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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.

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Overview of the Research Process

• First Online: 01 January 2012

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Research is a rigorous problem-solving process whose ultimate goal is the discovery of new knowledge. Research may include the description of a new phenomenon, definition of a new relationship, development of a new model, or application of an existing principle or procedure to a new context. Research is systematic, logical, empirical, reductive, replicable and transmittable, and generalizable. Research can be classified according to a variety of dimensions: basic, applied, or translational; hypothesis generating or hypothesis testing; retrospective or prospective; longitudinal or cross-sectional; observational or experimental; and quantitative or qualitative. The ultimate success of a research project is heavily dependent on adequate planning.

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Supino, P.G. (2012). Overview of the Research Process. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_1

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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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst... there’s more!

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

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10 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

how to cite this page

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

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

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

Introduction

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

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

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

General Structure and Writing Style

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

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

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

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

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

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

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

Action Research Design

Definition and Purpose

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

What do these studies tell you ?

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

What these studies don't tell you ?

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

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

Case Study Design

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

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

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

Causal Design

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

Conditions necessary for determining causality:

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

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

Cohort Design

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

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

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

Cross-Sectional Design

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

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

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

Descriptive Design

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

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

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

Experimental Design

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

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

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

Exploratory Design

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

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

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

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

Field Research Design

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

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

What these studies don't tell you

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

Historical Design

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

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

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

Longitudinal Design

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

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

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

Meta-Analysis Design

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

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

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

Mixed-Method Design

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

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

Observational Design

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

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

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

Philosophical Design

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

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

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

Sequential Design

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

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

Systematic Review

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

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

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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:

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.

Scientific Method

4. research methods: modeling.

LEGO ® bricks have been a staple of the toy world since they were first manufactured in Denmark in 1953. The interlocking plastic bricks can be assembled into an endless variety of objects (see Figure 1). Some kids (and even many adults) are interested in building the perfect model – finding the bricks of the right color, shape, and size, and assembling them into a replica of a familiar object in the real world, like a castle, the space shuttle , or London Bridge. Others focus on using the object they build – moving LEGO knights in and out of the castle shown in Figure 1, for example, or enacting a space shuttle mission to Mars. Still others may have no particular end product in mind when they start snapping bricks together and just want to see what they can do with the pieces they have.

On the most basic level, scientists use models in much the same way that people play with LEGO bricks. Scientific models may or may not be physical entities, but scientists build them for the same variety of reasons: to replicate systems in the real world through simplification, to perform an experiment that cannot be done in the real world, or to assemble several known ideas into a coherent whole to build and test hypotheses .

Types of models: Physical, conceptual, mathematical

At the St. Anthony Falls Laboratory at the University of Minnesota, a group of engineers and geologists have built a room-sized physical replica of a river delta to model a real one like the Mississippi River delta in the Gulf of Mexico (Paola et al., 2001). These researchers have successfully incorporated into their model the key processes that control river deltas (like the variability of water flow, the deposition of sediments transported by the river, and the compaction and subsidence of the coastline under the pressure of constant sediment additions) in order to better understand how those processes interact. With their physical model, they can mimic the general setting of the Mississippi River delta and then do things they can’t do in the real world, like take a slice through the resulting sedimentary deposits to analyze the layers within the sediments. Or they can experiment with changing parameters like sea level and sedimentary input to see how those changes affect deposition of sediments within the delta, the same way you might “experiment” with the placement of the knights in your LEGO castle.

Not all models used in scientific research are physical models. Some are conceptual, and involve assembling all of the known components of a system into a coherent whole. This is a little like building an abstract sculpture out of LEGO bricks rather than building a castle. For example, over the past several hundred years, scientists have developed a series of models for the structure of an atom . The earliest known model of the atom compared it to a billiard ball, reflecting what scientists knew at the time – they were the smallest piece of an element that maintained the properties of that element. Despite the fact that this was a purely conceptual model, it could be used to predict some of the behavior that atoms exhibit. However, it did not explain all of the properties of atoms accurately. With the discovery of subatomic particles like the proton and electron , the physicist Ernest Rutherford proposed a “solar system” model of the atom, in which electrons orbited around a nucleus that included protons (see our Atomic Theory I: The Early Days module for more information). While the Rutherford model is useful for understanding basic properties of atoms, it eventually proved insufficient to explain all of the behavior of atoms. The current quantum model of the atom depicts electrons not as pure particles, but as having the properties of both particles and waves , and these electrons are located in specific probability density clouds around the atom’s nucleus.

Both physical and conceptual models continue to be important components of scientific research . In addition, many scientists now build models mathematically through computer programming. These computer-based models serve many of the same purposes as physical models, but are determined entirely by mathematical relationships between variables that are defined numerically. The mathematical relationships are kind of like individual LEGO bricks: They are basic building blocks that can be assembled in many different ways. In this case, the building blocks are fundamental concepts and theories like the mathematical description of turbulent flow in a liquid , the law of conservation of energy, or the laws of thermodynamics, which can be assembled into a wide variety of models for, say, the flow of contaminants released into a groundwater reservoir or for global climate change.

Comprehension Checkpoint

All models are exact replicas of physical things.

Modeling as a scientific research method

Whether developing a conceptual model like the atomic model, a physical model like a miniature river delta , or a computer model like a global climate model, the first step is to define the system that is to be modeled and the goals for the model. “System” is a generic term that can apply to something very small (like a single atom), something very large (like the Earth’s atmosphere), or something in between, like the distribution of nutrients in a local stream. So defining the system generally involves drawing the boundaries (literally or figuratively) around what you want to model, and then determining the key variables and the relationships between those variables.

Though this initial step may seem straightforward, it can be quite complicated. Inevitably, there are many more variables within a system than can be realistically included in a model , so scientists need to simplify. To do this, they make assumptions about which variables are most important. In building a physical model of a river delta , for example, the scientists made the assumption that biological processes like burrowing clams were not important to the large-scale structure of the delta, even though they are clearly a component of the real system.

Determining where simplification is appropriate takes a detailed understanding of the real system – and in fact, sometimes models are used to help determine exactly which aspects of the system can be simplified. For example, the scientists who built the model of the river delta did not incorporate burrowing clams into their model because they knew from experience that they would not affect the overall layering of sediments within the delta. On the other hand, they were aware that vegetation strongly affects the shape of the river channel (and thus the distribution of sediments), and therefore conducted an experiment to determine the nature of the relationship between vegetation density and river channel shape (Gran & Paola, 2001).

Once a model is built (either in concept, physical space, or in a computer), it can be tested using a given set of conditions. The results of these tests can then be compared against reality in order to validate the model. In other words, how well does the model do at matching what we see in the real world? In the physical model of delta sediments , the scientists who built the model looked for features like the layering of sand that they have seen in the real world. If the model shows something really different than what the scientists expect, the relationships between variables may need to be redefined or the scientists may have oversimplified the system . Then the model is revised, improved, tested again, and compared to observations again in an ongoing, iterative process . For example, the conceptual “billiard ball” model of the atom used in the early 1800s worked for some aspects of the behavior of gases, but when that hypothesis was tested for chemical reactions , it didn’t do a good job of explaining how they occur – billiard balls do not normally interact with one another. John Dalton envisioned a revision of the model in which he added “hooks” to the billiard ball model to account for the fact that atoms could join together in reactions , as conceptualized in Figure 3.

Once a model is built, it is never changed or modified.

While conceptual and physical models have long been a component of all scientific disciplines, computer-based modeling is a more recent development, and one that is frequently misunderstood. Computer models are based on exactly the same principles as conceptual and physical models, however, and they take advantage of relatively recent advances in computing power to mimic real systems .

The beginning of computer modeling: Numerical weather prediction

In the late 19 th century, Vilhelm Bjerknes , a Norwegian mathematician and physicist, became interested in deriving equations that govern the large-scale motion of air in the atmosphere . Importantly, he recognized that circulation was the result not just of thermodynamic properties (like the tendency of hot air to rise), but of hydrodynamic properties as well, which describe the behavior of fluid flow. Through his work, he developed an equation that described the physical processes involved in atmospheric circulation, which he published in 1897. The complexity of the equation reflected the complexity of the atmosphere, and Bjerknes was able to use it to describe why weather fronts develop and move.

Using calculations predictively

Bjerknes had another vision for his mathematical work, however: He wanted to predict the weather. The goal of weather prediction, he realized, is not to know the paths of individual air molecules over time, but to provide the public with “average values over large areas and long periods of time.” Because his equation was based on physical principles , he saw that by entering the present values of atmospheric variables like air pressure and temperature, he could solve it to predict the air pressure and temperature at some time in the future. In 1904, Bjerknes published a short paper describing what he called “the principle of predictive meteorology” (Bjerknes, 1904) (see the Research links for the entire paper). In it, he says:

Based upon the observations that have been made, the initial state of the atmosphere is represented by a number of charts which give the distribution of seven variables from level to level in the atmosphere. With these charts as the starting point, new charts of a similar kind are to be drawn, which represent the new state from hour to hour.

In other words, Bjerknes envisioned drawing a series of weather charts for the future based on using known quantities and physical principles . He proposed that solving the complex equation could be made more manageable by breaking it down into a series of smaller, sequential calculations, where the results of one calculation are used as input for the next. As a simple example, imagine predicting traffic patterns in your neighborhood. You start by drawing a map of your neighborhood showing the location, speed, and direction of every car within a square mile. Using these parameters , you then calculate where all of those cars are one minute later. Then again after a second minute. Your calculations will likely look pretty good after the first minute. After the second, third, and fourth minutes, however, they begin to become less accurate. Other factors you had not included in your calculations begin to exert an influence, like where the person driving the car wants to go, the right- or left-hand turns that they make, delays at traffic lights and stop signs, and how many new drivers have entered the roads.

Trying to include all of this information simultaneously would be mathematically difficult, so, as proposed by Bjerknes, the problem can be solved with sequential calculations. To do this, you would take the first step as described above: Use location, speed, and direction to calculate where all the cars are after one minute. Next, you would use the information on right- and left-hand turn frequency to calculate changes in direction, and then you would use information on traffic light delays and new traffic to calculate changes in speed. After these three steps are done, you would solve your first equation again for the second minute time sequence, using location, speed, and direction to calculate where the cars are after the second minute. Though it would certainly be rather tiresome to do by hand, this series of sequential calculations would provide a manageable way to estimate traffic patterns over time.

Although this method made calculations tedious, Bjerknes imagined “no intractable mathematical difficulties” with predicting the weather. The method he proposed (but never used himself) became known as numerical weather prediction, and it represents one of the first approaches towards numerical modeling of a complex, dynamic system .

Advancing weather calculations

Bjerknes’ challenge for numerical weather prediction was taken up sixteen years later in 1922 by the English scientist Lewis Fry Richardson . Richardson related seven differential equations that built on Bjerknes’ atmospheric circulation equation to include additional atmospheric processes. One of Richardson’s great contributions to mathematical modeling was to solve the equations for boxes within a grid; he divided the atmosphere over Germany into 25 squares that corresponded with available weather station data (see Figure 4) and then divided the atmosphere into five layers, creating a three-dimensional grid of 125 boxes. This was the first use of a technique that is now standard in many types of modeling. For each box, he calculated each of nine variables in seven equations for a single time step of three hours. This was not a simple sequential calculation, however, since the values in each box depended on the values in the adjacent boxes, in part because the air in each box does not simply stay there – it moves from box to box.

Richardson’s attempt to make a six-hour forecast took him nearly six weeks of work with pencil and paper and was considered an utter failure, as it resulted in calculated barometric pressures that exceeded any historically measured value (Dalmedico, 2001). Probably influenced by Bjerknes, Richardson attributed the failure to inaccurate input data , whose errors were magnified through successive calculations (see more about error propagation in our Uncertainty, Error, and Confidence module).

In addition to his concerns about inaccurate input parameters , Richardson realized that weather prediction was limited in large part by the speed at which individuals could calculate by hand. He thus envisioned a “forecast factory,” in which thousands of people would each complete one small part of the necessary calculations for rapid weather forecasting.

First computer for weather prediction

Richardson’s vision became reality in a sense with the birth of the computer, which was able to do calculations far faster and with fewer errors than humans. The computer used for the first one-day weather prediction in 1950, nicknamed ENIAC (Electronic Numerical Integrator and Computer), was 8 feet tall, 3 feet wide, and 100 feet long – a behemoth by modern standards, but it was so much faster than Richardson’s hand calculations that by 1955, meteorologists were using it to make forecasts twice a day (Weart, 2003). Over time, the accuracy of the forecasts increased as better data became available over the entire globe through radar technology and, eventually, satellites.

The process of numerical weather prediction developed by Bjerknes and Richardson laid the foundation not only for modern meteorology , but for computer-based mathematical modeling as we know it today. In fact, after Bjerknes died in 1951, the Norwegian government recognized the importance of his contributions to the science of meteorology by issuing a stamp bearing his portrait in 1962 (Figure 5).

Weather prediction is based on _____________ modeling.

• mathematical

Modeling in practice: The development of global climate models

The desire to model Earth’s climate on a long-term, global scale grew naturally out of numerical weather prediction. The goal was to use equations to describe atmospheric circulation in order to understand not just tomorrow’s weather, but large-scale patterns in global climate, including dynamic features like the jet stream and major climatic shifts over time like ice ages. Initially, scientists were hindered in the development of valid models by three things: a lack of data from the more inaccessible components of the system like the upper atmosphere , the sheer complexity of a system that involved so many interacting components, and limited computing powers. Unexpectedly, World War II helped solve one problem as the newly-developed technology of high altitude aircraft offered a window into the upper atmosphere (see our Technology module for more information on the development of aircraft). The jet stream, now a familiar feature of the weather broadcast on the news, was in fact first documented by American bombers flying westward to Japan.

As a result, global atmospheric models began to feel more within reach. In the early 1950s, Norman Phillips, a meteorologist at Princeton University, built a mathematical model of the atmosphere based on fundamental thermodynamic equations (Phillips, 1956). He defined 26 variables related through 47 equations, which described things like evaporation from Earth’s surface , the rotation of the Earth, and the change in air pressure with temperature. In the model, each of the 26 variables was calculated in each square of a 16 x 17 grid that represented a piece of the northern hemisphere. The grid represented an extremely simple landscape – it had no continents or oceans, no mountain ranges or topography at all. This was not because Phillips thought it was an accurate representation of reality, but because it simplified the calculations. He started his model with the atmosphere “at rest,” with no predetermined air movement, and with yearly averages of input parameters like air temperature.

Phillips ran the model through 26 simulated day-night cycles by using the same kind of sequential calculations Bjerknes proposed. Within only one “day,” a pattern in atmospheric pressure developed that strongly resembled the typical weather systems of the portion of the northern hemisphere he was modeling (see Figure 6). In other words, despite the simplicity of the model, Phillips was able to reproduce key features of atmospheric circulation , showing that the topography of the Earth was not of primary importance in atmospheric circulation. His work laid the foundation for an entire subdiscipline within climate science: development and refinement of General Circulation Models (GCMs).

By the 1980s, computing power had increased to the point where modelers could incorporate the distribution of oceans and continents into their models . In 1991, the eruption of Mt. Pinatubo in the Philippines provided a natural experiment: How would the addition of a significant volume of sulfuric acid , carbon dioxide, and volcanic ash affect global climate? In the aftermath of the eruption, descriptive methods (see our Description in Scientific Research module) were used to document its effect on global climate: Worldwide measurements of sulfuric acid and other components were taken, along with the usual air temperature measurements. Scientists could see that the large eruption had affected climate , and they quantified the extent to which it had done so. This provided a perfect test for the GCMs . Given the inputs from the eruption, could they accurately reproduce the effects that descriptive research had shown? Within a few years, scientists had demonstrated that GCMs could indeed reproduce the climatic effects induced by the eruption, and confidence in the abilities of GCMs to provide reasonable scenarios for future climate change grew. The validity of these models has been further substantiated by their ability to simulate past events, like ice ages, and the agreement of many different models on the range of possibilities for warming in the future, one of which is shown in Figure 7.

Limitations and misconceptions of models

The widespread use of modeling has also led to widespread misconceptions about models , particularly with respect to their ability to predict. Some models are widely used for prediction, such as weather and streamflow forecasts, yet we know that weather forecasts are often wrong. Modeling still cannot predict exactly what will happen to the Earth’s climate , but it can help us see the range of possibilities with a given set of changes. For example, many scientists have modeled what might happen to average global temperatures if the concentration of carbon dioxide (CO 2 ) in the atmosphere is doubled from pre-industrial levels (pre-1950); though individual models differ in exact output, they all fall in the range of an increase of 2-6° C (IPCC, 2007).

All models are also limited by the availability of data from the real system . As the amount of data from a system increases, so will the accuracy of the model. For climate modeling, that is why scientists continue to gather data about climate in the geologic past and monitor things like ocean temperatures with satellites – all those data help define parameters within the model. The same is true of physical and conceptual models, too, well-illustrated by the evolution of our model of the atom as our knowledge about subatomic particles increased.

__________ can result in a flawed model.

• Lack of data about a system
• Too much data about a system

Modeling in modern practice

The various types of modeling play important roles in virtually every scientific discipline, from ecology to analytical chemistry and from population dynamics to geology. Physical models such as the river delta take advantage of cutting edge technology to integrate multiple large-scale processes. As computer processing speed and power have increased, so has the ability to run models on them. From the room-sized ENIAC in the 1950s to the closet-sized Cray supercomputer in the 1980s to today’s laptop, processing speed has increased over a million-fold, allowing scientists to run models on their own computers rather than booking time on one of only a few supercomputers in the world. Our conceptual models continue to evolve, and one of the more recent theories in theoretical physics digs even deeper into the structure of the atom to propose that what we once thought were the most fundamental particles – quarks – are in fact composed of vibrating filaments, or strings. String theory is a complex conceptual model that may help explain gravitational force in a way that has not been done before. Modeling has also moved out of the realm of science into recreation, and many computer games like SimCity® involve both conceptual modeling (answering the question, “What would it be like to run a city?”) and computer modeling, using the same kinds of equations that are used model traffic flow patterns in real cities. The accessibility of modeling as a research method allows it to be easily combined with other scientific research methods, and scientists often incorporate modeling into experimental, descriptive, and comparative studies.

Scientific modeling is a research method scientists use to replicate real-world systems – whether it’s a conceptual model of an atom, a physical model of a river delta, or a computer model of global climate. This module describes the principles that scientists use when building models and shows how modeling contributes to the process of science.

Key Concepts

• Modeling involves developing physical, conceptual, or computer-based representations of systems.
• Scientists build models to replicate systems in the real world through simplification, to perform an experiment that cannot be done in the real world, or to assemble several known ideas into a coherent whole to build and test hypotheses.
• Computer modeling is a relatively new scientific research method, but it is based on the same principles as physical and conceptual modeling.

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Research Model

A National Research Model for Online Learning By Tanya Joosten

Downloadable PDF version

In developing the grant proposal for the U.S. Department of Education’s Fund for Improvement in Postsecondary Education (FIPSE), several researchers from the University of Wisconsin-Milwaukee (UWM) spent a Friday afternoon discussing the types of research projects we would propose to be conducted by the new National Research Center for Distance Education and Technological Advancements (DETA).  What became clear in that meeting room was evidence of a broader issue in distance education research.  Individuals who are studying distance education, including eLearning, blended learning, and online learning, are heterogeneous.  These individuals represent an array of disciplines, including different paradigmatic, theoretical, and methodological approaches to studying distance education, just as we were witnessing in the room that day.  The opportunity of this diversity in research approaches has the potential to provide our higher education communities a greater understanding of the complexity of human interaction in distance education.  The opportunity identified also presented a new problem to solve – we don’t all speak the same language about research in distance education.  Evident from this discussion was a need for coherency about how to approach the study of this phenomenon.  

In distance education, a common language or ground has not yet been established.  Although existing scholarship attempts to establish an identity for teaching and learning on the fringe or margins (see Moore, 2013), such as distance education, there is still much work to be done.  It is common in other disciplines to struggle with finding this common ground as well (e.g., Corman & Poole, 2000).  Yet, unlike many other disciplines that have models illustrative of the phenomenon of interest or research models that guide the design of research, distance education has seen little traction in this area.  A cohesive approach to researching distance education from a transdisciplinary lens is pertinent.        

The lack of common language and work being conducted in disciplinary silos has led to a disregard or lack of acknowledgement of previous developments in the field.  Furthermore, the disconnect many times between the fast moving development of practice and redundant research of already proven practices is less than helpful to developing distance education. Several authors over the last several years have noted this dilemma.  Saba (2013) discusses that “authors, editors, and reviewers are not familiar with the historical origin and conceptual growth of the field of distance education…history starts from when they become interested in the field” (p. 50).   Dziuban and Picciano (2015) refer to Roberts (2007) and Diamond (1999) in describing this as a type of amnesia where “we tend to trust what we have seen for ourselves and dismiss events that have occurred in the distance past…we forget anything but what we are experiencing at the moment and assume that the present is a way it has always been” (p. 179).  Moore and Kiersey (2011) have discussed this tendency as a threat to good practice and good scholarship.  

Our initial goal, as outlined in the grant, is to solve this problem and create a language that will have sustainability across disciplines and temporal barriers.  At least in the first year, it was apparent that there was a need for grant efforts to focus on creating a language we can all understand as well as to engage distance education stakeholders from across the country in the attempt to create an interdisciplinary lens for examining distance education. In so doing, the aim is to facilitate research efforts regarding cross-institutional distance education research as a strategy for ensuring quality in teaching and learning for all students.  The research fellows on the grant team felt a desire to identify a model or models that represented research in distance education, in particular, with regard to the research that would be conducted as part of the grant activities.  Moreover, the development of a framework of inquiry that included detailed representations, which illustrates the varying levels of inquiry as characterized by input-throughput-output processes facilitating an interdisciplinary approach to studying distance education, was needed as well as research models.      

Development

The first goal of the grant activities is to develop research models for online learning that provide guidance in the practice of distance education research.  The models were intended to facilitate the exploration of instructional practices, inform future instructional practices, serve as a model for future research practices across educational institutions, and enhance consistency in the field.  In the development process, it became clear that a more general research model was needed to represent the various research designs that would be deployed as part of the DETA research efforts rather than several specific research models.  The development of this model included the following steps:

• Review of the literature on desired outcomes in distance education, including blended and online research, to determine key desired outcomes in practice and research in the field.       
• Identify and engaging with national experts, including researchers and practitioners, in the field to identify pertinent research questions and variables of interest for enhancing the understanding of the desired outcomes.  
• Review germane research and current national efforts to ensure alignment with the development of research model and the framework of inquiry, including identifying any gaps and future areas of research needed.
• Create research designs, including formulating measures, instrumentation, and coding to conduct cross-institutional research within the framework of inquiry.
• Develop a research model for online learning appropriate for interdisciplinary research and diverse methodologies to be brought to fruition in the development and use of research toolkits by researchers and practitioners across the country.

The National DETA Research Model for Online Learning

Prior to the DETA national summit, held at the 2015 EDUCAUSE Learning Initiative (ELI)  meeting, the DETA Research Center reviewed pertinent literature and documents in developing the desired outcomes (see https://uwm.edu/deta/desired-outcomes/).  These desired outcomes were published on the DETA community site and feedback was solicited from the national experts who participated in the summit.  The desired outcomes guiding the activities at the DETA national summit are also appended.

Participants at the DETA national summit (see https://uwm.edu/deta/summit/ ) were asked to participate in two key sets of activities related to developing and prioritizing research questions  and the process of creating a framework of inquiry to guide current and future research by identifying key variables for research model.   

The research questions and associated votes were statistically analyzed for prioritization.  The top research questions were identified by highlighting those that were one standard deviation at or above the mean.  The top research questions can be viewed at: https://uwm.edu/deta/top-research-questions/ .  Additionally, the variables were examined to identify conceptual alignment with existing literature and to sort based on level of inquiry, which resulted in the framework of inquiry (see Figure 1, General Framework of Inquiry).  The detailed version of the framework of inquiry, including variables, can be viewed here .

Figure 1, General Framework of Inquiry

Situated within the framework of inquiry, several research designs were created, including formulating measures, developing instrumentation, and coding to conduct cross-institutional research within the framework of inquiry.  These research designs included experimental and survey study designs to address the top research questions.  Experimental designs included interventions identified for testing that burgeoned from discussions at the DETA national summit.  Survey studies and instrumentation (applicable to both survey and experimental studies) were developed from existing research at UWM and a review of the literature, including utilized instrumentation.  Survey studies included questions to gather qualitative data for analysis to address research questions of exploratory nature.  Both the survey and experimental research designs are complemented by data mining of student information systems to provide learner characteristics (low-income, minority, first generation, and disabled) and outcome data (grade, completion).      

Model Description

Taking a structured approach to model development, a research model for online learning appropriate for interdisciplinary research and diverse methodologies was derived from a grounded and theoretical approach (see Figure 2, Developing Research Model of Online Learning).  The model is considered grounded because it is a reflection of the research questions, framework of inquiry, including variables, and research designs developed as part of the grant activities.  The model is considered theoretical since social and learning theories inform the development.

Figure 2, Developing Research Model of Online Learning

There are four primary components that compose the research model for online learning.  The four components include (1) inputs and outputs, (2) process, (3) context, and (4) interventions.  The inputs and outputs include both agency and structural level inputs.  Agency level inputs include students (learners) and instructors.  Structural level inputs include the characteristics of the course, instruction, and the program that provide structure, rules, and resources to agents to facilitate online learning process.  The second component is the process, which includes in-class and out-of-class interactions that are online learning.  The third component is that of the context.  The context for the research of this grant is institutions of postsecondary higher education. Although much learning may happen in informal settings, it is not a focus of this model.  The final component of the model is intervention.  Interventions create variable conditions intended to result in a predetermined outcome, usually to increase student success.       

There are three facets of the model that describe the relationship between and among the components of the model.  First, the model is cyclical in nature in that learning is conducted in cycles with each end playing the role of input and output through an interactive process representing a continuous lifecycle of online learning.  Second, the model is transactional .   This means that online learning is a simultaneous engagement of students and instructors in the learning process.  Students and instructors are linked reciprocally.  Third, the model can be structurational .   Courses, instructional, and program characteristics are outcomes from human action (instructors and staff) in design, development, and modification.  Also, these facilitate and constrain student interactions in online learning.  Furthermore, institutional properties influence individuals in their online learning interaction through instructional and professional norms, design standards, and available resources.  Likewise, the interactions in online learning will influence institutional properties through reinforcing or transforming structures.    

The proposed model describes a series of inputs that can have a relationship with online learning, which is a throughput or process, inside and outside the classroom within the contexts of institutions.  For DETA research the institutional context is postsecondary institutions of higher education.  The cyclical elements of the model are evident in the inputs, including the characteristics of students, instructors, course as well as instruction, and programs, may influence the online learning process, which, in return, will influence future inputs of online learning process in a cyclical fashion.  For instance, a course is designed by an instructor in such a way that it leads to increased rates of completion, which eventually can alter the program profile and potentially future course designs.  Therefore, the inputs will influence the online learning process, which will in return influence the inputs through a feedback loop process.  For example, students may become more confident and have a greater growth or mindset for achievement in future courses, instructors may learn from what works in the classroom and improve future instructional methods and course designs, and programs may have greater success.  Not only is there a lifecycle of online learning, but an important interplay between the success of students in a course and the continued development of courses and programs by instructors and staff within the institution.  

There are individual agents in the model, including students and instructors, that have characteristics of which have a relationship with online learning.  First, these students and instructors are agents within the context of institutions but have influences from beyond the institution, too.  The cognition and experiences (from within and outside of the institution) of students and instructors will potentially affect online learning interactions within and outside a class.  Second, there are also course, instructional, and program characteristics. The design of these, in particular, will have a relationship with and potentially enhancing or hindering the process of online learning.  These five inputs will have relationships with the online learning process.  

Interventions can be employed at any level of these input variables in order to enhance the probability that the online learning process will be positively influenced.  Interventions can be at the agent level to develop students or instructors, or at the course, instructional, or program levels to potentially improve the interactions of students and instructors to enhance online learning.  At the learner level, an intervention may be a workshop about taking an online course.  At the instructor level, an intervention may be a faculty development program for teaching online.  At the course and instructional level, an intervention may be focused on how content is designed to  meet the course learning outcomes to enhance the student-content interaction.  At the program level, an intervention may be the receipt of tutoring support during the course.  Interventions at the agent or structural levels are intended to increase student success by enhancing online learning.

The model represents an array of research designs, including experimental, quasi experimental, survey, and qualitative appropriate for DETA research.  Input variables, such as student or course characteristics, can be mined through institutional technology systems, such as student information systems, or can be reported on surveys.  This information can be used for all research designs.  Experimental or quasi experimental studies would focus on comparisons of the control and experimental condition based on the intervention applied usually through the comparison of student assessments.  Survey studies can examine the ability to predict student outcome variables based on the student self-report of instructional and program/institutional characteristics including reports of behaviors taking place or perceptions of in-class and out-of-class.  Finally, qualitative data can be collected through surveys and other methods to better understand or develop measurement for an array of constructs (e.g., student motivation, ecosystem components).    

This research model and the associated toolkits serves to guide research conducted across institutions and disciplines, including both experimental and survey studies.  The DETA Research Center will disseminate a call for proposals in the grant’s second year, October 2015, to identify partners across the country who are interested in using the research toolkits to gather data to better understand the key factors in distance education courses and programs that are impacting student success.  Once research has been conducted, an evaluation of the model and toolkits will be conducted to improve the quality of the grant products for dissemination in the final year of the grant.    

References:

Corman, S. R., & Poole, M. S. (2000) Perspectives on organizational communication: Finding common ground. Guilford Press.

Dziuban, C. D., & Picciano, A. G. (2015).  What the future might hold for online and blended learning research.  In Dziuban, C. D., Picciano, A. G., Graham, C. R., & Moskal, P. D. (Eds). Conducting research in online and blended learning environments.

Moore, M. G. (Ed.). (2013). Handbook of distance education. Routledge.

Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Cengage Learning.

Orlikowski, W. J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization science, 3(3), 398-427.

Saba, F. (2013).  Building the future: A theoretical perspective. In Moore, M. G. (Ed.). Handbook of distance education. Routledge.

Top Research Questions

Framework of Inquiry

Analysis of Summit Data:

Key Research Questions and Variables

Key Themes from Discussion Notes

Microsoft Research Blog

Gigapath: whole-slide foundation model for digital pathology.

Published May 22, 2024

By Hoifung Poon , General Manager, Health Futures Naoto Usuyama , Principal Researcher

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The confluence of digital transformation in biomedicine and the current generative AI revolution creates an unprecedented opportunity for drastically accelerating progress in precision health. Digital pathology is emblematic of this exciting frontier. In cancer care, whole-slide imaging has become routinely available, which transforms a microscopy slide of tumor tissue into a high-resolution digital image. Such whole-slide images contain key information for deciphering the tumor microenvironment, which is critical for precision immunotherapy (for example differentiating hot versus cold tumors based on lymphocyte infiltration). Digital pathology can also be combined with other multimodal, longitudinal patient information in multimodal generative AI for scaling population-level, real-world evidence generation. 

This is an exciting time, tempered by the reality that digital pathology poses unique computational challenges, as a standard gigapixel slide may be thousands of times larger than typical natural images in both width and length. Conventional vision transformers struggle with such an enormous size as computation for self-attention grows dramatically with the input length. Consequently, prior work in digital pathology often ignores the intricate interdependencies across image tiles in each slide, thus missing important slide-level context for key applications such as modeling the tumor microenvironment. 

In this blog post, we introduce GigaPath (opens in new tab) , a novel vision transformer that attains whole-slide modeling by leveraging dilated self-attention to keep computation tractable. In joint work with Providence Health System and the University of Washington, we have developed Prov-GigaPath (opens in new tab) , an open-access whole-slide pathology foundation model pretrained on more than one billion 256 X 256 pathology images tiles in more than 170,000 whole slides from real-world data at Providence.  All computation was conducted within Providence’s private tenant, approved by Providence Institutional Review Board (IRB).  

To our knowledge, this is the first whole-slide foundation model for digital pathology with large-scale pretraining on real-world data. Prov-GigaPath attains state-of-the-art performance on standard cancer classification and pathomics tasks, as well as vision-language tasks. This demonstrates the importance of whole-slide modeling on large-scale real-world data and opens new possibilities to advance patient care and accelerate clinical discovery.

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Adapting dilated attention and LongNet to digital pathology

GigaPath adopts two-stage curriculum learning comprising tile-level pretraining using DINOv2 and slide-level pretraining using masked autoencoder with LongNet (see Figure 1). DINOv2 is a standard self-supervision method that combines contrastive loss and masked reconstruction loss in training teacher and student vision transformers. However, due to the computational challenge for self-attention, its application is limited to small images such as 256 × 256 tiles. For slide-level modeling, we adapt dilated attention from LongNet to digital pathology (see Figure 2). To handle the long sequence of image tiles for a whole slide, we introduce a series of increasing sizes for subdividing the tile sequence into segments of the given size. For larger segments, we introduce sparse attention with sparsity proportional to segment length, thus canceling out the quadratic growth. The largest segment would cover the entire slide, though with sparsely subsampled self-attention. This enables us to capture long-range dependencies in a systematic way while maintaining tractability in computation (linear in context length).

GigaPath on cancer classification and pathomics tasks

We construct a digital pathology benchmark comprising nine cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data. With large-scale pretraining and whole-slide modeling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best model on 18 tasks.

On cancer subtyping, the goal is to classify fine-grained subtypes based on the pathology slide. For example, for ovarian cancer, the model needs to differentiate among six subtypes: Clear Cell Ovarian Cancer, Endometrioid Ovarian Cancer, High-Grade Serous Ovarian Cancer, Low-Grade Serous Ovarian Cancer, Mucinous Ovarian Cancer, and Ovarian Carcinosarcoma. Prov-GigaPath attained state-of-the-art performance in all nine tasks, with significant improvement over the second best in six out of nine tasks (see Figure 3). For six cancer types (breast, kidney, liver, brain, ovarian, central nervous system), Prov-GigaPath attains an AUROC of 90% or higher. This bodes well for downstream applications in precision health such as cancer diagnostics and prognostics. 

On pathomics tasks, the goal is to classify whether the tumor exhibits specific clinically relevant genetic mutations based on the slide image alone. This may uncover meaningful connections between tissue morphology and genetic pathways that are too subtle to be picked up by human observation. Aside from a few well-known pairs of specific cancer type and gene mutations, it is unclear how much signal there exists from the slide alone. Moreover, in some experiments, we consider the pan-cancer scenario, where we are trying to identify universal signals for a gene mutation across all cancer types and very diverse tumor morphologies. In such challenging scenarios, Prov-GigaPath once again attained state-of-the-art performance in 17 out of 18 tasks, significantly outperforming the second best in 12 out of 18 tasks (see Figure 4). For example, in the pan-cancer 5-gene analysis, Prov-GigaPath outperformed the best competing methods by 6.5% in AUROC and 18.7% in AUPRC. We also conducted head-to-head comparison on TCGA data to assess the generalizability of Prov-GigaPath and found that Prov-GigaPath similarly outperformed all competing methods there. This is all the more remarkable given that the competing methods were all pretrained on TCGA. That Prov-Gigapath can extract genetically linked pan-cancer and subtype-specific morphological features at the whole-slide level highlights the biological relevance of the underlying learned embeddings, and opens the door to using real-world data for future research directions around the complex biology of the tumor microenvironment. 

GigaPath on vision-language tasks

We further demonstrate the potential of GigaPath on vision-language tasks by incorporating the pathology reports. Prior work on pathology vision-language pretraining tends to focus on small images at the tile level. We instead explore slide-level vision-language pretraining. By continuing pretraining on slide-report pairs, we can leverage the report semantics to align the pathology slide representation, which can be used for downstream prediction tasks without supervised fine-tuning (e.g., zero-shot subtyping). Specifically, we use Prov-GigaPath as the whole-slide image encoder and PubMedBERT as the text encoder, and conduct contrastive learning using the slide-report pairs. This is considerably more challenging than traditional vision-language pretraining, as we do not have fine-grained alignment information between individual image tiles and text snippets. Prov-GigaPath substantially outperforms three state-of-the-art pathology vision-language models in standard vision-language tasks, such as zero-shot cancer subtyping and gene mutation prediction, demonstrating the potential for Prov-GigaPath in whole-slide vision-language modeling (see Figure 5).

GigaPath is a promising step toward multimodal generative AI for precision health

We have conducted thorough ablation studies to establish the best practices in whole-slide pretraining and vision-language modeling. We also observed early indications of the scaling law in digital pathology, where larger-scale pretraining generally improved downstream performance, although our experiments were still limited due to computational constraints.

Going forward, there are many opportunities for progress. Prov-GigaPath attained state-of-the-art performance compared to prior best models, but there is still significant growth space in many downstream tasks. While we have conducted initial exploration on pathology vision-language pretraining, there is still a long way to go to pursue the potential of a multimodal conversational assistant, specifically by incorporating advanced multimodal frameworks such as LLaVA-Med (opens in new tab) . Most importantly, we have yet to explore the impact of GigaPath and whole-slide pretraining in many key precision health tasks such as modeling tumor microenvironment and predicting treatment response.

GigaPath is joint work with Providence Health System and the University of Washington’s Paul G. Allen School of Computer Science & Engineering, and brings collaboration from multiple teams within Microsoft*. It reflects Microsoft’s larger commitment on advancing multimodal generative AI for precision health, with exciting progress in other digital pathology research collaborations such as Cyted (opens in new tab) , Volastra (opens in new tab) , and Paige (opens in new tab) as well as other technical advances such as BiomedCLIP (opens in new tab) , LLaVA-Rad (opens in new tab) , BiomedJourney (opens in new tab) , BiomedParse (opens in new tab) , MAIRA (opens in new tab) , Rad-DINO (opens in new tab) , Virchow (opens in new tab) . 

(Acknowledgment footnote) *: Within Microsoft, it is a wonderful collaboration among Health Futures, MSRA, MSR Deep Learning, and Nuance. Paper co-authors: Hanwen Xu, Naoto Usuyama, Jaspreet Bagga, Sheng Zhang, Rajesh Rao, Tristan Naumann, Cliff Wong, Zelalem Gero, Javier Gonz ́alez, Yu Gu, Yanbo Xu, Mu Wei, Wenhui Wang, Shuming Ma, Furu Wei, Jianwei Yang, Chunyuan Li, Jianfeng Gao, Jaylen Rosemon, Tucker Bower, Soohee Lee, Roshanthi Weerasinghe, Bill J. Wright, Ari Robicsek, Brian Piening, Carlo Bifulco, Sheng Wang, Hoifung Poon. 

Related publications

Llava-med: training a large language-and-vision assistant for biomedicine in one day, maira-1: a specialised large multimodal model for radiology report generation, longnet: scaling transformers to 1,000,000,000 tokens, biomedjourney: counterfactual biomedical image generation by instruction-learning from multimodal patient journeys, meet the authors.

Hoifung Poon

General Manager, Health Futures

Naoto Usuyama

Principal Researcher

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NASA, IBM Research to Release New AI Model for Weather, Climate

By Jessica Barnett

Working together, NASA and IBM Research have developed a new artificial intelligence model to support a variety of weather and climate applications. The new model – known as the Prithvi-weather-climate foundational model – uses artificial intelligence (AI) in ways that could vastly improve the resolution we’ll be able to get, opening the door to better regional and local weather and climate models.  

Foundational models are large-scale, base models which are trained on large, unlabeled datasets and can be fine-tuned for a variety of applications. The Prithvi-weather-climate model is trained on a broad set of data – in this case NASA data from NASA’s Modern-Era Retrospective analysis for Research and Applications (MERRA-2)– and then makes use of AI learning abilities to apply patterns gleaned from the initial data across a broad range of additional scenarios.  

“Advancing NASA’s Earth science for the benefit of humanity means delivering actionable science in ways that are useful to people, organizations, and communities. The rapid changes we’re witnessing on our home planet demand this strategy to meet the urgency of the moment,” said Karen St. Germain, director of the Earth Science Division of NASA’s Science Mission Directorate. “The NASA foundation model will help us produce a tool that people can use: weather, seasonal and climate projections to help inform decisions on how to prepare, respond and mitigate.”  

With the Prithvi-weather-climate model, researchers will be able to support many different climate applications that can be used throughout the science community. These applications include detecting and predicting severe weather patterns or natural disasters, creating targeted forecasts based on localized observations, improving spatial resolution on global climate simulations down to regional levels, and improving the representation of how physical processes are included in weather and climate models.

“These transformative AI models are reshaping data accessibility by significantly lowering the barrier of entry to using NASA’s scientific data,” said Kevin Murphy, NASA’s chief science data officer, Science Mission Directorate at NASA Headquarters. “Our open approach to sharing these models invites the global community to explore and harness the capabilities we’ve cultivated, ensuring that NASA’s investment enriches and benefits all.” 

Prithvi-weather-climate was developed through an open collaboration with IBM Research, Oak Ridge National Laboratory, and NASA, including the agency’s Interagency Implementation and Advanced Concepts Team (IMPACT) at Marshall Space Flight Center in Huntsville, Alabama. 

Prithvi-weather-climate can capture the complex dynamics of atmospheric physics even when there is missing information thanks to the flexibility of the model’s architecture. This foundational model for weather and climate can scale to both global and regional areas without compromising resolution. 

“This model is part of our overall strategy to develop a family of AI foundation models to support NASA’s science mission goals,” said Rahul Ramachandran, who leads IMPACT at Marshall. “These models will augment our capabilities to draw insights from our vast archives of Earth observations.”  

Prithvi-weather-climate is part of a larger model family– the Prithvi family– which includes models trained on NASA’s Harmonized LandSat and Sentinel-2 data. The latest model serves as an open collaboration in line with NASA’s open science principles to make all data accessible and usable by communities everywhere. It will be released later this year on Hugging Face, a machine learning and data science platform that helps users build, deploy, and train machine learning models. 

“The development of the NASA foundation model for weather and climate is an important step towards the democratization of NASA’s science and observation mission,” said Tsendgar Lee, program manager for NASA’s Research and Analysis Weather Focus Area, High-End Computing Program, and Data for Operation and Assessment. “We will continue developing new technology for climate scenario analysis and decision making.” 

Along with IMPACT and IBM Research, development of Prithvi-weather-climate featured significant contributions from NASA’s Office of the Chief Science Data Officer, NASA’s Global Modeling and Assimilation Office at Goddard Space Flight Center, Oak Ridge National Laboratory, the University of Alabama in Huntsville, Colorado State University, and Stanford University. 

Learn more about Earth data and previous Prithvi models:  https://www.earthdata.nasa.gov/news/impact-ibm-hls-foundation-model

Jonathan Deal   Marshall Space Flight Center, Huntsville, Ala.    256.544.0034    [email protected]   

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Rising seas and extreme storms fueled by climate change are combining to generate more frequent and severe floods in cities along rivers and coasts, and aging infrastructure is poorly equipped for the new reality. But when governments and planners try to prepare communities for worsening flood risks by improving infrastructure, the benefits are often unfairly distributed.

A new modeling approach from Stanford University and University of Florida researchers offers a solution: an easy way for planners to simulate future flood risks at the neighborhood level under conditions expected to become commonplace with climate change, such as extreme rainstorms that coincide with high tides elevated by rising sea levels.

The approach, described May 28 in Environmental Research Letters , reveals places where elevated risk is invisible with conventional modeling methods designed to assess future risks based on data from a single past flood event. “Asking these models to quantify the distribution of risk along a river for different climate scenarios is kind of like asking a microwave to cook a sophisticated souffle. It’s just not going to go well,” said senior study author Jenny Suckale, an associate professor of geophysics at the Stanford Doerr School of Sustainability . “We don’t know how the risk is distributed, and we don’t look at who benefits, to which degree.”

Helping other flood-prone communities

The new approach to modeling flood risk can help city and regional planners create better flood risk assessments and avoid creating new inequities, Suckale said. The algorithm is publicly available for other researchers to adapt to their location.

A history of destructive floods

The new study came about through collaboration with regional planners and residents in bayside cities including East Palo Alto, which faces rising flood risks from the San Francisco Bay and from an urban river that snakes along its southeastern border.

The river, known as the San Francisquito Creek, meanders from the foothills above Stanford’s campus down through engineered channels to the bay – its historic floodplains long ago developed into densely populated cities. “We live around it, we drive around it, we drive over it on the bridges,” said lead study author Katy Serafin , a former postdoctoral scholar in Suckale’s research group. 

The river has a history of destructive floods. The biggest one, in 1998, inundated 1,700 properties, caused more than $40 million in damages, and led to the creation of a regional agency tasked with mitigating future flood risk.

Nearly 20 years after that historic flood, Suckale started thinking about how science could inform future flood mitigation efforts around urban rivers like the San Francisquito when she was teaching a course in East Palo Alto focused on equity, resilience, and sustainability in urban areas. Designated as a Cardinal Course for its strong public service component, the course was offered most recently under the title Shaping the Future of the Bay Area . 

Around the time Suckale started teaching the course, the regional agency – known as the San Francisquito Creek Joint Powers Authority – had developed plans to redesign a bridge to allow more water to flow underneath it and prevent flooding in creekside cities. But East Palo Alto city officials told Suckale and her students that they worried the plan could worsen flood risks in some neighborhoods downstream of the bridge.

Suckale realized that if the students and scientists could determine how the proposed design would affect the distribution of flood risks along the creek, while collaborating with the agency to understand its constraints, then their findings could guide decisions about how to protect all neighborhoods. “It’s actionable science, not just science for science’s sake,” she said.

San Francisquito Creek waters rose along a temporary wooden floodwall in East Palo Alto, California, during a storm event on Dec. 31, 2022. | Jim Wiley, courtesy of the San Francisquito Creek Joint Powers Authority

Science that leads to action

The Joint Powers Authority had developed the plan using a flood-risk model commonly used by hydrologists around the world. The agency had considered the concerns raised by East Palo Alto city staff about downstream flood risks, but found that the standard model couldn’t substantiate them.

“We wanted to model a wider range of factors that will contribute to flood risk over the next few decades as our climate changes,” said Serafin, who served as a mentor to students in the Cardinal Course and is now an assistant professor at University of Florida.

Serafin created an algorithm to simulate millions of combinations of flood factors, including sea-level rise and more frequent episodes of extreme rainfall – a consequence of global warming that is already being felt in East Palo Alto and across California .

Serafin and Suckale incorporated their new algorithm into the widely used model to compute the statistical likelihood that the San Francisquito Creek would flood at different locations along the river. They then overlaid these results with aggregated household income and demographic data and a federal index of social vulnerability .

They found that the redesign of the upstream bridge would provide adequate protection against a repeat of the 1998 flood, which was once considered a 75-year flood event. But the modeling revealed that the planned design would leave hundreds of low-income households in East Palo Alto exposed to increased flood risk as climate change makes once-rare severe weather and flood events more common.

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Sea-level rise may worsen existing Bay Area inequities

A beneficial collaboration.

When the scientists shared their findings with the city of East Palo Alto, the Joint Powers Authority, and other community collaborators in conversations over several years, they emphasized that the conventional model wasn’t wrong – it just wasn’t designed to answer questions about equity.

The results provided scientific evidence to guide the Joint Powers Authority’s infrastructure plans, which expanded to include construction of a permanent floodwall beside the creek in East Palo Alto. The agency also adopted a plan to build up the creek’s bank in a particularly low area to better protect neighboring homes and streets.

Ruben Abrica, East Palo Alto’s elected representative to the Joint Powers Authority board, said researchers, planners, city staff, and policymakers have a responsibility to work together to “carry out projects that don’t put some people in more danger than others.”

Bay Area coastal flooding triggers regionwide commute disruptions

The results of the Stanford research demonstrated how seemingly neutral models that ignore equity can lead to uneven distributions of risks and benefits. “Scientists have to become more aware of the impact of the research, because the people who read the research or the people who then do the planning are relying on them,” he said.

Serafin and Suckale said their work with San Francisquito Creek demonstrates the importance of mutual respect and trust among researchers and communities positioned not as subjects of study, but active contributors to the creation of knowledge. “Our community collaborators made sure we, as scientists, understood the realities of these different communities,” Suckale said. “We’re not training them to be hydrological modelers. We are working with them to make sure that the decisions they’re making are transparent and fair to the different communities involved.”

For more information

Co-authors of the study include Derek Ouyang, Research Manager of the Regulation, Evaluation, and Governance Lab (RegLab) at Stanford and Jeffrey Koseff , the William Alden Campbell and Martha Campbell Professor in the School of Engineering , Professor of Civil and Environmental Engineering in the School of Engineering and the Stanford Doerr School of Sustainability, and a Senior Fellow at the Woods Institute for the Environment . Koseff is also the Faculty Director for the Change Leadership for Sustainability Program and Professor of Oceans in the Stanford Doerr School of Sustainability.

This research was supported by Stanford’s Bill Lane Center for the American West. The work is the product of the Stanford Future Bay Initiative, a research-education-practice collaboration committed to co-production of actionable intelligence with San Francisco Bay Area communities to shape a more equitable, resilient and sustainable urban future.

Jenny Suckale, Stanford Doerr School of Sustainability: [email protected] Katy Serafin, University of Florida: [email protected] Josie Garthwaite, Stanford Doerr School of Sustainability: (650) 497-0947, [email protected]

• Open access
• Published: 24 May 2024

VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images

• Anindita Saha   ORCID: orcid.org/0000-0002-5780-9252 1 ,
• Shahid Mohammad Ganie   ORCID: orcid.org/0000-0001-9925-0402 2 ,
• Pijush Kanti Dutta Pramanik   ORCID: orcid.org/0000-0001-9438-9309 3 ,
• Rakesh Kumar Yadav   ORCID: orcid.org/0000-0002-0151-4981 4 ,
• Saurav Mallik   ORCID: orcid.org/0000-0003-4107-6784 5 &
• Zhongming Zhao   ORCID: orcid.org/0000-0002-3477-0914 6  

BMC Medical Imaging volume  24 , Article number:  120 ( 2024 ) Cite this article

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Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease. Towards this, machine learning and deep learning techniques are successfully being applied. Due to several advantages, transfer learning has become popular for disease detection based on image data.

In this work, we build a novel transfer learning model (VER-Net) by stacking three different transfer learning models to detect lung cancer using lung CT scan images. The model is trained to map the CT scan images with four lung cancer classes. Various measures, such as image preprocessing, data augmentation, and hyperparameter tuning, are taken to improve the efficacy of VER-Net. All the models are trained and evaluated using multiclass classifications chest CT images.

The experimental results confirm that VER-Net outperformed the other eight transfer learning models compared with. VER-Net scored 91%, 92%, 91%, and 91.3% when tested for accuracy, precision, recall, and F1-score, respectively. Compared to the state-of-the-art, VER-Net has better accuracy.

VER-Net is not only effectively used for lung cancer detection but may also be useful for other diseases for which CT scan images are available.

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Introduction

Lung cancer is one of the leading causes of cancer-related deaths globally. It is broadly classified as small and non-small-cell lung cancer [ 1 ]. Lung cancer is a significant contributor to cancer-related deaths worldwide, with the highest mortality rate among all types of cancer. According to the World Health Organization Footnote 1 , cancer is a significant contributor to global mortality, resulting in approximately 10 million fatalities in 2020, which accounts for roughly one out of every six deaths. WHO estimated that one in 16 people would be diagnosed with lung cancer worldwide by 2022. Figure  1 represents the incidence cases and deaths of cancers for both sexes and all age groups worldwide Footnote 2 . The x-axis represents the number of people, while the y-axis denotes the types of cancers. Amongst all cancers, lung cancer has a significantly higher mortality rate. Additionally, when considering the number of incident cases, lung cancer ranks second among all types of cancer.

Roughly one-third of cancer-related deaths can be attributed to tobacco usage, a high body mass index, alcohol consumption, inadequate consumption of fruits and vegetables, and a lack of physical activity [ 2 ]. In addition, international agencies for cancer research have identified several risk factors that contribute to the development of various cancers, including alcohol, dietary exposures, infections, obesity, radiation, and many more that contribute towards cancer diseases. Lung cancer is caused by the abnormal growth of cells that form a tumour and can have serious consequences if left untreated. Early detection and effective treatment can lead to successful cures for many forms of cancer. Also, it is crucial for improving the survival rate and reducing mortality [ 3 ].

Lung cancer is a respiratory illness that affects people of all ages. Symptoms of lung cancer include changes in voice, coughing, chest pain, shortness of breath, weight loss, wheezing, and other painful symptoms [ 4 ]. Non-small-cell lung cancer has various subtypes, including Adenocarcinoma, squamous cell cancer, and large cell carcinoma, and is frequently observed [ 5 ]. However, small-cell lung cancer spreads faster and is often fatal.

Over the decades, clinical pathways and pathological treatments for lung cancer have included chemotherapy, targeted drugs, and immunotherapy [ 6 ]. In hospitals, doctors use different imaging techniques; while chest X-rays are the most cost-effective method of diagnosis, they require skilled radiologists to interpret the images accurately, as these can be complex and may overlap with other lung conditions [ 7 ]. Various lung diagnosis methods exist in the medical industry that use CT (computed tomography), isotopes, X-rays, MRI (magnetic resonance imaging), etc. [ 8 , 9 ].

Manual identification of lung cancer can be a time-consuming process subject to interpretation, causing delays in diagnosis and treatment. Additionally, the severity of the disease infection may not be apparent on X-ray images.

Incident cases and mortality rate of different cancers

As artificial intelligence (AI) has advanced, deep learning has become increasingly popular in analyzing medical images. Deep learning is a technique that can automatically discover high dimensionality, as compared to the more intuitive visual assessment of images that is often performed by skilled clinicians [ 10 , 11 , 12 ]. Convolutional neural networks (CNNs) are promising for extracting more powerful and deeper features from these images [ 13 ]. Significant improvements have been achieved in the potential to identify images and extract features inside images due to the development of CNN [ 14 , 15 ]. Advanced CNNs have been shown to improve the accuracy of predictions significantly. In recent years, the development of computer-aided detection (CAD) has shown promising results in medical image analysis [ 16 , 17 ]. Deep learning techniques, particularly transfer learning, have emerged as a powerful technique for leveraging pre-trained models and improving the performance of deep learning models [ 18 ].

Transfer learning has gained significant attention and success in various fields of AI, including medical image diagnosis [ 19 ], computer vision [ 20 ], natural language processing [ 21 ], speech recognition [ 22 ], and many more. Transfer learning involves using pre-trained neural networks to take the knowledge gained from one task (source task) and apply it to a different but related task (target task) [ 23 ]. In transfer learning, a model pre-trained on a large dataset for a specific task can be fine-tuned on similar datasets for different tasks.

Transfer learning has recently shown much promise in making it easier to detect lung cancer from medical imaging data. Integrating transfer learning methodologies into pipelines for lung cancer detection has demonstrated enhanced accuracy and efficiency across multiple research investigations. It offers a practical and effective way to leverage existing knowledge and resources to develop accurate and efficient models for lung cancer detection. It starts with a pre-trained CNN model and fine-tunes its layers on a dataset of lung images. This allows the model to quickly learn to identify relevant features associated with lung cancer without requiring extensive labelled lung cancer images. The advantages of transfer learning for lung cancer detection are listed in Fig.  2 .

Advantages of transfer learning for lung cancer detection

In this paper, we employed different transfer learning models for lung cancer detection using CT images. We proposed a hybrid model to enhance the prediction capability of the pre-trained models. The key contributions of this paper are:

The original image dataset is resized into 460 × 460 × 3.

Random oversampling is applied to fuse synthetic images in the minority class.

Data augmentation is applied by applying shear_range, zoom_range, rotation_range, horizontal_flip, and vertical_flip.

Eight transfer learning models, viz. NASNetLarge, Xception, DenseNet201, MobileNet, ResNet101, EfficientNetB0, EfficientNetB4, and VGG19 are tried with the processed dataset.

A novel transfer learning model (VER-Net) is built by stacking VGG19, EfficientNetB0, and ResNet101. The outputs of all three models are individually flattened and concatenated afterwards.

Seven deep dense layers are added to optimize the performance of VER-Net.

The performance of VER-Net is validated on eight different matrices (accuracy, loss, precision, recall, F1-score, macro average, weighted average, and standard deviation) and compared with the other eight considered models.

The accuracy of VER-Net is compared with the state-of-the-art.

The rest of the paper is organized as follows. Similar recent research addressing identifying lung cancer through transfer learning is discussed in Sect. 2. The working principle, details of the dataset preparation, and considered transfer learning models are discussed in Sect. 3. Section 4 presents the details of the proposed stacking model, including architecture and parameters. Section 5 presents the proposed model’s experimental details, results, and performance analysis. Section 6 concludes the paper, mentioning the limitations of this study and future scopes.

Related work

Deep learning techniques provide reliable, consistent, and accurate results. Due to this, they are widely applied across multiple domains to solve real-world problems [ 24 , 25 , 26 , 27 ]. Researchers have carried out diverse literature that includes datasets, algorithms, and methodology to facilitate future research in the classification and detection of lung cancer. Some of the prominent attempts to detect lung cancer using transfer learning are discussed in this section.

Wang et al. [ 28 ] experimented with a novel residual neural network with a transfer learning technique to identify pathology in lung cancer subtypes from medical images for an accurate and reliable diagnosis. The suggested model was pre-trained on the public medical image dataset luna16 and fine-tuned using their intellectual property lung cancer dataset from Shandong Provincial Hospital. Their approach accurately identifies pathological lung cancer from CT scans at 85.71%. Han et al. [ 29 ] developed a framework to assess the potential of PET/CT images in distinguishing between different histologic subtypes of non-small cell lung cancer (NSCLC). They evaluated ten feature selection techniques, ten machine learning models, and the VGG16 deep learning algorithm to construct an optimal classification model. The VGG16 achieved the highest accuracy rate of 84.1% among all the models. Vijayan et al. [ 30 ] employed three optimizers with six deep learning models. These models included AlexNet, GoogleNet, ResNet, Inception V3, EfficientNet b0, and SqueezeNet. While evaluating the various models, their effectiveness is measured by comparing their results with a stochastic gradient, momentum, Adam, and RMSProp optimization strategies. According to the findings of their study, GoogleNet using Adam as the optimizer achieves an accuracy of 92.08%. Nóbrega et al. [ 31 ] developed the classification model using deep transfer learning based on CT scan lung images. Several feature extraction models, including VGG16, VGG19, MobileNet, Xception, InceptionV3, ResNet50, Inception-ResNet-V2, DenseNet169, DenseNet201, NASNetMobile and NASNetLarge, were utilized to analyze the Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI). Among all the algorithms, the CNN-ResNet50 and SVM-RBF (support vector machine– radial basis function) combination was found to be the most effective deep extractor and classifier for identifying lung nodule malignancy in chest CT images, achieving an accuracy of 88.41% and an AUC of 93.19%. The authors have calculated the other performance evaluation matrices to validate the proposed model. Dadgar & Neshat [ 32 ] proposed a novel hybrid convolutional deep transfer learning model to detect three common types of lung cancer - Squamous Cell Carcinoma (SCC), Large Cell Carcinoma (LCC), and Adenocarcinoma. The model included several pre-trained deep learning architectures, such as VGG16, ResNet152V2, MobileNetV3 (small and large), InceptionResNetV2, and EfficientNetV2, which were compared and evaluated in combination with fully connected, dropout, and batch-normalization layers, with adjustments made to the hyper-parameters. After preprocessing 1000 CT scans from a public dataset, the best-performing model was identified as InceptionResNetV2 with transfer learning, achieving an accuracy of 91.1%, precision of 84.9%, AUC of 95.8%, and F1-score of 81.5% in classifying three types of lung cancer from normal samples. Worku et al. [ 33 ] proposed a denoising first two-path CNN (DFD-Net) for lung cancer detection. During preprocessing, a residual learning denoising model (DR-Net) is used to remove the noise. Then, a two-path convolutional neural network was used to identify lung cancer, with the denoised image from DR-Net as an input. The combined integration of local and global aspects is the main emphasis of the two pathways. Further, the performance of the model was enhanced, and a method other than the traditional feature concatenation techniques was employed, which directly integrated two sets of features from several CNN layers. Also, the authors overcame image label imbalance difficulties and achieved an accuracy of 87.8% for predicting lung cancer. Sari et al. [ 34 ] implemented CAD system using deep learning on CT images to classify lung cancer. They used transfer learning and a modified ResNet50 architecture to classify lung cancer images into four categories. The results obtained from this modified architecture show an accuracy of 93.33%, sensitivity of 92.75%, precision of 93.75%, F1-score of 93.25%, and AUC of 0.559. The study found that the modified ResNet50 outperforms the other two architectures, EfficientNetB1 and AlexNet, in accurately classifying lung cancer images into Adenocarcinoma, large carcinoma, normal, and squamous carcinoma categories.

Overall, these studies show that transfer learning has the potential to improve how well medical imaging data can be used to find lung cancer. Using pre-trained deep neural networks can significantly reduce the need for large datasets and reduce training time, making them more accessible for clinical applications. However, more research is needed to find the best architecture for transfer learning and the best fine-tuning strategy for spotting lung cancer. Further studies can focus on improving the interpretability and generalization of transfer learning models for real-world applications.

Research methodology

The details of the requirements and experimental steps carried out in this paper are discussed in this section.

The proposed model follows seven phases of structure, as shown in Fig.  3 . After acquiring the chest CT scan images, they were preprocessed and augmented to make the experiment suitable. The processed dataset is divided into training, validation, and testing sets. Eight popular transfer learning models were executed based on this data. Among them, the top three were selected and stacked to build a new prediction model. The model was fine-tuned repeatedly to improve the classification accuracy while reducing the required training time. The model was trained and validated to classify three cancer classes and a normal class. Finally, the model was tested.

Framework of the proposed methodology

Dataset description

The chest CT images utilized in this study were obtained from Kaggle Footnote 3 . The dataset contains CT scan images of three types of lung cancers: Adenocarcinoma, Large cell carcinoma, and Squamous cell carcinoma. During the cancer prediction process, the lung cancer image dataset taken from Kaggle consists of 1653 CT images, of which 1066 images are used for training, 446 images for testing and the remaining 141 for validation purposes to determine the efficiency of the cancer prediction system. Class-wise samples of lung cancer CT images are depicted in Fig.  4 . The detailed distribution of the dataset in terms of the total images, number of images in each class, number of classes, and labelling in each category is elucidated in Table  1 .

Sample images from chest CT imaging dataset (a) large cell, (b) squamous cell, (c) adenocarcinoma, and (d) normal

Adenocarcinoma

Lung adenocarcinoma Footnote 4 is the most common form of lung cancer, accounting for 30% of all cases and about 40% of all non-small cell lung cancer occurrences. Adenocarcinomas are found in several common cancers, including breast, prostate and colorectal. Adenocarcinomas of the lung are found in the outer region of the lung in glands that secrete mucus and help us breathe. Symptoms include coughing, hoarseness, weight loss and weakness.

Large cell carcinoma

Large-cell undifferentiated carcinoma Footnote 5 lung cancer grows and spreads quickly and can be found anywhere in the lung. This type of lung cancer usually accounts for 10 to 15% of all cases. Large-cell undifferentiated carcinoma tends to grow and spread quickly.

Squamous cell carcinoma

Squamous cell carcinoma Footnote 6 is found centrally in the lung, where the larger bronchi join the trachea to the lung or in one of the main airway branches. Squamous cell lung cancer is responsible for about 30% of all non-small cell lung cancers and is generally linked to smoking.

The last category is the normal CT scan images.

Data preprocessing

To develop a robust and reliable automated system, data preprocessing plays a crucial role in the model-building process [ 35 , 36 , 37 ]. Preprocessing is an essential step to eliminate the distortions from the images. In this study, data preprocessing, image resizing, and data augmentation were used for better classification and detection of lung cancer, as discussed in the subsections below.

Image resizing

The loaded images are standardized and normalized using a standard scaler and min-max scaler as the normalization functions. The files are resized from 224 × 224 to 460 × 460 using a resize function. The classes undergo label encoding, i.e., 0 for class Adenocarcinoma, 1 for class Large cell carcinoma, 2 for class Normal and 3 for class Squamous cell carcinoma.

Data augmentation

Random oversampling was applied afterwards to add randomly duplicate examples in the minority class by adding additional images to the classes containing fewer samples in the dataset. Initially, the dataset comprised 1000 images, with each class containing 338, 187, 260 and 215 images. The final dataset after oversampling contains 1653 images, with each class containing 411, 402, 374 and 466 images, as shown in Table  2 .

After that, data augmentation was applied by applying shear_range = 0.2, zoom_range = 0.2, rotation_range = 24, horizontal_flip = True, and vertical_flip = True. Finally, the dataset is split into training, testing and validation in 64.48%, 26.98% and 8.52%, respectively. After the preprocessing followed by the Train-test split, the data is fed to models for training.

Transfer learning models

Transfer learning models play a significant role in healthcare for medical image processing [ 23 , 31 ]. Medical imaging technologies, such as X-rays, CT scans, MRI scans, and histopathology slides, generate vast amounts of visual data that require accurate and efficient analysis. Transfer learning enables the utilization of pre-trained models trained on large datasets from various domains, such as natural images, to tackle medical image processing tasks [ 28 ]. The transfer learning models that are considered in this experiment are described in this section.

NasNetLarge

Google created the NasNetLarge [ 38 ], a neural architecture search network designed for powerful computational resources. This model addresses the issue of crafting an ideal CNN architecture by formulating it as a reinforcement learning challenge. NasNetLarge introduces an approach where a machine assists in designing neural network architecture and constructing a deep neural network without relying on traditional underlying models that concentrate on tensor decomposition or quantization techniques. Notably, NasNetLarge demonstrated exceptional performance in the ImageNet competition, showcasing its state-of-the-art capabilities. The model is tailored to a specific image input size of 331 × 331, which remains fixed and unmodifiable.

The unique advantages of NasNetLarge are:

Efficient architecture design using neural architecture search.

Achieves state-of-the-art performance on various image classification tasks.

Good balance between accuracy and computational efficiency.

The Xception architecture is a popular and strong convolutional neural network through various significant principles, including the convolutional layer, depth-wise separable convolution layer, residual connections, and the inception module [ 39 ]. Additionally, the activation function in the CNN architecture plays a crucial role, where the Swish activation function has been introduced to enhance the conventional activation function. The foundation of Xception is rooted in the Inception module, which effectively separates cross-channel correlations and spatial relationships within CNN feature maps, resulting in a fully independent arrangement.

The unique advantages of Xception are:

Deep and efficient convolutional neural network architecture.

Achieves high accuracy on image classification tasks.

Separable convolutions reduce the number of parameters and operations.

DenseNet201

DenseNet201 [ 40 ] is a CNN with 201 layers. It is based on the DenseNet concept of densely connecting every layer to every other layer in a feedforward manner, which helps improve the flow of information and gradient propagation through the network. It is a part of the DenseNet family of models, designed to address the problem of vanishing gradients in very deep neural networks. The output of densely connected and transition layers can be calculated using Eq.  1 and Eq.  2 .

where H i is the output of the current layer, f is the activation function, and [ H 0 , H 1 , H 2 , …, H i−1 ] are the outputs of all previous layers concatenated together. Also, W i+1 is the set of weights for the convolutional layer, BN is the batch normalization operation, f is the activation function, and W i+1 is the output of the transition layer.

The unique advantages of DenseNet201 are:

Dense connectivity pattern between layers, allowing for feature reuse.

Reduces the vanishing gradient problem and encourages feature propagation.

Achieves high accuracy while using fewer parameters compared to other models.

MobileNet [ 38 ] is a popular deep neural network architecture designed for mobile and embedded devices with limited computational resources. The architecture is based on a lightweight building block called a MobileNet unit, which consists of a depth-wise separable convolution layer followed by a pointwise convolution layer. The depth-wise separable convolution is a factorized convolution that decomposes a standard convolution into a depth-wise convolution and a pointwise convolution, which reduces the number of parameters and computations. The output of a MobileNet unit and inverted residual block can be calculated using Eq.  3 to Eq.  7 .

where X is the input tensor, DW is the depth-wise convolution operation, Conv 1 × 1 is the pointwise convolution operation, σ is the activation function, BN is the batch normalization operation, and Y is the output tensor. Also, X in is the input tensor, X is the output tensor of the bottleneck layer, Conv 1 × 1 and DW are the pointwise and depthwise convolution operations.

The unique advantages of MobileNet are:

Specifically designed for mobile and embedded vision applications.

Lightweight architecture with depth-wise separable convolutions.

Achieves a good balance of accuracy and model size, making it ideal for resource-constrained environments.

Residual Neural Networks (ResNets) are a type of deep learning model that has become increasingly popular in recent years, particularly for computer vision applications. The ResNet101 [ 41 ] model allows us to train extremely deep neural networks with 101 layers successfully. It addresses the vanishing gradient problem by using skip connections, which allow the output of one layer to be added to the previous layer’s output. This creates a shortcut that bypasses the intermediate layers, which helps to preserve the gradient and makes it easier to train very deep networks. This model architecture results in a more efficient network for training and provides good performance in terms of accuracy. Mathematically, the residual block can be expressed as given by Eq.  8

where x is the input to the block, F is a set of convolutional layers with weights W i , and y is the block output. The skip connection adds the input x to the output y to produce the final output of the block.

The unique advantages of ResNet101 are:

Residual connections that mitigate the vanishing gradient problem.

Permits deeper network architecture without compromising performance.

It is easy to train and achieves excellent accuracy.

• EfficientNetB0

EfficientNetB0 [ 42 ] is a CNN architecture belonging to the EfficientNet model family. These models are specifically crafted to achieve top-tier performance while maintaining computational efficiency, rendering them suitable for various computer vision tasks. The central concept behind EfficientNet revolves around harmonizing model depth, width, and resolution to attain optimal performance. This is achieved through a compound scaling technique that uniformly adjusts these three dimensions to generate a range of models, with EfficientNetB0 as the baseline. The network comprises 16 blocks, each characterized by its width, determined by the number of channels (filters) in every convolutional layer. The number of channels is adjusted using a scaling coefficient. Additionally, the input image resolution for EfficientNetB0 typically remains fixed at 224 × 224 pixels.

The unique advantages of EfficientNetB0 are:

Achieve state-of-the-art accuracy on image classification tasks.

Use a compound scaling method to balance model depth, width, and resolution.

A more accurate and computationally efficient architecture design.

EfficientNetB4

The EfficientB4 [ 43 ] neural network, consisting of blocks and segments, has residual units and parallel GPU utilization points. It is a part of the EfficientNet family of models, designed to be more computationally efficient than previous models while achieving state-of-the-art accuracy on various computer vision tasks, including image classification and object detection. The CNN backbone in EfficientNetB4 consists of a series of convolutional blocks, each with a set of operations, including convolution, batch normalization, and activation. The output of each block is fed into the next block as input. The final convolutional block is followed by a set of fully connected layers responsible for classifying the input image. The output of a convolutional block can be calculated using Eq.  9 .

where x i−1 is the input to the current block, W i is the set of weights for the convolutional layer, BN is the batch normalization operation, f is the activation function, and y i is the block output.

Being in the same family, EfficientB4 shares the advantages of EfficientNetB0.

Visual Geometry Group (VGG) is a traditional CNN architecture. The VGG19 [ 44 ] model consists of 19 layers with 16 convolutional layers and three fully connected layers. The max-pooling layers are applied after every two or three convolutional layers. It has achieved high accuracy on various computer vision tasks, including image classification, object detection, and semantic segmentation. One of the main contributions of the VGG19 network is the use of very small convolution filters (3 × 3) in each layer, which allows for deeper architectures to be built with fewer parameters. The output of the convolutional layers can be calculated using Eq.  10 .

where x is the input image, W is the weight matrix of the convolutional layer, b is the bias term, and f is the activation function, which is usually a rectified linear unit (ReLU) in VGG19. The output y is a feature map that captures the important information from the input image.

The unique advantages of VGG19 are:

Simple and straightforward architecture.

Achieves good performance on various computer vision tasks.

Its simplicity and ease of use make it a favourite among educators.

Proposed VER-Net model

To find out the best-performing models among the ones discussed in the previous section, we ran them and assessed their performance individually. Among them, VGG19 and EfficientNetB0 were the best performers in all metrics. However, EfficientNetB4 and ResNet101 competed with each other to take the third spot. In some metrics, EfficientNetB4 did better, while in some, ResNet101 was better. Nevertheless, we picked ResNet101 over EfficientNetB4 because it has better testing accuracy and precision, which is crucial for detecting life-threatening diseases like cancer. Therefore, we stacked VGG19, EfficientNetB0, and ResNet101 in our proposed VER-Net model. The complete algorithm for this procedure is shown in Algorithm 1.

Model Architecture

The architecture of the proposed VER-Net model is shown in Fig.  5 . The input shape is 460 × 460 × 3, which is mapped to four classes as output. We used three different dense layers for three stacked transfer learning models in the model. Thereafter, the same convolution layers of 7 × 7 × 1024 for all three and three different max-pooling layers are used. The outputs are flattened before sending to three 3 fully connected layers (1024 × 512 × 256). The three outputs of these connected layers are then concatenated using majority voting, and accordingly, the classified outputs are generated. The architectural description of VER-Net is shown in Table  3 .

VER-Net’s architecture

Model parameters

The details of hyperparameters settings for VER-Net are listed in Table  4 . In Table  5 , the details of data augmentation are listed. Here, we used the RandomFlip and RandomRotation functions available in TensorFlow.Keras for data augmentation.

Experiment, results and performance analysis

In this section, the experimental details, including system setup and evaluation metrics, are covered. Also, the results are elaborately presented, and the performance of the proposed model is extensively assessed.

Experimental system setup

The experiment was conducted on a Dell workstation with a Microsoft Windows environment. Python was used to program on the Anaconda framework. The details of the system are given in Table  6 .

Evaluation Metrics

Evaluation metrics are used to assess the performance of a model on a problem statement. Different evaluation metrics are used depending on the problem type and the data’s nature. In this study, the experimental findings for the presented models are evaluated using various performance metrics, summarised in Table  7 .

VER-Net model implementation

After background and designing the VER-Net model, we implemented it. The results are discussed in the following.

Confusion matrix

The classification performance of VER-Net is evaluated using a confusion matrix, as shown in Fig.  6 . Since there are four output classes, the confusion matrix is a 4 × 4 matrix. Every column in the matrix represents a predicted class, whereas every row represents an actual class. The principal diagonal cells denote the respective classes’ correct predictions (TP). Besides the TP cell, all other cells in the same row denote TN. For example, in the first row, except the first column, five of the Adenocarcinoma were falsely classified as large cell carcinoma, and four were categorized as Squamous cell carcinoma. So, 9 (5 + 0 + 4) are TN classifications for the Adenocarcinoma class. Similarly, all other cells in the same column denote FP besides the TP cell. For example, in the first column, except the first row, four Large cell carcinoma, four normal cells, and 21 Squamous cell carcinoma are falsely classified as Adenocarcinoma. So, 29 (4 + 4 + 21) FN classifications exist for the Adenocarcinoma class. The rest of the cells denote FN predictions.

Confusion matrix of VER-Net

Accuracy and loss of VER-Net

The accuracy and loss of our VER-Net model are plotted in Figs.  7 and 8 , respectively. The x-axis denotes the number of epochs (100), while the y-axis reflects accuracy in Fig.  7 and loss in Fig.  8 . The training curve suggests how well VER-Net is trained. It can be observed that both accuracy and loss for validation/testing converge approximately after 20 epochs. It is further noticed that the model did not exhibit significant underfitting and overfitting upon hyperparameter tuning. In our experiment, we tried with different epoch numbers (40, 60, 100, and 200). We got the best results with 100 epochs.

Training and validation/test accuracy VER-Net model

Training and validation/test loss VER-Net model

Performance analysis of VER-Net

In this section, we exhaustively analyze the performance of VER-Net model. For this, we adopted a comparative analysis approach. We compared VER-Net with other transfer learning models and the results of similar research works.

Comparing VER-Net with other transfer learning models

First, we compare the performance of VER-Net with the individual transfer learning models, mentioned in Sect. 3.4. All the models were trained and tested on the same dataset and validated with the same parameters.

Figures  9 and 10 present the accuracy and loss comparisons. VER-Net and VGG19 both achieved the highest accuracy of 97.47% for training, but for testing, VER-Net emerged as the sole highest accuracy achiever with 91%. NASNetLarge got the lowest accuracy on both occasions, with 69.51% and 64% training and testing accuracy, respectively. Similar to accuracy, VER-Net and VGG19 both managed the lowest loss of 0.07% for training, and VER-Net was the sole lowest loss achiever with 0.34%. Here also, NASNetLarge performed worst on both occasions with 0.66% and 0.80% training and testing loss, respectively.

Accuracy comparison of the proposed ensemble method (VER-Net) with other transfer learning models

Loss comparison of the proposed ensemble method (VER-Net) with other transfer learning models

Table  8 notes all classes’ precision, recall and F1-score values to compare VER-Net with other models. The macro average of these metrics for all four classes is shown in Fig.  11 . For all three instances, i.e., precision, recall and F1-score, VER-Net outperformed with 0.920, 0.910, and 0.913, respectively. VGG19 and EficientNetB0 emerged as the second and third-best performers, whereas NASNetLarge was the worst performer with 0.693, 0.645, and 0.645 for precision, recall and F1-score, respectively.

In Fig.  12 , VER-Net is compared with others in terms of weighted average for precision, recall and F1-score. Here, we used a uniform weight of 1.5 for all classes. Like the macro average, VER-Net was the top performer for all three metrics, followed by VGG19 and EficientNetB0, and NasNetLarge was the worst performer. As shown in Table  8 , NasNetLarge classifies the non-cancerous cells with 100% accuracy; in fact, it performs the best among all models but performs very poorly for the cancerous cells.

Macro average comparison of VER-Net and other models

Weighted average comparison of VER-Net and other models

To assess the performance variations of VER-Net, we calculated the standard deviation to calculate the mean-variance across the classes for precision, recall and F1-score. A lower value suggests that the model is effective for all classes equally. In contrast, a higher variation suggests bias to a certain class. From Fig.  13 , it can be observed that VER-Net has the lowest variations for recall and F1-score of 0.062 and 0.04, respectively. However, as an exception in the case of precision, VER-Net is bettered by DenseNet201 with a margin of 0.042 variations. This can be reasoned as VER-Net attained 100% precision for the Normal class. Nevertheless, VER-Net has significantly lower variance across three metrics than DenseNet201.

Standard deviation for precision, recall and F1-score of all classes

Comparing VER-Net with literature

In the previous section, we established the superiority of VER-Net over other established transfer learning models. To prove the ascendency of VER-Net further, we compared it with the results of some similar recent experiments, available in the literature pertaining to detecting lung cancer based on CT scan images using transfer learning methods. A comparative summary is given in Table  9 .

The above experiments and results clearly show that the proposed VER-NET performed well in detecting lung cancer in most of the performance testing. It is the overall best performer among the nine transfer learning models. One of the reasons for this is that we incorporated the best three models (considered in this experiment) into the VER-NET. Besides, we optimally designed the VER-NET architecture for its best performance. Furthermore, to make the model more generalized, we generated additional synthetic lung cancer images in addition to the original image dataset.

To balance the dataset, we performed image augmentation, which might make slight changes in the real images. So, the performance of VER-Net might vary little on a balanced real dataset where there is no need for synthetic augmentation. The images were generated with 64 × 64 pixels, which is insufficient for the analysis of medical images. For cancer cell detection based on cell images, high-resolution images are crucial.

Since VER-Net is an ensembled model comprising three transfer learning, it is obvious that it should increase the computational complexity, requiring longer for training. However, this should not be a discouraging factor in a lifesaving application like cancer detection, where accuracy and precision matter most.

Conclusions and future scope

Incorporating transfer learning into lung cancer detection models has shown improved performance and robustness in various studies. In this paper, we concatenated three transfer learning models, namely, VGG19 + EfficientNetB0 + ResNet101, to build an ensembled VER-Net model to detect lung cancer. We used CT scan images as input to the model. To make VER-Net effective, we conducted data preprocessing and data augmentation. We compared the performance of VER-Net with eight other transfer learning models. The comparative results were assessed through various performance evaluation metrics. It was observed that VER-Net performed best in all metrics. VER-Net also exhibited better accuracy than similar empirical studies from the recent literature.

Here, we incorporated the three top-performing transfer models in the hybrid VER-Net architecture. Further experimentation can be done on this ensembling approach. For example, other models can be tried in different combinations. Also, transfer learning models of different families can be tried.

We plan to extend the use of the VER-Net model for identifying lung cancer where only chest X-ray images are available. Furthermore, this model can also be applied to assess the severity of lung cancer if the patient is already infested. Considering the success of VER-Net in detecting lung cancer, it can be used for other diseases where CT scan images are useful to identify the disease.

Data availability

No datasets were generated or analysed during the current study.

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ZZ is partially funded by his startup fund at The University of Texas Health Science Center at Houston, Houston, Texas, USA.

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Anindita Saha

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Shahid Mohammad Ganie

School of Computer Applications and Technology, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India

Pijush Kanti Dutta Pramanik

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Saha, A., Ganie, S.M., Pramanik, P.K.D. et al. VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images. BMC Med Imaging 24 , 120 (2024). https://doi.org/10.1186/s12880-024-01238-z

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Researchers find ai model outperforms human stock forecasters.

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Traders work during the Reddit Inc. initial public offering (IPO) on the floor of the New York Stock ... [+] Exchange (NYSE) in New York, US, on Thursday, March 21, 2024. Reddit Inc. shares jumped as much as 67% over their initial public offering price after the social media company and its shareholders raised $748 million priced at the top of a marketed range, sending a strong signal that the window for US IPOs is reopening. Photographer: Michael Nagle/Bloomberg

Researchers have found that AI models can outperform humans in forecasting future company earnings. ChatGPT 4.0 produces superior directional earnings forecasts than human analysts. That’s after ChatGPT examined anonymized historic balance sheet and income statement data for prior years. The researchers also found that investing on that AI-based analysis could historically lead to stock market outperformance.

The Research

The paper ‘Financial Statement Analysis with Large Language Models’ was published by researchers at the University of Chicago in May 2024. They provided the AI tool with standardized balance sheet and income statement data and used a detailed “chain-of-thought” prompt outlining a range of analytical techniques and metrics common in earnings forecast analysis.

Importantly the Large Language Model did not have any of the industry context that human analysts can benefit from. Nor did the models know specific company detail beyond numerical financial statement data. Yet the results were relatively impressive. The goal was to determine whether earnings would grow or decline in a subsequent period and to indicate broad magnitude confidence.

Measured Performance

The model outperformed consensus forecasts. It showed comparable, if not superior, performance to better public industry models. Overall accuracy in predicting whether earnings would increase or decline was approximately 60%.

The model was also able to produce similar results for 2023 using 2022 data, which suggested that the model was not somehow drawing on actual retrieval of historic performance. The researchers were able to know this as 2023 results, disclosed by companies in 2024 were outside of ChatGPT 4.0’s training window.

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Interestingly, ChatGPT 4.0 produced superior forecasting accuracy to ChatGPT 3.5. Results for Google’s Google Gemini Pro 1.5, though tested over a more limited sample, were broadly similar to ChatGPT 4.0.

However, as is often the case with LLMs the researchers are unable to pinpoint exactly what the model is doing that is resulting in forecast accuracy. That said, the researchers did assess the most common descriptors use in the model’s output finding terms such as “operating margin” and “current ratio” to be more commonly used across a broad set of terminology.

The researchers also suspect that the combination of human and AI models are likely to result in superior forecasts because humans can bring additional insight that LLMs may not currently have access to, whereas LLMs can avoid common human biases and perform robust and comprehensive analysis.

Alpha Generation

The researchers found that the model could outperform the broader stock market if annual portfolios were formed based on its predictions with performance measured monthly. The Sharpe ratio from this strategy was superior to that of an artificial neural net trained for earnings prediction on an equal-weighted basis, though the artificial neural net outperformed the ChatGPT model in terms of Sharpe ratio on a value-weighted basis.

The bulk of the model’s returns especially in recent history appear to have come from its long positions rather than its short exposure. It also appears that forecasting accuracy has declined somewhat in recent decades, though that is true for other models too and the results remain generally above that of human consensus forecasts.

What’s Next?

Of course, many useful stock market models are not public because investors who are profit from them have little incentive publicly share them. Therefore, there may be superior models out that that ChatGPT 4.0 is unable outperform.

Still, the success of ChatGPT 4.0 in predicting earnings directions with relatively limited financial data and, importantly, the considerable improvement in performance relative to ChatGPT 3.5 is impressive. As in many fields, it is likely that LLMs will be increasingly disruptive and effective in areas of financial analysis and prediction.

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Adhesive coatings can prevent scarring around medical implants

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When medical devices such as pacemakers are implanted in the body, they usually provoke an immune response that leads to buildup of scar tissue around the implant. This scarring, known as fibrosis, can interfere with the devices’ function and may require them to be removed.

In an advance that could prevent that kind of device failure, MIT engineers have found a simple and general way to eliminate fibrosis by coating devices with a hydrogel adhesive. This adhesive binds the devices to tissue and prevents the immune system from attacking it.

“The dream of many research groups and companies is to implant something into the body that over the long term the body will not see, and the device can provide therapeutic or diagnostic functionality. Now we have such an ‘invisibility cloak,’ and this is very general: There’s no need for a drug, no need for a special polymer,” says Xuanhe Zhao, an MIT professor of mechanical engineering and of civil and environmental engineering.

The adhesive that the researchers used in this study is made from cross-linked polymers called hydrogels, and is similar to a surgical tape they previously developed to help seal internal wounds. Other types of hydrogel adhesives can also protect against fibrosis, the researchers found, and they believe this approach could be used for not only pacemakers but also sensors or devices that deliver drugs or therapeutic cells.

Zhao and Hyunwoo Yuk SM ’16, PhD ’21, a former MIT research scientist who is now the chief technology officer at SanaHeal, are the senior authors of the study, which appears today in Nature . MIT postdoc Jingjing Wu is the lead author of the paper.

Preventing fibrosis

In recent years, Zhao’s lab has developed adhesives for a variety of medical applications, including double-sided and single-sided tapes that could be used to heal surgical incisions or internal injuries. These adhesives work by rapidly absorbing water from wet tissues, using polyacrylic acid, an absorbent material used in diapers. Once the water is cleared, chemical groups called NHS esters embedded in the polyacrylic acid form strong bonds with proteins at the tissue surface. This process takes about five seconds.

Several years ago, Zhao and Yuk began exploring whether this kind of adhesive could also help keep medical implants in place and prevent fibrosis from occurring.

To test this idea, Wu coated polyurethane devices with their adhesive and implanted them on the abdominal wall, colon, stomach, lung, or heart of rats. Weeks later, they removed the device and found that there was no visible scar tissue. Additional tests with other animal models showed the same thing: Wherever the adhesive-coated devices were implanted, fibrosis did not occur, for up to three months.

“This work really has identified a very general strategy, not only for one animal model, one organ, or one application,” Wu says. “Across all of these animal models, we have consistent, reproducible results without any observable fibrotic capsule.”

Using bulk RNA sequencing and fluorescent imaging, the researchers analyzed the animals’ immune response and found that when devices with adhesive coatings were first implanted, immune cells such as neutrophils began to infiltrate the area. However, the attacks quickly quenched out before any scar tissue could form.

“For the adhered devices, there is an acute inflammatory response because it is a foreign material,” Yuk says. “However, very quickly that inflammatory response decayed, and then from that point you do not have this fibrosis formation.”

One application for this adhesive could be coatings for epicardial pacemakers — devices that are placed on the heart to help control the heart rate. The wires that contact the heart often become fibrotic, but the MIT team found that when they implanted adhesive-coated wires in rats, they remained functional for at least three months, with no scar tissue formation.

“The formation of fibrotic tissue at the interface between implanted medical devices and the target tissue is a longstanding problem that routinely causes failure of the device. The demonstration that robust adhesion between the device and the tissue obviates fibrotic tissue formation is an important observation that has many potential applications in the medical device space,” says David Mooney, a professor of bioengineering at Harvard University, who was not involved in the study.

Mechanical cues

The researchers also tested a hydrogel adhesive that includes chitosan, a naturally occurring polysaccharide, and found that this adhesive also eliminated fibrosis in animal studies. However, two commercially available tissue adhesives that they tested did not show this antifibrotic effect because the commercially available adhesives eventually detached from the tissue and allowed the immune system to attack.

In another experiment, the researchers coated implants in hydrogel adhesives but then soaked them in a solution that removed the polymers’ adhesive properties, while keeping their overall chemical structure the same. After being implanted in the body, where they were held in place by sutures, fibrotic scarring occurred. This suggests that there is something about the mechanical interaction between the adhesive and the tissue that prevents the immune system from attacking, the researchers say.

“Previous research in immunology has been focused on chemistry and biochemistry, but mechanics and physics may play equivalent roles, and we should pay attention to those mechanical and physical cues in immunological responses,” says Zhao, who now plans to further investigate how those mechanical cues affect the immune system.

Yuk, Zhao, and others have started a company called SanaHeal, which is now working on further developing tissue adhesives for medical applications.

“As a team, we are interested in reporting this to the community and sparking speculation and imagination as to where this can go,” Yuk says. “There are so many scenarios in which people want to interface with foreign or manmade material in the body, like implantable devices, drug depots, or cell depots.”

The research was funded by the National Institutes of Health and the National Science Foundation.

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MIT engineers have developed a new adhesive, low-cost hydrogel that can stop fibrosis often experienced by people with pacemakers and other medical devices, reports for Maria Bolevich Interesting Engineering . “These findings may offer a promising strategy for long-term anti-fibrotic implant–tissue interfaces,” explains Prof. Xuanhe Zhao. 

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