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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 approach Quantitative approach

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

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

Type of design Purpose and characteristics

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.

Type of design Purpose and characteristics
Grounded theory

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 Non-probability sampling

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.

Questionnaires 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.

Quantitative observation

Other methods of data collection

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

Field Examples of data collection methods
Media & communication Collecting a sample of texts (e.g., speeches, articles, or social media posts) for data on cultural norms and narratives
Psychology Using technologies like neuroimaging, eye-tracking, or computer-based tasks to collect data on things like attention, emotional response, or reaction time
Education Using tests or assignments to collect data on knowledge and skills
Physical sciences Using scientific instruments to collect data on things like weight, blood pressure, or chemical composition

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.


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.

Reliability Validity

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 .

Approach Characteristics
Thematic analysis
Discourse analysis

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Research Design 101

Everything You Need To Get Started (With Examples)

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

Research design for qualitative and quantitative studies

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.

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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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example of research model

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 .

The four most common quantitative research design types are descriptive, correlational, experimental and quasi-experimental.

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.

example of research model

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.

Case study design often involves investigating an individual to gain an in-depth understanding of their experiences, behaviours or outcomes.

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.

example of research model

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 .

example of research model

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Wei Leong YONG

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

Solly Khan

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?

Sam Msongole

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.

Robyn Pritchard

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


how to cite this page


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


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

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Research Methods - University of Southampton Library

Strategies and Models

The choice of qualitative or quantitative approach to research has been traditionally guided by the subject discipline. However, this is changing, with many “applied” researchers taking a more holistic and integrated approach that combines the two traditions. This methodology reflects the multi-disciplinary nature of many contemporary research problems.

In fact, it is possible to define many different types of research strategy. The following list ( Business research methods / Alan Bryman & Emma Bell. 4th ed. Oxford : Oxford University Press, 2015 ) is neither exclusive nor exhaustive.

  • Clarifies the nature of the problem to be solved
  • Can be used to suggest or generate hypotheses
  • Includes the use of pilot studies
  • Used widely in market research
  • Provides general frequency data about populations or samples
  • Does not manipulate variables (e.g. as in an experiment)
  • Describes only the “who, what, when, where and how”
  • Cannot establish a causal relationship between variables
  • Associated with descriptive statistics
  • Breaks down factors or variables involved in a concept, problem or issue
  • Often uses (or generates) models as analytical tools
  • Often uses micro/macro distinctions in analysis
  • Focuses on the analysis of bias, inconsistencies, gaps or contradictions in accounts, theories, studies or models
  • Often takes a specific theoretical perspective, (e.g. feminism; labour process theory)
  • Mainly quantitative
  • Identifies measurable variables
  • Often manipulates variables to produce measurable effects
  • Uses specific, predictive or null hypotheses
  • Dependent on accurate sampling
  • Uses statistical testing to establish causal relationships, variance between samples or predictive trends
  • Associated with organisation development initiatives and interventions
  • Practitioner based, works with practitioners to help them solve their problems
  • Involves data collection, evaluation and reflection
  • Often used to review interventions and plan new ones
  • Focuses on recognised needs, solving practical problems or answering specific questions
  • Often has specific commercial objectives (e.g. product development )

Approaches to research

For many, perhaps most, researchers, the choice of approach is straightforward. Research into reaction mechanisms for an organic chemical reaction will take a quantitative approach, whereas qualitative research will have a better fit in the social work field that focuses on families and individuals. While some research benefits from one of the two approaches, other research yields more understanding from a combined approach.

In fact, qualitative and quantitative approaches to research have some important shared aspects. Each type of research generally follows the steps of scientific method, specifically:

example of research model

In general, each approach begins with qualitative reasoning or a hypothesis based on a value judgement. These judgements can be applied, or transferred to quantitative terms with both inductive and deductive reasoning abilities. Both can be very detailed, although qualitative research has more flexibility with its amount of detail.

Selecting an appropriate design for a study involves following a logical thought process; it is important to explore all possible consequences of using a particular design in a study. As well as carrying out a scoping study, a researchers should familiarise themselves with both qualitative and quantitative approaches to research in order to make the best decision. Some researchers may quickly select a qualitative approach out of fear of statistics but it may be a better idea to challenge oneself. The researcher should also be prepared to defend the paradigm and chosen research method; this is even more important if your proposal or grant is for money, or other resources.

Ultimately, clear goals and objectives and a fit-for-purpose research design is more helpful and important than old-fashioned arguments about which approach to research is “best”. Indeed, there is probably no such thing as a single “correct” design – hypotheses can be studied by different methods using different research designs. A research design is probably best thought of as a series of signposts to keep the research headed in the right direction and should not be regarded as a highly specific plan to be followed without deviation.

Research models

There is no common agreement on the classification of research models but, for the purpose of illustration, five categories of research models and their variants are outlined below.

A physical model is a physical object shaped to look like the represented phenomenon, usually built to scale e.g. atoms, molecules, skeletons, organs, animals, insects, sculptures, small-scale vehicles or buildings, life-size prototype products. They can also include 3-dimensional alternatives for two-dimensional representations e.g. a physical model of a picture or photograph.

In this case, the term model is used loosely to refer to any theory phrased in formal, speculative or symbolic styles. They generally consist of a set of assumptions about some concept or system; are often formulated, developed and named on the basis of an analogy between the object, or system that it describes and some other object or different system; and they are considered an approximation that is useful for certain purposes. Theoretical models are often used in biology, chemistry, physics and psychology.

A mathematical model refers to the use of mathematical equations to depict relationships between variables, or the behaviour of persons, groups, communities, cultural groups, nations, etc.

It is an abstract model that uses mathematical language to describe the behaviour of a system. They are used particularly in the natural sciences and engineering disciplines (such as physics, biology, and electrical engineering) but also in the social sciences (such as economics, sociology and political science). Types of mathematical models include trend (time series), stochastic, causal and path models. Examples include models of population and economic growth, weather forecasting and the characterisation of large social networks.

Mechanical (or computer) models tend to use concepts from the natural sciences, particularly physics, to provide analogues for social behaviour. They are often an extension of mathematical models. Many computer-simulation models have shown how a research problem can be investigated through sequences of experiments e.g. game models; microanalytic simulation models (used to examine the effects of various kinds of policy on e.g. the demographic structure of a population); models for predicting storm frequency, or tracking a hurricane.

These models are used to untangle meanings that individuals give to symbols that they use or encounter. They are generally simulation models, i.e. they are based on artificial (contrived) situations, or structured concepts that correspond to real situations. They are characterised by symbols, change, interaction and empiricism and are often used to examine human interaction in social settings.

The advantages and disadvantages of modelling

Take a look at the advantages and disadvantages below. It might help you think about what type of model you may use.

  • The determination of factors or variables that most influence the behaviour of phenomena
  • The ability to predict, or forecast the long term behaviour of phenomena
  • The ability to predict the behaviour of the phenomenon when changes are made to the factors influencing it
  • They allow researchers a view on difficult to study processes (e.g. old, complex or single-occurrence processes)
  • They allow the study of mathematically intractable problems (e.g. complex non-linear systems such as language)
  • They can be explicit, detailed, consistent, and clear (but that can also be a weakness)
  • They allow the exploration of different parameter settings (i.e. evolutionary, environmental, individual and social factors can be easily varied)
  • Models validated for a category of systems can be used in many different scenarios e.g. they can be reused in the design, analysis, simulation, diagnosis and prediction of a technical system
  • Models enable researchers to generate unrealistic scenarios as well as realistic ones
  • Difficulties in validating models
  • Difficulties in assessing the accuracy of models
  • Models can be very complex and difficult to explain
  • Models do not “provide proof”

The next section describes the processes and design of research.

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Conceptual Models and Theories

Developing a research framework.

Premkumar, Beulah M.Sc (N)., M.Phil * ; David, Shirley M.Sc (N)., Ph.D (N) ** ; Ravindran, Vinitha M.Sc (N)., Ph.D (N) ***

* Professor, College of Nursing, CMC, Vettore

** Professor, College of Nursing, CMC, Vettore

*** Professor, College of Nursing, CMC, Vettore

Conceptual models and theories provide structure for research. Research without a theoretical base provides isolated information which may not be used or applied effectively. The challenge for nurse researchers is to identify a model or theory that would a best fit for their area of study interest. In this research series article the authors unravel the simple steps that can be followed in identifying, choosing, and applying the constructs and concepts in the models or theories to develop a research framework. A research framework guides the researcher in developing research questions, refining their hypotheses, selecting interventions, defining and measuring variables. Roy's adaptation model and a study intending to assess the effectiveness of grief counseling on adaptation to spousal loss are used as an example in this article to depict the theory- research congruence.


The history of professional Nursing started with Florence Nightingale who envisioned nurses as a knowledgeable force who can bring positive changes in health care delivery (Alligood, 2014). It was 100 years later, during 1950s, a need to develop nursing knowledge apart from medical knowledge was felt to guide nursing practice. This beginning led to the awareness of the need to develop nursing theories (Alligood, 2010). Until then, nursing practice was based on principles and traditions that were handed down through apprenticeship model of education and individual hospital procedure manuals. It reflected the vocational heritage more than a professional vision. This transition from vocation to profession involves successive eras of history in nursing: the curriculum era, research emphasis era, research era, graduate education era, and the theory era (Alligood, 2014).

The theory era was a natural outcome of research era and graduate education era, where an understanding oí research and knowledge development increased. It became obvious that research without conceptual and theoretical framework produced isolated information. This awareness and acceptance paved way to another era, the theory utilization era, which placed emphasis on theory application in nursing practice, education, administration, and research (Alligood, 2014). Conceptual models and theories are structures that provide nurses with a perspective of the patient and the professional practice. Conceptual models provide structure for a phenomenon, direct thinking, observations, and interpretations and further provide direction for actions (Fawcett & Desanto-Madeya, 2005). In research, a framework is the underpinning of the study and if a framework is based on a theory it is called as theoretica framework and if it represents a conceptual model then it is generally called the conceptual framework. More often it is known as a research framework. However the terms conceptual framework, conceptual model theoretica framework, and research framework are often usee interchangeably (Polit & Beck, 2014).

Definitions of Terminologies

When nurse researchers are making decisions about theories and models for their study, it is important to understand the definitions of different related terminology. According Grove, Burns and Gray (2013) conceptual models are examples of grand theories and are highly abstract with related constructs. “A conceptual model broadly explains phenomenon of interest, expresses assumption, and reflects a philosophical stance” (Grove et al., 2013). A conceptual model is an image of a phenomenon. A theory in contrast represents a set of defined concepts that offers a systematic explanation about how two or more concepts are interrelated. Theories can be used to describe, explain, predict, or control the phenomenon that is of interest to a researcher (Grove et al., 2013).

Constructs are abstract descriptions of a phenomenon or the experiences or the contextual factors. Concepts are the terms or names given to a phenomena or idea or an object and are often considered as the building blocks of a theory (Grove et al., 2013). Many conceptual models are made of constructs. Concepts are derived from constructs or vice versa. For example, in the Transactional model of Stress and Coping by Lazarus and Folkman (1984) the constructs included are stressors, mediating processes, moderators and the outcomes. The examples of related concepts to these constructs are shown in Figure 1 .


Conceptual Framework in Research

Conceptual models and theories serve as the foundation on which a study can be developed or as a map to aid in the design of the study (Fawcett, 1989). When concepts related to the study are integrated and formulated into a workable model for the specific study it is generally known as a research framework (Grove, Gray & Burns, 2015). When concepts or constructs in the models or theory are converted into measurable terms they are known as variables (Grove et al., 2013). According to the purposes explicated by Sharma (2014) and Polit and Beck (2014) the use of conceptual/research framework m research can be summarized as follows:

  • - It provides a structure for the study which helps the researcher to organize the process of investigation
  • - It helps in formulating hypothesis, developing a research question and defining the variables
  • - It guides development, use, and testing of interventions and selection of data collection instruments
  • - It provides direction for explaining the study results and situate the findings in the gaps identified in the literature

Nurse researchers regularly select and use conceptual frameworks for carrying out their studies. Conceptual models and theories explicitly or implicitly guide research (Radwin & Fawcett, 2002). Researchers use both nursing and non- nursing models to provide a framework for their studies. There are however, two challenges for researchers and students in relation to using conceptual frameworks in their investigations. The first is to identify the conceptual model or a theory that will be the best fit and will be useful in guiding their research and the second is to incorporate and clearly articulate the model in relation to their study variables, interventions and the outcomes to convert it into their research framework (Radwin & Fawcett, 2002). A few essential steps need to be followed to choose a conceptual model and to incorporate it into the individual studies. Let us consider the steps with an example of a study intending “ to assess the effectiveness of grief counseling intervention in helping individuals cope and adapt after the loss of their spouse”.

1. The Purpose of the Study

The choice of conceptual model to guide the research first and foremost depends on the purpose of one's research. It can be educating staff/patient/families, improving academic and clinical teaching, changing practice by translating research evidence into practice, implementing a quality improvement strategy, encouraging behavior change, supporting individuals during crisis, assisting to cope etc. The researcher should look for a model or a theory that addresses similar purpose. It would be useful to identify and select the key concept in which the researcher is interested in at this stage (Sharma, 2014). In the example mentioned above the key concept of interest is ‘adaptation’ after a loss which is a traumatic life event.

2. Study Variables

The second step is to identify general variables that are included or may be included in the study. The variables are related to the constructs/concepts of interest in the study. The concepts and variables may be based on previous research findings, experiential knowledge or hunches and intuitions (Sharma, 2014). In the adaptation to spousal loss study in addition to the main concept of adaptation, other variables such as grief, coping, quality of life, and demographic and social factors that may influence adaptation may be included in the objectives.

3. Gathering Relevant Information

Once the researcher has identified the concepts and variables of interest, the next step involves in-depth study of existing models and theories to gather information about the relevant concepts and variables. The researcher can quickly skim through the literature to seek a few models that relate to the concepts and variables of interest in the study. The researcher must then read about them from primary sources to obtain comprehensive evidence about each model or theory (Sharma, 2014). When choosing a model for the study the researcher needs to analyze and evaluate the models she /he considers to understand its most important features (Fawcett, 1989). Some questions that need to be asked are: What is the historical evolution of the model? What methods are indicated for nursing knowledge development? What are the assumptions listed? How are the concepts person, environment, health, and nursing defined? How are these metaparadigm concepts linked and how is nursing process described? What is the major area of concern in the care identified in the model?

The researcher's previous experience and knowledge about theories and models greatly assist in quick decisions on choosing a model that would best fit their study purpose. Nurse researchers who have an interdisciplinary knowledge and experience may choose an overarching model or theory to develop their research framework for their study. It is the novice researchers who often find it difficult to decide on a model and have confusions regarding explicating their research framework. The above listed questions, if carefully considered will help them in choosing an appropriate model. Once a theory or conceptual model is identified the researcher need to studv it in-depth to understand each concent and propositions so that it can appropriately be integrated into the study (Sharma, 2014).

In the study example in this paper the researcher intends to assess how people adjust after the death of their spouse and how grief counseling will help in their adjustment to life after their loss. As the process of interest, as indicated already, is adaptation to traumatic life event, adaptation model that is purported by Sr. Callista Roy (1976) is chosen as the best fit as Roy's adaptation model focuses on how individuals cope after a stimuli and manifest adaptive behaviors (see Figure 2 ).


4. Understanding the Premises and Principles of the Selected Model: Roy's Conceptual Model

Once a model that is relevant to the study is selected the underlying premises and philosophy of the model or theory have to be explicated. The definitions of the concepts in the model have to be understood to enable the researcher to formulate her/his study framework which can be integrated with the chosen model (Mock et al., 2007). An in-depth review of literature on how the conceptual model was developed and refined, background information about the theorist or author, and the definitions of concepts included in the model is mandatory to examine how the researcher's study can be designed and executed. In Roy's adaptation model (Fawcett, 1989), Roy considers human being as an open system who is in constant interaction with the environment. She explains health as a process of being or becoming an integrated whole person. The goal of nursing is to assist individuals in positively adapting to environmental changes or what she terms ‘stimuli’. Three types of stimuli are explained in the model: 1. ‘Focal’ which is the life event itself, 2. ‘contextual’ which are the factors associated and contributing to or opposing the stimuli and 3.‘residual’ which are present innately which may not be altered, explained, or reasoned. The adaptation occurs through coping process at the regulator and cognator subsystem levels. The regulator subsystem refers to the automatic response that occurs naturally in the chemical, neural, and endocrine systems. The cognator subsystem respond through four cognitive emotional channels: perceptual and information processing, learning, judgment, and emotion. Adaptation in Roy's model is explained as conscious choice of individuals to create successful human and environmental integration which can be manifested as integrated adaptation in four adaptive behavioural modes. The four adaptive modes are the physical/physiological, self-concept, role functioning, and interdependence. If integrated adaptation did not happen it can result in compensatory or compromised adaptation.

5. Finalizing the Study Design, Variables, Tool, and Intervention

In a nursing theory or a conceptual model how a theorist defines nursing action and what is expected as outcomes helps the researcher to choose the research design and intervention (Mock et al., 2007). Further the concepts in the model guides the researcher to choose variables that would be of interest to nursing. In Roy's adaptation theory, nursing assessment and interventions that promote adaption is purported. On the basis of this premise the investigator can choose a specific intervention that would enhance integrated adaptation of an individual after a crisis (stimuli). The investigator then can measure whether the intervention has been effective in promoting adaptation by looking at the four modes of adaptive behavior (physical/physiological, self- concept, role functioning, and interdependence). The congruence of the constructs of Roy's adaptation model and the study variables is depicted in Figure 3 .


In the example being discussed the focal stimuli is the death of a spouse. The contextual stimuli are the grief reaction, social and spiritual support systems available for the person who has experienced loss and her or his economic status. The residual stimuli include variables such as the age, gender, years spent with spouse, race, or ethnic background. The researcher has chosen grief counseling as the intervention in the study. This is based on Roy's model which explains that when interventions are aimed at how contextual stimuli can be addressed it will result in better coping process and this will facilitate adaptation (Fawcett, 1989). When choosing the intervention it is vital to know that there is established evidence for the intervention (Mock et al., 2007). In this study grief counseling is chosen because of its established evidence on the effect on person's adjustment (Neimeyer & Currier, 2009). Other variables which relate to the adaptation model include coping with grief and the outcome variables as adaptive behaviours in physiological, interpersonal, role functioning, and self-concept domains (see Figure 3 ).

Once there is clarity about the variables of interest and the intervention, it becomes relatively simpler to decide on the study design and the instruments/tools which can be used to measure the variables. As shown in Figure 4 , the contextual variables can be measured using a socio-demographic data profile. Grief which is another contextual stimuli will be measured by the grief scale (Fireman, 2010). The grief scale measures the thoughts, feelings, and behaviours of people who are in the grieving process after a loss. A part of the demographic profile will measure the influence of the residual variables. The intervention which is the grief counseling will be administered by the researcher who has had special training in this method of counseling. How individuals cope to loss can be measured by the Ways of Coping Scale (Folkman & Lazarus, 1988). This scale consists of coping in eight domains namely problem focused coping, wishful thinking, distancing, seeking social support emphasizing the positive, self-blame, tension reduction, and self-isolation. The coping and the adaptation behaviors may be measured using the “Coping and Adaptation Processing Scale” (CAPS Short form, 2015) which is developed by Roy herself. CAPS is a tool which can be used to measure coping and adaptation in people suffering with chronic or acute health issues and can be used when the stimuli is an acute loss.


As the researcher intends to use grief counseling as an intervention, the research design will be an experimental design and the coping and adaptation process can be measured prior to and after the counseling sessions using both ways of coping and CAPS scales. As there may not be adequate number of samples available to represent the phenomenon of interest the study can be designed as a one group pretest posttest quasi experimental design instead a true experimental design with a control group. The research framework that is developed from the adaptation model may be modified as follows based on the research design (see Figure 4 ).

6. Using the Research Framework for Analysis and Interpretation of the Results

The framework that is developed for the study can guidi the analysis and will also help in interpretation of the finding The research report can easily incorporate the concepts in th(original model and also the developed framework and can b< discussed in relation to current study findings. As th(researcher's background knowledge that is gained in th(framework development process is elaborate, the concepts o: the model can guide the researcher to situate the findings wit! in the theoretical literature (Mock et al., 2007).

Choosing and applying a conceptual model or theory to develop a research frame work is a challenging but an educative process. It also involves an iterative process of moving back and forth between what is the phenomenon and variables of interest to the researcher and what and how the theorists explain and define concepts in their models. The first and foremost step to be remembered is to identify the core concept that the researcher is interested in which will pave way for searching the literature on the model that will match the researcher's interest. The researcher must understand that all variables in a given model may not be of interest to him or her or variables from more than one model may apply to the areas of interest to be studied. Both are acceptable. Researchers need to be creative in developing the research framework based on the model/models that is/are of interest. The nursing conceptual models serve as guides for articulating, reporting and recording nursing thought anc action in research. Further, the models also ultimately assist researchers in applying findings in clinical practice.

Conflicts of Interest: The authors have declared no conflicts of interest.

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What is a Conceptual Framework and How to Make It (with Examples)

What is a Conceptual Framework and How to Make It (with Examples)

What is a Conceptual Framework and How to Make It (with Examples)

A strong conceptual framework underpins good research. A conceptual framework in research is used to understand a research problem and guide the development and analysis of the research. It serves as a roadmap to conceptualize and structure the work by providing an outline that connects different ideas, concepts, and theories within the field of study. A conceptual framework pictorially or verbally depicts presumed relationships among the study variables.

The purpose of a conceptual framework is to serve as a scheme for organizing and categorizing knowledge and thereby help researchers in developing theories and hypotheses and conducting empirical studies.

In this post, we explain what is a conceptual framework, and provide expert advice on how to make a conceptual framework, along with conceptual framework examples.

Table of Contents

What is a Conceptual Framework in Research

Definition of a conceptual framework.

A conceptual framework includes key concepts, variables, relationships, and assumptions that guide the academic inquiry. It establishes the theoretical underpinnings and provides a lens through which researchers can analyze and interpret data. A conceptual framework draws upon existing theories, models, or established bodies of knowledge to provide a structure for understanding the research problem. It defines the scope of research, identifying relevant variables, establishing research questions, and guiding the selection of appropriate methodologies and data analysis techniques.

Conceptual frameworks can be written or visual. Other types of conceptual framework representations might be taxonomic (verbal description categorizing phenomena into classes without showing relationships between classes) or mathematical descriptions (expression of phenomena in the form of mathematical equations).

example of research model

Figure 1: Definition of a conceptual framework explained diagrammatically

Conceptual Framework Origin

The term conceptual framework appears to have originated in philosophy and systems theory, being used for the first time in the 1930s by the philosopher Alfred North Whitehead. He bridged the theological, social, and physical sciences by providing a common conceptual framework. The use of the conceptual framework began early in accountancy and can be traced back to publications by William A. Paton and John B. Canning in the first quarter of the 20 th century. Thus, in the original framework, financial issues were addressed, such as useful features, basic elements, and variables needed to prepare financial statements. Nevertheless, a conceptual framework approach should be considered when starting your research journey in any field, from finance to social sciences to applied sciences.

Purpose and Importance of a Conceptual Framework in Research

The importance of a conceptual framework in research cannot be understated, irrespective of the field of study. It is important for the following reasons:

  • It clarifies the context of the study.
  • It justifies the study to the reader.
  • It helps you check your own understanding of the problem and the need for the study.
  • It illustrates the expected relationship between the variables and defines the objectives for the research.
  • It helps further refine the study objectives and choose the methods appropriate to meet them.

What to Include in a Conceptual Framework

Essential elements that a conceptual framework should include are as follows:

  • Overarching research question(s)
  • Study parameters
  • Study variables
  • Potential relationships between those variables.

The sources for these elements of a conceptual framework are literature, theory, and experience or prior knowledge.

How to Make a Conceptual Framework

Now that you know the essential elements, your next question will be how to make a conceptual framework.

For this, start by identifying the most suitable set of questions that your research aims to answer. Next, categorize the various variables. Finally, perform a rigorous analysis of the collected data and compile the final results to establish connections between the variables.

In short, the steps are as follows:

  • Choose appropriate research questions.
  • Define the different types of variables involved.
  • Determine the cause-and-effect relationships.

Be sure to make use of arrows and lines to depict the presence or absence of correlational linkages among the variables.

Developing a Conceptual Framework

Researchers should be adept at developing a conceptual framework. Here are the steps for developing a conceptual framework:

1. Identify a research question

Your research question guides your entire study, making it imperative to invest time and effort in formulating a question that aligns with your research goals and contributes to the existing body of knowledge. This step involves the following:

  • Choose a broad topic of interest
  • Conduct background research
  • Narrow down the focus
  • Define your goals
  • Make it specific and answerable
  • Consider significance and novelty
  • Seek feedback.

 2. Choose independent and dependent variables

The dependent variable is the main outcome you want to measure, explain, or predict in your study. It should be a variable that can be observed, measured, or assessed quantitatively or qualitatively. Independent variables are the factors or variables that may influence, explain, or predict changes in the dependent variable.

Choose independent and dependent variables for your study according to the research objectives, the nature of the phenomenon being studied, and the specific research design. The identification of variables is rooted in existing literature, theories, or your own observations.

3. Consider cause-and-effect relationships

To better understand and communicate the relationships between variables in your study, cause-and-effect relationships need to be visualized. This can be done by using path diagrams, cause-and-effect matrices, time series plots, scatter plots, bar charts, or heatmaps.

4. Identify other influencing variables

Besides the independent and dependent variables, researchers must understand and consider the following types of variables:

  • Moderating variable: A variable that influences the strength or direction of the relationship between an independent variable and a dependent variable.
  • Mediating variable: A variable that explains the relationship between an independent variable and a dependent variable and clarifies how the independent variable affects the dependent variable.
  • Control variable: A variable that is kept constant or controlled to avoid the influence of other factors that may affect the relationship between the independent and dependent variables.
  • Confounding variable: A type of unmeasured variable that is related to both the independent and dependent variables.

Example of a Conceptual Framework

Let us examine the following conceptual framework example. Let’s say your research topic is “ The Impact of Social Media Usage on Academic Performance among College Students .” Here, you want to investigate how social media usage affects academic performance in college students. Social media usage (encompassing frequency of social media use, time spent on social media platforms, and types of social media platforms used) is the independent variable, and academic performance (covering grades, exam scores, and class attendance) is the dependent variable.

This conceptual framework example also includes a mediating variable, study habits, which may explain how social media usage affects academic performance. Study habits (time spent studying, study environment, and use of study aids or resources) can act as a mechanism through which social media usage influences academic outcomes. Additionally, a moderating variable, self-discipline (level of self-control and self-regulation, ability to manage distractions, and prioritization skills), is included to examine how individual differences in self-control and discipline may influence the relationship between social media usage and academic performance.

Confounding variables are also identified (socioeconomic status, prior academic achievement), which are potential factors that may influence both social media usage and academic performance. These variables need to be considered and controlled in the study to ensure that any observed effects are specifically attributed to social media usage. A visual representation of this conceptual framework example is seen in Figure 2.

example of research model

Figure 2: Visual representation of a conceptual framework for the topic “The Impact of Social Media Usage on Academic Performance among College Students”

Key Takeaways

Here is a snapshot of the basics of a conceptual framework in research:

  • A conceptual framework is an idea or model representing the subject or phenomena you intend to study.
  • It is primarily a researcher’s perception of the research problem. It can be used to develop hypotheses or testable research questions.
  • It provides a preliminary understanding of the factors at play, their interrelationships, and the underlying reasons.
  • It guides your research by aiding in the formulation of meaningful research questions, selection of appropriate methods, and identification of potential challenges to the validity of your findings.
  • It provides a structure for organizing and understanding data.
  • It allows you to chalk out the relationships between concepts and variables to understand them.
  • Variables besides dependent and independent variables (moderating, mediating, control, and confounding variables) must be considered when developing a conceptual framework.

Frequently Asked Questions

What is the difference between a moderating variable and a mediating variable.

Moderating and mediating variables are easily confused. A moderating variable affects the direction and strength of this relationship, whereas a mediating explains how two variables relate.

What is the difference between independent variables, dependent variables, and confounding variables?

Independent variables are the variables manipulated to affect the outcome of an experiment (e.g., the dose of a fat-loss drug administered to rats). Dependent variables are variables being measured or observed in an experiment (e.g., changes in rat body weight as a result of the drug). A confounding variable distorts or masks the effects of the variables being studied because it is associated both with dependent variable and with the independent variable. For instance, in this example, pre-existing metabolic dysfunction in some rats could interact differently with the drug being studied and also affect rat body weight.

Should I have more than one dependent or independent variable in a study?

The need for more than one dependent or independent variable in a study depends on the research question, study design, and relationships being investigated. Note the following when making this decision for your research:

  • If your research question involves exploring the relationships between multiple variables or factors, it may be appropriate to have more than one dependent or independent variable.
  • If you have specific hypotheses about the relationships between several variables, it may be necessary to include multiple dependent or independent variables.
  • Adequate resources, sample size, and data collection methods should be considered when determining the number of dependent and independent variables to include.

What is a confounding variable?

A confounding variable is not the main focus of the study but can unintentionally influence the relationship between the independent and dependent variables. Confounding variables can introduce bias and give rise to misleading conclusions. These variables must be controlled to ensure that any observed relationship is genuinely due to the independent variable.

What is a control variable?

A control variable is something not of interest to the study’s objectives but is kept constant because it could influence the outcomes. Control variables can help prevent research biases and allow for a more accurate assessment of the relationship between the independent and dependent variables. Examples are (i) testing all participants at the same time (e.g., in the morning) to minimize the potential effects of circadian rhythms, (ii) ensuring that instruments are calibrated consistently before each measurement to minimize the influence of measurement errors, and (iii) randomization of participants across study groups.

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The Four Types of Research Paradigms: A Comprehensive Guide

The Four Types of Research Paradigms: A Comprehensive Guide

  • 5-minute read
  • 22nd January 2023

In this guide, you’ll learn all about the four research paradigms and how to choose the right one for your research.

Introduction to Research Paradigms

A paradigm is a system of beliefs, ideas, values, or habits that form the basis for a way of thinking about the world. Therefore, a research paradigm is an approach, model, or framework from which to conduct research. The research paradigm helps you to form a research philosophy, which in turn informs your research methodology.

Your research methodology is essentially the “how” of your research – how you design your study to not only accomplish your research’s aims and objectives but also to ensure your results are reliable and valid. Choosing the correct research paradigm is crucial because it provides a logical structure for conducting your research and improves the quality of your work, assuming it’s followed correctly.

Three Pillars: Ontology, Epistemology, and Methodology

Before we jump into the four types of research paradigms, we need to consider the three pillars of a research paradigm.

Ontology addresses the question, “What is reality?” It’s the study of being. This pillar is about finding out what you seek to research. What do you aim to examine?

Epistemology is the study of knowledge. It asks, “How is knowledge gathered and from what sources?”

Methodology involves the system in which you choose to investigate, measure, and analyze your research’s aims and objectives. It answers the “how” questions.

Let’s now take a look at the different research paradigms.

1.   Positivist Research Paradigm

The positivist research paradigm assumes that there is one objective reality, and people can know this reality and accurately describe and explain it. Positivists rely on their observations through their senses to gain knowledge of their surroundings.

In this singular objective reality, researchers can compare their claims and ascertain the truth. This means researchers are limited to data collection and interpretations from an objective viewpoint. As a result, positivists usually use quantitative methodologies in their research (e.g., statistics, social surveys, and structured questionnaires).

This research paradigm is mostly used in natural sciences, physical sciences, or whenever large sample sizes are being used.

2.   Interpretivist Research Paradigm

Interpretivists believe that different people in society experience and understand reality in different ways – while there may be only “one” reality, everyone interprets it according to their own view. They also believe that all research is influenced and shaped by researchers’ worldviews and theories.

As a result, interpretivists use qualitative methods and techniques to conduct their research. This includes interviews, focus groups, observations of a phenomenon, or collecting documentation on a phenomenon (e.g., newspaper articles, reports, or information from websites).

3.   Critical Theory Research Paradigm

The critical theory paradigm asserts that social science can never be 100% objective or value-free. This paradigm is focused on enacting social change through scientific investigation. Critical theorists question knowledge and procedures and acknowledge how power is used (or abused) in the phenomena or systems they’re investigating.

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Researchers using this paradigm are more often than not aiming to create a more just, egalitarian society in which individual and collective freedoms are secure. Both quantitative and qualitative methods can be used with this paradigm.

4.   Constructivist Research Paradigm

Constructivism asserts that reality is a construct of our minds ; therefore, reality is subjective. Constructivists believe that all knowledge comes from our experiences and reflections on those experiences and oppose the idea that there is a single methodology to generate knowledge.

This paradigm is mostly associated with qualitative research approaches due to its focus on experiences and subjectivity. The researcher focuses on participants’ experiences as well as their own.

Choosing the Right Research Paradigm for Your Study

Once you have a comprehensive understanding of each paradigm, you’re faced with a big question: which paradigm should you choose? The answer to this will set the course of your research and determine its success, findings, and results.

To start, you need to identify your research problem, research objectives , and hypothesis . This will help you to establish what you want to accomplish or understand from your research and the path you need to take to achieve this.

You can begin this process by asking yourself some questions:

  • What is the nature of your research problem (i.e., quantitative or qualitative)?
  • How can you acquire the knowledge you need and communicate it to others? For example, is this knowledge already available in other forms (e.g., documents) and do you need to gain it by gathering or observing other people’s experiences or by experiencing it personally?
  • What is the nature of the reality that you want to study? Is it objective or subjective?

Depending on the problem and objective, other questions may arise during this process that lead you to a suitable paradigm. Ultimately, you must be able to state, explain, and justify the research paradigm you select for your research and be prepared to include this in your dissertation’s methodology and design section.

Using Two Paradigms

If the nature of your research problem and objectives involves both quantitative and qualitative aspects, then you might consider using two paradigms or a mixed methods approach . In this, one paradigm is used to frame the qualitative aspects of the study and another for the quantitative aspects. This is acceptable, although you will be tasked with explaining your rationale for using both of these paradigms in your research.

Choosing the right research paradigm for your research can seem like an insurmountable task. It requires you to:

●  Have a comprehensive understanding of the paradigms,

●  Identify your research problem, objectives, and hypothesis, and

●  Be able to state, explain, and justify the paradigm you select in your methodology and design section.

Although conducting your research and putting your dissertation together is no easy task, proofreading it can be! Our experts are here to make your writing shine. Your first 500 words are free !

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Research process models

How should we conceptualize the processes of doing research? Two models, one productive and one not.

Eleanor C Sayre

Oftentimes in school, we’re taught how to do research (or science labs) in a linear process that passes through discrete stages in a specific order. At the end of the process, you’re supposed to “write it up” in a lab report or scientific paper. However, this stage model of research is a lie. In this article, I articulate two models for doing research, and explore their implications.

Research stage model

example of research model

Under this model, research passes through discrete stages. The progression of the stages looks a bit like sections of a research paper, and it echoes the scientific method that’s often taught in schools. Each stage in this model has defined tasks. For example, when you’re doing a lit review , you gather papers, read them, and synthesize. Getting data is a separate stage that finishes before any data can be analyzed, and all data must be analyzed to generate your conclusions before you write up your papers.

It’s easy to teach a stage model because it feels simple and linear: the stages are distinct and well-determined, and they progress in an orderly fashion. It feels comforting to plan a project which progresses through these stages, because you can look at your calendar and know when you are ahead or behind. When we’re teaching people about doing research , we often simplify the research process so that it more closely mimics this model. Research-esque activities, like undergraduate labs (including course-based undergraduate research experiences), undergraduate “capstone” courses, and even some masters theses, can take up this model for doing research and use it to determine preliminary and intermediate deadlines: by this day, turn in your draft lit review; by this day, turn in your full data set. As instructors, we often simplify these research-esque projects in order to support this stage model of doing research.

However, the research stage model can create blocking tasks which impede progress on the project as a whole. Blocking tasks are ones where you can’t do them (because you don’t know how, because you are waiting for someone else, because they feel insurmountable, etc), but also you cannot avoid them and do something else. They block all work on the project until they can be resolved.

As research projects get bigger and more complex, there are a lot more opportunities for blocking tasks to emerge. For example, what if you are collecting data at three field sites, and one of them can’t host your visit until three months later? Or – this is super common – you don’t want to start writing until you’ve finished all your analysis, but there’s always one more exciting avenue to pursue?

The blocking tasks problem is especially bad for emerging researchers, because they’re more likely to only be working on one project at a time, and because there are more things they don’t know how to do yet.

Additionally, because the research stages are sequential, if you need to “go back” to a prior stage, that can feel like a failure. Suppose your data analysis doesn’t align well with the lit review you did ahead of time. If you need to read new papers to understand your data or bring in another theory to explain it better, you might feel like your research has a major setback or like you don’t have time to complete your project in the original timeline. This is a problem for emerging researchers because it can negatively impact their self-efficacy as researchers more strongly.

Parallel processes model for research

example of research model

Under this model, research processes are overlapping and generative. Each analysis that you do suggests new data for you to collect or new literature for you to read. Each paper that you read suggests new analyses to perform or data to collect. All throughout, your research questions are living questions: they grow and change in response to what you’re discovering. Instead of writing up your paper at the end, you engage in generative writing to help you record your results, generate new ideas, and document your work for your papers.

The parallel processes model for research can seem intimidating at first, but it creates fewer blocking tasks than the stage model. Because you’re doing a little bit of each thread at a time, if you’re stuck on one aspect of your project, you can work on another aspect to help unstick you (or to fill your time while you’re waiting for feedback). As you learn more, you can do more. This allows your research progress to grow with you as you learn and develop as a researcher.

Plus, the parallel processes model for research is more honest than the research stage model. This is how research actually gets done in big and complex projects (and small exploratory ones). It’s very common for experienced researchers to need to go back to the literature in the process of doing their analysis, or for inspiration for new data to strike while they’re reading a paper. Generative writing is good practice for research, and while it’s not required to write papers, it’s certainly more productive than the stage model.

Emerging researchers (from undergrads to faculty new to DBER) tend to have one of three major responses when they learn about the parallel processes model:

  • omg, I’ve never heard it explicitly like this and I feel so seen
  • whoa, that’s allowable? mind. blown.
  • hmm, this feels less deterministic and therefore more scary.

If you see yourself in one of these groups, you’re in good company.

Applying the parallel processes model

Something that is both great and terrible about the parallel processes model is that it does not prescribe where to begin the research process. You can start in any of the major strands of research and trust that your work will integrate activities from all strands as necessary and relevant.

If you’re new to research, this feature can be scary.

Where should you start? Here are some options:

  • Read something! When you pick up a research paper or popular press article, ask yourself what’s exciting about their study. The claims? the methods? the theory? the population? Doing a lit review can help you do this in a more structured way.
  • Write something! What are you interested in? Why is it interesting? Engage in some generative writing around a problem you see, or work on a reflective statement of research interests to help structure your thoughts and suggest a new study.
  • Notice something! Oftentimes as scientists, educators, and humans in the world, we see something interesting or unusual or problematic. Allow yourself the curiosity to ask questions of this thing you noticed: why does it do that? what would happen if it were different? The Research Process for Video-based Research article covers a formalized method for noticing and refining our noticing to generate a research project using classroom video.

You might notice that all of these articles push you to do generative writing in order to figure out new ideas and refine them. That’s because generative writing is a major research tool. It can help you improve at doing research, no matter what your skill level is.

Once you’re started, what comes next?

Well, what did your reading, writing, or noticing suggest to you? If your next idea doesn’t flow from your last one, try another one of the first options.

Unlike the stage model, using the parallel processes model isn’t prescriptive. There isn’t a simple script to follow for what comes next.

Research models and research advising

If, as a research advisor or lab instructor, you are strongly committed to the research stages model, you might find yourself simplifying the research-esque projects that your students work on, so that they are less likely to develop blocking tasks or sequencing setbacks. As long as this choice supports your learning goals, that’s probably ok. It works pretty well in instructional labs where the major goal is for students to learn something science-y with equipment. It also works pretty well if your primary interaction with research is reading published papers (e.g. for a journal club), or if the research work you engage in is primarily replication or repetition (e.g. for a CURE).

Alternately, if your students are engaging in original research, you might need to reconceptualize their projects in order to take advantage of the parallel processes model. You need to figure out reasonable timelines for their work and help them integrate each strand in these processes, from collecting pilot data before they’re “done” with a lit review to making space for preliminary analyses early enough to return to the literature. You also need to teach them how to turn to another part of the braid while they wait for your feedback, so that you are not a source of blocking tasks.

You will also need to work to help your students understand why you work this way, instead of in the more familiar stage model. By the time my students come to my lab, they have already engaged in many years of instructional labs (and sometimes also research-esque projects) in which the projects were simplified into fitting the research stages model. Often, they have already internalized feelings of failure when they need to return to literature during their analysis, and they have already experienced the paralysis of blocking tasks.

There are a few articles in this handbook to help you figure out how to mentor research with emerging scholars (singly and in groups) from a parallel processes model, or think more abstractly about how this works. I’m open to new suggestions: what else do you want to see?

Related articles

Planning research projects.

How to develop a timeline for an education research project that makes space for emergence.

Statement of research interests

How to write your statement of research interests

Evaluation and Research

What is the difference between evaluation and research?

This article was first written on June 2, 2018, and last modified on May 30, 2024.

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Selected Books on Theory

example of research model

Selected Journals on Theory

  • Historical Materialism Historical Materialism is an interdisciplinary journal dedicated to exploring and developing the critical and explanatory potential of Marxist theory.
  • International Journal of Social Research Methodology Focuses on current & emerging methodological debates across a wide range of social science disciplines & substantive interests.
  • Journal of Professional Counseling: Practice, Theory & Research Includes practical & unique applications of counseling techniques in schools & clinical settings, as well as significant quantitative & qualitative research.
  • Journal of Public Administration Research and Theory Peer reviewed coverage of the research and theory of public administration, including reports of empirical work, and both quantitative and qualitative areas of research.
  • Reconceptualizing Educational Research Methodology Reconceptualizing Educational Research Methodology (RERM) is an internationally refereed journal for researchers and practitioners investigating, tracing and theorizing practices, documentations and politics in education.

What is Meant by Theory?

A theory is a well-established principle that has been developed to explain some aspect of the natural world.

Theory of Everything comic by ThadGuy.com and used with permission.

Defining Theory

Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge, within the limits of the critical bounding assumptions.

  • The theoretical framework is the structure that can hold or support a theory of a research study.
  • The theoretical framework introduces and describes the theory which explains why the research problem under study exists.

The Importance of Theory

A theoretical framework consists of concepts, together with their definitions, and existing theory/theories that are used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your  research paper and that will relate it to the broader fields of knowledge in the class you are taking.

The theoretical framework is not something that is found readily available in the literature . You must review course readings and pertinent research literature for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.

The theoretical framework strengthens the study in the following ways .

  • An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
  • The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
  • Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to move from simply describing a phenomenon observed to generalizing about various aspects of that phenomenon.
  • Having a theory helps you to identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest. It alerts you to examine how those key variables might differ and under what circumstances.

By virtue of its application nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges of a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

The Conceptual Framework . College of Education. Alabama State University; Drafting an Argument . Writing@CSU. Colorado State University; Trochim, William M.K. Philosophy of Research . Research Methods Knowledge Base. 2006.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

Here are some strategies to develop of an effective theoretical framework:

  • Examine your thesis title and research problem . The research problem anchors your entire study and forms the basis from which you construct your theoretical framework.
  • Brainstorm on what you consider to be the key variables in your research . Answer the question, what factors contribute to the presumed effect?
  • Review related literature to find answers to your research question.
  • List  the constructs and variables that might be relevant to your study. Group these variables into independent and dependent categories.
  • Review the key social science theories that are introduced to you in your course readings and choose the theory or theories that can best explain the relationships between the key variables in your study [note the Writing Tip on this page].
  • Discuss the assumptions or propositions of this theory and point out their relevance to your research.

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint (framework) that the researcher will take in analyzing and interpreting the data to be gathered, understanding concepts and variables according to the given definitions, and building knowledge by validating or challenging theoretical assumptions.

II.  Purpose

Think of theories as the conceptual basis for understanding, analyzing, and designing ways to investigate relationships within social systems. To the end, the following roles served by a theory can help guide the development of your framework.*

  • Means by which new research data can be interpreted and coded for future use,
  • Response to new problems that have no previously identified solutions strategy,
  • Means for identifying and defining research problems,
  • Means for prescribing or evaluating solutions to research problems,
  • Way of telling us that certain facts among the accumulated knowledge are important and which facts are not,
  • Means of giving old data new interpretations and new meaning,
  • Means by which to identify important new issues and prescribe the most critical research questions that need to be answered to maximize understanding of the issue,
  • Means of providing members of a professional discipline with a common language and a frame of reference for defining boundaries of their profession, and
  • Means to guide and inform research so that it can, in turn, guide research efforts and improve professional practice.

*Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Incorporating Theory in Your Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, you are expected to test the validity of an existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism theory, which categorizes perceived differences between nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa.

A test of this theory could be the following: Does Peripheral Realism theory help explain intra-state actions, such as, the growing split between southern and northern Sudan that may likely lead to the creation of two nations?

However, you may not always be asked by your professor to test a specific theory in your paper, but to develop your own framework from which your analysis of the research problem is derived . Given this, it is perhaps easiest to understand the nature and function of a theoretical framework if it is viewed as the answer to two basic questions:

  • What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
  • Why is your approach a feasible solution? [I could choose to test Instrumentalist or Circumstantialists models developed among Ethnic Conflict Theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

The answers to these questions come from a thorough review of the literature and your course readings [summarized and analyzed in the next section of your paper] and the gaps in the research that emerge from the review process. With this in mind, a complete theoretical framework will likely not emerge until after you have completed a thorough review of the literature .

In writing this part of your research paper, keep in mind the following:

  • Clearly describe the framework, concepts, models, or specific theories that underpin your study . This includes noting who the key theorists are in the field who have conducted research on the problem you are investigating and, when necessary, the historical context that supports the formulation of that theory. This latter element is particularly important if the theory is relatively unknown or it is borrowed from another discipline.
  • Position your theoretical framework within a broader context of related frameworks , concepts, models, or theories . There will likely be several concepts, theories, or models that can be used to help develop a framework for understanding the research problem. Therefore, note why the framework you've chosen is the appropriate one.
  • The present tense is used when writing about theory.
  • You should make your theoretical assumptions as explicit as possible . Later, your discussion of methodology should be linked back to this theoretical framework.
  • Don’t just take what the theory says as a given! Reality is never accurately represented in such a simplistic way; if you imply that it can be, you fundamentally distort a reader's ability to understand the findings that emerge. Given this, always note the limitiations of the theoretical framework you've chosen [i.e., what parts of the research problem require further investigation because the theory does not explain a certain phenomena].

The Conceptual Framework . College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument . Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. A General Perspective on the Role of Theory in Qualitative Research. Journal of International Social Research 3 (Spring 2010); Trochim, William M.K. Philosophy of Research . Research Methods Knowledge Base. 2006.

Video on Creating a Theoretical Framework

  • Theoretical Framework A short introduction to theoretical frameworks and how to approach constructing one. Presented by Francois J. Desjardins, Associate Professor at University of Ontario Institute of Technology. NOTE: Dr. Desjardins speaks a bit quickly at times but the content of his presentation is very good.

Writing Tip | Borrowing Theoretical Constructs

Borrowing Theoretical Constructs from Elsewhere

A growing and increasingly important trend in the social sciences is to think about and attempt to understand specific research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories you've read about in a particular class, but to think about how an issue might be informed by theories developed in other disciplines.

For example, if you are a political science student studying the rhetorical strategies used by female incumbants in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies.

Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be fully engaged in the research topic.

Writing Tip | Don't Undertheorize

Never leave the theory hanging out there in the Introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you introduce should guide your study throughout the paper.

Be sure to always connect theory to the analysis and to explain in the discussion part of your paper how the theoretical framework you chose fit the research problem, or if appropriate, was inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Writing Tip | Theory vs Hypothesis

What's a Theory? What's a Hypothesis?

The terms theory and hypothesis are often used interchangeably in everyday use. However, the difference between them in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world.

Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted [e.g., rational choice theory; grounded theory].

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your research.

The key distinctions are:

  • A theory predicts events in a broad, general context;  a hypothesis makes a specific prediction about a specified set of circumstances.
  • A theory has been extensively tested and is generally accepted among scholars; a hypothesis is a speculative guess that has yet to be tested.

Cherry, Kendra. Introduction to Research Methods: Theory and Hypothesis . About.com Psychology; Gezae, Michael et al. Welcome Presentation on Hypothesis . Slideshare presentation.

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Research Method

Home » Research Report – Example, Writing Guide and Types

Research Report – Example, Writing Guide and Types

Table of Contents

Research Report

Research Report


Research Report is a written document that presents the results of a research project or study, including the research question, methodology, results, and conclusions, in a clear and objective manner.

The purpose of a research report is to communicate the findings of the research to the intended audience, which could be other researchers, stakeholders, or the general public.

Components of Research Report

Components of Research Report are as follows:


The introduction sets the stage for the research report and provides a brief overview of the research question or problem being investigated. It should include a clear statement of the purpose of the study and its significance or relevance to the field of research. It may also provide background information or a literature review to help contextualize the research.

Literature Review

The literature review provides a critical analysis and synthesis of the existing research and scholarship relevant to the research question or problem. It should identify the gaps, inconsistencies, and contradictions in the literature and show how the current study addresses these issues. The literature review also establishes the theoretical framework or conceptual model that guides the research.


The methodology section describes the research design, methods, and procedures used to collect and analyze data. It should include information on the sample or participants, data collection instruments, data collection procedures, and data analysis techniques. The methodology should be clear and detailed enough to allow other researchers to replicate the study.

The results section presents the findings of the study in a clear and objective manner. It should provide a detailed description of the data and statistics used to answer the research question or test the hypothesis. Tables, graphs, and figures may be included to help visualize the data and illustrate the key findings.

The discussion section interprets the results of the study and explains their significance or relevance to the research question or problem. It should also compare the current findings with those of previous studies and identify the implications for future research or practice. The discussion should be based on the results presented in the previous section and should avoid speculation or unfounded conclusions.

The conclusion summarizes the key findings of the study and restates the main argument or thesis presented in the introduction. It should also provide a brief overview of the contributions of the study to the field of research and the implications for practice or policy.

The references section lists all the sources cited in the research report, following a specific citation style, such as APA or MLA.

The appendices section includes any additional material, such as data tables, figures, or instruments used in the study, that could not be included in the main text due to space limitations.

Types of Research Report

Types of Research Report are as follows:

Thesis is a type of research report. A thesis is a long-form research document that presents the findings and conclusions of an original research study conducted by a student as part of a graduate or postgraduate program. It is typically written by a student pursuing a higher degree, such as a Master’s or Doctoral degree, although it can also be written by researchers or scholars in other fields.

Research Paper

Research paper is a type of research report. A research paper is a document that presents the results of a research study or investigation. Research papers can be written in a variety of fields, including science, social science, humanities, and business. They typically follow a standard format that includes an introduction, literature review, methodology, results, discussion, and conclusion sections.

Technical Report

A technical report is a detailed report that provides information about a specific technical or scientific problem or project. Technical reports are often used in engineering, science, and other technical fields to document research and development work.

Progress Report

A progress report provides an update on the progress of a research project or program over a specific period of time. Progress reports are typically used to communicate the status of a project to stakeholders, funders, or project managers.

Feasibility Report

A feasibility report assesses the feasibility of a proposed project or plan, providing an analysis of the potential risks, benefits, and costs associated with the project. Feasibility reports are often used in business, engineering, and other fields to determine the viability of a project before it is undertaken.

Field Report

A field report documents observations and findings from fieldwork, which is research conducted in the natural environment or setting. Field reports are often used in anthropology, ecology, and other social and natural sciences.

Experimental Report

An experimental report documents the results of a scientific experiment, including the hypothesis, methods, results, and conclusions. Experimental reports are often used in biology, chemistry, and other sciences to communicate the results of laboratory experiments.

Case Study Report

A case study report provides an in-depth analysis of a specific case or situation, often used in psychology, social work, and other fields to document and understand complex cases or phenomena.

Literature Review Report

A literature review report synthesizes and summarizes existing research on a specific topic, providing an overview of the current state of knowledge on the subject. Literature review reports are often used in social sciences, education, and other fields to identify gaps in the literature and guide future research.

Research Report Example

Following is a Research Report Example sample for Students:

Title: The Impact of Social Media on Academic Performance among High School Students

This study aims to investigate the relationship between social media use and academic performance among high school students. The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The findings indicate that there is a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students. The results of this study have important implications for educators, parents, and policymakers, as they highlight the need for strategies that can help students balance their social media use and academic responsibilities.


Social media has become an integral part of the lives of high school students. With the widespread use of social media platforms such as Facebook, Twitter, Instagram, and Snapchat, students can connect with friends, share photos and videos, and engage in discussions on a range of topics. While social media offers many benefits, concerns have been raised about its impact on academic performance. Many studies have found a negative correlation between social media use and academic performance among high school students (Kirschner & Karpinski, 2010; Paul, Baker, & Cochran, 2012).

Given the growing importance of social media in the lives of high school students, it is important to investigate its impact on academic performance. This study aims to address this gap by examining the relationship between social media use and academic performance among high school students.


The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The questionnaire was developed based on previous studies and was designed to measure the frequency and duration of social media use, as well as academic performance.

The participants were selected using a convenience sampling technique, and the survey questionnaire was distributed in the classroom during regular school hours. The data collected were analyzed using descriptive statistics and correlation analysis.

The findings indicate that the majority of high school students use social media platforms on a daily basis, with Facebook being the most popular platform. The results also show a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students.


The results of this study have important implications for educators, parents, and policymakers. The negative correlation between social media use and academic performance suggests that strategies should be put in place to help students balance their social media use and academic responsibilities. For example, educators could incorporate social media into their teaching strategies to engage students and enhance learning. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. Policymakers could develop guidelines and policies to regulate social media use among high school students.


In conclusion, this study provides evidence of the negative impact of social media on academic performance among high school students. The findings highlight the need for strategies that can help students balance their social media use and academic responsibilities. Further research is needed to explore the specific mechanisms by which social media use affects academic performance and to develop effective strategies for addressing this issue.


One limitation of this study is the use of convenience sampling, which limits the generalizability of the findings to other populations. Future studies should use random sampling techniques to increase the representativeness of the sample. Another limitation is the use of self-reported measures, which may be subject to social desirability bias. Future studies could use objective measures of social media use and academic performance, such as tracking software and school records.


The findings of this study have important implications for educators, parents, and policymakers. Educators could incorporate social media into their teaching strategies to engage students and enhance learning. For example, teachers could use social media platforms to share relevant educational resources and facilitate online discussions. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. They could also engage in open communication with their children to understand their social media use and its impact on their academic performance. Policymakers could develop guidelines and policies to regulate social media use among high school students. For example, schools could implement social media policies that restrict access during class time and encourage responsible use.


  • Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237-1245.
  • Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Journal of the Research Center for Educational Technology, 8(1), 1-19.
  • Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17(10), 652-657.
  • Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948-958.

Note*: Above mention, Example is just a sample for the students’ guide. Do not directly copy and paste as your College or University assignment. Kindly do some research and Write your own.

Applications of Research Report

Research reports have many applications, including:

  • Communicating research findings: The primary application of a research report is to communicate the results of a study to other researchers, stakeholders, or the general public. The report serves as a way to share new knowledge, insights, and discoveries with others in the field.
  • Informing policy and practice : Research reports can inform policy and practice by providing evidence-based recommendations for decision-makers. For example, a research report on the effectiveness of a new drug could inform regulatory agencies in their decision-making process.
  • Supporting further research: Research reports can provide a foundation for further research in a particular area. Other researchers may use the findings and methodology of a report to develop new research questions or to build on existing research.
  • Evaluating programs and interventions : Research reports can be used to evaluate the effectiveness of programs and interventions in achieving their intended outcomes. For example, a research report on a new educational program could provide evidence of its impact on student performance.
  • Demonstrating impact : Research reports can be used to demonstrate the impact of research funding or to evaluate the success of research projects. By presenting the findings and outcomes of a study, research reports can show the value of research to funders and stakeholders.
  • Enhancing professional development : Research reports can be used to enhance professional development by providing a source of information and learning for researchers and practitioners in a particular field. For example, a research report on a new teaching methodology could provide insights and ideas for educators to incorporate into their own practice.

How to write Research Report

Here are some steps you can follow to write a research report:

  • Identify the research question: The first step in writing a research report is to identify your research question. This will help you focus your research and organize your findings.
  • Conduct research : Once you have identified your research question, you will need to conduct research to gather relevant data and information. This can involve conducting experiments, reviewing literature, or analyzing data.
  • Organize your findings: Once you have gathered all of your data, you will need to organize your findings in a way that is clear and understandable. This can involve creating tables, graphs, or charts to illustrate your results.
  • Write the report: Once you have organized your findings, you can begin writing the report. Start with an introduction that provides background information and explains the purpose of your research. Next, provide a detailed description of your research methods and findings. Finally, summarize your results and draw conclusions based on your findings.
  • Proofread and edit: After you have written your report, be sure to proofread and edit it carefully. Check for grammar and spelling errors, and make sure that your report is well-organized and easy to read.
  • Include a reference list: Be sure to include a list of references that you used in your research. This will give credit to your sources and allow readers to further explore the topic if they choose.
  • Format your report: Finally, format your report according to the guidelines provided by your instructor or organization. This may include formatting requirements for headings, margins, fonts, and spacing.

Purpose of Research Report

The purpose of a research report is to communicate the results of a research study to a specific audience, such as peers in the same field, stakeholders, or the general public. The report provides a detailed description of the research methods, findings, and conclusions.

Some common purposes of a research report include:

  • Sharing knowledge: A research report allows researchers to share their findings and knowledge with others in their field. This helps to advance the field and improve the understanding of a particular topic.
  • Identifying trends: A research report can identify trends and patterns in data, which can help guide future research and inform decision-making.
  • Addressing problems: A research report can provide insights into problems or issues and suggest solutions or recommendations for addressing them.
  • Evaluating programs or interventions : A research report can evaluate the effectiveness of programs or interventions, which can inform decision-making about whether to continue, modify, or discontinue them.
  • Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies.

When to Write Research Report

A research report should be written after completing the research study. This includes collecting data, analyzing the results, and drawing conclusions based on the findings. Once the research is complete, the report should be written in a timely manner while the information is still fresh in the researcher’s mind.

In academic settings, research reports are often required as part of coursework or as part of a thesis or dissertation. In this case, the report should be written according to the guidelines provided by the instructor or institution.

In other settings, such as in industry or government, research reports may be required to inform decision-making or to comply with regulatory requirements. In these cases, the report should be written as soon as possible after the research is completed in order to inform decision-making in a timely manner.

Overall, the timing of when to write a research report depends on the purpose of the research, the expectations of the audience, and any regulatory requirements that need to be met. However, it is important to complete the report in a timely manner while the information is still fresh in the researcher’s mind.

Characteristics of Research Report

There are several characteristics of a research report that distinguish it from other types of writing. These characteristics include:

  • Objective: A research report should be written in an objective and unbiased manner. It should present the facts and findings of the research study without any personal opinions or biases.
  • Systematic: A research report should be written in a systematic manner. It should follow a clear and logical structure, and the information should be presented in a way that is easy to understand and follow.
  • Detailed: A research report should be detailed and comprehensive. It should provide a thorough description of the research methods, results, and conclusions.
  • Accurate : A research report should be accurate and based on sound research methods. The findings and conclusions should be supported by data and evidence.
  • Organized: A research report should be well-organized. It should include headings and subheadings to help the reader navigate the report and understand the main points.
  • Clear and concise: A research report should be written in clear and concise language. The information should be presented in a way that is easy to understand, and unnecessary jargon should be avoided.
  • Citations and references: A research report should include citations and references to support the findings and conclusions. This helps to give credit to other researchers and to provide readers with the opportunity to further explore the topic.

Advantages of Research Report

Research reports have several advantages, including:

  • Communicating research findings: Research reports allow researchers to communicate their findings to a wider audience, including other researchers, stakeholders, and the general public. This helps to disseminate knowledge and advance the understanding of a particular topic.
  • Providing evidence for decision-making : Research reports can provide evidence to inform decision-making, such as in the case of policy-making, program planning, or product development. The findings and conclusions can help guide decisions and improve outcomes.
  • Supporting further research: Research reports can provide a foundation for further research on a particular topic. Other researchers can build on the findings and conclusions of the report, which can lead to further discoveries and advancements in the field.
  • Demonstrating expertise: Research reports can demonstrate the expertise of the researchers and their ability to conduct rigorous and high-quality research. This can be important for securing funding, promotions, and other professional opportunities.
  • Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies. Producing a high-quality research report can help ensure compliance with these requirements.

Limitations of Research Report

Despite their advantages, research reports also have some limitations, including:

  • Time-consuming: Conducting research and writing a report can be a time-consuming process, particularly for large-scale studies. This can limit the frequency and speed of producing research reports.
  • Expensive: Conducting research and producing a report can be expensive, particularly for studies that require specialized equipment, personnel, or data. This can limit the scope and feasibility of some research studies.
  • Limited generalizability: Research studies often focus on a specific population or context, which can limit the generalizability of the findings to other populations or contexts.
  • Potential bias : Researchers may have biases or conflicts of interest that can influence the findings and conclusions of the research study. Additionally, participants may also have biases or may not be representative of the larger population, which can limit the validity and reliability of the findings.
  • Accessibility: Research reports may be written in technical or academic language, which can limit their accessibility to a wider audience. Additionally, some research may be behind paywalls or require specialized access, which can limit the ability of others to read and use the findings.

About the author

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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DrAfter123/DigitalVision Vectors

10 high-value use cases for predictive analytics in healthcare

Predictive analytics can support population health management, financial success, and better outcomes across the value-based care continuum..

  • Editorial Staff

As healthcare organizations pursue improved care delivery and increased operational efficiency, digital transformation remains a key strategy to help achieve these goals. Many health systems’ digital transformation journey involves identifying the value of their data and capitalizing on that value through big data analytics.

Of the four types of healthcare data analytics , predictive analytics currently has some of the highest potential for value generation. This type of analytics goes beyond showing stakeholders what happened and why, allowing users to gain insight into what’s likely to happen based on historical data trends.

Being able to forecast potential future patterns has game-changing potential as healthcare organizations aim to move from reactive to proactive, but those looking to leverage predictive analytics must first define relevant use cases.

In this primer, HealthITAnalytics will outline 10 predictive analytics use cases, in alphabetical order, that health systems can pursue as part of a successful predictive analytics strategy .


Improved care coordination can bolster patient outcomes and satisfaction, and predictive analytics is one way healthcare organizations can enhance these efforts. Predictive analytics is beneficial in hospital settings, where care coordination staff are trying to prevent outcomes like patient deterioration and readmission while optimizing patient flow.

Some healthcare organizations are already beginning to see success after deploying advanced analytics to reduce hospital readmissions .

In June, a research team from New York University (NYU) Grossman School of Medicine successfully built a large language model (LLM) known as NYUTron to predict multiple outcomes, including readmissions and length of stay.

The tool, detailed in a Nature study , can accurately forecast 30-day all-cause readmission, in-hospital mortality, comorbidity index, length of stay, and insurance denials using unaltered electronic health record (EHR) data. At the time of the study’s publication, NYUTron could predict 80 percent of all-cause readmissions, a five percent improvement over existing models.

According to a December 2023 NEJM Catalyst study , predictive models deployed at Corewell Health have seen similar success, keeping 200 patients from being readmitted and resulting in a $5 million cost savings.

In a 2022 interview with HealthITAnalytics , leadership from Children’s of Alabama discussed how real-time risk prediction allows the health system to tackle patient deterioration and pursue intensive care unit (ICU) liberation.

Alongside its applications for inpatient care management, predictive analytics is particularly useful for other preventive care uses, such as disease detection.


Effective disease management is vital to improving patient outcomes, but capturing and analyzing the necessary data only became plausible with the advent of predictive analytics.

Using predictive analytics for disease management requires healthcare organizations to pool extensive patient data — including EHRs, genomics, social determinants of health (SDOH), and other information — to identify relevant trends. These insights can then be used as a starting point to guide early disease detection and diagnosis efforts, anticipate disease progression, flag high-risk patients, and optimize treatment plans and resource allocation.

The promise of big data and predictive analytics is valuable in infectious disease monitoring.

In a February 2024 PLOS One study , researchers from the University of Virginia detailed the development of an online big data dashboard to track enteric infectious disease burden in low- and middle-income countries.

The dashboard is part of the Planetary Child Health & Enterics Observatory (Plan-EO) initiative, which aims to provide an evidence base to help geographically target child health interventions.

The dashboard will pull data from various sources to map transmission hotspots and predict outbreaks of diarrheal diseases, which public health stakeholders can use to better understand disease burden and guide decision-making.

The impacts of infectious disease are often inequitable, which may lead some to question the role that predictive analytics plays in concerns about health equity. Like any advanced data analytics approach, these tools must be used responsibly to avoid perpetuating health disparities, but when used responsibly, predictive tools can positively impact equity efforts.


Care disparities, bias and health inequity are rampant in the United States healthcare system. Researchers and clinicians are on the front lines of efforts to ensure that patients receive equitable care, but doing so requires healthcare stakeholders to gain a deep, nuanced understanding of how factors like SDOH impact patients .

Predictive analytics can help draw a wealth of information from the large, complex data needed to guide these efforts.

The health of those in marginalized communities is disproportionately impacted by housing, care access, social isolation and loneliness , food insecurity, and other issues. Effectively capturing data on these phenomena and designing interventions to address them is challenging, but predictive analytics has already bolstered these efforts.

Recently, researchers from Cleveland Clinic and MetroHealth were awarded over $3 million from the National Institutes of Health (NIH) to develop a digital twin-based, neighborhood-focused model to reduce disparities.

The Digital Twin Neighborhoods project uses de-identified EHR data to design digital replicas of real communities served by both organizations. Experts on the project indicated that by pulling geographic, biological, and SDOH information, researchers can better understand place-based health disparities.

Models developed using these data can simulate life course outcomes in a community. Tools that accurately predict the outcomes observed within a population’s EHRs can inform health equity interventions.

In 2021, United Healthcare launched a predictive analytics-based advocacy program to help address SDOH and improve care for its members. The system uses machine learning to identify individuals who may need social services support.

These insights are incorporated into an agent dashboard that member advocates can use, alongside more traditional tools like questionnaires, to gather more information from the patient about their situation. If necessary, the advocate connects the individual with support mechanisms.

Efforts like these also demonstrate the utility of predictive analytics tools in patient and member engagement.


Patient engagement plays a vital role in enhancing healthcare delivery. The advent of big data analytics in healthcare provides many opportunities for stakeholders to actively involve patients in their care.

Predictive analytics has shown promise in allowing health systems to proactively address barriers to patient engagement, such as appointment no-shows and medication adherence.

In a 2021 interview with HealthITAnalytics , Community Health Network leadership detailed how the health system bolsters its engagement efforts by using predictive analytics to reduce appointment no-shows and conduct post-discharge outreach.

A key aspect of this strategy is meeting patients where they are to effectively individualize their care journeys and improve their outcomes.

Appointment no-shows present a significant hurdle to achieving these aims, leading Community Health Network to implement automated, text message-based appointment reminders, with plans to deploy a two-way communication system to streamline the appointment scheduling process further.

The health system took a similar approach to post-discharge outreach, successfully deploying an automated solution during the COVID-19 pandemic.

To further enhance these systems, Community Health Network turned to predictive analytics.  By integrating a predictive algorithm into existing workflows, the health system could personalize outreach for appointment no-shows. Patients at low risk for no-shows may receive only one text message, but those at higher risk receive additional support, including outreach to determine whether unmet needs that the health system can help address are preventing them from making it to appointments.

Data analytics can also support medication adherence strategies by identifying non-adherence or predicting poor adherence.

One 2020 study published in Psychiatry Research showed that machine learning models can “accurately predict rates of medication adherence of [greater than or equal to 80 percent] across a clinical trial, adherence over the subsequent week, and adherence the subsequent day” among a large cohort of participants with a variety of conditions.

Research published in the March 2020 issue of BMJ Open Diabetes Research & Care found that a machine learning model tasked with identifying type 2 diabetes patients at high risk of medication nonadherence was accurate and sensitive, achieving good performance.

Outside the clinical sphere, predictive analytics is also useful for helping organizations like payers meet their strategic goals.


Payers are an integral part of the US healthcare system. As payer organizations work with providers to guide members' care journeys, they generate a wealth of data that provides insights into healthcare utilization, costs, and outcomes.

Predictive analytics can help transform these data and inform efforts to improve payer forecasting . With historical data, payers can use predictive modeling to identify care management trends, forecast membership shifts, project enrollment churn, and pinpoint changes in service demand, among other uses.

In June 2023, leaders from Elevance Health discussed how the payer’s emphasis on predictive analytics is key to improving member outcomes.

Elevance utilizes a predictive algorithm to personalize member experience by addressing diabetes management and fall risk. The predictive model pulls clinical indicators like demographics, comorbidities, and A1C levels to forecast future A1C patterns and identify individuals with uncontrolled or poorly controlled diabetes.

From there, the payer can help members manage their condition through at-home lab A1C test kits and increased member and care team engagement.

The second predictive tool incorporates data points — including past diagnoses, procedures, and medications, the presence of musculoskeletal-related conditions and connective tissue disorders, analgesic or opioid drug usage, and frailty indicators — to flag women over the age of 65 at higher risk of fracture from a fall.

Elevance then conducts outreach to these individuals to recommend bone density scans and other interventions to improve outcomes.

These efforts are one example of how predictive analytics can improve the health of specific populations, but these tools can also be applied to population health more broadly.


While much of healthcare is concerned with improving individual patients’ well-being, advancing the health of populations is extremely valuable for boosting health outcomes on a large scale. To that end, many healthcare organizations are pursuing data-driven population health management .

Predictive analytics tools can enhance these initiatives by guiding large-scale efforts in chronic disease management and population-wide care coordination.

In one 2021 American Journal of Preventive Medicine study , a research team from New York University’s School of Global Public Health and Tandon School showed that machine learning-driven models incorporating SDOH data can accurately predict cardiovascular disease burden. Further, insights from these tools can guide treatment recommendations.

The early identification of chronic disease risk is also helpful in informing preventive care interventions and flagging gaps in care .

Being closely related to population health , public health can also benefit from applying predictive analytics.

Researchers from the Center for Neighborhood Knowledge at UCLA Luskin, writing in the International Journal of Environmental Health in 2021, detailed how a predictive model successfully helped them identify which neighborhoods in Los Angeles County were at the greatest risk for COVID-19 infections.

The tool mapped the county on a neighborhood-by-neighborhood basis to evaluate residents’ vulnerability to infection using four indicators: barriers to accessing health care, socioeconomic challenges, built-environment characteristics, and preexisting medical conditions.

The model allowed stakeholders to harness existing local data to guide public health decision-making, prioritize vulnerable populations for vaccination, and prevent new COVID-19 infections.

Alongside large-scale initiatives like these, predictive modeling can also support the advancement of precision medicine.


The emergence of genomics and big data analytics has opened new doors in the realm of tailored health interventions. Precision and personalized medicine rely on individual patients’ data points to guide their care and improve their well-being.

From cancer to genetic conditions, predictive analytics is a crucial aspect of precision medicine.

In 2021, a meta-analysis presented at the American Society for Radiation Oncology (ASTRO) Annual Meeting showed that a genetic biomarker test could accurately predict treatment response in men with high-risk prostate cancer.

The test analyzes gene activity in prostate tumors to generate a score to represent the aggressiveness of a patient’s cancer. These insights can be used to personalize treatment plans that balance survival risk with quality of life.

Researchers from Arizona State University (ASU) revealed in a 2024 Cell Systems paper that they developed a machine learning model to predict how a patient’s immune system will respond to foreign pathogens.

The tool uses information on individualized molecular interactions to characterize how major histocompatibility complex-1 (MHC-1) proteins — key players in the body’s ability to recognize foreign cells — impact immune response.

MHC-1s exist on the cell surface and bind foreign peptides to present to the immune system for recognition and attack. These proteins also come in thousands of varieties across the human genome, making it difficult to forecast how various MHC-1s interact with a given pathogen.

The ASU research addressed this by analyzing just under 6,000 MHC-1 alleles, shedding light on how these molecules interact with peptides and revealing that individuals with a diverse range of MHC-1s were more likely to survive cancer treatment.

Using the model, providers could potentially forecast pathological outcomes for patients, bolstering treatment planning and clinical decision-making.

In addition to these successes at the microscopic level, predictive analytics is also useful on the macro level in healthcare.


Optimization of the supply chain and resource allocation ensures that providers and patients receive the equipment, medications, and other tools that they need to support positive outcomes. Data analytics plays a massive role in this, as supply chain management and resource use rely heavily on accurately recording and tracking resources as they move from the assembly line into the clinical setting.

Predictive analytics takes this one step further by helping stakeholders anticipate and address supply chain issues before they arise while optimizing resource use.

Seattle Children's Hospital is using predictive modeling in the form of digital twins to help the health system streamline hospital operations , particularly resource allocation.

By using digital twin simulation to “clone” the hospital, stakeholders can model how certain events, strategies, or policies might impact operational efficiency. This capability was critical in the wake of COVID-19, as it allowed the health system to identify how rapidly its personal protective equipment (PPE) supplies would diminish, forecast bed capacity, and generate insights around labor resources.

Predictive analytics can also be used by distinct parts of the supply chain to help prevent shortages.

The 2022 infant formula shortage is one example of how supply chain disruptions can significantly impact health.

One potential way for parents to deal with the formula shortage was to turn to human breast milk banks, which distribute donated milk to vulnerable babies and their families. However, accomplishing this vital work requires milk banks to effectively screen donors, accept donations, process and test them to ensure they’re safe, and dispense them.

In an interview with HealthITAnalytics , stakeholders from Mothers' Milk Bank at WakeMed Health & Hospitals described how data analytics can help optimize aspects of this process.

A crucial part of ensuring that milk is available to those who need it is tracking milk waste. Milk can be wasted for various reasons, but the presence of bacteria is one of the primary causes. To address this, the milk bank began analyzing donor records to determine what factors may make a batch of milk more likely to test positive for bacillus .

The milk bank can then use the insights generated from the analysis to predict which donors may be at high risk for having bacillus in their milk, allowing milk from these individuals to be tested separately. This removes any bacillus -positive samples before the milk is pooled for processing.

Predictive analytics is also helpful in assessing and managing risks in clinical settings.


Patient risk scores have the potential to improve care management initiatives, as they allow providers to formulate improved prevention strategies to eliminate or reduce adverse outcomes. Risk scores are used to help understand what characteristics may make a patient more susceptible to various conditions.

From there, the scores can inform risk stratification efforts, which enables health systems to categorize patients based on whether they are low-, medium- or high-risk. These data can show how one or more factors increase a patient's risk.

Risk stratification is one of the most valuable use cases for predictive analytics because of its ability to prevent adverse outcomes.

In February 2024, leaders from Parkland Health & Hospital System (PHHS) and Parkland Center for Clinical Innovation (PCCI) in Dallas, Texas, detailed one of these high-value use cases.

Parkland’s Universal Suicide Screening Program is an initiative designed to flag patients at risk of suicide who may have flown under the health system’s radar through proactive screening of all Parkland patients aged 10 or older, regardless of the reason for the clinical encounter.

During the encounter, nursing staff ask the patient a set of standardized, validated questions to assess their suicide risk. This information is then incorporated into the EHR for risk stratification.

These data are useful for stakeholders looking to better understand patients’ stories, including factors like healthcare utilization before suicide. Coupling these insights with state mortality could help predict and prevent suicide in the future.

Risk stratification is also crucial for improving outcomes for some of the youngest, most vulnerable patients: newborns.

Parkland also runs an initiative that uses SDOH data to identify at-risk pregnant patients and enable early interventions to help reduce preterm births .

The program’s risk prediction model and text message-based patient education program have been invaluable in understanding the nuances of preterm birth risk for Parkland patients. Major risk factors like cervical length and history of spontaneous preterm delivery may not be easy to determine for some patients. Further, many preterm births appear to be associated with additional risk factors outside of these – like prenatal visit attendance.

Using these additional factors to forecast risk, Parkland has developed clinical- and population-level interventions that have resulted in a 20 percent reduction in preterm births.

These use cases, among other things, demonstrate the key role predictive analytics can play in advancing value-based care.


Value-based care incentivizes healthcare providers to improve care quality and delivery by linking reimbursement to patient outcomes. To achieve value-based care success, providers rely on a host of tools: health information exchange (HIE), data analytics, artificial intelligence (AI) and machine learning (ML), population health management solutions, and price transparency technologies.

Predictive analytics can be utilized alongside these tools to drive long-term success for healthcare organizations pursuing value-based care.

Accountable care organizations (ACOs) are significant players in the value-based care space, and predictive modeling has already helped some achieve their goals in this area.

Buena Vida y Salud ACO partnered with the Health Data Analytics Institute (HDAI) in 2023 to explore how predictive analytics could help the organization keep patients healthy at home.

At the outset of the collaboration, the ACO’s leadership team was presented with multiple potential use cases in which data analysis could help with unplanned admissions, worsening heart failure, pneumonia development, and more.

However, providers were overwhelmed when given risk-stratified patient lists for multiple use cases. Upon working with its providers, the ACO found that allowing clinicians to choose the use cases or patient cohorts they wanted to focus on was much more successful.

The approach has helped the ACO engage its providers and enhance care management efforts through predictive modeling and digital twins. These tools provide fine-grain insights into the drivers of outcomes like pneumonia-related hospitalization, which guide the development of care management interventions.

These 10 use cases are just the beginning of predictive analytics' potential to transform healthcare. As data analytics technologies like AI, ML and digital twins continue to advance, the value of predictive analytics is likely to increase exponentially.

What Are the Benefits of Predictive Analytics in Healthcare?

  • How Can Predictive Analytics Help ACOs Boost Value-Based Care Delivery?
  • Putting the Pieces Together for a Successful Predictive Analytics Strategy

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What Is One Way Large Language Models Can Help In Daily Life?

What Is One Way Large Language Models Can Help In Daily Life?

In today’s digital age, large language models, such as ChatGPT, are revolutionizing how we interact with technology. These models, built on vast amounts of data and sophisticated algorithms, can perform a wide range of tasks that benefit our daily lives in numerous ways. Today, we will check what is one way large language models can help in daily life. So, let’s dive into the details.

What Are Large Language Models?

Table of Contents

Before diving into their practical applications, let’s briefly understand what large language models are. Simply put, they are advanced computer programs capable of understanding and generating human-like text based on the input they receive. These models learn from extensive datasets to predict and produce coherent responses.

Applications in Daily Life

1. enhanced communication.

Large language models excel in enhancing communication across various platforms. They can:

  • Assist in Learning: Students can use them to clarify concepts, improve writing skills, and even receive personalized tutoring.
  • Language Translation: They help break down language barriers by translating text and speech in real-time, facilitating global communication.
  • Accessibility: Individuals with disabilities can use these models to interact with devices and access information more independently.

2. Information Retrieval and Research

These models act as powerful research assistants:

  • Answering Questions: Large language models swiftly give correct answers on many subjects, like history and science.
  • Using Data: Businesses rely on these models to study markets, spot trends, and decide wisely using lots of data.

3. Creative Assistance

Large language models foster creativity and productivity:

  • Content Generation: They assist writers, journalists, and content creators by generating ideas, drafting articles, and suggesting improvements.
  • Artistic Expression: They can help in creating poetry, stories, and even generating music compositions based on user inputs.

Enhancing Communication and Accessibility

Large language models such as ChatGPT improve communication across languages and assist individuals with disabilities. They enable:

  • Language Translation: Facilitating seamless communication between people who speak different languages.
  • Personalized Assistance: Providing tailored support for learning and accessibility needs.
  • Voice Interfaces: Allowing hands-free interaction with devices, enhancing accessibility for visually impaired individuals.

These capabilities not only simplify everyday tasks but also foster inclusion and global connectivity, making information and communication more accessible to everyone.

Why Are They Helpful: This capability simplifies information retrieval, supports learning, and improves accessibility for individuals with disabilities.

How Are They Helpful: Large language models, such as ChatGPT, assist in answering questions and enhancing communication by understanding natural language inputs and generating relevant responses.

Example: A student can use ChatGPT to understand complex concepts, ask questions about their homework, and receive explanations in real-time. 

Similarly, someone with visual impairment can interact with devices using voice commands powered by these models, improving their independence and access to information.

Some Examples of Usage

Let’s look at some practical examples of how individuals can benefit from large language models:

Student researching a projectQuickly finds relevant information and summaries.
Traveler needing language translationCommunicates effectively in foreign countries.
Small business owner analyzing market trendsMakes data-driven decisions for business growth.
Writer brainstorming new ideasGenerates creative prompts and explores different angles.

How Will Large Language Models Change The World?

Large language models have the potential to significantly impact the world in various ways:

  • Enhanced Communication: They will improve global communication by breaking down language barriers through accurate translation and natural language understanding.
  • Advancing Education: These models can personalize learning experiences, offering tutoring, clarifying complex topics, and providing accessible educational resources globally.
  • Revolutionizing Business: Large language models aid in data analysis, market research, and customer interaction, enabling businesses to make informed decisions and streamline operations.
  • Fostering Innovation: They stimulate creativity by generating ideas, assisting in content creation across diverse fields such as art, literature, and music.
  • Improving Accessibility: They empower individuals with disabilities by enabling easier interaction with technology and access to information through voice commands and text-to-speech capabilities.
  • Ethical Considerations: As their capabilities grow, ensuring responsible use and addressing potential biases or misuse will be crucial for ethical implementation.

What Are The Use Cases For Large Language Models?

Large language models have numerous practical use cases across various domains:

Natural Language Understanding

  • Chatbots and Virtual Assistants: They power intelligent virtual assistants like Siri, Alexa, and customer support chatbots, handling inquiries and providing responses in natural language.
  • Information Retrieval: They assist in retrieving relevant information from vast datasets quickly and accurately.

Content Creation and Enhancement

  • Writing Assistance: They help in drafting content, suggesting improvements, and generating creative ideas for writers, journalists, and content creators.
  • Automated Summarization: They summarize lengthy documents or articles, saving time for readers and researchers.

Language Translation

  • Real-Time Translation: They facilitate real-time translation of text and speech across multiple languages, enhancing global communication and accessibility.

Education and Learning

  • Personalized Learning: They provide personalized tutoring, explanations of complex topics, and educational resources tailored to individual learning styles.
  • Assessment and Feedback: They assist in grading assignments, providing feedback, and assessing students’ understanding.

Research and Data Analysis

  • Data Insights: They analyze large volumes of data, identifying patterns, trends, and insights for businesses, researchers, and analysts.
  • Scientific Discovery: They aid researchers in accessing and understanding scientific literature, accelerating the pace of discovery in fields like medicine and biology.

Creative Applications

  • Artistic Creation: They contribute to generating poetry, stories, music compositions, and other forms of artistic expression based on user inputs and stylistic preferences.

Accessibility and Inclusion

  • Assistive Technologies: They support accessibility features such as text-to-speech and speech recognition, benefiting individuals with disabilities by enabling easier interaction with devices and information.

Ethical Considerations

  • Bias Mitigation: They address biases in language and decision-making processes to ensure fair and inclusive outcomes.
  • Privacy and Security: They uphold data privacy standards and ensure secure handling of sensitive information.

In conclusion (of what is one way large language models can help in daily life), large language models like ChatGPT are not just technological marvels but practical tools that significantly enhance our daily lives.

Whether it’s improving communication, aiding research, or fostering creativity, these models are reshaping how we interact with information and each other. 

As they continue to evolve, their potential to simplify tasks and enrich human experiences grows, promising a future where technology truly empowers and connects us in unprecedented ways.

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DiffiT: Diffusion Vision Transformers for Image Generation

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Diffusion models with their powerful expressivity and high sample quality have achieved State-Of-The-Art (SOTA) performance in the generative domain. The pioneering Vision Transformer (ViT) has also demonstrated strong modeling capabilities and scalability, especially for recognition tasks. In this paper, we study the effectiveness of ViTs in diffusion-based generative learning and propose a new model denoted as Diffusion Vision Transformers (DiffiT). Specifically, we propose a methodology for finegrained control of the denoising process and introduce the Time-dependant Multihead Self Attention (TMSA) mechanism. DiffiT is surprisingly effective in generating high-fidelity images with significantly better parameter efficiency. We also propose latent and image space DiffiT models and show SOTA performance on a variety of class-conditional and unconditional synthesis tasks at different resolutions. The Latent DiffiT model achieves a new SOTA FID score of 1.73 on ImageNet256 dataset while having 19.85%, 16.88% less parameters than other Transformer-based diffusion models such as MDT and DiT, respectively.

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  • 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Research question Explanation
The first question is not enough. The second question is more , using .
Starting with “why” often means that your question is not enough: there are too many possible answers. By targeting just one aspect of the problem, the second question offers a clear path for research.
The first question is too broad and subjective: there’s no clear criteria for what counts as “better.” The second question is much more . It uses clearly defined terms and narrows its focus to a specific population.
It is generally not for academic research to answer broad normative questions. The second question is more specific, aiming to gain an understanding of possible solutions in order to make informed recommendations.
The first question is too simple: it can be answered with a simple yes or no. The second question is , requiring in-depth investigation and the development of an original argument.
The first question is too broad and not very . The second question identifies an underexplored aspect of the topic that requires investigation of various  to answer.
The first question is not enough: it tries to address two different (the quality of sexual health services and LGBT support services). Even though the two issues are related, it’s not clear how the research will bring them together. The second integrates the two problems into one focused, specific question.
The first question is too simple, asking for a straightforward fact that can be easily found online. The second is a more question that requires and detailed discussion to answer.
? dealt with the theme of racism through casting, staging, and allusion to contemporary events? The first question is not  — it would be very difficult to contribute anything new. The second question takes a specific angle to make an original argument, and has more relevance to current social concerns and debates.
The first question asks for a ready-made solution, and is not . The second question is a clearer comparative question, but note that it may not be practically . For a smaller research project or thesis, it could be narrowed down further to focus on the effectiveness of drunk driving laws in just one or two countries.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Type of research Example question
Qualitative research question
Quantitative research question
Statistical research question

Other interesting articles

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


  • Sampling methods
  • Simple random sampling
  • Stratified sampling
  • Cluster sampling
  • Likert scales
  • Reproducibility


  • Null hypothesis
  • Statistical power
  • Probability distribution
  • Effect size
  • Poisson distribution

Research bias

  • Optimism bias
  • Cognitive bias
  • Implicit bias
  • Hawthorne effect
  • Anchoring bias
  • Explicit bias

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