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Exploring Experimental Research: Methodologies, Designs, and Applications Across Disciplines

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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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• What Is Action Research? | Examples & Definition

What Is Action Research? | Examples & Definition

Published on June 9, 2024 by Julia Merkus, MA .

Action research is a research method that combines investigation and intervention to solve a problem. Because of its interactive nature, action research is commonly used in the social sciences, particularly in educational contexts.

Educators frequently use this method as a means of structured inquiry, emphasizing reflective practice and combining theoretical knowledge and practical application.

The term “action research” was first introduced in 1944 by Kurt Lewin, a renowned MIT professor. Due to its cyclical nature, action research is also referred to as the action cycle, action model, or cycle of inquiry.

Table of contents

Types of action research, action research examples, action research models, action research vs traditional research, action research advantages, action research disadvantages, frequently asked questions about action research.

There are two main types of action research:

• Participatory action research focuses on involving community members as active participants in the research process. This approach prioritizes empowering individuals within the community being studied, recognizing their valuable insights and experiences as integral to shaping the research outcomes.
• Practical action research centers on the methodology used to conduct research and its direct application to resolving specific problems or challenges.

Both participatory and practical action research aim to enhance the skills of future professionals rather than solely contributing to theoretical advancements.

Action research is frequently used in fields like education due to its flexibility and iterative nature.

Part-time staff members walked the researcher through their days and suggested where breaks and changes to the schedule would be most beneficial. The findings were shared with school administrators to execute the improvements. Action research example: Practical action research Professors at a local university noticed a decline in student attendance during lectures. They conducted an in-depth study of the teaching methods, tools, and approaches used by each teacher to engage with students.

Action research approaches often draw upon three action research models: collaboration, critical reflection, and operational (aka technical).

• The collaboration model focuses on building a network of individuals with similar traits and collecting learning points through repeated feedback cycles.
• The critical reflection model examines existing systemic processes, seeking to understand why certain practices were developed and how they might be improved.
• The operational model typically involves a spiral process, such as planning, acting, observing, and reflecting.

Action research differs significantly from other types of research in its focus on producing actionable processes during the research process rather than contributing to an existing body of knowledge or drawing data-driven conclusions.

As such, action research is formative rather than summative in nature, and it’s conducted in an ongoing manner.

Action research has the following advantages:

• Action research offers immediate and practical solutions for pressing issues, avoiding complex, long-term remedies based on extensive data analysis.
• It is highly adaptable , so researchers can tailor their analysis to specific needs and implement practical, customized changes at the individual level.
• When conducted effectively, action research can empower participants and facilitate social change , enabling community members to drive meaningful transformations.
• The flexibility of action research studies limits their generalizability and replicability , leading to concerns about the validity of findings and the overall theoretical rigor of the research.
• Ethical challenges may arise in structuring action research projects, as participants could feel pressured to take part or conform to certain expectations.
• High susceptibility to research biases , such as selection bias or social desirability bias, poses a significant risk in action research studies.

Examples of action research papers are:

• “ Participatory Action Research for Conservation and Development: Experiences from the Amazon ” by Perz et al. (2022)
• “ Exploring Social Innovation through Co-creation in Rural India Using Action Research ” by Cornet and Barpanda (2021)

Topics for action research in education are:

• Developing a standards-based grading system to improve students’ understanding of assessment criteria
• Designing a technology-enhanced curriculum to improve student learning outcomes and engagements
• Developing a co-teaching model to improve student outcomes for students with special needs

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Action Research: What it is, Stages & Examples

The best way to get things accomplished is to do it yourself. This statement is utilized in corporations, community projects, and national governments. These organizations are relying on action research to cope with their continuously changing and unstable environments as they function in a more interdependent world.

In practical educational contexts, this involves using systematic inquiry and reflective practice to address real-world challenges, improve teaching and learning, enhance student engagement, and drive positive changes within the educational system.

This post outlines the definition of action research, its stages, and some examples.

Content Index

What is action research?

Stages of action research, the steps to conducting action research, examples of action research, advantages and disadvantages of action research.

Action research is a strategy that tries to find realistic solutions to organizations’ difficulties and issues. It is similar to applied research.

Action research refers basically learning by doing. First, a problem is identified, then some actions are taken to address it, then how well the efforts worked are measured, and if the results are not satisfactory, the steps are applied again.

It can be put into three different groups:

• Positivist: This type of research is also called “classical action research.” It considers research a social experiment. This research is used to test theories in the actual world.
• Interpretive: This kind of research is called “contemporary action research.” It thinks that business reality is socially made, and when doing this research, it focuses on the details of local and organizational factors.
• Critical: This action research cycle takes a critical reflection approach to corporate systems and tries to enhance them.

All research is about learning new things. Collaborative action research contributes knowledge based on investigations in particular and frequently useful circumstances. It starts with identifying a problem. After that, the research process is followed by the below stages:

Stage 1: Plan

For an action research project to go well, the researcher needs to plan it well. After coming up with an educational research topic or question after a research study, the first step is to develop an action plan to guide the research process. The research design aims to address the study’s question. The research strategy outlines what to undertake, when, and how.

Stage 2: Act

The next step is implementing the plan and gathering data. At this point, the researcher must select how to collect and organize research data . The researcher also needs to examine all tools and equipment before collecting data to ensure they are relevant, valid, and comprehensive.

Stage 3: Observe

Data observation is vital to any investigation. The action researcher needs to review the project’s goals and expectations before data observation. This is the final step before drawing conclusions and taking action.

Different kinds of graphs, charts, and networks can be used to represent the data. It assists in making judgments or progressing to the next stage of observing.

Stage 4: Reflect

This step involves applying a prospective solution and observing the results. It’s essential to see if the possible solution found through research can really solve the problem being studied.

The researcher must explore alternative ideas when the action research project’s solutions fail to solve the problem.

Action research is a systematic approach researchers, educators, and practitioners use to identify and address problems or challenges within a specific context. It involves a cyclical process of planning, implementing, reflecting, and adjusting actions based on the data collected. Here are the general steps involved in conducting an action research process:

Identify the action research question or problem

Clearly define the issue or problem you want to address through your research. It should be specific, actionable, and relevant to your working context.

Review existing knowledge

Conduct a literature review to understand what research has already been done on the topic. This will help you gain insights, identify gaps, and inform your research design.

Plan the research

Develop a research plan outlining your study’s objectives, methods, data collection tools, and timeline. Determine the scope of your research and the participants or stakeholders involved.

Collect data

Implement your research plan by collecting relevant data. This can involve various methods such as surveys, interviews, observations, document analysis, or focus groups. Ensure that your data collection methods align with your research objectives and allow you to gather the necessary information.

Analyze the data

Once you have collected the data, analyze it using appropriate qualitative or quantitative techniques. Look for patterns, themes, or trends in the data that can help you understand the problem better.

Reflect on the findings

Reflect on the analyzed data and interpret the results in the context of your research question. Consider the implications and possible solutions that emerge from the data analysis. This reflection phase is crucial for generating insights and understanding the underlying factors contributing to the problem.

Develop an action plan

Based on your analysis and reflection, develop an action plan that outlines the steps you will take to address the identified problem. The plan should be specific, measurable, achievable, relevant, and time-bound (SMART goals). Consider involving relevant stakeholders in planning to ensure their buy-in and support.

Implement the action plan

Put your action plan into practice by implementing the identified strategies or interventions. This may involve making changes to existing practices, introducing new approaches, or testing alternative solutions. Document the implementation process and any modifications made along the way.

Evaluate and monitor progress

Continuously monitor and evaluate the impact of your actions. Collect additional data, assess the effectiveness of the interventions, and measure progress towards your goals. This evaluation will help you determine if your actions have the desired effects and inform any necessary adjustments.

Reflect and iterate

Reflect on the outcomes of your actions and the evaluation results. Consider what worked well, what did not, and why. Use this information to refine your approach, make necessary adjustments, and plan for the next cycle of action research if needed.

Remember that participatory action research is an iterative process, and multiple cycles may be required to achieve significant improvements or solutions to the identified problem. Each cycle builds on the insights gained from the previous one, fostering continuous learning and improvement.

Explore Insightfully Contextual Inquiry in Qualitative Research

Here are two real-life examples of action research.

Action research initiatives are frequently situation-specific. Still, other researchers can adapt the techniques. The example is from a researcher’s (Franklin, 1994) report about a project encouraging nature tourism in the Caribbean.

In 1991, this was launched to study how nature tourism may be implemented on the four Windward Islands in the Caribbean: St. Lucia, Grenada, Dominica, and St. Vincent.

For environmental protection, a government-led action study determined that the consultation process needs to involve numerous stakeholders, including commercial enterprises.

First, two researchers undertook the study and held search conferences on each island. The search conferences resulted in suggestions and action plans for local community nature tourism sub-projects.

Several islands formed advisory groups and launched national awareness and community projects. Regional project meetings were held to discuss experiences, self-evaluations, and strategies. Creating a documentary about a local initiative helped build community. And the study was a success, leading to a number of changes in the area.

Lau and Hayward (1997) employed action research to analyze Internet-based collaborative work groups.

Over two years, the researchers facilitated three action research problem -solving cycles with 15 teachers, project personnel, and 25 health practitioners from diverse areas. The goal was to see how Internet-based communications might affect their virtual workgroup.

First, expectations were defined, technology was provided, and a bespoke workgroup system was developed. Participants suggested shorter, more dispersed training sessions with project-specific instructions.

The second phase saw the system’s complete deployment. The final cycle witnessed system stability and virtual group formation. The key lesson was that the learning curve was poorly misjudged, with frustrations only marginally met by phone-based technical help. According to the researchers, the absence of high-quality online material about community healthcare was harmful.

Role clarity, connection building, knowledge sharing, resource assistance, and experiential learning are vital for virtual group growth. More study is required on how group support systems might assist groups in engaging with their external environment and boost group members’ learning. 

Action research has both good and bad points.

• It is very flexible, so researchers can change their analyses to fit their needs and make individual changes.
• It offers a quick and easy way to solve problems that have been going on for a long time instead of complicated, long-term solutions based on complex facts.
• If It is done right, it can be very powerful because it can lead to social change and give people the tools to make that change in ways that are important to their communities.

Disadvantages

• These studies have a hard time being generalized and are hard to repeat because they are so flexible. Because the researcher has the power to draw conclusions, they are often not thought to be theoretically sound.
• Setting up an action study in an ethical way can be hard. People may feel like they have to take part or take part in a certain way.
• It is prone to research errors like selection bias , social desirability bias, and other cognitive biases.

LEARN ABOUT: Self-Selection Bias

This post discusses how action research generates knowledge, its steps, and real-life examples. It is very applicable to the field of research and has a high level of relevance. We can only state that the purpose of this research is to comprehend an issue and find a solution to it.

At QuestionPro, we give researchers tools for collecting data, like our survey software, and a library of insights for any long-term study. Go to the Insight Hub if you want to see a demo or learn more about it.

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Frequently Asked Questions(FAQ’s)

Action research is a systematic approach to inquiry that involves identifying a problem or challenge in a practical context, implementing interventions or changes, collecting and analyzing data, and using the findings to inform decision-making and drive positive change.

Action research can be conducted by various individuals or groups, including teachers, administrators, researchers, and educational practitioners. It is often carried out by those directly involved in the educational setting where the research takes place.

The steps of action research typically include identifying a problem, reviewing relevant literature, designing interventions or changes, collecting and analyzing data, reflecting on findings, and implementing improvements based on the results.

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How the Experimental Method Works in Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amanda Tust is a fact-checker, researcher, and writer with a Master of Science in Journalism from Northwestern University's Medill School of Journalism.

sturti/Getty Images

The Experimental Process

Types of experiments, potential pitfalls of the experimental method.

The experimental method is a type of research procedure that involves manipulating variables to determine if there is a cause-and-effect relationship. The results obtained through the experimental method are useful but do not prove with 100% certainty that a singular cause always creates a specific effect. Instead, they show the probability that a cause will or will not lead to a particular effect.

At a Glance

While there are many different research techniques available, the experimental method allows researchers to look at cause-and-effect relationships. Using the experimental method, researchers randomly assign participants to a control or experimental group and manipulate levels of an independent variable. If changes in the independent variable lead to changes in the dependent variable, it indicates there is likely a causal relationship between them.

What Is the Experimental Method in Psychology?

The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis.

For example, researchers may want to learn how different visual patterns may impact our perception. Or they might wonder whether certain actions can improve memory . Experiments are conducted on many behavioral topics, including:

The scientific method forms the basis of the experimental method. This is a process used to determine the relationship between two variables—in this case, to explain human behavior .

Positivism is also important in the experimental method. It refers to factual knowledge that is obtained through observation, which is considered to be trustworthy.

When using the experimental method, researchers first identify and define key variables. Then they formulate a hypothesis, manipulate the variables, and collect data on the results. Unrelated or irrelevant variables are carefully controlled to minimize the potential impact on the experiment outcome.

History of the Experimental Method

The idea of using experiments to better understand human psychology began toward the end of the nineteenth century. Wilhelm Wundt established the first formal laboratory in 1879.

Wundt is often called the father of experimental psychology. He believed that experiments could help explain how psychology works, and used this approach to study consciousness .

Wundt coined the term "physiological psychology." This is a hybrid of physiology and psychology, or how the body affects the brain.

Other early contributors to the development and evolution of experimental psychology as we know it today include:

• Gustav Fechner (1801-1887), who helped develop procedures for measuring sensations according to the size of the stimulus
• Hermann von Helmholtz (1821-1894), who analyzed philosophical assumptions through research in an attempt to arrive at scientific conclusions
• Franz Brentano (1838-1917), who called for a combination of first-person and third-person research methods when studying psychology
• Georg Elias Müller (1850-1934), who performed an early experiment on attitude which involved the sensory discrimination of weights and revealed how anticipation can affect this discrimination

Key Terms to Know

To understand how the experimental method works, it is important to know some key terms.

Dependent Variable

The dependent variable is the effect that the experimenter is measuring. If a researcher was investigating how sleep influences test scores, for example, the test scores would be the dependent variable.

Independent Variable

The independent variable is the variable that the experimenter manipulates. In the previous example, the amount of sleep an individual gets would be the independent variable.

A hypothesis is a tentative statement or a guess about the possible relationship between two or more variables. In looking at how sleep influences test scores, the researcher might hypothesize that people who get more sleep will perform better on a math test the following day. The purpose of the experiment, then, is to either support or reject this hypothesis.

Operational definitions are necessary when performing an experiment. When we say that something is an independent or dependent variable, we must have a very clear and specific definition of the meaning and scope of that variable.

Extraneous Variables

Extraneous variables are other variables that may also affect the outcome of an experiment. Types of extraneous variables include participant variables, situational variables, demand characteristics, and experimenter effects. In some cases, researchers can take steps to control for extraneous variables.

Demand Characteristics

Demand characteristics are subtle hints that indicate what an experimenter is hoping to find in a psychology experiment. This can sometimes cause participants to alter their behavior, which can affect the results of the experiment.

Intervening Variables

Intervening variables are factors that can affect the relationship between two other variables. 

Confounding Variables

Confounding variables are variables that can affect the dependent variable, but that experimenters cannot control for. Confounding variables can make it difficult to determine if the effect was due to changes in the independent variable or if the confounding variable may have played a role.

Psychologists, like other scientists, use the scientific method when conducting an experiment. The scientific method is a set of procedures and principles that guide how scientists develop research questions, collect data, and come to conclusions.

The five basic steps of the experimental process are:

• Identifying a problem to study
• Devising the research protocol
• Conducting the experiment
• Analyzing the data collected
• Sharing the findings (usually in writing or via presentation)

Most psychology students are expected to use the experimental method at some point in their academic careers. Learning how to conduct an experiment is important to understanding how psychologists prove and disprove theories in this field.

There are a few different types of experiments that researchers might use when studying psychology. Each has pros and cons depending on the participants being studied, the hypothesis, and the resources available to conduct the research.

Lab Experiments

Lab experiments are common in psychology because they allow experimenters more control over the variables. These experiments can also be easier for other researchers to replicate. The drawback of this research type is that what takes place in a lab is not always what takes place in the real world.

Field Experiments

Sometimes researchers opt to conduct their experiments in the field. For example, a social psychologist interested in researching prosocial behavior might have a person pretend to faint and observe how long it takes onlookers to respond.

This type of experiment can be a great way to see behavioral responses in realistic settings. But it is more difficult for researchers to control the many variables existing in these settings that could potentially influence the experiment's results.

Quasi-Experiments

While lab experiments are known as true experiments, researchers can also utilize a quasi-experiment. Quasi-experiments are often referred to as natural experiments because the researchers do not have true control over the independent variable.

A researcher looking at personality differences and birth order, for example, is not able to manipulate the independent variable in the situation (personality traits). Participants also cannot be randomly assigned because they naturally fall into pre-existing groups based on their birth order.

So why would a researcher use a quasi-experiment? This is a good choice in situations where scientists are interested in studying phenomena in natural, real-world settings. It's also beneficial if there are limits on research funds or time.

Field experiments can be either quasi-experiments or true experiments.

Examples of the Experimental Method in Use

The experimental method can provide insight into human thoughts and behaviors, Researchers use experiments to study many aspects of psychology.

A 2019 study investigated whether splitting attention between electronic devices and classroom lectures had an effect on college students' learning abilities. It found that dividing attention between these two mediums did not affect lecture comprehension. However, it did impact long-term retention of the lecture information, which affected students' exam performance.

An experiment used participants' eye movements and electroencephalogram (EEG) data to better understand cognitive processing differences between experts and novices. It found that experts had higher power in their theta brain waves than novices, suggesting that they also had a higher cognitive load.

A study looked at whether chatting online with a computer via a chatbot changed the positive effects of emotional disclosure often received when talking with an actual human. It found that the effects were the same in both cases.

One experimental study evaluated whether exercise timing impacts information recall. It found that engaging in exercise prior to performing a memory task helped improve participants' short-term memory abilities.

Sometimes researchers use the experimental method to get a bigger-picture view of psychological behaviors and impacts. For example, one 2018 study examined several lab experiments to learn more about the impact of various environmental factors on building occupant perceptions.

A 2020 study set out to determine the role that sensation-seeking plays in political violence. This research found that sensation-seeking individuals have a higher propensity for engaging in political violence. It also found that providing access to a more peaceful, yet still exciting political group helps reduce this effect.

While the experimental method can be a valuable tool for learning more about psychology and its impacts, it also comes with a few pitfalls.

Experiments may produce artificial results, which are difficult to apply to real-world situations. Similarly, researcher bias can impact the data collected. Results may not be able to be reproduced, meaning the results have low reliability .

Since humans are unpredictable and their behavior can be subjective, it can be hard to measure responses in an experiment. In addition, political pressure may alter the results. The subjects may not be a good representation of the population, or groups used may not be comparable.

And finally, since researchers are human too, results may be degraded due to human error.

What This Means For You

Every psychological research method has its pros and cons. The experimental method can help establish cause and effect, and it's also beneficial when research funds are limited or time is of the essence.

At the same time, it's essential to be aware of this method's pitfalls, such as how biases can affect the results or the potential for low reliability. Keeping these in mind can help you review and assess research studies more accurately, giving you a better idea of whether the results can be trusted or have limitations.

Colorado State University. Experimental and quasi-experimental research .

American Psychological Association. Experimental psychology studies human and animals .

Mayrhofer R, Kuhbandner C, Lindner C. The practice of experimental psychology: An inevitably postmodern endeavor . Front Psychol . 2021;11:612805. doi:10.3389/fpsyg.2020.612805

Mandler G. A History of Modern Experimental Psychology .

Stanford University. Wilhelm Maximilian Wundt . Stanford Encyclopedia of Philosophy.

Britannica. Gustav Fechner .

Britannica. Hermann von Helmholtz .

Meyer A, Hackert B, Weger U. Franz Brentano and the beginning of experimental psychology: implications for the study of psychological phenomena today . Psychol Res . 2018;82:245-254. doi:10.1007/s00426-016-0825-7

Britannica. Georg Elias Müller .

McCambridge J, de Bruin M, Witton J.  The effects of demand characteristics on research participant behaviours in non-laboratory settings: A systematic review .  PLoS ONE . 2012;7(6):e39116. doi:10.1371/journal.pone.0039116

Laboratory experiments . In: The Sage Encyclopedia of Communication Research Methods. Allen M, ed. SAGE Publications, Inc. doi:10.4135/9781483381411.n287

Schweizer M, Braun B, Milstone A. Research methods in healthcare epidemiology and antimicrobial stewardship — quasi-experimental designs . Infect Control Hosp Epidemiol . 2016;37(10):1135-1140. doi:10.1017/ice.2016.117

Glass A, Kang M. Dividing attention in the classroom reduces exam performance . Educ Psychol . 2019;39(3):395-408. doi:10.1080/01443410.2018.1489046

Keskin M, Ooms K, Dogru AO, De Maeyer P. Exploring the cognitive load of expert and novice map users using EEG and eye tracking . ISPRS Int J Geo-Inf . 2020;9(7):429. doi:10.3390.ijgi9070429

Ho A, Hancock J, Miner A. Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot . J Commun . 2018;68(4):712-733. doi:10.1093/joc/jqy026

Haynes IV J, Frith E, Sng E, Loprinzi P. Experimental effects of acute exercise on episodic memory function: Considerations for the timing of exercise . Psychol Rep . 2018;122(5):1744-1754. doi:10.1177/0033294118786688

Torresin S, Pernigotto G, Cappelletti F, Gasparella A. Combined effects of environmental factors on human perception and objective performance: A review of experimental laboratory works . Indoor Air . 2018;28(4):525-538. doi:10.1111/ina.12457

Schumpe BM, Belanger JJ, Moyano M, Nisa CF. The role of sensation seeking in political violence: An extension of the significance quest theory . J Personal Social Psychol . 2020;118(4):743-761. doi:10.1037/pspp0000223

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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• What Is Action Research? | Definition & Examples

What Is Action Research? | Definition & Examples

Published on 27 January 2023 by Tegan George . Revised on 21 April 2023.

Table of contents

Types of action research, action research models, examples of action research, action research vs. traditional research, advantages and disadvantages of action research, frequently asked questions about action research.

There are 2 common types of action research: participatory action research and practical action research.

• Participatory action research emphasises that participants should be members of the community being studied, empowering those directly affected by outcomes of said research. In this method, participants are effectively co-researchers, with their lived experiences considered formative to the research process.
• Practical action research focuses more on how research is conducted and is designed to address and solve specific issues.

Both types of action research are more focused on increasing the capacity and ability of future practitioners than contributing to a theoretical body of knowledge.

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Action research is often reflected in 3 action research models: operational (sometimes called technical), collaboration, and critical reflection.

• Operational (or technical) action research is usually visualised like a spiral following a series of steps, such as “planning → acting → observing → reflecting.”
• Collaboration action research is more community-based, focused on building a network of similar individuals (e.g., college professors in a given geographic area) and compiling learnings from iterated feedback cycles.
• Critical reflection action research serves to contextualise systemic processes that are already ongoing (e.g., working retroactively to analyse existing school systems by questioning why certain practices were put into place and developed the way they did).

Action research is often used in fields like education because of its iterative and flexible style.

After the information was collected, the students were asked where they thought ramps or other accessibility measures would be best utilised, and the suggestions were sent to school administrators. Example: Practical action research Science teachers at your city’s high school have been witnessing a year-over-year decline in standardised test scores in chemistry. In seeking the source of this issue, they studied how concepts are taught in depth, focusing on the methods, tools, and approaches used by each teacher.

Action research differs sharply from other types of research in that it seeks to produce actionable processes over the course of the research rather than contributing to existing knowledge or drawing conclusions from datasets. In this way, action research is formative , not summative , and is conducted in an ongoing, iterative way.

As such, action research is different in purpose, context, and significance and is a good fit for those seeking to implement systemic change.

Action research comes with advantages and disadvantages.

• Action research is highly adaptable , allowing researchers to mould their analysis to their individual needs and implement practical individual-level changes.
• Action research provides an immediate and actionable path forward for solving entrenched issues, rather than suggesting complicated, longer-term solutions rooted in complex data.
• Done correctly, action research can be very empowering , informing social change and allowing participants to effect that change in ways meaningful to their communities.

Disadvantages

• Due to their flexibility, action research studies are plagued by very limited generalisability  and are very difficult to replicate . They are often not considered theoretically rigorous due to the power the researcher holds in drawing conclusions.
• Action research can be complicated to structure in an ethical manner . Participants may feel pressured to participate or to participate in a certain way.
• Action research is at high risk for research biases such as selection bias , social desirability bias , or other types of cognitive biases .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

Sources for this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2023, April 21). What Is Action Research? | Definition & Examples. Scribbr. Retrieved 11 June 2024, from https://www.scribbr.co.uk/research-methods/action-research-cycle/

Cohen, L., Manion, L., & Morrison, K. (2017). Research methods in education (8th edition). Routledge.

Naughton, G. M. (2001).  Action research (1st edition). Routledge.

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education, community-building and change

Kurt Lewin: groups, experiential learning and action research

Kurt Lewin: groups, experiential learning and action research. Kurt Lewin was a seminal theorist who deepened our understanding of groups, experiential learning, and action research. What did he actually add to the theory and practice of pedagogy and informal education?

contents: introduction · life · field theory · group dynamics · democracy and groups · t-groups, facilitation and experience · action research · conclusion · further reading and references · links .

Kurt Lewin’s (1890-1947) work had a profound impact on social psychology and, more particularly for our purposes here, on our appreciation of experiential learning, group dynamics and action research. On this page, we provide a very brief outline of his life and an assessment of his continuing relevance to educators. Kurt Lewin was born on September 9, 1890, in the village of Mogilno in Prussia (now part of Poland). He was one of four children in a middle-class Jewish family (his father owned a small general store and a farm). They moved to Berlin when he was aged 15 and he was enrolled in the Gymnasium. In 1909 Kurt Lewin entered the University of Frieberg to study medicine. He then transferred to the University of Munich to study biology. Around this time he became in involved in the socialist movement. His particular concerns appear to have been the combating of anti-Semitism, the democratization of German institutions, and the need to improve the position of women. Along with other students he organized and taught an adult education program for working-class women and men (Marrow 1969).

His doctorate was undertaken at the University of Berlin where he developed an interest in the philosophy of science and encountered Gestalt psychology. His PhD was awarded in 1916, but by then he was serving in the German army (he was injured in combat). In 1921 Kurt Lewin joined the Psychological Institute of the University of Berlin – where he was to lecture and offer seminars in both philosophy and psychology. He was starting to make a name for himself both in terms of publishing and with regard to his teaching (he was an enthusiastic lecturer who attracted the interest of students). His work became known in America and he was invited to spend six months as a visiting professor at Stanford (1930). With the political position worsening considerably in Germany and in 1933 he and his wife and daughter settled in the USA (he became an American citizen in 1940). Kurt Lewin was first to work at the Cornell School of Home Economics and then, in 1935, at the University of Iowa (this was also the year when his first collection of papers in English – A Dynamic Theory of Personality – was published).

The University of Iowa remained Kurt Lewin’s base until 1944. There he continued to develop his interest in social processes, and to undertake research in that area. Significantly, he became involved in various applied research initiatives linked to the war effort (from 1940 onwards). These included exploring the morale of the fighting troops, psychological warfare, and reorienting food consumption away from foods in short supply. His social commitments were also still strong – and he was much in demand as a speaker on minority and inter-group relations. He wanted to establish a centre to research group dynamics – and in 1944 this dream was realized with the founding of the Research Center for Group Dynamics at MIT. At the same time Kurt Lewin was also engaged in a project for the American Jewish Congress in New York – the Commission of Community Interrelations. It made use of Lewin’s model of action research (research directed toward the solving of social problems) in a number of significant studies into religious and racial prejudice. It was also out of some of this work in 1946 with community leaders and group facilitators that the notion of ‘T’ groups emerged. He and his associates were able to get funding from the Office of Naval Research to set up the National Training Laboratories in 1947 in Bethel, Maine. However, Lewin died of a heart attack in Newtonville, Mass. on February 11, 1947, before the Laboratories were established.

Field theory

Here we will not enter into the detail of Kurt Lewin’s field theory (it is beyond our remit). However, it is necessary to note its key elements. To begin it is important to recognize its roots in Gestalt theory. (A gestalt is a coherent whole. It has its own laws, and is a construct of the individual mind rather than ‘reality’). For Kurt Lewin behaviour was determined by the totality of an individual’s situation. In his field theory, a ‘field’ is defined as ‘the totality of coexisting facts which are conceived of as mutually interdependent’ (Lewin 1951: 240). Individuals were seen to behave differently according to the way in which tensions between perceptions of the self and of the environment were worked through. The whole psychological field, or ‘life space’, within which people acted had to be viewed, in order to understand behaviour. Within this individuals and groups could be seen in topological terms (using map-like representations). Individuals participate in a series of life spaces (such as the family, work, school and church), and these were constructed under the influence of various force vectors (Lewin 1952).

Hall and Lindzey (1978: 386) summarize the central features of Kurt Lewin’s field theory as follows:

Behaviour is a function of the field that exists at the time the behaviour occurs, Analysis begins with the situation as a whole from which are differentiated the component parts, and The concrete person in a concrete situation can represented mathematically.

Kurt Lewin also looked to the power of underlying forces (needs) to determine behaviour and, hence, expressed ‘a preference for psychological as opposed to physical or physiological descriptions of the field’ ( op. cit. ).

In this we can see how Kurt Lewin drew together insights from topology (e.g. life space), psychology (need, aspiration etc.), and sociology (e.g. force fields – motives clearly being dependent on group pressures). As Allport in his foreword to Resolving Social Conflict (Lewin 1948: ix) put it, these three aspects of his thought were not separable. ‘All of his concepts, whatever root-metaphor they employ, comprise a single well-integrated system’. It was this, in significant part, which gave his work its peculiar power.

Group dynamics

It is not an exaggeration to say that Kurt Lewin had a profound impact on a generation of researchers and thinkers concerned with group dynamics. Brown (1988: 28-32) argues that two key ideas emerged out of field theory that are crucial to an appreciation of group process: interdependence of fate, and task interdependence.

The interdependence of fate. Here the basic line of argument is that groups come into being in a psychological sense ‘not because their members necessarily are similar to one another (although they may be); rather, a group exists when people in it realize their fate depends on the fate of the group as a whole’ (Brown 1988: 28). This is how Lewin (1946: 165-6) put it when discussing the position of Jews in 1939:

[I]t is not similarity or dissimilarity of individuals that constitutes a group, but rather interdependence of fate. Any normal group, and certainly any developed and organized one contains and should contain individuals of very different character…. It is easy enough to see that the common fate of all Jews makes them a group in reality. One who has grasped this simple idea will not feel that he has to break away from Judaism altogether whenever he changes his attitude toward a fundamental Jewish issue, and he will become more tolerant of differences of opinion among Jews. What is more, a person who has learned to see how much his own fate depends upon the fate of his entire group will ready and even eager to take over a fair share of responsibility for its welfare.

It could be argued that the position of Jews in 1939 constitutes a special case. That the particular dangers they faced in many countries make arguing a general case difficult. However, Lewin’s insight does seem to be applicable to many different group settings. Subsequently, there has been some experimental support for the need for some elementary sense of interdependence (Brown 1989).

Task interdependence. Interdependence of fate can be a fairly weak form of interdependence in many groups, argued Lewin. A more significant factor is where there is interdependence in the goals of group members. In other words, if the group’s task is such that members of the group are dependent on each other for achievement, then a powerful dynamic is created.

These implications can be positive or negative. In the former case one person’s success either directly facilitates others’ success of, in the strongest case, is actually necessary for those others to succeed also… In negative interdependence – known more usually as competition – one person’s success is another’s failure. (Brown (1989: 30)

Kurt Lewin had looked to the nature of group task in an attempt to understand the uniformity of some groups’ behaviour. He remained unconvinced of the explanatory power of individual motivational concepts such as those provided by psychoanalytical theory or frustration-aggression theory ( op. cit. ). He was able to argue that people may come to a group with very different dispositions, but if they share a common objective, they are likely to act together to achieve it. This links back to what is usually described as Lewin’s field theory. An intrinsic state of tension within group members stimulates or motivates movement toward the achievement of desired common goals (Johnson and Johnson 1995: 175). Interdependence (of fate and task) also results in the group being a ‘dynamic whole’. This means that a change in one member or subgroups impacts upon others. These two elements combined together to provide the basis for Deutch’s (1949) deeply influential exploration of the relationship of task to process (and his finding that groups under conditions of positive interdependence were generally more co-operative. Members tended to participate and communicate more in discussion; were less aggressive; liked each other more, and tended to be productive as compared to those working under negative task interdependence) (Brown 1989: 32; Johnson and Johnson 1995).

Democracy and groups

Gordon W. Allport, in his introduction to Resolving Social Conflicts (Lewin 1948: xi) argues that there is striking kinship between the work of Kurt Lewin and that of John Dewey.

Both agree that democracy must be learned anew in each generation, and that it is a far more difficult form of social structure to attain and to maintain than is autocracy. Both see the intimate dependence of democracy upon social science. Without knowledge of, and obedience to, the laws of human nature in group settings, democracy cannot succeed. And without freedom for research and theory as provided only in a democratic environment, social science will surely fail. Dewey, we might say, is the outstanding philosophical exponent of democracy, Lewin is its outstanding psychological exponent. More clearly than anyone else has he shown us in concrete, operational terms what it means to be a democratic leader, and to create democratic group structure.

One of the most interesting pieces of work in which Lewin was involved, concerned the exploration of different styles or types of leadership on group structure and member behaviour. This entailed a collaboration with Ronald Lippitt, among others (Lewin et. al 1939, also written up in Lewin 1948: 71-83). They looked to three classic group leadership models – democratic, autocratic and laissez-faire – and concluded that there was more originality, group-mindedness and friendliness in democratic groups. In contrast, there was more aggression, hostility, scapegoating and discontent in laissez-faire and autocratic groups (Reid 1981: 115). Lewin concludes that the difference in behaviour in autocratic, democratic and laissez-faire situations is not, on the whole, a result of individual differences. Reflecting on the group experiments conducted with children he had the following to say:

There have been few experiences for me as impressive as seeing the expression in children’s faces change during the first day of autocracy. The friendly, open, and co-operative group, full of life, became within a short half-hour a rather apathetic looking gathering without initiative. The change from autocracy to democracy seemed to take somewhat more time than from democracy to autocracy. Autocracy is imposed upon the individual. Democracy he has to learn. (Lewin 1948: 82)

This presentation of democratic of leadership in groups became deeply influential. Unfortunately, as Gastil (1994) notes, Lewin and his colleagues never developed their definition beyond this rough sketch. This has left them open to the charge that their vision of democratic leadership contains within it some worrying themes. In particular, Kariel (1956, discussed by Gastil 1994) argued that the notion is rather manipulative and élitist. What is more, there has also been some suggestion that Mao’s mass-line leadership in China, ‘used a model like Lewin’s to mask coercion under the guise of participative group processes’ (discussed by Gastil 1994). Such a possibility would have been disturbing to Lewin, whose commitments and intentions were democratic. He argued that democracy could not be imposed on people, that it had to be learnt by a process of voluntary and responsible participation (1948: 39). However, the problem becomes clearer when he discusses the nature of democratic leadership at moments of transition. Change needed to be facilitated and guided.

To instigate changes toward democracy a situation has to be created for a certain period where the leader is sufficiently in control to rule out influences he does not want and to manipulate the situation to a sufficient degree. The goal of the democratic leader in this transition period will have to be the same as any good teacher, namely to make himself superfluous, to be replaced by indigenous leaders from the group. (Lewin 1948: 39)

There are some elements here that ring a little of Rousseau’s view of the tutor’s role in Emile . Is it up to the leader to manipulate the situation in this way – or is there room for dialogue ?

‘T’ groups, facilitation and experience

In the summer of 1946 Kurt Lewin along with colleagues and associates from the Research Center for Group Dynamics (Ronald Lippitt, Leland Bradford and Kenneth Benne became involved in leadership and group dynamics training for the Connecticut State Interracial Commission. They designed and implemented a two-week programme that looked to encourage group discussion and decision-making, and where participants (including staff) could treat each other as peers. Research was woven into the event (as might be expected given Lewin’s concern for the generation of data and theory). The trainers and researchers collected detailed observations and recordings of group activities (and worked on these during the event). Initially, these meetings were just for the staff, but some of the other participants also wanted to be involved.

At the start of one of the early evening observers’ sessions, three of the participants asked to be present. Much to the chagrin of the staff, Lewin agreed to this unorthodox request. As the observers reported to the group, one of the participants – a woman – disagreed with the observer on the interpretation of her behaviour that day. One other participant agreed with her assertion and a lively discussion ensued about behaviours and their interpretations. Word of the session spread, and by the next night, more than half of the sixty participants were attending the feedback sessions which, indeed became the focus of the conference. Near the conference’s end, the vast majority of participants were attending these sessions, which lasted well into the night. ( NTL Institute )

Lippitt (1949) has described how Lewin responded to this and joined with participants in ‘active dialogue about differences of interpretation and observation of the events by those who had participated in them’. A significant innovation in training practice was established. As Kolb (1984: 10) has commented:

Thus the discovery was made that learning is best facilitated in an environment where there is dialectic tension and conflict between immediate, concrete experience and analytic detachment. By bringing together the immediate experiences of the trainees and the conceptual models of the staff in an open atmosphere where inputs from each perspective could challenge and stimulate the other, a learning environment occurred with remarkable vitality and creativity.

It was this experience that led to the establishment of the first National Training Laboratory in Group Development (held at Gould Academy in Bethel, Maine in the summer of 1947). By this time Lewin was dead, but his thinking and practice was very much a part of what happened. This is how Reid (1981: 153) describes what happened:

A central feature of the laboratory was “basic skills training,” in which an observer reported on group processes at set intervals. The skills to be achieved were intended to help an individual function in the role of “change agent”. A change agent was thought to be instrumental in facilitating communication and useful feedback among participants. He was also to be a paragon who was aware of the need for change, could diagnose the problems involved, and could plan for change, implement the plans, and evaluate the results. To become an effective change agent, an understanding of the dynamics of groups was believed necessary.

What we see here is the basic shape of T-group theory and the so-called ‘laboratory method’. Initially, the small discussion groups were known as ‘basic skill training groups’ but by 1949 they had been shortened to T-group. In 1950 a sponsoring organization, the National Training Laboratories (NTL) was set up, and the scene was set for a major expansion of the work (reaching its heyday in the 1960s) and the evolution of the encounter group (Yalom 1995: 488).

The approach was not without its critics – in part because of what was perceived as its Gestalt base. In part, because it was seen by some as lacking substance. Reid (1981: 154) reports that Grace Coyle, who had spent time at Bethel, felt that many of the training groups handled group situations badly; and that the leaders were starting to believe that they had ‘discovered everything there was to know about group relations and were unaware of the inquiry and work of others’. There may have been some element of this – but there was also innovation here. Four elements of the T-group are particularly noteworthy here according to Yalom (1995: 488-9) (and they owe a great deal to Lewin’s influence):

Feedback . Lewin had borrowed the term from electrical engineering and applied it to the behavioural sciences. Here it was broadly used to describe the adjustment of a process informed by information about its results or effects. An important element here is the difference between the desired and actual result. There was a concern that organizations, groups and relationships generally suffered from a lack of accurate information about what was happening around their performance. Feedback became a key ingredient of T-groups and was found to ‘be most effective when it stemmed from here-and-now observations, when it followed the generating event as closely as possible, and when the recipient checked with other group members to establish its validity and reduce perceptual distortion’ (Yalom 1995: 489). Unfreezing. This was taken directly from Kurt Lewin’s change theory. It describes the process of disconfirming a person’s former belief system. ‘Motivation for change must be generated before change can occur. One must be helped to re-examine many cherished assumptions about oneself and one’s relations to others’ ( op. cit. ). Part of the process of the group, then, had to address this. Trainers sought to create an environment in which values and beliefs could be challenged. Participant observation. ‘Members had to participate emotionally in the group as well as observe themselves and the group objectively’ ( op. cit. ). Connecting concrete (emotional) experience and analytical detachment is not an easy task, and is liable to be resisted by many participants, but it was seen as a essential if people were to learn and develop. Cognitive aids. This particular aspect was drawn from developments in psychoeducational and cognitive-behavioural group therapy. It entailed the provision of models or organizing ideas through the medium brief lectures and handouts (and later things like film clips or video). Perhaps the best known of these was the Johari Window (named after, and developed by, Joe Luft and Harry Ingram). Yalom (1995: 490) comments, ‘The use of such cognitive aids, lectures, reading assignments, and theory sessions demonstrates that the basic allegiance of the T-group was to the classroom rather than the consulting room. The participants were considered students; the task of the T-group was to facilitate learning for its members’.

Action research

Kurt Lewin is also generally credited as the person who coined the term ‘ action research ’.

The research needed for social practice can best be characterized as research for social management or social engineering. It is a type of action-research, a comparative research on the conditions and effects of various forms of social action, and research leading to social action. Research that produces nothing but books will not suffice (Lewin 1946, reproduced in Lewin 1948: 202-3)

His approach involves a spiral of steps, ‘each of which is composed of a circle of planning, action and fact-finding about the result of the action’ ( ibid. : 206). The basic cycle involves the following:

This is how Lewin describes the initial cycle:

The first step then is to examine the idea carefully in the light of the means available. Frequently more fact-finding about the situation is required. If this first period of planning is successful, two items emerge: namely, “an overall plan” of how to reach the objective and secondly, a decision in regard to the first step of action. Usually this planning has also somewhat modified the original idea. ( ibid. : 205)

The next step is ‘composed of a circle of planning, executing, and reconnaissance or fact-finding for the purpose of evaluating the results of the second step and preparing the rational basis for planning the third step, and for perhaps modifying again the overall plan’ ( ibid. : 206). What we can see here is an approach to research that is oriented to problem-solving in social and organizational settings, and that has a form that parallels Dewey’s conception of learning from experience.

The approach, as presented, does take a fairly sequential form – and it is open to a literal interpretation. Following it can lead to practice that is ‘correct’ rather than ‘good’ – as we will see. It can also be argued that the model itself places insufficient emphasis on analysis at key points. Elliott (1991: 70), for example, believed that the basic model allows those who use it to assume that the ‘general idea’ can be fixed in advance, ‘that “reconnaissance” is merely fact-finding, and that “implementation” is a fairly straightforward process’. As might be expected there was some questioning as to whether this was ‘real’ research. There were questions around action research’s partisan nature – the fact that it served particular causes. There were also questions concerning its rigour and the training of those undertaking it. However, as Bogdan and Biklen (1992: 223) point out, research is a frame of mind – ‘a perspective that people take toward objects and activities’. Once we have satisfied ourselves that the collection of information is systematic and that any interpretations made have proper regard for satisfying truth claims, then much of the critique aimed at action research disappears. In some of Lewin’s earlier work on action research (e.g. Lewin and Grabbe 1945), there was a tension between providing a rational basis for change through research, and the recognition that individuals are constrained in their ability to change by their cultural and social perceptions, and the systems of which they are a part. Having ‘correct knowledge’ does not of itself lead to change, attention also needs to be paid to the ‘matrix of cultural and psychic forces’ through which the subject is constituted (Winter 1987: 48).

Action research did suffer a decline in favour during the 1960s because of its association with radical political activism (Stringer 1999: 9). However, it has subsequently gained a significant foothold both within the realm of community-based, and participatory action research; and as a form of practice oriented to the improvement of educative encounters (e.g. Carr and Kemmis 1986). The use of action research to deepen and develop classroom practice has grown into a strong tradition of practice (one of the first examples being the work of Stephen Corey in 1949). For some, there is an insistence that action research must be collaborative and entail groupwork.

Action research is a form of collective self-reflective enquiry undertaken by participants in social situations in order to improve the rationality and justice of their own social or educational practices, as well as their understanding of those practices and the situations in which the practices are carried out… The approach is only action research when it is collaborative, though it is important to realise that action research of the group is achieved through the critically examined action of individual group members. (Kemmis and McTaggart 1988: 5-6)

Just why it must be collective is open to some question and debate (Webb 1996), but there is an important point here concerning the commitments and orientations of those involved in action research. One of the legacies Kurt Lewin left us is the ‘action research spiral’ – and with it there is the danger that action research becomes little more than a procedure. It is a mistake, according to McTaggart (1996: 248) to think that following the action research spiral constitutes ‘doing action research’. He continues, ‘Action research is not a ‘method’ or a ‘procedure’ for research but a series of commitments to observe and problematize through practice a series of principles for conducting social enquiry’. It is his argument that Lewin has been misunderstood or, rather, misused. When set in historical context, while Lewin does talk about action research as a method, he is stressing a contrast between this form of interpretative practice and more traditional empirical-analytic research. The notion of a spiral may be a useful teaching device – but it is all too easy to slip into using it as the template for practice (McTaggart 1996: 249).

As this brief cataloguing of his work shows, Lewin made defining contributions to a number of fields. He had a major impact on our appreciation of groups and how to work with them; he pioneered action research; he demonstrated that complex social phenomenon could be explored using controlled experiments, and he helped to move social psychology into a more rounded understanding of behaviour (being a function of people and the way they perceive the environment). This is a formidable achievement. Sixty years on, he still excites discussion and argument, and while we may want to qualify or rework various aspect of his work (and that of his associates) we are deeply indebted to him both for his insights and the way he tried to bring a commitment to democracy and justice to his work. The consistent theme in all Kurt Lewin’s work, according to David A. Kolb (1984: 9) was his concern for the integration of theory and practice. This was symbolized in his best-known quotation: ‘There is nothing so practical as a good theory’ (1951: 169). It’s a lesson that we still need to learn.

Further reading and references

Bogdan, R. C. and Biklen, S. K. (1992) Qualitative Research for Education , Boston: Allyn and Bacon.

Bradford, L. P., Gibb, J. R., Benn, K. D. (1964). T Group theory and laboratory method, New York: John Wiley.

Brown, R. (1988) Group Processes. Dynamics within and between groups , Oxford: Blackwell.

Carr, W. and Kemmis, S. (1986) Becoming Critical. Education, knowledge and action research , Lewes: Falmer Press.

Correy, S. M. (1949) ‘Action research, fundamental research and educational practices’, Teachers College Record 50: 509-14.

Deutch, M. (1949) ‘A theory of cooperation and competition’, Human Relations 2: 129-52

Elliott, J. (1991) Action Research for Educational Change , Buckingham: Open University Press.

Gastil, J. (1994) ‘A definition and illustration of democratic leadership’ Human Relations 47/8: 953-75. Reprinted in K. Grint (ed.) (1997) Leadership , Oxford: Oxford University Press.

Gold, M. (ed.) (1999) The Complete Social Scientist. A Kurt Lewin Reader.

Hall, C.S. and Lindzey, G. (1978) Theories of Personality 3e, New York: John Wiley and Sons.

Johnson, D. W. and Johnson, R. T. (1995) ‘Positive interdependence: key to effective cooperation’ in R. Hertz-Lazarowitz and N. Miller (eds.) Interaction in Cooperative Groups. The theoretical anatomy of group learning , Cambridge: Cambridge University Press.

Kariel, H. S. (1956) ‘Democracy unlimited. Kurt Lewin’s field theory’, American Journal of Sociology 62: 280-89.

Kemmis, S. and McTaggart, R. (1988) The Action Research Planner , Geelong, Victoria: Deakin University Press.

Kolb, D. A. (1984) Experiential Learning. Experience as the source of learning and development , Englewood Cliffs, NJ.: Prentice-Hall.

Lewin, K. (1935) A dynamic theory of personality. New York: McGraw-Hill.

Lewin, K. (1936) Principles of topological psychology. New York: McGraw-Hill.

Lewin, K. (1948) Resolving social conflicts; selected papers on group dynamics. Gertrude W. Lewin (ed.). New York: Harper & Row, 1948.

Lewin, K. (1951) Field theory in social science; selected theoretical papers. D. Cartwright (ed.). New York: Harper & Row.

Lewin, K. and Lippitt, R. (1938) ‘An experimental approach to the study of autocracy and democracy. A preliminary note’, Sociometry 1: 292-300.

Lewin, K., Lippitt, R. and White, R. (1939) ‘Patterns of aggressive behaviour in experimentally created “social climates”’, Journal of Social Psychology 10: 271-99.

Lewin, K. and Grabbe, P. (1945) ‘Conduct, knowledge and acceptance of new values’ Journal of Social Issues 2.

Lippitt, R. (1949) Training in Community Relations , New York: Harper and Row.

McTaggart, R. (1996) ‘Issues for participatory action researchers’ in O. Zuber-Skerritt (ed.) New Directions in Action Research , London: Falmer Press.

Marrow, A. J. (1969) The Practical Theorist. : The Life and Work of Kurt Lewin, New York: Basic Books

Reid, K. E. (1981) From Character Building to Social Treatment. The history of the use of groups in social work , Westpoint, Conn.: Greenwood Press.

Schein, E (1995) ‘Kurt Lewin’s Change Theory in the Field and in the Classroom: Notes Toward a Model of Managed Learning’, Systems Practice , http://www.solonline.org/res/wp/10006.html

Stringer, E. T. (1999) Action Research 2e, Thousand Oaks, CA.: Sage.

Ullman, D. (2000) ‘Kurt Lewin: His Impact on American Psychology, or Bridging the Gorge between Theory and Reality’, http://www.sonoma.edu/psychology/os2db/history3.html

Webb, G. (1996) ‘Becoming critical of action research for development’ in O. Zuber-Skerritt (ed.) New Directions in Action Research , London: Falmer Press.

Winter, R. (1987) Action-Research and the Nature of Social Inquiry. Professional innovation and educational work , Aldershot: Avebury.

Yalom, I. D. (1995) The Theory and Practice of Group Psychotherapy 4e,New York: Basic Books.

Force field analysis – brief article at accel-team.com

Kurt Lewin – timeline and brief biography – prepared by Julie Greathouse plus a brief description of his theoretical contribution to psychology

the groupwork pioneers series

Picture and diagram credits : Detail of plaque commemorating Kurt Lewin on the house where he was born.

Action research cycle (we believe to be in the public domain)

Bibliographical reference: Smith, M. K. (2001). ‘Kurt Lewin, groups, experiential learning and action research’, The encyclopedia of pedagogy and informal education. [ https://infed.org/mobi/kurt-lewin-groups-experiential-learning-and-action-research/ . Retrieved: insert date]

© Mark K. Smith 2001

First published June 2001.

Last Updated on July 20, 2020 by infed.org

Chapter 10 Experimental Research

Experimental research, often considered to be the “gold standard” in research designs, is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.

Experimental research is best suited for explanatory research (rather than for descriptive or exploratory research), where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalizability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments , conducted in field settings such as in a real organization, and high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.

Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.

Basic Concepts

Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favorably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receives a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.

Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the “cause” in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .

Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and assures that each unit in the population has a positive chance of being selected into the sample. Random assignment is however a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group, prior to treatment administration. Random selection is related to sampling, and is therefore, more closely related to the external validity (generalizability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.

Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.

• History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.
• Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.
• Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam. Not conducting a pretest can help avoid this threat.
• Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.
• Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.
• Regression threat , also called a regression to the mean, refers to the statistical tendency of a group’s overall performance on a measure during a posttest to regress toward the mean of that measure rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest was possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.

Two-Group Experimental Designs

The simplest true experimental designs are two group designs involving one treatment group and one control group, and are ideally suited for testing the effects of a single independent variable that can be manipulated as a treatment. The two basic two-group designs are the pretest-posttest control group design and the posttest-only control group design, while variations may include covariance designs. These designs are often depicted using a standardized design notation, where R represents random assignment of subjects to groups, X represents the treatment administered to the treatment group, and O represents pretest or posttest observations of the dependent variable (with different subscripts to distinguish between pretest and posttest observations of treatment and control groups).

Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.

Figure 10.1. Pretest-posttest control group design

The effect E of the experimental treatment in the pretest posttest design is measured as the difference in the posttest and pretest scores between the treatment and control groups:

E = (O 2 – O 1 ) – (O 4 – O 3 )

Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement (especially if the pretest introduces unusual topics or content).

Posttest-only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.

Figure 10.2. Posttest only control group design.

The treatment effect is measured simply as the difference in the posttest scores between the two groups:

E = (O 1 – O 2 )

The appropriate statistical analysis of this design is also a two- group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.

Covariance designs . Sometimes, measures of dependent variables may be influenced by extraneous variables called covariates . Covariates are those variables that are not of central interest to an experimental study, but should nevertheless be controlled in an experimental design in order to eliminate their potential effect on the dependent variable and therefore allow for a more accurate detection of the effects of the independent variables of interest. The experimental designs discussed earlier did not control for such covariates. A covariance design (also called a concomitant variable design) is a special type of pretest posttest control group design where the pretest measure is essentially a measurement of the covariates of interest rather than that of the dependent variables. The design notation is shown in Figure 10.3, where C represents the covariates:

Figure 10.3. Covariance design

Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:

Figure 10.4. 2 x 2 factorial design

Factorial designs can also be depicted using a design notation, such as that shown on the right panel of Figure 10.4. R represents random assignment of subjects to treatment groups, X represents the treatment groups themselves (the subscripts of X represents the level of each factor), and O represent observations of the dependent variable. Notice that the 2 x 2 factorial design will have four treatment groups, corresponding to the four combinations of the two levels of each factor. Correspondingly, the 2 x 3 design will have six treatment groups, and the 2 x 2 x 2 design will have eight treatment groups. As a rule of thumb, each cell in a factorial design should have a minimum sample size of 20 (this estimate is derived from Cohen’s power calculations based on medium effect sizes). So a 2 x 2 x 2 factorial design requires a minimum total sample size of 160 subjects, with at least 20 subjects in each cell. As you can see, the cost of data collection can increase substantially with more levels or factors in your factorial design. Sometimes, due to resource constraints, some cells in such factorial designs may not receive any treatment at all, which are called incomplete factorial designs . Such incomplete designs hurt our ability to draw inferences about the incomplete factors.

In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for 3 hours/week of instructional time than for 1.5 hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.

Hybrid Experimental Designs

Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomized bocks design, Solomon four-group design, and switched replications design.

Randomized block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full -time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between treatment group (receiving the same treatment) or control group (see Figure 10.5). The purpose of this design is to reduce the “noise” or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.

Figure 10.5. Randomized blocks design.

Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs but not in posttest only designs. The design notation is shown in Figure 10.6.

Figure 10.6. Solomon four-group design

Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organizational contexts where organizational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

Figure 10.7. Switched replication design.

Quasi-Experimental Designs

Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organization is used as the treatment group, while another section of the same class or a different organization in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of a certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impact by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.

Many true experimental designs can be converted to quasi-experimental designs by omitting random assignment. For instance, the quasi-equivalent version of pretest-posttest control group design is called nonequivalent groups design (NEGD), as shown in Figure 10.8, with random assignment R replaced by non-equivalent (non-random) assignment N . Likewise, the quasi -experimental version of switched replication design is called non-equivalent switched replication design (see Figure 10.9).

Figure 10.8. NEGD design.

Figure 10.9. Non-equivalent switched replication design.

In addition, there are quite a few unique non -equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.

Regression-discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to treatment or control group based on a cutoff score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardized test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program. The design notation can be represented as follows, where C represents the cutoff score:

Figure 10.10. RD design.

Because of the use of a cutoff score, it is possible that the observed results may be a function of the cutoff score rather than the treatment, which introduces a new threat to internal validity. However, using the cutoff score also ensures that limited or costly resources are distributed to people who need them the most rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design does not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.

Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.

Figure 10.11. Proxy pretest design.

Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data are not available from the same subjects.

Figure 10.12. Separate pretest-posttest samples design.

Nonequivalent dependent variable (NEDV) design . This is a single-group pre-post quasi-experimental design with two outcome measures, where one measure is theoretically expected to be influenced by the treatment and the other measure is not. For instance, if you are designing a new calculus curriculum for high school students, this curriculum is likely to influence students’ posttest calculus scores but not algebra scores. However, the posttest algebra scores may still vary due to extraneous factors such as history or maturation. Hence, the pre-post algebra scores can be used as a control measure, while that of pre-post calculus can be treated as the treatment measure. The design notation, shown in Figure 10.13, indicates the single group by a single N , followed by pretest O 1 and posttest O 2 for calculus and algebra for the same group of students. This design is weak in internal validity, but its advantage lies in not having to use a separate control group.

An interesting variation of the NEDV design is a pattern matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique, based on the degree of correspondence between theoretical and observed patterns is a powerful way of alleviating internal validity concerns in the original NEDV design.

Figure 10.13. NEDV design.

Perils of Experimental Research

Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, many experimental research use inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artifact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.

The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if doubt, using tasks that are simpler and familiar for the respondent sample than tasks that are complex or unfamiliar.

In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Experimental Research

• First Online: 25 February 2021

Cite this chapter

• C. George Thomas 2  

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Experiments are part of the scientific method that helps to decide the fate of two or more competing hypotheses or explanations on a phenomenon. The term ‘experiment’ arises from Latin, Experiri, which means, ‘to try’. The knowledge accrues from experiments differs from other types of knowledge in that it is always shaped upon observation or experience. In other words, experiments generate empirical knowledge. In fact, the emphasis on experimentation in the sixteenth and seventeenth centuries for establishing causal relationships for various phenomena happening in nature heralded the resurgence of modern science from its roots in ancient philosophy spearheaded by great Greek philosophers such as Aristotle.

The strongest arguments prove nothing so long as the conclusions are not verified by experience. Experimental science is the queen of sciences and the goal of all speculation . Roger Bacon (1214–1294)

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Bibliography

Best, J.W. and Kahn, J.V. 1993. Research in Education (7th Ed., Indian Reprint, 2004). Prentice–Hall of India, New Delhi, 435p.

Google Scholar  

Campbell, D. and Stanley, J. 1963. Experimental and quasi-experimental designs for research. In: Gage, N.L., Handbook of Research on Teaching. Rand McNally, Chicago, pp. 171–247.

Chandel, S.R.S. 1991. A Handbook of Agricultural Statistics. Achal Prakashan Mandir, Kanpur, 560p.

Cox, D.R. 1958. Planning of Experiments. John Wiley & Sons, New York, 308p.

Fathalla, M.F. and Fathalla, M.M.F. 2004. A Practical Guide for Health Researchers. WHO Regional Publications Eastern Mediterranean Series 30. World Health Organization Regional Office for the Eastern Mediterranean, Cairo, 232p.

Fowkes, F.G.R., and Fulton, P.M. 1991. Critical appraisal of published research: Introductory guidelines. Br. Med. J. 302: 1136–1140.

Gall, M.D., Borg, W.R., and Gall, J.P. 1996. Education Research: An Introduction (6th Ed.). Longman, New York, 788p.

Gomez, K.A. 1972. Techniques for Field Experiments with Rice. International Rice Research Institute, Manila, Philippines, 46p.

Gomez, K.A. and Gomez, A.A. 1984. Statistical Procedures for Agricultural Research (2nd Ed.). John Wiley & Sons, New York, 680p.

Hill, A.B. 1971. Principles of Medical Statistics (9th Ed.). Oxford University Press, New York, 390p.

Holmes, D., Moody, P., and Dine, D. 2010. Research Methods for the Bioscience (2nd Ed.). Oxford University Press, Oxford, 457p.

Kerlinger, F.N. 1986. Foundations of Behavioural Research (3rd Ed.). Holt, Rinehart and Winston, USA. 667p.

Kirk, R.E. 2012. Experimental Design: Procedures for the Behavioural Sciences (4th Ed.). Sage Publications, 1072p.

Kothari, C.R. 2004. Research Methodology: Methods and Techniques (2nd Ed.). New Age International, New Delhi, 401p.

Kumar, R. 2011. Research Methodology: A Step-by step Guide for Beginners (3rd Ed.). Sage Publications India, New Delhi, 415p.

Leedy, P.D. and Ormrod, J.L. 2010. Practical Research: Planning and Design (9th Ed.), Pearson Education, New Jersey, 360p.

Marder, M.P. 2011. Research Methods for Science. Cambridge University Press, 227p.

Panse, V.G. and Sukhatme, P.V. 1985. Statistical Methods for Agricultural Workers (4th Ed., revised: Sukhatme, P.V. and Amble, V. N.). ICAR, New Delhi, 359p.

Ross, S.M. and Morrison, G.R. 2004. Experimental research methods. In: Jonassen, D.H. (ed.), Handbook of Research for Educational Communications and Technology (2nd Ed.). Lawrence Erlbaum Associates, New Jersey, pp. 10211043.

Snedecor, G.W. and Cochran, W.G. 1980. Statistical Methods (7th Ed.). Iowa State University Press, Ames, Iowa, 507p.

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Thomas, C.G. (2021). Experimental Research. In: Research Methodology and Scientific Writing . Springer, Cham. https://doi.org/10.1007/978-3-030-64865-7_5

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Experimental Method In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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The experimental method in action research

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• Experimental Research Designs: Types, Examples & Methods

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes.

Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B.

If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment. Therefore, we can conclude that sunlight will aid growth in all similar plants.

What is Experimental Research?

Experimental research is a scientific approach to research, where one or more independent variables are manipulated and applied to one or more dependent variables to measure their effect on the latter. The effect of the independent variables on the dependent variables is usually observed and recorded over some time, to aid researchers in drawing a reasonable conclusion regarding the relationship between these 2 variable types.

The experimental research method is widely used in physical and social sciences, psychology, and education. It is based on the comparison between two or more groups with a straightforward logic, which may, however, be difficult to execute.

Mostly related to a laboratory test procedure, experimental research designs involve collecting quantitative data and performing statistical analysis on them during research. Therefore, making it an example of quantitative research method .

What are The Types of Experimental Research Design?

The types of experimental research design are determined by the way the researcher assigns subjects to different conditions and groups. They are of 3 types, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research Design

In pre-experimental research design, either a group or various dependent groups are observed for the effect of the application of an independent variable which is presumed to cause change. It is the simplest form of experimental research design and is treated with no control group.

Although very practical, experimental research is lacking in several areas of the true-experimental criteria. The pre-experimental research design is further divided into three types

• One-shot Case Study Research Design

In this type of experimental study, only one dependent group or variable is considered. The study is carried out after some treatment which was presumed to cause change, making it a posttest study.

• One-group Pretest-posttest Research Design: 

This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end.

• Static-group Comparison: 

In a static-group comparison study, 2 or more groups are placed under observation, where only one of the groups is subjected to some treatment while the other groups are held static. All the groups are post-tested, and the observed differences between the groups are assumed to be a result of the treatment.

Quasi-experimental Research Design

  The word “quasi” means partial, half, or pseudo. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same.  In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible.

 This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples.

Some examples of quasi-experimental research design include; the time series, no equivalent control group design, and the counterbalanced design.

True Experimental Research Design

The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects.

The true experimental research design must contain a control group, a variable that can be manipulated by the researcher, and the distribution must be random. The classification of true experimental design include:

• The posttest-only Control Group Design: In this design, subjects are randomly selected and assigned to the 2 groups (control and experimental), and only the experimental group is treated. After close observation, both groups are post-tested, and a conclusion is drawn from the difference between these groups.
• The pretest-posttest Control Group Design: For this control group design, subjects are randomly assigned to the 2 groups, both are presented, but only the experimental group is treated. After close observation, both groups are post-tested to measure the degree of change in each group.
• Solomon four-group Design: This is the combination of the pretest-only and the pretest-posttest control groups. In this case, the randomly selected subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only method, while the other two are tested using the pretest-posttest method.

Examples of Experimental Research

Experimental research examples are different, depending on the type of experimental research design that is being considered. The most basic example of experimental research is laboratory experiments, which may differ in nature depending on the subject of research.

Administering Exams After The End of Semester

During the semester, students in a class are lectured on particular courses and an exam is administered at the end of the semester. In this case, the students are the subjects or dependent variables while the lectures are the independent variables treated on the subjects.

Only one group of carefully selected subjects are considered in this research, making it a pre-experimental research design example. We will also notice that tests are only carried out at the end of the semester, and not at the beginning.

Further making it easy for us to conclude that it is a one-shot case study research. 

Employee Skill Evaluation

Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment.

In the course of employment, organizations also carry out employee training to improve employee productivity and generally grow the organization. Further evaluation is carried out at the end of each training to test the impact of the training on employee skills, and test for improvement.

Here, the subject is the employee, while the treatment is the training conducted. This is a pretest-posttest control group experimental research example.

Evaluation of Teaching Method

Let us consider an academic institution that wants to evaluate the teaching method of 2 teachers to determine which is best. Imagine a case whereby the students assigned to each teacher is carefully selected probably due to personal request by parents or due to stubbornness and smartness.

This is a no equivalent group design example because the samples are not equal. By evaluating the effectiveness of each teacher’s teaching method this way, we may conclude after a post-test has been carried out.

However, this may be influenced by factors like the natural sweetness of a student. For example, a very smart student will grab more easily than his or her peers irrespective of the method of teaching.

What are the Characteristics of Experimental Research?  

Experimental research contains dependent, independent and extraneous variables. The dependent variables are the variables being treated or manipulated and are sometimes called the subject of the research.

The independent variables are the experimental treatment being exerted on the dependent variables. Extraneous variables, on the other hand, are other factors affecting the experiment that may also contribute to the change.

The setting is where the experiment is carried out. Many experiments are carried out in the laboratory, where control can be exerted on the extraneous variables, thereby eliminating them. 

Other experiments are carried out in a less controllable setting. The choice of setting used in research depends on the nature of the experiment being carried out.

• Multivariable

Experimental research may include multiple independent variables, e.g. time, skills, test scores, etc.

Why Use Experimental Research Design?  

Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. 

Some uses of experimental research design are highlighted below.

• Medicine: Experimental research is used to provide the proper treatment for diseases. In most cases, rather than directly using patients as the research subject, researchers take a sample of the bacteria from the patient’s body and are treated with the developed antibacterial

The changes observed during this period are recorded and evaluated to determine its effectiveness. This process can be carried out using different experimental research methods.

• Education: Asides from science subjects like Chemistry and Physics which involves teaching students how to perform experimental research, it can also be used in improving the standard of an academic institution. This includes testing students’ knowledge on different topics, coming up with better teaching methods, and the implementation of other programs that will aid student learning.
• Human Behavior: Social scientists are the ones who mostly use experimental research to test human behaviour. For example, consider 2 people randomly chosen to be the subject of the social interaction research where one person is placed in a room without human interaction for 1 year.

The other person is placed in a room with a few other people, enjoying human interaction. There will be a difference in their behaviour at the end of the experiment.

• UI/UX: During the product development phase, one of the major aims of the product team is to create a great user experience with the product. Therefore, before launching the final product design, potential are brought in to interact with the product.

For example, when finding it difficult to choose how to position a button or feature on the app interface, a random sample of product testers are allowed to test the 2 samples and how the button positioning influences the user interaction is recorded.

What are the Disadvantages of Experimental Research?  

• It is highly prone to human error due to its dependency on variable control which may not be properly implemented. These errors could eliminate the validity of the experiment and the research being conducted.
• Exerting control of extraneous variables may create unrealistic situations. Eliminating real-life variables will result in inaccurate conclusions. This may also result in researchers controlling the variables to suit his or her personal preferences.
• It is a time-consuming process. So much time is spent on testing dependent variables and waiting for the effect of the manipulation of dependent variables to manifest.
• It is expensive. 
• It is very risky and may have ethical complications that cannot be ignored. This is common in medical research, where failed trials may lead to a patient’s death or a deteriorating health condition.
• Experimental research results are not descriptive.
• Response bias can also be supplied by the subject of the conversation.
• Human responses in experimental research can be difficult to measure. 

What are the Data Collection Methods in Experimental Research?  

Data collection methods in experimental research are the different ways in which data can be collected for experimental research. They are used in different cases, depending on the type of research being carried out.

1. Observational Study

This type of study is carried out over a long period. It measures and observes the variables of interest without changing existing conditions.

When researching the effect of social interaction on human behavior, the subjects who are placed in 2 different environments are observed throughout the research. No matter the kind of absurd behavior that is exhibited by the subject during this period, its condition will not be changed.

This may be a very risky thing to do in medical cases because it may lead to death or worse medical conditions.

2. Simulations

This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.

This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.

Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.

A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.

Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.

Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.

Differences between Experimental and Non-Experimental Research 

1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.

This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.

2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change

3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.

Conclusion  

Experimental research designs are often considered to be the standard in research designs. This is partly due to the common misconception that research is equivalent to scientific experiments—a component of experimental research design.

In this research design, one or more subjects or dependent variables are randomly assigned to different treatments (i.e. independent variables manipulated by the researcher) and the results are observed to conclude. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables.

Experimental research is suitable for research whose goal is to examine cause-effect relationships, e.g. explanatory research. It can be conducted in the laboratory or field settings, depending on the aim of the research that is being carried out. 

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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A Comprehensive Guide to Different Types of Research

Published: June 15, 2024

When embarking on a research project, selecting the right methodology can be the difference between success and failure. With various methods available, each suited to different types of research, it’s essential you make an informed choice. This blog post will provide tips on how to choose a research methodology that best fits your research goals .

We’ll start with definitions: Research is the systematic process of exploring, investigating, and discovering new information or validating existing knowledge. It involves defining questions, collecting data, analyzing results, and drawing conclusions.

Meanwhile, a research methodology is a structured plan that outlines how your research is to be conducted. A complete methodology should detail the strategies, processes, and techniques you plan to use for your data collection and analysis.

Research Methods

The first step of a research methodology is to identify a focused research topic, which is the question you seek to answer. By setting clear boundaries on the scope of your research, you can concentrate on specific aspects of a problem without being overwhelmed by information. This will produce more accurate findings. 

Along with clarifying your research topic, your methodology should also address your research methods. Let’s look at the four main types of research: descriptive, correlational, experimental, and diagnostic.

Descriptive Research

Descriptive research is an approach designed to describe the characteristics of a population systematically and accurately. This method focuses on answering “what” questions by providing detailed observations about the subject. Descriptive research employs surveys, observational studies, and case studies to gather qualitative or quantitative data. 

A real-world example of descriptive research is a survey investigating consumer behavior toward a competitor’s product. By analyzing the survey results, the company can gather detailed insights into how consumers perceive a competitor’s product, which can inform their marketing strategies and product development.

Correlational Research

Correlational research examines the statistical relationship between two or more variables to determine whether a relationship exists. Correlational research is particularly useful when ethical or practical constraints prevent experimental manipulation. It is often employed in fields such as psychology, education, and health sciences to provide insights into complex real-world interactions, helping to develop theories and inform further experimental research.

An example of correlational research is the study of the relationship between smoking and lung cancer. Researchers observe and collect data on individuals’ smoking habits and the incidence of lung cancer to determine if there is a correlation between the two variables. This type of research helps identify patterns and relationships, indicating whether increased smoking is associated with higher rates of lung cancer.

Experimental Research

Experimental research is a scientific approach where researchers manipulate one or more independent variables to observe their effect on a dependent variable. This method is designed to establish cause-and-effect relationships. Fields like psychology, medicine, and social sciences frequently employ experimental research to test hypotheses and theories under controlled conditions. 

A real-world example of experimental research is Pavlov’s Dog experiment. In this experiment, Ivan Pavlov demonstrated classical conditioning by ringing a bell each time he fed his dogs. After repeating this process multiple times, the dogs began to salivate just by hearing the bell, even when no food was presented. This experiment helped to illustrate how certain stimuli can elicit specific responses through associative learning.

Diagnostic Research

Diagnostic research tries to accurately diagnose a problem by identifying its underlying causes. This type of research is crucial for understanding complex situations where a precise diagnosis is necessary for formulating effective solutions. It involves methods such as case studies and data analysis and often integrates both qualitative and quantitative data to provide a comprehensive view of the issue at hand. 

An example of diagnostic research is studying the causes of a specific illness outbreak. During an outbreak of a respiratory virus, researchers might conduct diagnostic research to determine the factors contributing to the spread of the virus. This could involve analyzing patient data, testing environmental samples, and evaluating potential sources of infection. The goal is to identify the root causes and contributing factors to develop effective containment and prevention strategies.

Using an established research method is imperative, no matter if you are researching for marketing , technology , healthcare , engineering, or social science. A methodology lends legitimacy to your research by ensuring your data is both consistent and credible. A well-defined methodology also enhances the reliability and validity of the research findings, which is crucial for drawing accurate and meaningful conclusions. 

Additionally, methodologies help researchers stay focused and on track, limiting the scope of the study to relevant questions and objectives. This not only improves the quality of the research but also ensures that the study can be replicated and verified by other researchers, further solidifying its scientific value.

How to Choose a Research Methodology

Choosing the best research methodology for your project involves several key steps to ensure that your approach aligns with your research goals and questions. Here’s a simplified guide to help you make the best choice.

Understand Your Goals

Clearly define the objectives of your research. What do you aim to discover, prove, or understand? Understanding your goals helps in selecting a methodology that aligns with your research purpose.

Consider the Nature of Your Data

Determine whether your research will involve numerical data, textual data, or both. Quantitative methods are best for numerical data, while qualitative methods are suitable for textual or thematic data.

Understand the Purpose of Each Methodology

Becoming familiar with the four types of research – descriptive, correlational, experimental, and diagnostic – will enable you to select the most appropriate method for your research. Many times, you will want to use a combination of methods to gather meaningful data. 

Evaluate Resources and Constraints

Consider the resources available to you, including time, budget, and access to data. Some methodologies may require more resources or longer timeframes to implement effectively.

Review Similar Studies

Look at previous research in your field to see which methodologies were successful. This can provide insights and help you choose a proven approach.

By following these steps, you can select a research methodology that best fits your project’s requirements and ensures robust, credible results.

Completing Your Research Project

Upon completing your research, the next critical step is to analyze and interpret the data you’ve collected. This involves summarizing the key findings, identifying patterns, and determining how these results address your initial research questions. By thoroughly examining the data, you can draw meaningful conclusions that contribute to the body of knowledge in your field. 

It’s essential that you present these findings clearly and concisely, using charts, graphs, and tables to enhance comprehension. Furthermore, discuss the implications of your results, any limitations encountered during the study, and how your findings align with or challenge existing theories.

Your research project should conclude with a strong statement that encapsulates the essence of your research and its broader impact. This final section should leave readers with a clear understanding of the value of your work and inspire continued exploration and discussion in the field.

Now that you know how to perform quality research, it’s time to get started! Applying the right research methodologies can make a significant difference in the accuracy and reliability of your findings. Remember, the key to successful research is not just in collecting data, but in analyzing it thoughtfully and systematically to draw meaningful conclusions. So, dive in, explore, and contribute to the ever-growing body of knowledge with confidence. Happy researching!

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For more advanced server-side validation, you can use a library like zod to validate the form fields before mutating the data:

Once the fields have been validated on the server, you can return a serializable object in your action and use the React useFormState hook to show a message to the user.

• By passing the action to useFormState , the action's function signature changes to receive a new prevState or initialState parameter as its first argument.
• useFormState is a React hook and therefore must be used in a Client Component.

Then, you can pass your action to the useFormState hook and use the returned state to display an error message.

Good to know: Before mutating data, you should always ensure a user is also authorized to perform the action. See Authentication and Authorization .

Optimistic updates

You can use the React useOptimistic hook to optimistically update the UI before the Server Action finishes, rather than waiting for the response:

Nested elements

You can invoke a Server Action in elements nested inside <form> such as <button> , <input type="submit"> , and <input type="image"> . These elements accept the formAction prop or event handlers .

This is useful in cases where you want to call multiple server actions within a form. For example, you can create a specific <button> element for saving a post draft in addition to publishing it. See the React <form> docs for more information.

Programmatic form submission

You can trigger a form submission using the requestSubmit() method. For example, when the user presses ⌘ + Enter , you can listen for the onKeyDown event:

This will trigger the submission of the nearest <form> ancestor, which will invoke the Server Action.

Non-form Elements

While it's common to use Server Actions within <form> elements, they can also be invoked from other parts of your code such as event handlers and useEffect .

Event Handlers

You can invoke a Server Action from event handlers such as onClick . For example, to increment a like count:

To improve the user experience, we recommend using other React APIs like useOptimistic and useTransition to update the UI before the Server Action finishes executing on the server, or to show a pending state.

You can also add event handlers to form elements, for example, to save a form field onChange :

For cases like this, where multiple events might be fired in quick succession, we recommend debouncing to prevent unnecessary Server Action invocations.

You can use the React useEffect hook to invoke a Server Action when the component mounts or a dependency changes. This is useful for mutations that depend on global events or need to be triggered automatically. For example, onKeyDown for app shortcuts, an intersection observer hook for infinite scrolling, or when the component mounts to update a view count:

Remember to consider the behavior and caveats of useEffect .

Error Handling

When an error is thrown, it'll be caught by the nearest error.js or <Suspense> boundary on the client. We recommend using try/catch to return errors to be handled by your UI.

For example, your Server Action might handle errors from creating a new item by returning a message:

Good to know: Aside from throwing the error, you can also return an object to be handled by useFormState . See Server-side validation and error handling .

Revalidating data

You can revalidate the Next.js Cache inside your Server Actions with the revalidatePath API:

Or invalidate a specific data fetch with a cache tag using revalidateTag :

Redirecting

If you would like to redirect the user to a different route after the completion of a Server Action, you can use redirect API. redirect needs to be called outside of the try/catch block:

You can get , set , and delete cookies inside a Server Action using the cookies API:

See additional examples for deleting cookies from Server Actions.

Authentication and authorization

You should treat Server Actions as you would public-facing API endpoints, and ensure that the user is authorized to perform the action. For example:

Closures and encryption

Defining a Server Action inside a component creates a closure where the action has access to the outer function's scope. For example, the publish action has access to the publishVersion variable:

Closures are useful when you need to capture a snapshot of data (e.g. publishVersion ) at the time of rendering so that it can be used later when the action is invoked.

However, for this to happen, the captured variables are sent to the client and back to the server when the action is invoked. To prevent sensitive data from being exposed to the client, Next.js automatically encrypts the closed-over variables. A new private key is generated for each action every time a Next.js application is built. This means actions can only be invoked for a specific build.

Good to know: We don't recommend relying on encryption alone to prevent sensitive values from being exposed on the client. Instead, you should use the React taint APIs to proactively prevent specific data from being sent to the client.

Overwriting encryption keys (advanced)

When self-hosting your Next.js application across multiple servers, each server instance may end up with a different encryption key, leading to potential inconsistencies.

To mitigate this, you can overwrite the encryption key using the process.env.NEXT_SERVER_ACTIONS_ENCRYPTION_KEY environment variable. Specifying this variable ensures that your encryption keys are persistent across builds, and all server instances use the same key.

This is an advanced use case where consistent encryption behavior across multiple deployments is critical for your application. You should consider standard security practices such key rotation and signing.

Good to know: Next.js applications deployed to Vercel automatically handle this.

Allowed origins (advanced)

Since Server Actions can be invoked in a <form> element, this opens them up to CSRF attacks .

Behind the scenes, Server Actions use the POST method, and only this HTTP method is allowed to invoke them. This prevents most CSRF vulnerabilities in modern browsers, particularly with SameSite cookies being the default.

As an additional protection, Server Actions in Next.js also compare the Origin header to the Host header (or X-Forwarded-Host ). If these don't match, the request will be aborted. In other words, Server Actions can only be invoked on the same host as the page that hosts it.

For large applications that use reverse proxies or multi-layered backend architectures (where the server API differs from the production domain), it's recommended to use the configuration option serverActions.allowedOrigins option to specify a list of safe origins. The option accepts an array of strings.

Learn more about Security and Server Actions .

Additional resources

For more information on Server Actions, check out the following React docs:

• "use server"
• <form>
• useFormStatus
• useFormState
• useOptimistic

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• Open access
• Published: 11 June 2024

BUB1 regulates non-homologous end joining pathway to mediate radioresistance in triple-negative breast cancer

• Sushmitha Sriramulu 1   na1 ,
• Shivani Thoidingjam 1   na1 ,
• Wei-Min Chen 2 ,
• Oudai Hassan 3 ,
• Farzan Siddiqui 1 , 4 , 5 ,
• Stephen L. Brown 1 , 4 , 5 ,
• Benjamin Movsas 1 , 4 , 5 ,
• Michael D. Green 6 ,
• Anthony J. Davis 2 ,
• Corey Speers 6 , 7 ,
• Eleanor Walker 1 , 4 , 5 &
• Shyam Nyati 1 , 4 , 5  

Journal of Experimental & Clinical Cancer Research volume  43 , Article number:  163 ( 2024 ) Cite this article

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Metrics details

Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer subtype often treated with radiotherapy (RT). Due to its intrinsic heterogeneity and lack of effective targets, it is crucial to identify novel molecular targets that would increase RT efficacy. Here we demonstrate the role of BUB1 (cell cycle Ser/Thr kinase) in TNBC radioresistance and offer a novel strategy to improve TNBC treatment.

Gene expression analysis was performed to look at genes upregulated in TNBC patient samples compared to other subtypes. Cell proliferation and clonogenic survivals assays determined the IC 50 of BUB1 inhibitor (BAY1816032) and radiation enhancement ratio (rER) with pharmacologic and genomic BUB1 inhibition. Mammary fat pad xenografts experiments were performed in CB17/SCID. The mechanism through which BUB1 inhibitor sensitizes TNBC cells to radiotherapy was delineated by γ-H2AX foci assays, BLRR, Immunoblotting, qPCR, CHX chase, and cell fractionation assays.

BUB1 is overexpressed in BC and its expression is considerably elevated in TNBC with poor survival outcomes. Pharmacological or genomic ablation of BUB1 sensitized multiple TNBC cell lines to cell killing by radiation, although breast epithelial cells showed no radiosensitization with BUB1 inhibition. Kinase function of BUB1 is mainly accountable for this radiosensitization phenotype. BUB1 ablation also led to radiosensitization in TNBC tumor xenografts with significantly increased tumor growth delay and overall survival. Mechanistically, BUB1 ablation inhibited the repair of radiation-induced DNA double strand breaks (DSBs). BUB1 ablation stabilized phospho-DNAPKcs (S2056) following RT such that half-lives could not be estimated. In contrast, RT alone caused BUB1 stabilization, but pre-treatment with BUB1 inhibitor prevented stabilization (t 1/2 , ~8 h). Nuclear and chromatin-enriched fractionations illustrated an increase in recruitment of phospho- and total-DNAPK, and KAP1 to chromatin indicating that BUB1 is indispensable in the activation and recruitment of non-homologous end joining (NHEJ) proteins to DSBs. Additionally, BUB1 staining of TNBC tissue microarrays demonstrated significant correlation of BUB1 protein expression with tumor grade.

Conclusions

BUB1 ablation sensitizes TNBC cell lines and xenografts to RT and BUB1 mediated radiosensitization may occur through NHEJ. Together, these results highlight BUB1 as a novel molecular target for radiosensitization in women with TNBC.

Breast cancer (BC) affects more than 2 million women worldwide each year. Triple-negative breast cancer (TNBC) is the most lethal subtype of BC and while effective targeted therapies exist for the prevention and treatment of ER-positive breast cancer, no effective targeted therapy exists for TNBC. TNBC tend to be more aggressive, occur in younger women, and are less likely to be cured by adjuvant therapy [ 1 ]. As radiotherapy is standard in the management of BC, there is a need to identify molecular targets with potential to increase the efficacy of radiation therapy (RT). To this end, DNA damage repair pathways are of interest.

DNA damage is a critical determinant of radiation-induced cell death [ 2 ]. Radiation mediated base damages and single strand breaks (SSBs) are more efficiently repaired by cells, whereas double strand breaks (DSBs) are more difficult to repair and, if unrepaired, lead to lethality in cells. The ability of cells to recognize and respond to DSBs is fundamental in determining the sensitivity (or resistance) of cells to radiation [ 3 ]. DSB repair is comprised of two major and mechanistically distinct processes: non-homologous end-joining (NHEJ) and homologous recombination (HR). NHEJ involves directly ligating two broken DNA ends and is initiated by binding of Ku70/Ku80 hetero dimers at DSB sites [ 4 ]. Ku70/Ku80 localization recruits DNA-dependent protein kinase (DNAPKcs) to the DSB site, followed by Artemis-dependent end-processing, strand synthesis by DNA polymerase-beta (POLβ) and strand ligation by XRCC4, ligase IV, and XLF complex [ 5 ]. HR on the other hand is initiated by lesion recognition by ATM and processing of DSB ends by MRN complex (Mre1—Rad50-Nbs1). 53BP1 protein may play a role in pathway choice between NHEJ and HR [ 6 , 7 ]. Target-based radiosensitization approaches increase radiotherapy efficiency by selectively sensitizing tumor tissue to ionizing radiation [ 8 ]. Several new molecular targets are currently being evaluated in clinical trials to measure their radiation sensitization potential [ 9 ].

Following DNA damage, cell cycle checkpoints are activated to block cell cycle progression and prevent propagation of cells with damaged DNA. Both DNA damage repair and cell cycle checkpoints are positively regulated by several kinases, including BUB1 (Budding uninhibited by benzimidazoles-1). BUB1 is a serine/threonine kinase implicated in chromosomal segregation during mitosis. BUB1 regulates cell-cycle and is known to impact DNA damage signaling. However, it is still uncertain how BUB1 contributes to radioresistance in TNBC. BUB1 is known to localize near DSB sites where early DNA damage sensor proteins such as phosphorylated H2AX are also recruited [ 10 ]. Moreover, BUB1 co-localizes with 53BP1 suggesting a role in NHEJ pathway [ 10 ]. Knockdown of BUB1 results in prolonged γH2AX foci and comet tail formation as well as hypersensitivity in response to ionizing radiation [ 11 ]. Increased expression of BUB1 is associated with resistance to DNA-damaging agents (i.e. radiotherapy and some chemotherapies) [ 12 ] and we have shown that BUB1 inhibition reduces invasion and migration in cancer cell lines [ 13 ] through direct interaction with TGFβ receptors [ 14 , 15 ]. Moreover, BUB1 regulates cell cycle through its roles in spindle assembly checkpoint and chromosome alignment [ 16 , 17 , 18 ].

Here, we demonstrate that BUB1 is overexpressed in TNBC, and that its overexpression correlates with poorer outcome and radiation resistance. Moreover, we confirm that pharmacological or genomic ablation of BUB1 is cytotoxic to TNBC cell lines and leads to radiation sensitization. BUB1 ablation delays DSB repair as evident by prolonged γH2AX foci and affects NHEJ as evaluated by bioluminescent DNA damage repair reporters (BLRR). BUB1 inhibition causes significant decrease in tumor volume when combined with radiation in SUM159 mammary fat pad tumor xenograft models and demonstrates significant reduction in tumor cell proliferation as evaluated by Ki67 immunostaining of tumor sections. Additionally, our mechanistic studies show that BUB1 mediates radioresistance through impacting chromatin localization of core NHEJ proteins and increasing radiation mediated DNAPKcs phosphorylation and stability. Overall, our results provide evidence that BUB1 mediated radiation resistance takes place through NHEJ, specifically by regulating chromatin binding of key proteins and that combining BUB1 ablation with radiation could be an effective approach for radiosensitization of TNBC.

Gene expression data

Normalized expression data for the cell lines were downloaded from the EMBL-EBI ArrayExpress website as described in the original publication [ 19 ]. The Hatzis gene expression and survival data were downloaded from the Gene Expression Omnibus (GEO) database with series number GSE25066 [ 20 ]. A log-rank (Mantel-Cox) test was used for survival curve analyses. Data for the TCGA cohort was downloaded from http://tcga-data.nci.nih.gov . Expression levels were log transformed, median centered and scaled, subtype calls were based on previous description [ 21 ].

A receiver operating characteristic curve (ROC) was generated as an alternate way to measure the performance of BUB1 as a biomarker using area under the curve (AUC) as a metric, with an AUC >0.65 being considered of significant clinical value. BUB1 expression was evaluated as a continuous variable. BUB1 expression was measured by using RNA isolated from patients tumors at time of surgical expression, then log transformed values from the Affymetrix Human Genome U133A Array were assessed. Other clinical covariates included ER, PR, overall stage, size, nodal status, and PAM50 classification (p =0.0003).

Gene expression and metastasis correlation

In vivo screening for metastases was performed using Chick Chorioallantoic Membrane assays in 21 preclinical breast cancer models with data published previously [ 22 ]. Correlation coefficients were calculated using Pearson’s correlation methods.

Cell culture

Triple-negative breast cancer cell lines (MDA-MB-231, MDA-MB-468, BT-549), normal breast epithelial cell line (MCF10A) and Estrogen Receptor (+), Progesterone Receptor (+) breast cancer cell line T47D were obtained from the American Type Culture Collection (ATCC). SUM159 cells were originally sourced from Steve P. Either (University of Michigan) and were acquired from Sofia Merajver (University of Michigan). SUM159 cells were grown in HAM’S F-12 media (Catalog No. 31765035, Thermo Fisher Scientific) supplemented with 5% FBS, 10 mM HEPES, 1 μg/ml Hydrocortisone, 6 μg/ml Insulin, and 1% Penicillin-Streptomycin. MDA-MB-231 and MDA-MB-468 cells were grown in DMEM media (Catalog No. 30-2002, ATCC) supplemented with 10% FBS and 1% Penicillin-Streptomycin. BT-549 and T-47D cells were grown in RPMI-1640 media (Catalog No. 30-2001, ATCC) supplemented with 10% FBS, 0.023 U/ml insulin, and 1% Penicillin-Streptomycin. MCF10A cells were also grown in RPMI-1640 media supplemented with 10% FBS, and 1% Penicillin-Streptomycin. All cell lines were maintained at 37⁰C in a 5% CO 2 incubator and passaged at 70% confluence. Cell lines were routinely tested for mycoplasma contamination. Mutations in key genes is listed in Supplementary Table S1 (See Additional file 1 for Supplementary figures and tables).

Drug treatment and irradiation

A BUB1 inhibitor (BUB1i) BAY1816032 (Catalog No. HY-103020, MedChemExpress) and DNAPK inhibitor (DNAPKi) NU7441 (Catalog No. S2638, Selleckchem) were dissolved in DMSO (20 mM BUB1i and 15 mM DNAPKi) and stored at -80⁰C. For each experiment, a fresh vial was thawed, and any remaining stock solution was discarded. Working concentrations were made in media with serum and supplements and cells were exposed to a range of concentrations, from 125 nM to 1000 nM. Irradiation was performed 1 h after the drug treatment using a CIX-3 orthovoltage unit (Xstrahl Life Sciences) operating at 320 kV and 10 mA with 1 mm Cu filter.

Proliferation assay

To investigate the effect of BAY1816032 on cell proliferation in TNBC cell lines, 2 x 10 3 cells were plated into a 96-well plate 24 h prior to treatment. Cells were exposed to different concentrations of BUB1 inhibitor (BUB1i) ranging from 1 nM to 10 μM and cultured for 72 h. Cell proliferation was measured using alamarBlue (Catalog No. DAL1025, Thermo Fisher Scientific) following the manufacturer’s protocols. Absorbance was read at 570 nM on Synergy H1 Hybrid Reader (BioTek Instruments). Values were normalized to mock (DMSO/vehicle) treated cells. The IC 50 values were estimated on GraphPad Prism (V9) using a non-linear regression best-fit equation.

Clonogenic survival assay

Cells were plated in 6-well plates at different cell densities overnight. The next morning, cells were treated with BAY1816032 (125 nM to 1000 nM) for 1 hr and irradiated (2 to 6 Gy). Cells were allowed to grow for 7-23 days until visible colonies formed before being fixed and stained with methanol and crystal violet. All the colonies with >50 cells were manually counted, and the cell survival was plotted using GraphPad (V9). Plating efficiency (PE %) was estimated as: (100 x Number of colonies formed / Number of cells plated x 100). Radiation enhancement ratios (rER) were determined from the survival curve using the formula: D bar of varying inhibitor concentrations / D bar of vehicle (DMSO) (Microsoft Excel) which indicates radiation dose to produce some level of cell killing in the absence of inhibitor (i.e., vehicle) divided by the radiation dose in the presence of the inhibitor to produce the same level of cell kill. rER >1 was considered to be radiation sensitization while rER <1 was radiation resistance/protection.

Transfections

Cells were seeded in 6-well plates overnight and the transfection was performed with 60% confluent cells. The siGENOME SMARTPool siRNA for human BUB1 and DNAPK (gene ID: PRKDC) were purchased from Dharmacon. Next morning, 100 nM siRNAs were diluted in Opti-MEM reduced serum media (Catalog No. 31985062, Thermo Fisher Scientific) and transfected using Lipofectamine RNAiMAX (Catalog No. 13778075, Thermo Fisher Scientific). Diluted siRNAs and lipofectamine reagent were separately incubated for 5 mins at RT before being combined and incubated further for 20-30 mins after combining them. The siRNA-lipid complex was added to the cells in plain media without serum and antibiotics. After 48 h of transfection, the transfected cells were used for further experiments. BUB1 Wild-type (WT) and Kinase-dead (KD) plasmids were a kind gift from Dr. Hongtao Yu (UT Southwestern). The BUB1 plasmids or siRNA with plasmids were transfected with Lipofectamine 2000 (Catalog No. 11668500, Thermo Fisher Scientific) per manufacture’s protocol.

Generation of BUB1 CRISPR knockout cell lines

Cells were transfected with CRISPR/CAS9 ribonucleoprotein (RNP) for generating BUB1 knockout cell lines. BUB1 sgRNAs were designed with the CRISPR tool ( www.benchling.com ) and synthesized by Integrated DNA Technologies (IDT). We combined two sgRNAs (sgRNA1 and sgrna2) in this experiment each targeted different exons (exon 2 and 3) for better knockout efficiency. Purified CAS9 protein was purchased from IDT (Catalog No. 1081058) while Lipofectamine RNAiMAX (Catalog No. 11668027) was from Thermo Fisher Scientific. TNBC cell lines were plated in 2 wells of a 24-well plate overnight. Next morning, the cells in one well were transfected by combining BUB1 sgRNA1 and sgRNA2 (each 300 ng/well), Cas9 protein (1 µg/well) using Lipofectamine RNAiMAX (3 µl/well) and the other well was used as a negative control without sgRNAs [ 23 , 24 , 25 ]. 24-hours after transfection, cells were trypsinized and plated in 96-well plate at 1 cell/well. Cells were allowed to grow until colonies formed (2-4 weeks) and expanded into 24 well plates. Genomic DNA was isolated from these clones using the QuickExtract DNA extraction solution (Catalog No. QE09050, Lucigen) following manufacturer’s protocol. The extracted DNA was PCR-amplified with following conditions: 98⁰C for 30 s, 98⁰C for 10 s, 61.5⁰C for 30 s, 72⁰C for 23 s, and 72⁰C for 10 mins for 34 cycles. The putative BUB1 null clones were sequence verified (Sanger sequencing, Azenta Life Sciences, NJ, USA) and absence of BUB1 protein was confirmed by western immunoblotting. The efficiency of BUB1 CRISPR knockout was estimated by Synthego ICE software. gRNA sequences for BUB1 knockout, primer sequences for PCR amplification and Sanger sequencing are listed in Supplementary Table S2.

Immunoblotting

Total protein was extracted using IP-lysis buffer (50mM Tris PH 7.4, 1% NP40, 0.25% Deoxycholate sodium salt, 150mM NaCl, 10% Glycerol, and 1mM EDTA) supplemented with PhosStop (Roche), Protease inhibitor (Roche), Sodium Ortho Vanadate, Sodium fluoride, PMSF, and β-Glycerol phosphate (2 µM each). Protein concentrations were determined using Pierce BCA protein assay kit (Catalog No. 23225, Thermo Fisher Scientific) and equal amounts of samples were loaded on NuPAGE 4-12%, Bis-Tris Midi protein gels (Catalog No. WG1402BOX, Thermo Fisher Scientific) along with SeeBlue Plus2 Pre-stained Protein Standard (Catalog No. LC5925, Thermo Fisher Scientific). Samples were transferred to Immobilon-P PVDF membranes (Catalog No. IPVH00010, Millipore). The blots were blocked using 5% non-fat dry milk (Catalog No. 1706404, BioRad) and/or 5% BSA and incubated with primary antibodies at 4⁰C overnight. Membranes were incubated with HRP-tagged secondary antibodies and protein bands were detected using ECL Prime western blotting system (Catalog No. GERPN2232, Millipore Sigma). Protein band density was measured using ImageJ 1.52a. Specific antibody information and dilutions are listed in Supplementary Table S3.

Animal studies

Fox Chase SCID female mice (CB17/lcr Prkdcscid/lcrlcoCrl; 8 weeks old) (N = 52) were procured from Charles River Laboratories through the Department of Bioresources, Henry Ford Health. Mice were acclimatized for a week and housed at the Animal Facility, E&R building, Henry Ford Hospital. Experimental animals were housed and handled in accordance with protocols approved by IACUC of Henry Ford Health (protocol # 00001298). We used >9-10 mice per treatment group (2 tumors/mouse). After injecting SUM159 cells (1 x 10 6 bilaterally) into the 4 th mammary fat pads, animals were randomly assigned to receive treatment once the tumors reached a size of about 80 mm 3 . BAY1816032 (25 mg/kg, in vivo grade, Catalog No. CT-BAY181, Chemietek) dissolved in 50% PEG 400, 10% DMSO, and 40% saline was given orally twice daily (5 days) for four weeks. RT was administered in three 5 Gy fractions over 5 days (total 15 Gy) using the small animal radiation research platform (SARRP, Xstrahl Life Sciences). Animals wherein tumors were generated with SUM159 BUB1 CRISPR KO cells were treated only with radiation or sham irradiated. Tumor volume and animal body weights were measured twice a week using a digital vernier caliper and tumor volume was calculated using the formula: (Length x Width 2 ) x 3.14/6. When the tumor volume reached >1000 mm 3 , mice were euthanized according to IACUC guidelines. Linear mixed model (LMM) of log2 (tumor volume) was built on time and time* arm interaction. LMM was clustered by each tumor and nested within each mouse. 95% CI was 0.1 while p-value <0.001 was considered significant. Animal survival was estimated and depicted in a Kaplan-Meier survival plot. Logrank test were performed to estimate if the arms (treatment groups) were different (p<0.0001). Cox proportional hazards model with Firth’s penalized maximum likelihood bias reduction method was used to compare if experimental conditions resulted in significant differences.

Immunohistochemical staining

Five random tumors from each treatment groups were harvested, fixed in buffered formalin and paraffin embedded. Histological sections from individual paraffin-embedded xenograft tumor tissues were initially deparaffinized and rehydrated. These tumor sections were stained at the Histology core (Henry Ford Health) according to the manufacturer’s protocols (Ki-67 IHC MIB-1, Dako Omnis). Proliferating cells were immunostained with FLEX monoclonal mouse anti-human Ki-67 (Catalog No. GA626, Ready-to-use (Dako Omnis), Clone MIB-1, Agilent). Images of the microscopic slides were taken under the light microscope at 20x magnification in two to three random fields for each tumor ( N = 5, each arm). The % of Ki-67 positive cells was calculated using the formula: 100 x number of Ki-67 positive cells in treated / sham. H&E staining was also performed to assess the structural changes in the tumor sections.

γH2AX foci formation assay

Cells (1 x 10 5 ) were plated into a 6-well plate containing glass coverslips (12 mm Catalog No. 633029, Carolina). After treatment with BUB1i or DNAPKi (1 μM) for an hour, cells were irradiated (4 Gy) and coverslips were collected at different time points. Coverslips were washed with ice-cold PBS, fixed with 2% w/v Sucrose, 0.2% Triton X-100 in formaldehyde, and permeabilized 0.5% triton in PBS for 10 min. Coverslips were blocked for 30 min at 4⁰C (2.5% Horse serum, 2.5% FBS, 0.5% w/v BSA, 0.05% Triton X-100 in PBS) and were incubated with Anti-phospho-Histone H2A.X (Ser139); Millipore) for 1 h at room temperature. After washing, coverslips were incubated with Goat anti-Mouse-Alexa Fluor 488 (Thermo Fisher Scientific) for 30 min at 4⁰C. DAPI (1 µg/ml) was used as a nuclear counter stain and the coverslips were mounted using ProLong Gold Antifade Mountant (Catalog No. P10144, Thermo Fisher Scientific) onto glass slides and observed under a microscope (Zeiss Axio Imager 2). At least 3 random fields were imaged for each condition. Cells with more than 10 γH2AX foci were scored as positive. The percentage of γH2AX foci positive cells was calculated as: (100 x number of γH2AX foci positive cells / Total number of cells counted).

Bioluminescent NHEJ and HR reporter assays

The rate of DNA repair by NHEJ and HR was measured by the bioluminescent repair reporter (BLRR) kindly gifted by Dr. Christian E Badr, Harvard Medical School [ 26 ]. Cells (2.5 x 10 5 ) were plated in a 6-well plate and transfected with pLenti-BLRR (Addgene # 158958), pLenti-trGluc (Addgene # 158959), and pX330-gRNA (Addgene # 158973) plasmids with Lipofectamine 2000. After 48 h of transfection, the cells were reseeded into a 96-well plate, treated with BUB1i or DNAPKi (1 μM) for 1 h followed by RT (4 Gy), and replaced with fresh media. After 48 h of treatment, the cell supernatant was collected, centrifuged and 20 μl was transferred in a white opaque 96-well plate (Catalog No. IP-DP35F-96-W, Stellar Scientific). 1 mM Coelenterazine (Catalog No. 16123, Cayman Chemical) diluted to 80 μl was added to the supernatant and Gaussia luciferase activity (GLuc; HR efficiency) was measured for 0.8 s on Synergy H1 Hybrid (Biotek Instruments). 6.16 mM Vargulin (Catalog No. 305, NanoLight Technology) diluted in 50 μl was added to measure Cypridina luciferase activity (VLuc; NHEJ efficiency) with integration time of 1 s.

Quantitative PCR

Cells (1.5 x 10 5 ) were seeded in 6-well plates 24 h prior to treatment with BUB1i or DNAPKi (1 μM) and irradiation (4 Gy). Cells were harvested after 72 h and stored at -80⁰C. Total RNA was isolated with TRIzol (Catalog No. 15596026, Thermo Fisher Scientific) and concentration was measured on Nanodrop (Nanodrop 2000c, Thermo Fisher Scientific). RNA was reverse transcribed into cDNA using Super Script III Reverse Transcriptase kit (Catalog No. 18080044, Thermo Fisher Scientific), dNTPs (Catalog No. R0191, Thermo Fisher Scientific), and Random Primers (Catalog No. 48190011, Thermo Fisher Scientific). The qPCR was performed using Takyon Low ROX SYBR 2X MasterMix (Catalog No. UF-LSMT-B0701, Eurogentec) and KiCqStart pre-designed SYBR green gene-specific primers (Supplementary Table S4) in QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems). Expression level for each gene was normalized to GAPDH for each experiment. All QRTPCR reactions were performed in triplicates and all experiments were repeated at least three times.

Cycloheximide-chase assay

1.5 x 10 5 cells were seeded in a 6-well plate over-night. Next morning cells were treated with 50 μM Cycloheximide (CHX; Catalog No. 14126, Cayman Chemical) to block nascent protein synthesis followed by BAY1816032 (1 μM) and 4 Gy irradiation. BUB1 CRISPR KO cells were treated with CHX followed by 4 Gy irradiation. Proteins were eluted at different time points (0 - 24h) by direct lysis (IP lysis buffer with 1.25X SDS protein loading buffer), sonicated, boiled for 7-8 mins before loading on the SDS-PAGE gels. Protein band density was quantified using ImageJ 1.52a software and calculated fold change using Microsoft Excel. The graphs were plotted in GraphPad Prism 9 software and the average half-life of BUB1 protein (t 1/2 ) was determined using Microsoft Excel.

Subcellular fractionation

The effect of BUB1 ablation on localization and movement of key DNA repair proteins on break sites and chromatin was investigated by subcellular fractionation assays. Different protein fractions were collected using Subcellular Protein Fractionation Kit (Catalog No. 78840, Thermo Fisher Scientific) according to the manufacturer’s protocols. Briefly, 2.5 x 10 6 cells were plated in 100 mm petri dishes 48 h prior to treatment. BUB1i was added 1 h prior to irradiation (8 Gy) and allowed to recover for 10 mins. Cells were harvested, protein fractions were eluted as recommended and 40 μg protein was loaded onto NuPAGE 4-12%, Bis-Tris gels for western blot analysis.

Laser micro-irradiation

U2-OS cells expressing YFP-tagged Ku80 and YFP-tagged DNA-PKcs were generated in the earlier studies (PMID: 22179609, PMID: 35580045). YFP-Ku80 and YFP-DNA-PKcs were transfected into U2-OS DNA-PKcs +/+ and −/− cells with JetPrime® (Polyplus transfection reagent, Catalog No. 101000027) following the manufacturer's instructions. To observe the role of BUB1 in the accumulation of DNA-PKcs and KU80 at DNA DSBs, BUB1 was inhibited, and the cells were subjected to laser micro-irradiation. Twenty-four hours after the transfection, laser micro-irradiation and real-time recruitment were carried out using a Carl Zeiss Axiovert 200M microscope with a Plan-Apochromat 63X/NA 1.40 oil immersion objective (Carl Zeiss) as described in previous studies [ 27 ]. A 365-nm pulsed nitrogen laser (Spectra-Physics) connected directly to the microscope's epifluorescence path was used to create DSBs [ 27 ]. During micro-irradiation, the cells were kept in an Invitrogen CO 2 -independent medium at 37°C. The fluorescence intensities of the micro-irradiated and control areas were measured using the Carl Zeiss Axiovision software, version 4.5. The irradiated area's intensity was then normalized to the non-irradiated control area in accordance with earlier descriptions [ 28 , 29 ].

Tissue Microarray

Tissue microarray (TMA) panels of human breast carcinoma with adjacent normal breast tissues (BC081120f - 110 cores/110 cases and BR1191 – 119 cores/119 cases) were purchased from Tissue Array (formerly US Biomax, Derwood, MD). Breast TMAs were stained by the Histology Core-HFH with an anti-BUB1 antibody, (Catalog # ab195268, Clone EPR18947, Abcam, 1:50 dilution) following standard protocols. The slides were scanned/imaged using Aperio digital pathology slide scanner (Leica Biosystems). The TMAs were reviewed (manual scoring) by a blinded pathologist who provided the score of 0, 1+, 2+, 3+ that measures the staining intensity of BUB1, and the percentage of cells stained positive for BUB1. Graphs were plotted based on the staining intensity and % of cells positive for BUB1 to compare between normal and breast cancer tissues, molecular subtypes, tumor grades and stages.

Statistical analysis

For the analyses of in vitro data, the Student’s t-test method was used in GraphPad Prism 9 software. Results are presented as mean ± standard error of the Mean (SEMs). All experiments were performed in triplicates and were repeated at least three times. Correlation coefficients were calculated using Pearson’s correlation methods. P < 0.05 was considered statistically significant. The statistical analysis of in vivo tumor growth data is presented under that section.

BUB1 is overexpressed in TNBC and correlates with poorer survival and metastatic potential

In an effort to identify novel therapeutic targets for radiosensitization, we performed a screen focused on the human kinome to identify kinases upregulated in across 21 breast cancer cell lines that also impacted radiation sensitivity in human breast tumors [ 30 ]. We identified a list of 52 kinases whose expression was significantly elevated in triple-negative breast cancer. We hypothesized that many of these kinases would govern mitogenic, metastatic, survival, or growth regulatory pathways critical to the development and dissemination of triple-negative breast cancer that could be readily targeted for the treatment of patients with triple-negative and basal-like breast cancer. To further characterize which of these 52 kinases played an important role in the aggressive features of triple-negative breast cancer, we combined expression, phenotypic, and clinical outcomes data to prioritize kinases that warranted further interrogation. We prioritized those kinases that had the highest level of differential expression in triple-negative breast cancer, showed limited to no expression in normal tissues (including the mammary gland, thus were specific for breast cancer), were associated with clinically relevant outcomes, and for which we would be able to obtain or generate a specific inhibitor that was of clinical-grade quality to aid in translational efforts. To that end, BUB1 was one of the top nominated of the 52 kinases as it showed significantly elevated expression in triple-negative and basal-like breast cancers and limited expression in normal tissues (Fig. 1 A-B, data based on expression in over 1000 patient tumors from TCGA). Additionally, BUB1 expression is significantly associated with basal-like and luminal B tumors and in triple-negative breast cancers (Fig. 1 C-D). BUB1 expression is also much higher in breast cancer cell lines with basal-like characteristics and in cell lines with increased metastatic potential (Fig. 1 E) [ 19 ]. To further investigate the association of BUB1 expression with the metastatic potential of various breast cancer cell lines, we performed chick chorioallantoic membrane (CAM) assays on 21 breast cancer cell lines and quantitated the number of metastatic cells in the lungs and liver of chick embryos after injection of each of these 21 cell lines. This data was then correlated with BUB1 expression and there was a significant association between BUB1 expression and metastatic potential in this in vivo system (Fig. 1 F, R 2 =0.64, p-value 0.004).

BUB1 is highly expressed in breast cancer compared to normal, non-malignant breast tissue and is associated with triple-negative and basal-like breast cancers. A-B , BUB1 expression is significantly increased in breast tumors compared to normal breast tissue. C , BUB1 expression is strongly associated with the PAM50-defined basal-like subtype of breast cancer and ( D ), is also significantly elevated in TNBC. E , BUB1 expression is significantly increased in basal-like breast cancer cell lines. F , BUB1 expression strongly correlates with metastatic potential to the lungs and liver as measured by CAM assay in vivo . All CAM assays performed at least in triplicate. G , Kaplan-Meier survival plot demonstrate that high BUB1 levels are associated with worse overall survival in breast cancer patients (data from Hatzis et al, JAMA 2011). H , On multivariable analysis, BUB1 expression discriminates overall survival with high sensitivity and specificity (AUC: 0.68, <0.01). I , Raw data that was used for the analysis of the receiver operating characteristic curve (ROC). z statistic 3.631, *** P = 0.0003, a DeLong et al., 1988 [ 31 ], b Binomial exact

To investigate the clinical relevance of our findings, we assessed the impact of BUB1 expression on clinical outcomes. We found that BUB1 expression was significantly associated with poor outcomes (including higher mortality and increased rates of recurrence) in both women treated with chemotherapy and radiation therapy, the two most common adjuvant treatment modalities for women with breast cancer, with high BUB1 expression being strongly associated with worse overall survival in women with breast cancer (Fig. 1 G) [ 20 , 32 ]. Furthermore, we demonstrate that BUB1 outperforms every other clinical or pathologic parameter (i.e., T-stage, grade, age, nodal status, ER, PR, Her2, margin, etc.) as a predictive biomarker of response (as measured by metastasis-free survival) to chemotherapy in a dataset of patients treated with paclitaxel and anthracycline-based chemotherapy with an AUC of 0.68 (Fig. 1 H; *** P = 0.0003). The control group, tissue sample type, and detection method for Fig. 1 H are described in the Fig. 1 I.

Pharmacological inhibition of BUB1 reduces viability of breast cancer cells

To study the effect of BUB1 inhibition in TNBC, we used the selective inhibitor of BUB1 kinase, BAY1816032. We assessed the effects of BAY1816032 on proliferation of TNBC (SUM159, MDA-MB-231, MDA-MB-468, BT-549) cells (Fig. 2 A-D), luminal A subtype (T-47D) (Fig. 2 E) and the non-tumorigenic human breast epithelial cell line, MCF10A (Fig. 2 F). BAY1816032 is cytotoxic in all breast cancer cell lines tested with IC 50 values ranging from 1.6 μM to 3.9 μM. However, BAY1816032 had less cell killing and/or growth inhibitory effects in MCF10A with IC 50 around 18 μM. This response correlated with differential BUB1 mRNA expression (Fig. 1 E) and BUB1 protein expression (Fig 2 G). Based on these observations, we hypothesize that breast cancer cell lines that express high BUB1 would be radiosensitized by BAY1816032 while the cell lines that express low to moderate BUB1 would not.

Effect of BUB1 inhibitor on cell proliferation in TNBC cell lines. BAY1816032 is cytotoxic to cells at low micromolar range ( A ) SUM159, IC 50 : 2.90 μM; ( B ) MDA-MB-231, IC 50 : 2.10 μM; ( C ) MDA-MB-468, IC 50 : 2.59 μM; ( D ) BT-549 IC 50 : 1.59 μM; ( E ) T-47D, IC 50 : 3.9μM; ( F ) MCF10A, IC 50 : 18 μM. G BUB1 protein expression in cell lines by immunoblotting; gray scale values of BUB1 are normalized over Actin for each cell line. Pharmacological inhibition of BUB1 induces radiosensitivity in TNBC cell lines: ( H ) SUM159, ( I ) MDA-MB-231, ( J ) MDA-MB-468, ( K ) BT-549, ( L ) T-47D, and (M) MCF10A. P -values were defined as * P ≤0.05, ** P ≤0.01, *** P ≤0.001, **** P ≤0.0001

BUB1 inhibition causes durable radiosensitization in TNBC cell lines

We evaluated the effect of BAY1816032 on radiation sensitivity in Basal A (MDA-MB-468), Basal B (MDA-MB-231, SUM159, BT-549) (Fig. 2 H-K) and Luminal A (T-47D) (Fig. 2 L) cell lines by clonogenic survival assays. High levels of BUB1 expressed in selected Basal A and B cell lines ( BUB1-high ) while expressed at low level in Luminal cells ( BUB1-low ). BUB1-high cells were radiosensitized by BAY1816032 (rER from 1.1 to 1.38) while the radiation sensitivity of BUB1-low cells did not increase with BAY1816032 (rER 0.91). As expected, BAY1816032 had no effect on radiosensitivity in MCF10A cells (Fig. 2 M). BAY1816032 led to a significant dose-dependent reduction in the surviving fraction at 2 Gy (SF-2 Gy) in BUB1-high cells indicating that BUB1 kinase function is important for radioresistance. Moreover, BAY1816032 did not significantly impacted SF-2Gy in BUB1-low cells.

Genomic depletion of BUB1 is cytotoxic and makes TNBC cells radiosensitive

We evaluated the effect of BUB1 genomic depletion on cell survival and radiation sensitivity. SUM159 and MDA-MB-231 cells were transiently transfected with an increasing concentration of BUB1 siRNA (20, 60 and 100 nM) or control siRNA (100 nM) and cell viability was measured by alamarBlue assay (Fig. 3 A-B). The siRNA-mediated BUB1 depletion demonstrated a dose-dependency on cell survival. Additionally, BUB1 was depleted in MDA-MB-468, BT-549 and T-47D cells which also exhibited significant reduction in cell viability as compared to control siRNA (Fig. 3 C-E). DNAPKcs (gene ID: 5591, PRKDC ) siRNA was used as a positive control since its inhibition or knockdown is known to reduce cell survival [ 33 ] because of the role it plays in DNA DSB repair process [ 34 ].

Effect of BUB1 genomic depletion on cell survival and radiation sensitivity. Transient transfection of BUB1 siRNA (20, 60 and 100 nM) or control siRNA (100 nM) measured cell viability using alamarBlue assay in ( A ) SUM159, ( B ) MDA-MB-231, ( C ) MDA-MB-468, ( D ) BT-549, and ( E ) T-47D. Effect of siRNA-mediated BUB1 depletion on radiosensitization was measured in these cell lines. Transient BUB1 siRNA transfection led to moderate radiosensitization with rER 1.0 to 1.2 in ( F ) SUM159, and ( G ) MDA-MB-231; After silencing of BUB1, BUB1-WT re-expression rescues the radiosensitization phenotype while BUB1-KD does not in ( H ) SUM159 and ( I ) MDA-MB-231. Genomic depletion of BUB1 by CRISPR/Cas9 leads to radiosensitization in ( J ) SUM159 and ( K ) MDA-MB-231 cells; Re-expression of BUB1-WT rescues the radiosensitization phenotype in BUB1 CRISPR KO ( L ) SUM159 and ( M ) MDA-MB-231 cells but BUB1-KD does not in. P -values were defined as * P ≤0.05, ** P ≤0.01, and *** P ≤0.001

Effect of siRNA-mediated BUB1 depletion on radiosensitization was measured in all the selected breast cancer cell lines (Fig. 3 F-G; Supplementary Fig. S5). We observed moderate radiosensitization (rER 1.0 to 1.2) when BUB1 was transiently depleted by siRNA. BUB1 depletion led to a significant reduction in the surviving fraction at 2 Gy (SF-2 Gy). Western blot analyses of total cell lysates following transfection of siRNA revealed that BUB1 could be efficiently repressed. In order to confirm that these effects are mediated by BUB1, we performed the same experiments in SUM159 and MDA-MB-231 (BUB1 depleted) cells with reintroduction of wild-type or kinase dead BUB1 (BUB1-wt, BUB1-kd) (F i g. 3 H-I). Addition of BUB1-wt restored radioresistance in both the cell lines (rER 0.9) while BUB1-kd addition did not (rER 1.0 to 1.1) and this response was correlated with immunoblotting analyses.

Since transient BUB1 depletion by siRNA did not lead to significant radiation sensitization, we generated BUB1 knockout (BUB1 KO) SUM159 and MDA-MB-231 cell lines by CRISPR-CAS9 RNP transfection. Multiple BUB1 CRISPR clones were validated by Western blotting and Sanger sequencing to confirm complete BUB1 KO (Supplementary Fig. S6). Two different BUB1 KO clones for each cell line were used for subsequent experiments. SUM159 BUB1 KO clones demonstrated significant radiation sensitization (clone #18 rER 1.24, clone #48 rER 1.27) (Fig. 3 J). There was also significant decrease in surviving fractions at 2 Gy (SF-2 Gy) in these clones. Similarly, significant radiation sensitization was observed in MDA-MB-231 BUB1 KO clones (clone #12 rER 1.57, clone #15 rER 1.37) and also significant reduction in surviving fractions at 2 Gy (Fig. 3 K). To further confirm a role for BUB1 in radiation sensitization, BUB1-wt and BUB1-kd plasmids were transfected in one BUB1 CRISPR KO SUM159 and MDA-MB-231 clone each and clonogenic survival assay was performed (Fig. 3 L-M). In both the cases, we observed significant radiation sensitization which was reversed when BUB1-wt was expressed (rER 0.9) but not in BUB1-kd expressed cells (rER 1.0), as demonstrated by immunoblotting. The rER value of SUM159 and MDA-MB-231 BUB1 KO clones presented in Fig. 3 L-M is lower than that of Fig. 3 J-K due to the toxicity that is commonly observed with the Lipofectamine 2000 transfection reagent, which we used to transfect the BUB1-wt and BUB1-kd plasmids.

BUB1 inhibition radiosensitizes SUM159 tumor xenografts and prolongs animal survival

To determine the effects of BUB1 inhibition on radiosensitization in vivo , xenograft tumors ( N = >9-10/arm) were generated by injecting SUM159 cells into the 4 th mammary fat pads of female CB17/SCID mice. Mice were randomized to different treatment groups once the tumors reached ~80 mm 3 . Mice received either BUB1 inhibitor BAY1816032 (25 mg/kg, twice daily for 4 weeks, week days only), RT (5Gy X3, 2 days apart), combination or sham irradiation/vehicle (Fig. 4 A). We initially tested the doses/fractions (2.5 GyX8, 5 GyX3 and 10 GyX1) that yielded similar equivalent dose (EQD2; 21.7-23.3Gy) and biologically effective dose (BED; 32.5-35Gy) using an alpha/beta ratio of 4 which enabled us to explore whether high dose/fraction was more effective than standard fractionation. We observed insignificant benefits of adding 2.5GyX8 and 10 GyX1 radiation with BUB1i while 5GyX3 schema demonstrated superior tumor control (Supplementary Fig. S7A) which was selected for the subsequent repeat experiments. In combination treatment arm, RT started 24 h after the first treatment with BUB1i. BAY1816032 with RT significantly reduced tumor growth (Fig. 4 B) compared with inhibitor or RT alone and significantly extended animal survival (Fig. 4 C-E). There was no toxicity of BUB1 inhibitor since body weight of experimental animals remained constant during the study period (Supplementary Fig. S7B). Immunohistochemical staining of Ki67 (marker for proliferation) from tumors collected at the study end point revealed a significant reduction in Ki67 positivity in combination treatment than either treatment alone which also correlated with H&E staining pattern (Fig. 4 F-G).

BUB1 inhibition sensitizes SUM159 tumor xenografts to radiation ( A ) Timeline of the experiment; ( B ) Representative images of tumor growth in different treatment groups; ( C ) Combination treatment of BAY1816032 + RT reduces tumor volume in vivo; ( D, E ) Combination treatment increases tumor volume doubling time in Fox Chase SCID mice; Representative images of ( F ) H&E staining showing structural changes and Ki67 staining (a proliferation marker) revealed a significant reduction in combination treatment of SUM159 xenografts; ( G ) Ki-67 plot showing decrease in % of positive cells in combination treatment of BUB1i + RT. P-value was defined as **** P ≤0.0001

Additionally, we generated mammary fat pad tumor xenografts in CB17/SCID mice ( N = 4-10/arm) using SUM159 BUB1 CRISPR KO cell line (clone #48). Animals were randomly divided into treatment groups once the tumors established (~80 mm 3 ) and treated with RT (5Gy X3) or sham irradiated (Fig. 5 A). There was a significant increase in mouse survival in combination treatment group as compared to sham irradiation (Fig. 5 B-E).

Tumor xenograft of BUB1 CRISPR KO SUM159 cells are sensitive to irradiation. A Timeline of the experiment; ( B ) Representative images of tumor growth in different treatment groups; ( C ) Treatment of BUB1 KO + RT reduces tumor volume in vivo; ( D, E ) Treatment of BUB1 KO + RT increases tumor volume doubling time in Fox Chase SCID mice. P -value was defined as * P ≤0.05

BUB1 inhibition reduces radiation induced DSB repair as visualized by γH2AX foci

We next investigated the effect of BAY1816032 on dsDNA break repair. γH2AX foci (> 10 foci per cell), a marker for unresolved double strand DNA damage was assessed in cells treated with DMSO and 1 μM BAY1816032, either with or without RT (4 Gy) at different time points (30 min, 4 h, 16 h, 24 h). NU7441 (DNAPK inhibitor) was used as a positive control. Representative images are shown of γH2AX (16 h) in SUM159 and MDA-MB-231 cell lines (Fig. 6 A and C). Non-irradiated cells had fewer γH2AX positive cells. RT induced the formation of γH2AX foci in approximately 40% of cells within 30 mins post-irradiation, peaked at 4 h, gradually decreased by 16 h and reached near baseline levels by 24 h. However, pretreatment with BAY1816032 resulted in a slight increase in the number of foci (approximately 90% of cells) at 30 min post-irradiation and the expression of γH2AX foci continued to remain elevated thereafter; even at 16 and 24 h with a significantly higher number of foci in the BAY1816032 pre-treated group compared to RT alone group (Fig. 6 B and D). Cells treated with RT alone efficiently repaired the RT-induced dsDNA damage than the combination over the time, suggesting that BUB1 inhibition delayed the RT induced dsDNA break repair efficiency.

BUB1 ablation radiosensitize through NHEJ. Representative images of ( A ) SUM159 and ( C ) MDA-MB-231 γH2AX foci at 16 h. Original magnification, ×63; Combination treatment of BUB1i and RT leads to delayed resolution of γH2AX foci in ( B ) SUM159, and ( D ) MDA-MB-231 cell lines. Inhibition of BUB1 kinase function by BAY1816032, at 1 μM and 10 μM, decreases NHEJ efficiency (V Luc) and increases HR efficiency (G Luc) in ( E ) SUM159, and ( F ) MDA-MB-231. Effect of DNAPK inhibitor (NU7441) on cell proliferation in TNBC cell lines. NU7441 is cytotoxic to cells at low nanomolar range ( G ) SUM159, IC 50 : 368 nM; ( H ) MDA-MB-231, IC 50 : 503 nM; Combination of BAY1816032 and NU7441 does not increase DNAPKcs-mediated radiosensitization in ( I ) SUM159 ( J ) MDA-MB-231 cell lines. Inhibition of BUB1 increased transcription of DNA damage genes after radiation. Significant upregulation of H2AFX and downregulation of PRKDC levels in ( K ) SUM159 and ( L ) SUM159 BUB1 CRISPR KO cells were observed. P -values were defined as * P ≤0.05, ** P ≤0.01, *** P ≤0.001, **** P ≤0.0001

We also assessed the dsDNA break repair using BUB1 siRNA in SUM159 and MDA-MB-231 cell lines. Representative images of γH2AX (16 h) are shown in Supplementary Fig. S8. Almost all cells treated with BUB1 siRNA were γH2AX foci positive in presence or absence of RT (4 Gy) at 30 min and 4 h. The largest differences were seen at subsequent time points (16 and 24 h) in which BUB1 depletion resulted in persistence of γH2AX foci, whereas the foci began to resolve in presence of BUB1. These results indicate that inhibition of BUB1 kinase activity most likely results in a slower rate of DNA damage repair.

BUB1 inhibition reduces non-homologous end joining (NHEJ) repair

The two major pathways for repair of DNA DSBs include HR and NHEJ [ 35 ]. Though either may be involved in repairing dsDNA breaks, earlier reports suggested a potential link between BUB1 expression and NHEJ pathway [ 10 ]. Thus, we hypothesized that reduced NHEJ repair efficiency is partly responsible for BUB1-mediated radiosensitization and prolonged unresolved dsDNA breaks. Following the induction of a DSB, we used BLRR approach to simultaneously monitor the NHEJ and HR dynamics [ 26 ]. We aimed to confirm if BUB1 inhibition impacted NHEJ or HR since it has been previously demonstrated that knockdown of BUB1 reduces NHEJ efficiency [ 10 ]. BLRR transfected cells treated with BAY1816032 at two different concentrations (1 and 10 μM) in presence of RT (4 Gy) led to a significant decrease in NHEJ (VLuc activity) signal as the GLuc signal (HR activity) increased reciprocally in a dose-dependent manner (Fig. 6 E-F). NU7441 was used as a positive control. These results indicate that BUB1 inhibition decreases NHEJ-mediated DNA damage repair efficiency and BUB1-mediated radiosensitization may take place through the NHEJ pathway.

BUB1 inhibition does not increase DNAPKi-mediated radiosensitization

The above results encouraged us to further assess the effect of BUB1 inhibition in combination with a DNAPK specific inhibitor NU7441, which is well-known to impair NHEJ-mediated radiation-induced DSB repair [ 36 ]. Initially, we investigated the cytotoxicity of NU7441 in SUM159 and MDA-MB-231 cells at 72 h. The IC 50 value of NU7441 on these cells ranges from 300 - 500 nM (Fig. 6 G-H). The radiosensitization effects following treatment with a combination of BAY1816032 and NU7441 (250 nM each) in presence of radiation (0, 2, 4, 6 Gy) were assessed using clonogenic survival assays. When combined, BUB1 inhibition does not increase DNAPK inhibitor driven radiosensitization (combination rER ranges from 1 to 1.3) which further confirms that BUB1-mediated radiosensitization takes place through NHEJ pathway (F i g. 6 I-J). Furthermore, the surviving fraction at 2 Gy (SF-2 Gy) was significantly reduced by the combined effect of BAY1816032 and NU7441 (Fig. 6 I-J; inset plots) but it was not significantly different than either agent alone.

Pharmacological and genomic ablation of BUB1 causes increased transcription of DNA damage genes after radiation

Cells were pre-treated with BUB1i, irradiated (4 Gy) 1h after BUB1i and harvested 72h post RT to examine the impact of BUB1 inhibition on NHEJ pathway associated genes by qPCR. The expression of H2AFX, XRCC5, XRCC6, PRKDC, and BUB1 was measured and normalized against GAPDH . These results demonstrated an increase in H2AFX, XRCC5, and XRCC6 in BUB1i treated SUM159 (Fig. 6 K) and MDA-MB-231 cells (Supplementary Fig. S9, top panel). We observed significant downregulation of PRKDC and BUB1 in BUB1i treated cells. (Fig. 6 K and Supplementary Fig. S9). Similar results were obtained in both BUB1 CRISPR KO cell lines (SUM159 KO #48; Fig. 6 L and MDA-MB-231 KO #12; Supplementary S9, bottom panel) further supporting a role for BUB1 in regulating mRNA levels of key NHEJ genes in response to radiation.

BUB1 ablation increases DNAPKcs phosphorylation and stabilizes it after irradiation

DNAPK catalytic subunit (DNAPKcs) is a well-known mediator of DNA DSB repair through the activation of NHEJ [ 37 , 38 ]. DNAPK autophosphorylates at Ser2056 (PQR cluster) and Thr2609 (ABCDE cluster) in response to DSB induction [ 39 , 40 , 41 ] which may limit or promote DNA end processing during NHEJ [ 40 , 42 ]. Thus, we evaluated if BUB1 ablation had any effect of DNAPK phosphorylation at Ser2056 (S2056) in MDA-MB-231 (Fig. 7 A) and MDA-MB-468 (Supplementary S10A) cells. As expected, radiation treatment led to an increase in DNAPK phosphorylation (pDNAPKcs) at S2056 which was significantly increased in samples that had been pre-treated with BUB1i (Fig. 7 A). DNAPK inhibitor NU7441 was used as a positive control in parallel experiments. Not surprisingly, pre-treatment with NU7441 almost completely blocked radiation induced DNAPKcs S2056 phosphorylation in these cells (Fig. 7 A and Supplementary S10A). There were no noticeable changes in the expression of KU70, KU80, or total DNAPKcs. Since radiation induced DNAPKcs autophosphorylation can be observed within minutes [ 28 , 39 , 40 ] and phospho-DNAPKcs levels decrease afterwards [ 41 ], we next investigated if BUB1 ablation changes pDNAPKcs dynamics following radiation. Cells were pre-treated with BUB1i for 1hr followed by 4 Gy radiation and collected at various intervals (0, 15, 30, and 120 min). We observed that the pre-treatment with BAY1816032 augmented the expression of pDNAPKcs (S2056), which was noticeable up to 2h while pDNAPKcs started to decrease after 30 minutes in the radiation alone group in MDA-MB-231 while it was noticeable in MDA-MB-468 only at 120 minutes in RT only lanes (Fig. 7 B and Supplementary S10B). This data indicates that BUB1 ablation increases the amplitude and duration of radiation induced pDNAPKcs within a PQR cluster site.

BUB1 ablation leads to increased phosphorylation of DNAPKcs, alters chromatin localization of key NHEJ factors and induces apoptotic cell death upon irradiation. A MDA-MB-231 cells were treated with BUB1i or DNAPKi an hour prior to radiation treatment. Cells were harvested 30 minutes post RT (4Gy) and resolved on SDS-PAGE gels and probed with indicated antibodies. B MDA-MB-231 cells were treated as (A) and harvested at 1-, 15-, 30- and 120-minutes post-RT and immunoblotted as specified. C SUM159 (top panel) MDA-MB-231 cells (bottom panel) were treated with cycloheximide followed by BUB1i or DNAPKi and radiation (4Gy). Total protein lysates were made at the indicated time-points and resolved on gels. D BUB1 CRISPR KO SUM159 (left panel) or MDA-MB-231 (right panel) cells were treated with cycloheximide, and radiation and samples were harvested at different time points. E Quantitation of pDNAPKcs protein levels in SUM159 and MDA-MB-231 cells (from 7C and other experiments). F Quantitation of BUB1 protein levels in SUM159 and MDA-MB-231 cells (from above experiments). G Nuclear and chromatin fractions of SUM159 and MDA-MB-231 cells treated with BUB1i, DNAPKi and RT (left) and BUB1 CRISPR KO SUM159 and MDA-MB-231 cells treated with RT (right panels). H Effect of BUB1 inhibitor (red circles) on initial recruitment of YFP-tagged KU80 and YFP-DNAPKcs by laser microirradiation in U2OS cells. I effect of BUB1 inhibition on the accumulation of YFP-KU80 and YFP-DNAPKcs at laser-induced DSBs for up to 120 minutes. J QRT-PCR of BAX, BCL2, PCNA, CASP3 and CASP9 in SUM159 cells treated with BUB1i, DNAPKi and radiation (4 Gy, 72 hours). (I) QRT-PCR of BAX, BCL2, PCNA, CASP3 and CASP9 in SUM159 BUB1 CRISPR cells 72 hours post-irradiation (4 Gy). P -values were defined as * P ≤0.05, ** P ≤0.01, and *** P ≤0.001

To validate if observed increase in amplitude and duration of pDNAPKcs was due to the stabilization of pDNAPKcs-S2056, we carried out an experiment wherein nascent protein synthesis was blocked by cycloheximide (CHX). MDA-MB-231 and SUM159 cells were treated with CHX, followed by BUB1i, DNAPKi, vehicle/mock and radiation (4Gy). Protein samples were collected at various time points (0 min, 30 min, 2 h, 8 h, 16 h, and 24 h) and resolved on SDS-PAGE gels (Fig. 7 C). Densitometric analysis yielded half-life (t 1/2 ) of pDNAPKcs at >24h in radiation treated samples which significantly increased upon DNAPKi treatment in SUM159 cells. Surprisingly, combination of BUB1i with RT significantly stabilized pDNAPKcs up to the longest time point evaluated (24h) such that t 1/2 could not be estimated (Fig. 7 C). These results demonstrate that BUB1 ablation stabilizes radiation induced pDNAPKcs. In BUB1 CRISPR KO cell lines (SUM159 KO#48 and MDA-MB-231 KO#12), DNAPKcs phosphorylation was detectable up to 24 h in the presence of RT further confirming a role for BUB1 in stabilizing pDNAPKcs (i.e., active DNAPKcs) in response to radiation (Fig. 7 D). Interestingly, we observed that BUB1 protein was stabilized upon radiation treatment (t 1/2 = ∞, Fig. 7 C and 7E) which was reversed in cells pre-treated with BAY1816032 (t 1/2 = 8h, Fig. 7 F). BUB1 inhibitor at clonogenic concentrations did not affect BUB1 protein levels in MCF10A cells (Supplementary Fig. S11).

BUB1 ablation alters chromatin localization of NHEJ proteins

Chromatin remodeling increases the accessibility of the region surrounding a DNA lesion for proteins involved in DNA damage response and repair [ 43 ]. DNA damage sensors and early signal transducers are rapidly attracted to damaged DNA sites right after the radiation exposure [ 44 ]. We postulated that the initial local chromatin relaxation brought about by BUB1 kinase activity is necessary for the rapid loading of the NHEJ machinery to DSBs. To examine this, nuclear and chromatin fractions were isolated 10 min post-DNA damage with 8 Gy RT in BUB1i-treated and BUB1 CRISPR KO SUM159 and MDA-MB-231 cell lines (Fig. 7 G). We observed an increased recruitment of phospho-DNAPKcs, total-DNAPKcs, and KAP1 in both nuclear and chromatin-enriched fractions suggesting that BUB1 plays a crucial role in the activation and recruitment of key NHEJ proteins to DSBs. There was no change in the enrichment of KU70 and KU80 proteins in these fractions. The hypothesis that BUB1 is necessary for the quick recruitment of the NHEJ factors to DNA damage sites was supported by laser micro-irradiation experimental findings. Since BUB1 interacts with DNAPKcs just after DSB induction [ 10 ], we additionally looked at whether BUB1 regulates DNAPKcs at DSBs. Inhibition of BUB1 does not affect the initial recruitment of YFP-KU80 as viewed in Fig. 7 H (top, 200 sec) while BUB1i results in rapid recruitment of YFP-DNAPKcs to DSBs compared to the vehicle treated cells (Fig. 7 H, bottom). In contrast, BUB1 inhibition resulted in prolonged retention of KU80 and DNAPKcs at DSBs for up to 120 minutes (Fig. 7 I). Gene correlation analysis on the METABRIC dataset identified very strong correlation between BUB1 and H2AX (spearman correlation 0.58), PRKDC (0.39), and moderate correlation with XRCC5 (0.05) and XRCC6 (0.29) further corroborating a strong link between BUB1 and NHEJ mediators (Supplementary S12).

BUB1 ablation increases transcription of apoptotic genes after irradiation

Since BUB1 increased radiation induced cell death (Fig. 2 H-M, and Fig. 3 F-M) and led to increased loading of key NHEJ factors chromatin fractions (i.e., DNA damage; Fig. 7 G), we next sought out to elucidate cell death mechanisms mediated by the combination treatment. qRT-PCR for pro-apoptotic, anti-apoptotic and proliferation genes demonstrated significant upregulation of BAX , CASP3 and CASP9 while significant downregulation of PCNA and BCL2 was observed after BUB1 ablation in SUM159 and MDA-MB-231 cell lines (Fig. 7 J and Supplementary S13). Gene correlation studies using the METABRIC dataset identified very strong correlation between BUB1 and MKI67 (spearman correlation 0.71), CASP3 (0.44), BAX (0.27), BCL2 (-0.42) and PCNA (0.48) all with p <*** (Supplementary S13) further supporting a role for BUB1 in facilitating radiation induced apoptosis.

BUB1 is overexpressed in tumors and its expression correlates with tumor grade

We examined the expression of BUB1 in breast tumors (N = 202) and compared with normal breast tissues (N = 15). Expression levels of BUB1 protein were graded based on staining intensity and percentage of cells positively stained for BUB1. Levels of immunopositivity were scored as follows: 0 (No staining); 1+ (Weak staining); 2+ (Moderate staining); 3+ (Strong staining). Scores of 0 designated as negative, and scores of 1, 2, and 3 were designated as positive. Examples of BUB1 staining are illustrated in Fig. 8 A under 4x and 20x magnifications. Immunohistochemical analysis revealed a significantly high BUB1 protein expression in breast tumors compared to normal breast tissue. We observed significant correlation between BUB1 protein expression (staining intensity) and tumor grades and stages (Fig. 8 B). Furthermore, BUB1 was overexpressed in TNBC ( N = 50; P<0.05 ), ER+/PR+ ( N = 63; P<0.001 ), ER+/PR+/HER2+ ( N = 19; P<0.05 ), ER+ ( N = 37; P<0.05 ), ER+/HER2+ ( N = 12; P<0.01 ), HER2+ ( N = 18; P<0.0001 ), and PR+ ( N = 3; P<0.0001 ) compared to normal breast ( N = 15). Although, we observed highest BUB1 staining intensity in PR+ tumors, the number of PR+ samples in the current TMA are too small to statistically support the findings.

BUB1 is overexpressed in breast tumors. A Representative images of BUB1 staining intensity at 4x and 20x magnifications in breast TMA. B Quantification of BUB1 staining in breast tumor TMA. C Proposed model for a role of BUB1 in mediating radiation induced NHEJ signaling. We propose that radiation induced DNA DSB are repaired efficiently when BUB1 is present (left panel) leading to radiation resistance. In the absence of BUB1 activity or availability, radiation induces hyper phosphorylation of DNAPKcs (Ser2056) and increased binding of NHEJ mediators at the DNA DSB sites (right panel). These NHEJ mediators may not stay on the extended chromatin thus hamper end processing causing radiation-sensitization. P -values were defined as * P ≤0.05, ** P ≤0.01, *** P ≤0.001, and **** P ≤0.0001

BUB1 is a serine/threonine kinase required for optimal DNA damage response as there is increasing evidence that DNA damage response elements and spindle assembly checkpoint components crosstalk [ 11 ]. We identified BUB1 as a key kinase associated to radiosensitivity in a focused human kinome screen [ 30 ]. Nevertheless, there is no data that links BUB1 to radiation therapy or DNA damage repair in TNBC. Here, we demonstrate that BUB1-specific inhibitor BAY1816032 radiosensitized TNBC models, a subtype of breast cancer known to have limited treatment options with poorest prognosis [ 45 ]. Previous studies have shown the advantage of radiation therapy in reducing local recurrence rates, and this was validated in a randomized controlled trial in patients with TNBC [ 46 ]. However, radioresistance is a major cause of treatment failure or locoregional relapse in TNBC. Here, we provide evidence that BUB1 mediates radiation resistance in TNBC through modulating DNA DSB repair.

Our study showed that BUB1 is overexpressed (differential mRNA levels) in breast cancer with the highest expression in TNBC (Fig. 1 D). However, BUB1 protein expression is found to be slightly different when compared to the differential mRNA expression (Fig. 2 G). We hypothesize that several factors including delayed protein synthesis, post-transcriptional and post-translational modifications, different protein half-lives cause reduced mRNA/protein correlations [ 47 , 48 , 49 ]. Generally, only 20 – 40% correlation is observed between protein expression and corresponding mRNA levels [ 50 , 51 ]. Our findings that BUB1i was effective at a log lower concentration in cancer cells (Fig. 2 A-E) compared to normal breast epithelial MCF10A cell line (Fig. 2 F) demonstrate the selectivity and potentially minimal toxicity of BUB1i in future translational studies given it was found to be safe in large animal models [ 52 ]. Our observations that BUB1 ablation sensitizes TNBC cell lines (SUM159, MDA-MB-231, MDA-MB-468, and BT-549) but not Luminal A subtype (T47D) further support a role for BUB1 in mediating radiation-resistance phenotype in TNBC [ 30 ]. PI3K family kinases including ATM, ATR, and DNAPK phosphorylate Ser139 in H2AX upon DNA damage which is necessary to sustain the stable association of repair factors at DSB sites [ 53 ]. ATM phosphorylates BUB1 at Ser314 that activates BUB1 resulting in optimal DNA damage response [ 11 ]. By re-expressing BUB1-WT and BUB1-KD in BUB1 knockout cells, we confirmed that BUB1 activity plays a role in radiation (Fig. 3 ) and DDR responses (Fig. 6 A-D). Biochemical or genomic BUB1 ablation radiosensitized SUM159 mouse xenograft model (Figs. 4 and 5 ) further corroborating a role for BUB1 in mediating radiation response. Prolonged presence of γH2AX foci after irradiation in BUB1 ablated cells supports earlier reports on delayed or unrepaired DSB after BUB1 ablation [ 10 , 54 ]. The BLRR assay confirmed that BUB1i radiosensitizes TNBC through NHEJ (Fig. 6 E-F) which was further confirmed by no increase in DNAPKi mediated radiosensitization by BUB1i (Fig. 6 I-J). Similar observations with DNAPKi have been reported [ 55 ].

Recent evidence has shown that the DNAPKcs and DNA methyltransferase inhibitors are effective at sensitizing TNBC to PARPi and radiation [ 56 ]. Autophosphorylation of DNAPKcs at Ser2056, a known autophosphorylation site within the PQR cluster regulates DNA end processing and possibly DSB repair pathway choice [ 57 , 58 ]. Surprisingly, we identified that BUB1 ablation increased the level and amplitude of pDNAPKcs-S2056 following radiation. The pDNAPKcs was stabilized till the longest time point evaluated (24hr). Our observations support previous studies which demonstrated higher or persistent pDNAPKcs following DNAPKi, ATMi with IR or other DNA damaging agents [ 39 , 58 , 59 , 60 ]. Although, we have not confirmed the mechanism of pDNAPKcs stabilization by BUB1, we are tempted to speculate that known (RNF144A [ 61 ], MARCH5 [ 62 ], CRL4ADTL [ 63 ]) or yet unrelated E3-ubiquitin ligase(s) may be involved. Although tumor suppressor protein P53 (p53) has been linked to BUB1 expression [ 64 ], the data has been lacking that demonstrated BUB1 protein regulation by radiotherapy. Our cycloheximide chase assays clearly demonstrates that BUB1 is stabilized upon RT while pretreatment with BUB1i or DNAPKi reverses this and causes BUB1 degradation after irradiation (Fig. 7 C, 7F). Future studies will determine the mechanism of BUB1 regulation by radiation.

Chromatin remodeling increases the accessibility of DNA damage response and repair proteins in area around a DNA lesion [ 43 ]. Phosphorylation of H2AX and KAP1 are key steps that enhance chromatin relaxation and allow the recruitment of the DDR machinery to a DSB [ 65 , 66 ]. Our findings (Fig. 7 G) that BUB1 ablation causes increased loading of pDNAPKc, pKAP1, KAP1, and pATM to chromatin fractions and alters the recruitment of YFP-KU80 and YFP-DNAPKcs (F i g. 7 H-I) support a role for BUB1 in this step. Lu et al., identified a role for DNAPK kinase activity wherein attenuated chromatin recruitment of MRN complex was detected in DNAPK-KD or null (-/-) cells [ 28 ]. Additionally, they observed decreased localization of NHEJ factors including LIG4, XRCC4 and XLF in chromatin fractions in these cells further supporting a role for DNAPKcs activity in NHEJ. The above data supports our hypothesis wherein BUB1 mediate radiation induced NHEJ through regulating activation (phosphorylation) of DNAPKcs thus chromatin relaxation and access of NHEJ factors. Taken together, our results provide evidence to the hypothesis that BUB1 is necessary for the quick recruitment of the NHEJ factors to DNA damage sites (F i g. 7 G-I). In future, we will perform in-depth mechanistic studies such as micronuclease digestion, immunoprecipitation (CO-IP), and proximality ligation assay (PLA) to confirm a role for BUB1 in this step of NHEJ. Since mutagenesis of DNAPKcs Ser2056 confirmed that it limits end-processing [ 58 ], additionally we will evaluate if BUB1 ablation impacts this process. Because DNAPKcs inhibition or depletion leads to reduction in NHEJ and reciprocally a shift to HR [ 67 , 68 ] and since certain phosphorylation in DNAPKcs promote HR while inhibiting NHEJ [ 67 ], it would be interesting to see if these phospho sites in DNAPKcs are affected by BUB1 and thus downstream signaling (HR). Moreover, ATM phosphorylates members of MRN complex (that initiates HR cascade) [ 69 ], NHEJ factors (including DNAPKcs [ 70 ], and H2AX) and is shown to phosphorylate BUB1 at Ser314 in response to irradiation [ 11 ], it would be fascinating to investigate if BUB1 indeed affects HR response through ATM or some other mechanism.

BUB1 transcripts are significantly higher in breast cancer cell lines and in high-grade primary breast cancer tissues compared to normal mammary epithelial cells, or in normal breast tissues [ 71 ]. High BUB1 expression (transcript) correlates with extremely poor outcome in breast cancer [ 72 , 73 ]. Our meta-analysis that BUB1 expression significantly correlates with Ki67 (Supplementary Fig. S12) supports earlier findings [ 72 , 73 , 74 ] and signifies our in-vivo observations that tumors harvested from mice treated with a combination of BUB1i and radiation have statistically significant reduction in Ki67 (Fig. 4 F-G) or PCNA in cells (Fig. 7 J). Our TMA analysis found strong correlation between BUB1 protein expression and tumor grade (Fig. 8 A-B) and identified high BUB1 expression in TNBC samples. Our BUB1 immunostaining TMA data support earlier findings wherein nuclear BUB1 staining was found to strongly correlate with stage, pathological tumor factors, lymph node metastasis, distant metastasis, histological grade, and proliferation [ 74 ]. In future it will be important to assess if BUB1 protein expression correlates with treatment naïve or radioresistant-recurrence cases. Based on our data that BUB1 is stabilized upon radiation treatment (Fig. 7 C) we speculate higher BUB1 expression in radiation resistant, recurrent cases compared to treatment naïve cases.

Our results are consistent with previous reports where knockdown of BUB1 was demonstrated to prolong γH2AX foci, comet tail as well as hypersensitivity in response to ionizing radiation [ 11 ]. Since BUB1 co-localizes with 53BP1 [ 10 ] and interacts with NHEJ factors [ 10 ] and we identified that BUB1 ablation increases the amplitude and duration of DNAPKcs phosphorylation and increases chromatin localization of key NHEJ factors, we describe a model on BUB1’s role in NHEJ (Graphical Abstract, Fig. 8 C). In the presence of BUB1 (left panel), the NHEJ is efficient and can repair radiation induced DSB thus causes radio-resistance. On the other hand, BUB1 inhibition or depletion causes increased phosphorylation of DNAPKcs and increased binding of NHEJ factors at the DSB sites (right panel). These NHEJ mediators do not stay on the extended/open chromatin required for proper end processing and ligation of the DNA ends. This leads to reduced NHEJ repair leading to radiation-sensitization. DNAPKcs phosphorylation is essential for its dissociation from Ku bound DNA [ 46 , 75 , 76 ]. Although we observed increased binding of NHEJ factors at the chromatin following BUB1i+RT (10 minutes post RT), we cannot rule out that these factors fall off at a later time without repairing broken DNA ends (limited end processing) as has been demonstrated using DNAPKcs phospho-site mutants [ 76 ]. Taken together, our data demonstrate that BUB1 is overexpressed in breast cancer including TNBC and BUB1 ablation leads to radiosensitization through regulating DNAPKcs phosphorylation and chromatin localization of key NHEJ factors. Our findings strongly support nomination of BUB1 as a potential biomarker and a therapeutic target for radiosensitization in TNBC.

Availability of data and material

All the relevant data are already presented in the manuscript. Any additional data will be available upon request to the corresponding author.

Abbreviations

Breast cancer

Bioluminescent repair reporter

Budding uninhibited by benzimidazoles-1

Chick chorioallantoic membrane

Cycloheximide

Dulbecco's modified eagle medium

Dimethyl sulfoxide

DNA dependent protein kinase

Double-strand breaks

Electrogenerated chemiluminescence

Ethylenediaminetetraacetic acid

Estrogen receptor

Fetal bovine serum

Gene expression omnibus

Human epidermal growth factor receptor 2

Homologous recombination

Institutional Animal Care and Use Committee

Integrated DNA technologies

KRAB-associated protein 1

Kinase-dead

Linear mixed models

Mre11, Rad50 and Nbs1

Non-homologous end joining

Poly (ADP-ribose) polymerase inhibitor

Plating efficiency

Polyethylene glycol

Proximality ligation assay

DNA polymerase-beta

Progesterone receptor

polyvinylidene difluoride

Quantitative polymerase chain reaction

Radiation enhancement ratio

Ribonucleoprotein

Radiotherapy

Standard error of the mean

Survival fraction

Single-strand breaks

The Cancer Genome Atlas

Tissue microarray

Triple-negative breast cancer

Kyndi M, Sorensen FB, Knudsen H, Overgaard M, Nielsen HM, Overgaard J, et al. Estrogen receptor, progesterone receptor, HER-2, and response to postmastectomy radiotherapy in high-risk breast cancer: the Danish Breast Cancer Cooperative Group. J Clin Oncol. 2008;26(9):1419–26.

Article   CAS   PubMed   Google Scholar  

Mladenov E, Magin S, Soni A, Iliakis G. DNA double-strand break repair as determinant of cellular radiosensitivity to killing and target in radiation therapy. Front Oncol. 2013;3:113.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Morgan MA, Lawrence TS. Molecular Pathways: Overcoming Radiation Resistance by Targeting DNA Damage Response Pathways. Clin Cancer Res. 2015;21(13):2898–904.

Britton S, Coates J, Jackson SP. A new method for high-resolution imaging of Ku foci to decipher mechanisms of DNA double-strand break repair. J Cell Biol. 2013;202(3):579–95.

Ahnesorg P, Smith P, Jackson SP. XLF interacts with the XRCC4-DNA ligase IV complex to promote DNA nonhomologous end-joining. Cell. 2006;124(2):301–13.

Chapman JR, Taylor MR, Boulton SJ. Playing the end game: DNA double-strand break repair pathway choice. Mol Cell. 2012;47(4):497–510.

Gupta A, Hunt CR, Chakraborty S, Pandita RK, Yordy J, Ramnarain DB, et al. Role of 53BP1 in the regulation of DNA double-strand break repair pathway choice. Radiat Res. 2014;181(1):1–8.

Berry MR, Fan TM. Target-Based Radiosensitization Strategies: Concepts and Companion Animal Model Outlook. Front Oncol. 2021;11:768692.

Sriramulu S, Thoidingjam S, Brown SL, Siddiqui F, Movsas B, Nyati S. Molecular targets that sensitize cancer to radiation killing: From the bench to the bedside. Biomed Pharmacother. 2022;158:114126.

Article   PubMed   Google Scholar  

Jessulat M, Malty RH, Nguyen-Tran DH, Deineko V, Aoki H, Vlasblom J, et al. Spindle Checkpoint Factors Bub1 and Bub2 Promote DNA Double-Strand Break Repair by Nonhomologous End Joining. Mol Cell Biol. 2015;35(14):2448–63.

Yang C, Wang H, Xu Y, Brinkman KL, Ishiyama H, Wong ST, et al. The kinetochore protein Bub1 participates in the DNA damage response. DNA Repair (Amst). 2012;11(2):185–91.

Komura K, Inamoto T, Tsujino T, Matsui Y, Konuma T, Nishimura K, et al. Increased BUB1B/BUBR1 expression contributes to aberrant DNA repair activity leading to resistance to DNA-damaging agents. Oncogene. 2021;40(43):6210–22.

Nyati S, Schinske-Sebolt K, Pitchiaya S, Chekhovskiy K, Chator A, Chaudhry N, et al. The kinase activity of the Ser/Thr kinase BUB1 promotes TGF-beta signaling. Sci Signal. 2015;8(358):ra1.

Article   PubMed   PubMed Central   Google Scholar  

Nyati S, Gregg B, Xu JQ, Young G, Kimmel L, Mukesh N, et al. TGFBR2 mediated phosphorylation of BUB1 at Ser-318 is required for transforming growth factor-beta signaling. Cancer Res. 2019;79(13):3430.

Article   Google Scholar  

Nyati S, Gregg BS, Xu J, Young G, Kimmel L, Nyati MK, et al. TGFBR2 mediated phosphorylation of BUB1 at Ser-318 is required for transforming growth factor-beta signaling. Neoplasia. 2020;22(4):163–78.

Tang ZY, Shu HJ, Oncel D, Chen S, Yu HT. Phosphorylation of Cdc20 by Bub1 provides a catalytic mechanism for APC/C inhibition by the spindle checkpoint. Mol Cell. 2004;16(3):387–97.

Tang ZY, Sun YX, Harley SE, Zou H, Yu HT. Human Bub1 protects centromeric sister-chromatid cohesion through Shugoshin during mitosis. Proc Natl Acad Sci U S A. 2004;101(52):18012–7.

Yu H, Tang Z. Bub1 multitasking in mitosis. Cell Cycle. 2005;4(2):262–5.

Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, Fevr T, et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell. 2006;10(6):515–27.

Hatzis C, Pusztai L, Valero V, Booser DJ, Esserman L, Lluch A, et al. A genomic predictor of response and survival following taxane-anthracycline chemotherapy for invasive breast cancer. JAMA. 2011;305(18):1873–81.

Speers C, Zhao SG, Kothari V, Santola A, Liu M, Wilder-Romans K, et al. Maternal Embryonic Leucine Zipper Kinase (MELK) as a Novel Mediator and Biomarker of Radioresistance in Human Breast Cancer. Clin Cancer Res. 2016;22(23):5864–75.

Zhao SG, Shilkrut M, Speers C, Liu M, Wilder-Romans K, Lawrence TS, et al. Development and validation of a novel platform-independent metastasis signature in human breast cancer. PLoS One. 2015;10(5):e0126631.

Zuris JA, Thompson DB, Shu Y, Guilinger JP, Bessen JL, Hu JH, et al. Cationic lipid-mediated delivery of proteins enables efficient protein-based genome editing in vitro and in vivo. Nat Biotechnol. 2015;33(1):73–80.

Liang X, Potter J, Kumar S, Zou Y, Quintanilla R, Sridharan M, et al. Rapid and highly efficient mammalian cell engineering via Cas9 protein transfection. J Biotechnol. 2015;208:44–53.

Serçin Ö, Reither S, Roidos P, Ballin N, Palikyras S, Baginska A, et al. A solid-phase transfection platform for arrayed CRISPR screens. Mol Syst Biol. 2019;15(12):e8983.

Chien JC, Tabet E, Pinkham K, da Hora CC, Chang JC, Lin S, et al. A multiplexed bioluminescent reporter for sensitive and non-invasive tracking of DNA double strand break repair dynamics in vitro and in vivo. Nucleic Acids Res. 2020;48(17):e100.

So S, Davis AJ, Chen DJ. Autophosphorylation at serine 1981 stabilizes ATM at DNA damage sites. J Cell Biol. 2009;187(7):977–90.

Lu H, Saha J, Beckmann PJ, Hendrickson EA, Davis AJ. DNA-PKcs promotes chromatin decondensation to facilitate initiation of the DNA damage response. Nucleic Acids Res. 2019;47(18):9467–79.

Lu H, Shamanna RA, de Freitas JK, Okur M, Khadka P, Kulikowicz T, et al. Cell cycle-dependent phosphorylation regulates RECQL4 pathway choice and ubiquitination in DNA double-strand break repair. Nat Commun. 2017;8(1):2039.

Speers C, Zhao S, Liu M, Bartelink H, Pierce LJ, Feng FY. Development and Validation of a Novel Radiosensitivity Signature in Human Breast Cancer. Clin Cancer Res. 2015;21(16):3667–77.

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.

Servant N, Bollet MA, Halfwerk H, Bleakley K, Kreike B, Jacob L, et al. Search for a gene expression signature of breast cancer local recurrence in young women. Clin Cancer Res. 2012;18(6):1704–15.

Goodwin JF, Knudsen KE. Beyond DNA repair: DNA-PK function in cancer. Cancer Discov. 2014;4(10):1126–39.

Yue X, Bai C, Xie D, Ma T, Zhou PK. DNA-PKcs: A Multi-Faceted Player in DNA Damage Response. Front Genet. 2020;11:607428.

Mao Z, Bozzella M, Seluanov A, Gorbunova V. DNA repair by nonhomologous end joining and homologous recombination during cell cycle in human cells. Cell Cycle. 2008;7(18):2902–6.

Dong J, Ren Y, Zhang T, Wang Z, Ling CC, Li GC, et al. Inactivation of DNA-PK by knockdown DNA-PKcs or NU7441 impairs non-homologous end-joining of radiation-induced double strand break repair. Oncol Rep. 2018;39(3):912–20.

CAS   PubMed   PubMed Central   Google Scholar  

Chang HHY, Pannunzio NR, Adachi N, Lieber MR. Non-homologous DNA end joining and alternative pathways to double-strand break repair. Nat Rev Mol Cell Biol. 2017;18(8):495–506.

Kurimasa A, Kumano S, Boubnov NV, Story MD, Tung CS, Peterson SR, et al. Requirement for the kinase activity of human DNA-dependent protein kinase catalytic subunit in DNA strand break rejoining. Mol Cell Biol. 1999;19(5):3877–84.

Neal JA, Sugiman-Marangos S, VanderVere-Carozza P, Wagner M, Turchi J, Lees-Miller SP, et al. Unraveling the complexities of DNA-dependent protein kinase autophosphorylation. Mol Cell Biol. 2014;34(12):2162–75.

Cui X, Yu Y, Gupta S, Cho YM, Lees-Miller SP, Meek K. Autophosphorylation of DNA-dependent protein kinase regulates DNA end processing and may also alter double-strand break repair pathway choice. Mol Cell Biol. 2005;25(24):10842–52.

Chan DW, Chen BP, Prithivirajsingh S, Kurimasa A, Story MD, Qin J, et al. Autophosphorylation of the DNA-dependent protein kinase catalytic subunit is required for rejoining of DNA double-strand breaks. Genes Dev. 2002;16(18):2333–8.

Block WD, Yu Y, Merkle D, Gifford JL, Ding Q, Meek K, et al. Autophosphorylation-dependent remodeling of the DNA-dependent protein kinase catalytic subunit regulates ligation of DNA ends. Nucleic Acids Res. 2004;32(14):4351–7.

Price BD, D’Andrea AD. Chromatin remodeling at DNA double-strand breaks. Cell. 2013;152(6):1344–54.

Harper JW, Elledge SJ. The DNA damage response: ten years after. Mol Cell. 2007;28(5):739–45.

Zagami P, Carey LA. Triple negative breast cancer: Pitfalls and progress. NPJ Breast Cancer. 2022;8(1):95.

Abdulkarim BS, Cuartero J, Hanson J, Deschenes J, Lesniak D, Sabri S. Increased risk of locoregional recurrence for women with T1–2N0 triple-negative breast cancer treated with modified radical mastectomy without adjuvant radiation therapy compared with breast-conserving therapy. J Clin Oncol. 2011;29(21):2852–8.

Gedeon T, Bokes P. Delayed protein synthesis reduces the correlation between mRNA and protein fluctuations. Biophys J. 2012;103(3):377–85.

Liu Y, Beyer A, Aebersold R. On the Dependency of Cellular Protein Levels on mRNA Abundance. Cell. 2016;165(3):535–50.

Greenbaum D, Colangelo C, Williams K, Gerstein M. Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 2003;4(9):117.

Nie L, Wu G, Zhang W. Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: a multiple regression to identify sources of variations. Biochem Biophys Res Commun. 2006;339(2):603–10.

Tian Q, Stepaniants SB, Mao M, Weng L, Feetham MC, Doyle MJ, et al. Integrated genomic and proteomic analyses of gene expression in Mammalian cells. Mol Cell Proteomics. 2004;3(10):960–9.

Siemeister G, Mengel A, Fernandez-Montalvan AE, Bone W, Schroder J, Zitzmann-Kolbe S, et al. Inhibition of BUB1 Kinase by BAY 1816032 Sensitizes Tumor Cells toward Taxanes, ATR, and PARP Inhibitors In Vitro and In Vivo. Clin Cancer Res. 2019;25(4):1404–14.

Griesbach E, Schlackow M, Marzluff WF, Proudfoot NJ. Dual RNA 3’-end processing of H2A.X messenger RNA maintains DNA damage repair throughout the cell cycle. Nat Commun. 2021;12(1):359.

Morales AG, Pezuk JA, Brassesco MS, de Oliveira JC, de Paula Queiroz RG, Machado HR, et al. BUB1 and BUBR1 inhibition decreases proliferation and colony formation, and enhances radiation sensitivity in pediatric glioblastoma cells. Childs Nerv Syst. 2013;29(12):2241–8.

Chandler BC, Moubadder L, Ritter CL, Liu M, Cameron M, Wilder-Romans K, Zhang A, Pesch AM, Michmerhuizen AR, Hirsh N, Androsiglio M, Ward T, Olsen E, Niknafs YS, Merajver S, Thomas DG, Brown PH, Lawrence TS, Nyati S, Pierce LJ, Chinnaiyan A, Speers C. TTK inhibition radiosensitizes basal-like breast cancer through impaired homologous recombination. J Clin Invest. 2020;130(2):958–73.

Fok JHL, Ramos-Montoya A, Vazquez-Chantada M, Wijnhoven PWG, Follia V, James N, et al. AZD7648 is a potent and selective DNA-PK inhibitor that enhances radiation, chemotherapy and olaparib activity. Nat Commun. 2019;10(1):5065.

Mohiuddin IS, Kang MH. DNA-PK as an Emerging Therapeutic Target in Cancer. Front Oncol. 2019;9:635.

Jiang W, Crowe JL, Liu X, Nakajima S, Wang Y, Li C, et al. Differential phosphorylation of DNA-PKcs regulates the interplay between end-processing and end-ligation during nonhomologous end-joining. Mol Cell. 2015;58(1):172–85.

Quanz M, Chassoux D, Berthault N, Agrario C, Sun JS, Dutreix M. Hyperactivation of DNA-PK by double-strand break mimicking molecules disorganizes DNA damage response. PLoS One. 2009;4(7):e6298.

Wang Y, Xu H, Liu T, Huang M, Butter PP, Li C, et al. Temporal DNA-PK activation drives genomic instability and therapy resistance in glioma stem cells. JCI Insight. 2018;3(3):e98096.

Ho SR, Mahanic CS, Lee YJ, Lin WC. RNF144A, an E3 ubiquitin ligase for DNA-PKcs, promotes apoptosis during DNA damage. Proc Natl Acad Sci USA. 2014;111(26):E2646-55.

Heo J, Park YJ, Kim Y, Lee HS, Kim J, Kwon SH, et al. Mitochondrial E3 ligase MARCH5 is a safeguard against DNA-PKcs-mediated immune signaling in mitochondria-damaged cells. Cell Death Dis. 2023;14(12):788.

Feng M, Wang Y, Bi L, Zhang P, Wang H, Zhao Z, et al. CRL4A(DTL) degrades DNA-PKcs to modulate NHEJ repair and induce genomic instability and subsequent malignant transformation. Oncogene. 2021;40(11):2096–111.

Elango R, Vishnubalaji R, Shaath H, Alajez NM. Transcriptional alterations of protein coding and noncoding RNAs in triple negative breast cancer in response to DNA methyltransferases inhibition. Cancer Cell Int. 2021;21(1):515.

Ziv Y, Bielopolski D, Galanty Y, Lukas C, Taya Y, Schultz DC, et al. Chromatin relaxation in response to DNA double-strand breaks is modulated by a novel ATM- and KAP-1 dependent pathway. Nat Cell Biol. 2006;8(8):870–6.

Nakamura AJ, Rao VA, Pommier Y, Bonner WM. The complexity of phosphorylated H2AX foci formation and DNA repair assembly at DNA double-strand breaks. Cell Cycle. 2010;9(2):389–97.

Neal JA, Dang V, Douglas P, Wold MS, Lees-Miller SP, Meek K. Inhibition of homologous recombination by DNA-dependent protein kinase requires kinase activity, is titratable, and is modulated by autophosphorylation. Mol Cell Biol. 2011;31(8):1719–33.

Neal JA, Meek K. Choosing the right path: does DNA-PK help make the decision? Mutat Res. 2011;711(1–2):73–86.

Blackford AN, Jackson SP. ATM, ATR, and DNA-PK: The Trinity at the Heart of the DNA Damage Response. Mol Cell. 2017;66(6):801–17.

Lu H, Zhang Q, Laverty DJ, Puncheon AC, Augustine MM, Williams GJ, et al. ATM phosphorylates the FATC domain of DNA-PKcs at threonine 4102 to promote non-homologous end joining. Nucleic Acids Res. 2023;51(13):6770–83.

Yuan B, Xu Y, Woo JH, Wang Y, Bae YK, Yoon DS, et al. Increased expression of mitotic checkpoint genes in breast cancer cells with chromosomal instability. Clin Cancer Res. 2006;12(2):405–10.

Dai H, van’t Veer L, Lamb J, He YD, Mao M, Fine BM, et al. A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients. Cancer Res. 2005;65(10):4059–66.

Chen DL, Cai JH, Wang CCN. Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis. Genes (Basel). 2022;13(5):902.

Takagi K, Miki Y, Shibahara Y, Nakamura Y, Ebata A, Watanabe M, et al. BUB1 immunolocalization in breast carcinoma: its nuclear localization as a potent prognostic factor of the patients. Horm Cancer. 2013;4(2):92–102.

Meek K, Douglas P, Cui X, Ding Q, Lees-Miller SP. trans Autophosphorylation at DNA-dependent protein kinase’s two major autophosphorylation site clusters facilitates end processing but not end joining. Mol Cell Biol. 2007;27(10):3881–90.

Uematsu N, Weterings E, Yano K, Morotomi-Yano K, Jakob B, Taucher-Scholz G, et al. Autophosphorylation of DNA-PKCS regulates its dynamics at DNA double-strand breaks. J Cell Biol. 2007;177(2):219–29.

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Acknowledgements

The authors thank Grahm Valadie for help with animal treatment using SARRP and Katheryn Meek (MSU) with data interpretation. We thank Transgenics and CRISPR (TGEF) core at MSU for help with BUB1 gRNA design and Histology core-HFH for immunohistological staining. We thank Pin Li and Sunita Ghosh from Public Health Sciences for statistical analyses. Grant support to SN ((NIH/NCI R21 CA252010-01A1), Henry Ford Cancer Institute (HFCI) and Henry Ford Health Research Administration Start Up grant, HFH Proposal Development Award, HFH Near Miss Award, HFH Radiation Oncology Start Up grant, and Game on Cancer award), HFCI Translational Oncology Postdoctoral Fellowship to SS.

This work was supported by NCI R21 (1R21CA252010-01A1), HFHS Research Administration Start up, HFHS Proposal Development Award, HFHS-Radiation Oncology Start Up, and Game on Cancer award to SN. We also thank HFCI for providing a Translational Oncology Postdoctoral Fellowship to SS.

Author information

Sushmitha Sriramulu and Shivani Thoidingjam contributed equally to this work.

Authors and Affiliations

Department of Radiation Oncology, Henry Ford Cancer Institute, Henry Ford Health, 1 Ford Place, Detroit, 5D-42, MI-48202, USA

Sushmitha Sriramulu, Shivani Thoidingjam, Farzan Siddiqui, Stephen L. Brown, Benjamin Movsas, Eleanor Walker & Shyam Nyati

Department of Radiation Oncology, UT Southwestern Medical School, Dallas, TX-75390, USA

Wei-Min Chen & Anthony J. Davis

Department of Surgical Pathology, Henry Ford Cancer Institute, Henry Ford Health, Detroit, MI-48202, USA

Oudai Hassan

Henry Ford Health + Michigan State University Health Sciences, Detroit, MI-48202, USA

Farzan Siddiqui, Stephen L. Brown, Benjamin Movsas, Eleanor Walker & Shyam Nyati

Department of Radiology, Michigan State University, East Lansing, MI-48824, USA

Department of Radiation Oncology, University of Michigan, Ann Arbor, MI-48109, USA

Michael D. Green & Corey Speers

Department of Radiation Oncology, UH Seidman Cancer Center, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, OH-44106, USA

Corey Speers

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Contributions

SN conceived and designed the study; SS and ST performed the experiments; WMC performed laser micro irradiation experiments, CS performed the bioinformatic analysis; SN, SS, and ST wrote the original manuscript; OH analyzed the immunohistological staining of BUB1 on TMA slides; SS, ST, WMC, OH, FS, SLB, BM, MDG, AJD, CS, EW, and SN interpreted the data; SS, ST, WMC, OH, FS, SLB, BM, MDG, AJD, CS, EW, and SN revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Shyam Nyati .

Ethics declarations

Ethics approval and consent to participate.

Experimental animals were housed and handled in accordance with protocols approved by IACUC of Henry Ford Health (protocol # 00001298).

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Not applicable.

Competing interests

SS, ST, WMC, OH, SLB, MDG, AJD, SN: No competing interests, FS: Varian Medical Systems Inc - Honorarium and travel reimbursement for lectures and talks, Varian Noona – Member of Medical Advisory Board - Honorarium (no direct conflict), BM: Research support from Varian, ViewRay, and Philips (no direct conflict), CS: Exact Sciences (paid consultant - no direct conflict), EW: Genentech research support for clinical trials.

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Supplementary Information

13046_2024_3086_moesm1_esm.pdf.

Additional file 1: Table S1. List of mutated genes in the TNBC cell lines. Table S2. Guide RNA (gRNA) sequences used to knock out BUB1, primer sequences for PCR amplification of BUB1-edited section, and primer sequence for Sanger sequencing. Table S3. List of antibodies used for Western Blotting/Immunohistochemical /Immunofluorescence studies. Table S4. Primer sequences used in quantitative PCR (qRT-PCR) analysis. Fig. S5. Clonogenic assays using BUB1 siRNA and RT in (A) MDA-MB-468, (B) BT-549, (C) T-47D cell lines. PRKDC siRNA is used as a positive control. Fig. S6. (A) CRISPR-CAS9 RNP transfection method was utilized to knock out BUB1 (B) BUB1 knockouts were confirmed through Immunoblotting in SUM159, and MDA-MB-231 cell lines followed by (C) PCR amplification and (D) Sanger Sequencing to further validate the BUB1 KO’s (E) Sanger Sequencing chromatograms of SUM159 BUB1 KO #48 and #18, and MDA-MB-231 KO #12 and #15. Fig. S7. (A) Initial radiation dose-response studies in SUM159 tumor xenograft in CB17 SCID mice. SUM159 xenograft mammary fat pad tumors were conformally irradiated at 2.5 GyX8, 5 Gy X3 or 10 GyX1 by SARRP (light blue curves). Additionally, mice were treated with a BUB1 inhibitor (25 mg/kg, orally, twice daily, 5 days/week for 4 weeks) along with radiation (red curves). (B) A spaghetti plot for animal body weight change during the treatment. Fig. S8. Immunofluorescence studies using BUB1 siRNA and RT (16 h time point) in (a) SUM159 and (b) MDA-MB-231 cell lines. Fig. S9. qRT-PCR of NHEJ pathway related genes in MDA-MB-231 cell line with BUB1i (top panel) and BUB1 CRISPR-KO #12. Fig. S10. Effect of BUB1 inhibition with IR on DNAPKcs phosphorylation using Immunoblotting in (A) MDA-MB-468 cell line, and (B) shown at different time points up to 2 h. Fig. S11. The effect of BUB1 inhibitor (BAY1816032) on BUB1 protein levels in normal mammary epithelial cell line MCF 10A. The cells were treated for 1 hour with the same doses of BUB1i that were used for the colony formation assays (250 nM, 500 nM and 1000 nM). Fig. S12. mRNA expression plots showing correlation of BUB1 vs. NHEJ pathway-related genes, apoptotic, and proliferation genes in Breast cancer (METABRIC, 2509 samples) from cBIOPORTAL. Fig. S13. qRT-PCR of apoptotic and proliferation genes in MDA-MB-231 cell line with (A) BUB1i and (B) BUB1 CRISPR-KO #12.

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Sriramulu, S., Thoidingjam, S., Chen, WM. et al. BUB1 regulates non-homologous end joining pathway to mediate radioresistance in triple-negative breast cancer. J Exp Clin Cancer Res 43 , 163 (2024). https://doi.org/10.1186/s13046-024-03086-9

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Received : 14 February 2024

Accepted : 30 May 2024

Published : 11 June 2024

DOI : https://doi.org/10.1186/s13046-024-03086-9

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