct of a systematic review involves several sequential key steps.[ 3 , 4 ] As in other research study designs, a clearly stated research question and a well-written research protocol are essential before commencing a systematic review.

Step 1: Stating the review question

Systematic reviews can be carried out in any field of medical research, e.g. efficacy or safety of interventions, diagnostics, screening or health economics. In this article, we focus on systematic reviews of studies looking at the efficacy of interventions. As for the other study designs, for a systematic review too, the question is best framed using the Population, Intervention, Comparator, and Outcome (PICO) format.

For example, Safi et al . carried out a systematic review on the effect of beta-blockers on the outcomes of patients with myocardial infarction.[ 5 ] In this review, the Population was patients with suspected or confirmed myocardial infarction, the Intervention was beta-blocker therapy, the Comparator was either placebo or no intervention, and the Outcomes were all-cause mortality and major adverse cardiovascular events. The review question was “ In patients with suspected or confirmed myocardial infarction, does the use of beta-blockers affect mortality or major adverse cardiovascular outcomes? ”

Step 2: Listing the eligibility criteria for studies to be included

It is essential to explicitly define a priori the criteria for selection of studies which will be included in the review. Besides the PICO components, some additional criteria used frequently for this purpose include language of publication (English versus non-English), publication status (published as full paper versus unpublished), study design (randomized versus quasi-experimental), age group (adults versus children), and publication year (e.g. in the last 5 years, or since a particular date). The PICO criteria used may not be very specific, e.g. it is possible to include studies that use one or the other drug belonging to the same group. For instance, the systematic review by Safi et al . included all randomized clinical trials, irrespective of setting, blinding, publication status, publication year, or language, and reported outcomes, that had used any beta-blocker and in a broad range of doses.[ 5 ]

Step 3: Comprehensive search for studies that meet the eligibility criteria

A thorough literature search is essential to identify all articles related to the research question and to ensure that no relevant article is left out. The search may include one or more electronic databases and trial registries; in addition, it is common to hand-search the cross-references in the articles identified through such searches. One could also plan to reach out to experts in the field to identify unpublished data, and to search the grey literature non-peer-reviewednon-peer-reviewed. This last option is particularly helpful non-pharmacologic (theses, conference abstracts, and non-peer-reviewed journals). These sources are particularly helpful when the intervention is relatively new, since data on these may not yet have been published as full papers and hence are unlikely to be found in literature databases. In the review by Safi et al ., the search strategy included not only several electronic databases (Cochrane, MEDLINE, EMBASE, LILACS, etc.) but also other resources (e.g. Google Scholar, WHO International Clinical Trial Registry Platform, and reference lists of identified studies).[ 5 ] It is not essential to include all the above databases in one's search. However, it is mandatory to define in advance which of these will be searched.

Step 4: Identifying and selecting relevant studies

Once the search strategy defined in the previous step has been run to identify potentially relevant studies, a two-step process is followed. First, the titles and abstracts of the identified studies are processed to exclude any duplicates and to discard obviously irrelevant studies. In the next step, full-text papers of the remaining articles are retrieved and closely reviewed to identify studies that meet the eligibility criteria. To minimize bias, these selection steps are usually performed independently by at least two reviewers, who also assign a reason for non-selection to each discarded study. Any discrepancies are then resolved either by an independent reviewer or by mutual consensus of the original reviewers. In the Cochrane review on beta-blockers referred to above, two review authors independently screened the titles for inclusion, and then, four review authors independently reviewed the screen-positive studies to identify the trials to be included in the final review.[ 5 ] Disagreements were resolved by discussion or by taking the opinion of a separate reviewer. A summary of this selection process, showing the degree of agreement between reviewers, and a flow diagram that depicts the numbers of screened, included and excluded (with reason for exclusion) studies are often included in the final review.

Step 5: Data extraction

In this step, from each selected study, relevant data are extracted. This should be done by at least two reviewers independently, and the data then compared to identify any errors in extraction. Standard data extraction forms help in objective data extraction. The data extracted usually contain the name of the author, the year of publication, details of intervention and control treatments, and the number of participants and outcome data in each group. In the review by Safi et al ., four review authors independently extracted data and resolved any differences by discussion.[ 5 ]

Handling missing data

Some of the studies included in the review may not report outcomes in accordance with the review methodology. Such missing data can be handled in two ways – by contacting authors of the original study to obtain the necessary data and by using data imputation techniques. Safi et al . used both these approaches – they tried to get data from the trial authors; however, where that failed, they analyzed the primary outcome (mortality) using the best case (i.e. presuming that all the participants in the experimental arm with missing data had survived and those in the control arm with missing mortality data had died – representing the maximum beneficial effect of the intervention) and the worst case (all the participants with missing data in the experimental arm assumed to have died and those in the control arm to have survived – representing the least beneficial effect of the intervention) scenarios.

Evaluating the quality (or risk of bias) in the included studies

The overall quality of a systematic review depends on the quality of each of the included studies. Quality of a study is inversely proportional to the potential for bias in its design. In our previous articles on interventional study design in this series, we discussed various methods to reduce bias – such as randomization, allocation concealment, participant and assessor blinding, using objective endpoints, minimizing missing data, the use of intention-to-treat analysis, and complete reporting of all outcomes.[ 6 , 7 ] These features form the basis of the Cochrane Risk of Bias Tool (RoB 2), which is a commonly used instrument to assess the risk of bias in the studies included in a systematic review.[ 8 ] Based on this tool, one can classify each study in a review as having low risk of bias, having some concerns regarding bias, or at high risk of bias. Safi et al . used this tool to classify the included studies as having low or high risk of bias and presented these data in both tabular and graphical formats.[ 5 ]

In some reviews, the authors decide to summarize only studies with a low risk of bias and to exclude those with a high risk of bias. Alternatively, some authors undertake a separate analysis of studies with low risk of bias, besides an analysis of all the studies taken together. The conclusions from such analyses of only high-quality studies may be more robust.

Step 6: Synthesis of results

The data extracted from various studies are pooled quantitatively (known as a meta-analysis) or qualitatively (if pooling of results is not considered feasible). For qualitative reviews, data are usually presented in the tabular format, showing the characteristics of each included study, to allow for easier interpretation.

Sensitivity analyses

Sensitivity analyses are used to test the robustness of the results of a systematic review by examining the impact of excluding or including studies with certain characteristics. As referred to above, this can be based on the risk of bias (methodological quality), studies with a specific study design, studies with a certain dosage or schedule, or sample size. If results of these different analyses are more-or-less the same, one can be more certain of the validity of the findings of the review. Furthermore, such analyses can help identify whether the effect of the intervention could vary across different levels of another factor. In the beta-blocker review, sensitivity analysis was performed depending on the risk of bias of included studies.[ 5 ]

IMPORTANT RESOURCES FOR CARRYING OUT SYSTEMATIC REVIEWS AND META-ANALYSES

Cochrane is an organization that works to produce good-quality, updated systematic reviews related to human healthcare and policy, which are accessible to people across the world.[ 9 ] There are more than 7000 Cochrane reviews on various topics. One of its main resources is the Cochrane Library (available at https://www.cochranelibrary.com/ ), which incorporates several databases with different types of high-quality evidence to inform healthcare decisions, including the Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL), and Cochrane Clinical Answers.

The Cochrane Handbook for Systematic Reviews of Interventions

The Cochrane handbook is an official guide, prepared by the Cochrane Collaboration, to the process of preparing and maintaining Cochrane systematic reviews.[ 10 ]

Review Manager software

Review Manager (RevMan) is a software developed by Cochrane to support the preparation and maintenance of systematic reviews, including tools for performing meta-analysis.[ 11 ] It is freely available in both online (RevMan Web) and offline (RevMan 5.3) versions.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement is an evidence-based minimum set of items for reporting of systematic reviews and meta-analyses of randomized trials.[ 12 ] It can be used both by authors of such studies to improve the completeness of reporting and by reviewers and readers to critically appraise a systematic review. There are several extensions to the PRISMA statement for specific types of reviews. An update is currently underway.

Meta-analysis of Observational Studies in Epidemiology statement

The Meta-analysis of Observational Studies in Epidemiology statement summarizes the recommendations for reporting of meta-analyses in epidemiology.[ 13 ]

PROSPERO is an international database for prospective registration of protocols for systematic reviews in healthcare.[ 14 ] It aims to avoid duplication of and to improve transparency in reporting of results of such reviews.

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