types of experimental research studies

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• v.9(4); Oct-Dec 2018

Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

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

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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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 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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Experimental Research: What it is + Types of designs

Any research conducted under scientifically acceptable conditions uses experimental methods. The success of experimental studies hinges on researchers confirming the change of a variable is based solely on the manipulation of the constant variable. The research should establish a notable cause and effect.

What is Experimental Research?

Experimental research is a study conducted with a scientific approach using two sets of variables. The first set acts as a constant, which you use to measure the differences of the second set. Quantitative research methods , for example, are experimental.

If you don’t have enough data to support your decisions, you must first determine the facts. This research gathers the data necessary to help you make better decisions.

You can conduct experimental research in the following situations:

• Time is a vital factor in establishing a relationship between cause and effect.
• Invariable behavior between cause and effect.
• You wish to understand the importance of cause and effect.

Experimental Research Design Types

The classic experimental design definition is: “The methods used to collect data in experimental studies.”

There are three primary types of experimental design:

• Pre-experimental research design
• True experimental research design
• Quasi-experimental research design

The way you classify research subjects based on conditions or groups determines the type of research design  you should use.

0 1. Pre-Experimental Design

A group, or various groups, are kept under observation after implementing cause and effect factors. You’ll conduct this research to understand whether further investigation is necessary for these particular groups.

You can break down pre-experimental research further into three types:

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

0 2. True Experimental Design

It relies on statistical analysis to prove or disprove a hypothesis, making it the most accurate form of research. Of the types of experimental design, only true design can establish a cause-effect relationship within a group. In a true experiment, three factors need to be satisfied:

• There is a Control Group, which won’t be subject to changes, and an Experimental Group, which will experience the changed variables.
• A variable that can be manipulated by the researcher
• Random distribution

This experimental research method commonly occurs in the physical sciences.

0 3. Quasi-Experimental Design

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

Importance of Experimental Design

Experimental research is a powerful tool for understanding cause-and-effect relationships. It allows us to manipulate variables and observe the effects, which is crucial for understanding how different factors influence the outcome of a study.

But the importance of experimental research goes beyond that. It’s a critical method for many scientific and academic studies. It allows us to test theories, develop new products, and make groundbreaking discoveries.

For example, this research is essential for developing new drugs and medical treatments. Researchers can understand how a new drug works by manipulating dosage and administration variables and identifying potential side effects.

Similarly, experimental research is used in the field of psychology to test theories and understand human behavior. By manipulating variables such as stimuli, researchers can gain insights into how the brain works and identify new treatment options for mental health disorders.

It is also widely used in the field of education. It allows educators to test new teaching methods and identify what works best. By manipulating variables such as class size, teaching style, and curriculum, researchers can understand how students learn and identify new ways to improve educational outcomes.

In addition, experimental research is a powerful tool for businesses and organizations. By manipulating variables such as marketing strategies, product design, and customer service, companies can understand what works best and identify new opportunities for growth.

Advantages of Experimental Research

When talking about this research, we can think of human life. Babies do their own rudimentary experiments (such as putting objects in their mouths) to learn about the world around them, while older children and teens do experiments at school to learn more about science.

Ancient scientists used this research to prove that their hypotheses were correct. For example, Galileo Galilei and Antoine Lavoisier conducted various experiments to discover key concepts in physics and chemistry. The same is true of modern experts, who use this scientific method to see if new drugs are effective, discover treatments for diseases, and create new electronic devices (among others).

It’s vital to test new ideas or theories. Why put time, effort, and funding into something that may not work?

This research allows you to test your idea in a controlled environment before marketing. It also provides the best method to test your theory thanks to the following advantages:

• Researchers have a stronger hold over variables to obtain desired results.
• The subject or industry does not impact the effectiveness of experimental research. Any industry can implement it for research purposes.
• The results are specific.
• After analyzing the results, you can apply your findings to similar ideas or situations.
• You can identify the cause and effect of a hypothesis. Researchers can further analyze this relationship to determine more in-depth ideas.
• Experimental research makes an ideal starting point. The data you collect is a foundation for building more ideas and conducting more action research .

Whether you want to know how the public will react to a new product or if a certain food increases the chance of disease, experimental research is the best place to start. Begin your research by finding subjects using  QuestionPro Audience  and other tools today.

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Experimental Research

• First Online: 25 February 2021

Cite this chapter

• C. George Thomas 2  

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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Neag School of Education

Educational Research Basics by Del Siegle

Experimental research.

The major feature that distinguishes experimental research from other types of research is that the researcher manipulates the independent variable.  There are a number of experimental group designs in experimental research. Some of these qualify as experimental research, others do not.

• In true experimental research , the researcher not only manipulates the independent variable, he or she also randomly assigned individuals to the various treatment categories (i.e., control and treatment).
• In quasi experimental research , the researcher does not randomly assign subjects to treatment and control groups. In other words, the treatment is not distributed among participants randomly. In some cases, a researcher may randomly assigns one whole group to treatment and one whole group to control. In this case, quasi-experimental research involves using intact groups in an experiment, rather than assigning individuals at random to research conditions. (some researchers define this latter situation differently. For our course, we will allow this definition).
• In causal comparative ( ex post facto ) research, the groups are already formed. It does not meet the standards of an experiment because the independent variable in not manipulated.

The statistics by themselves have no meaning. They only take on meaning within the design of your study. If we just examine stats, bread can be deadly . The term validity is used three ways in research…

• I n the sampling unit, we learn about external validity (generalizability).
• I n the survey unit, we learn about instrument validity .
• In this unit, we learn about internal validity and external validity . Internal validity means that the differences that we were found between groups on the dependent variable in an experiment were directly related to what the researcher did to the independent variable, and not due to some other unintended variable (confounding variable). Simply stated, the question addressed by internal validity is “Was the study done well?” Once the researcher is satisfied that the study was done well and the independent variable caused the dependent variable (internal validity), then the research examines external validity (under what conditions [ecological] and with whom [population] can these results be replicated [Will I get the same results with a different group of people or under different circumstances?]). If a study is not internally valid, then considering external validity is a moot point (If the independent did not cause the dependent, then there is no point in applying the results [generalizing the results] to other situations.). Interestingly, as one tightens a study to control for treats to internal validity, one decreases the generalizability of the study (to whom and under what conditions one can generalize the results).

There are several common threats to internal validity in experimental research. They are described in our text.  I have review each below (this material is also included in the  PowerPoint Presentation on Experimental Research for this unit):

• Subject Characteristics (Selection Bias/Differential Selection) — The groups may have been different from the start. If you were testing instructional strategies to improve reading and one group enjoyed reading more than the other group, they may improve more in their reading because they enjoy it, rather than the instructional strategy you used.
• Loss of Subjects ( Mortality ) — All of the high or low scoring subject may have dropped out or were missing from one of the groups. If we collected posttest data on a day when the honor society was on field trip at the treatment school, the mean for the treatment group would probably be much lower than it really should have been.
• Location — Perhaps one group was at a disadvantage because of their location.  The city may have been demolishing a building next to one of the schools in our study and there are constant distractions which interferes with our treatment.
• Instrumentation Instrument Decay — The testing instruments may not be scores similarly. Perhaps the person grading the posttest is fatigued and pays less attention to the last set of papers reviewed. It may be that those papers are from one of our groups and will received different scores than the earlier group’s papers
• Data Collector Characteristics — The subjects of one group may react differently to the data collector than the other group. A male interviewing males and females about their attitudes toward a type of math instruction may not receive the same responses from females as a female interviewing females would.
• Data Collector Bias — The person collecting data my favors one group, or some characteristic some subject possess, over another. A principal who favors strict classroom management may rate students’ attention under different teaching conditions with a bias toward one of the teaching conditions.
• Testing — The act of taking a pretest or posttest may influence the results of the experiment. Suppose we were conducting a unit to increase student sensitivity to prejudice. As a pretest we have the control and treatment groups watch Shindler’s List and write a reaction essay. The pretest may have actually increased both groups’ sensitivity and we find that our treatment groups didn’t score any higher on a posttest given later than the control group did. If we hadn’t given the pretest, we might have seen differences in the groups at the end of the study.
• History — Something may happen at one site during our study that influences the results. Perhaps a classmate dies in a car accident at the control site for a study teaching children bike safety. The control group may actually demonstrate more concern about bike safety than the treatment group.
• Maturation –There may be natural changes in the subjects that can account for the changes found in a study. A critical thinking unit may appear more effective if it taught during a time when children are developing abstract reasoning.
• Hawthorne Effect — The subjects may respond differently just because they are being studied. The name comes from a classic study in which researchers were studying the effect of lighting on worker productivity. As the intensity of the factor lights increased, so did the work productivity. One researcher suggested that they reverse the treatment and lower the lights. The productivity of the workers continued to increase. It appears that being observed by the researchers was increasing productivity, not the intensity of the lights.
• John Henry Effect — One group may view that it is competition with the other group and may work harder than than they would under normal circumstances. This generally is applied to the control group “taking on” the treatment group. The terms refers to the classic story of John Henry laying railroad track.
• Resentful Demoralization of the Control Group — The control group may become discouraged because it is not receiving the special attention that is given to the treatment group. They may perform lower than usual because of this.
• Regression ( Statistical Regression) — A class that scores particularly low can be expected to score slightly higher just by chance. Likewise, a class that scores particularly high, will have a tendency to score slightly lower by chance. The change in these scores may have nothing to do with the treatment.
• Implementation –The treatment may not be implemented as intended. A study where teachers are asked to use student modeling techniques may not show positive results, not because modeling techniques don’t work, but because the teacher didn’t implement them or didn’t implement them as they were designed.
• Compensatory Equalization of Treatmen t — Someone may feel sorry for the control group because they are not receiving much attention and give them special treatment. For example, a researcher could be studying the effect of laptop computers on students’ attitudes toward math. The teacher feels sorry for the class that doesn’t have computers and sponsors a popcorn party during math class. The control group begins to develop a more positive attitude about mathematics.
• Experimental Treatment Diffusion — Sometimes the control group actually implements the treatment. If two different techniques are being tested in two different third grades in the same building, the teachers may share what they are doing. Unconsciously, the control may use of the techniques she or he learned from the treatment teacher.

When planning a study, it is important to consider the threats to interval validity as we finalize the study design. After we complete our study, we should reconsider each of the threats to internal validity as we review our data and draw conclusions.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

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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.

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Observational Research – Methods and Guide

Quantitative Research – Methods, Types and...

Qualitative Research Methods

Explanatory Research – Types, Methods, Guide

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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 (generalisability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments are conducted in field settings such as in a real organisation, and are 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 favourably 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 receiving 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 ensures that each unit in the population has a positive chance of being selected into the sample. Random assignment, however, is 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 (generalisability) 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 to regress toward the mean during a posttest 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 were 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

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.

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.

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

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.

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:

Due to the presence of covariates, the right statistical analysis of this design is a two-group analysis of covariance (ANCOVA). This design has all the advantages of posttest-only design, but with internal validity due to the controlling of covariates. Covariance designs can also be extended to pretest-posttest control group design.

Factorial designs

Two-group designs are inadequate if your research requires manipulation of two or more independent variables (treatments). In such cases, you would need four or higher-group designs. Such designs, quite popular in experimental research, are commonly called factorial designs. Each independent variable in this design is called a factor , and each subdivision of a factor is called a level . Factorial designs enable the researcher to examine not only the individual effect of each treatment on the dependent variables (called main effects), but also their joint effect (called interaction effects).

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 three hours/week of instructional time than for one and a half 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 randomised bocks design, Solomon four-group design, and switched replications design.

Randomised 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 the treatment group (receiving the same treatment) and the 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.

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.

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 organisational contexts where organisational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

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 organisation is used as the treatment group, while another section of the same class or a different organisation 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 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 impacted 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.

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 the treatment or control group based on a cut-off 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 standardised 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.

Because of the use of a cut-off score, it is possible that the observed results may be a function of the cut-off score rather than the treatment, which introduces a new threat to internal validity. However, using the cut-off 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 do 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.

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, say 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 is not available from the same subjects.

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.

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, often experimental research uses 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 artefact 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 in doubt, use tasks that are simple and familiar for the respondent sample rather 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 (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

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

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

Descriptive studies

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

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

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

Observational studies

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

Cross-sectional study:

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

Cohort study:

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

Case-Control Study:

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

Experimental Studies

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

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

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

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

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

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

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

References (pdf)

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

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

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

Very informative and easy understandable

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

Wow very erll explained and easy to understand

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

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

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

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

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

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

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

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

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

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

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

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

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

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

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

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Experimental Research: Definition, Types, Design, Examples

Appinio Research · 14.05.2024 · 31min read

Experimental research is a cornerstone of scientific inquiry, providing a systematic approach to understanding cause-and-effect relationships and advancing knowledge in various fields. At its core, experimental research involves manipulating variables, observing outcomes, and drawing conclusions based on empirical evidence. By controlling factors that could influence the outcome, researchers can isolate the effects of specific variables and make reliable inferences about their impact. This guide offers a step-by-step exploration of experimental research, covering key elements such as research design, data collection, analysis, and ethical considerations. Whether you're a novice researcher seeking to understand the basics or an experienced scientist looking to refine your experimental techniques, this guide will equip you with the knowledge and tools needed to conduct rigorous and insightful research.

What is Experimental Research?

Experimental research is a systematic approach to scientific inquiry that aims to investigate cause-and-effect relationships by manipulating independent variables and observing their effects on dependent variables. Experimental research primarily aims to test hypotheses, make predictions, and draw conclusions based on empirical evidence.

By controlling extraneous variables and randomizing participant assignment, researchers can isolate the effects of specific variables and establish causal relationships. Experimental research is characterized by its rigorous methodology, emphasis on objectivity, and reliance on empirical data to support conclusions.

Importance of Experimental Research

• Establishing Cause-and-Effect Relationships : Experimental research allows researchers to establish causal relationships between variables by systematically manipulating independent variables and observing their effects on dependent variables. This provides valuable insights into the underlying mechanisms driving phenomena and informs theory development.
• Testing Hypotheses and Making Predictions : Experimental research provides a structured framework for testing hypotheses and predicting the relationship between variables . By systematically manipulating variables and controlling for confounding factors, researchers can empirically test the validity of their hypotheses and refine theoretical models.
• Informing Evidence-Based Practice : Experimental research generates empirical evidence that informs evidence-based practice in various fields, including healthcare, education, and business. Experimental research contributes to improving outcomes and informing decision-making in real-world settings by identifying effective interventions, treatments, and strategies.
• Driving Innovation and Advancement : Experimental research drives innovation and advancement by uncovering new insights, challenging existing assumptions, and pushing the boundaries of knowledge. Through rigorous experimentation and empirical validation, researchers can develop novel solutions to complex problems and contribute to the advancement of science and technology.
• Enhancing Research Rigor and Validity : Experimental research upholds high research rigor and validity standards by employing systematic methods, controlling for confounding variables, and ensuring replicability of findings. By adhering to rigorous methodology and ethical principles, experimental research produces reliable and credible evidence that withstands scrutiny and contributes to the cumulative body of knowledge.

Experimental research plays a pivotal role in advancing scientific understanding, informing evidence-based practice, and driving innovation across various disciplines. By systematically testing hypotheses, establishing causal relationships, and generating empirical evidence, experimental research contributes to the collective pursuit of knowledge and the improvement of society.

Understanding Experimental Design

Experimental design serves as the blueprint for your study, outlining how you'll manipulate variables and control factors to draw valid conclusions.

Experimental Design Components

Experimental design comprises several essential elements:

• Independent Variable (IV) : This is the variable manipulated by the researcher. It's what you change to observe its effect on the dependent variable. For example, in a study testing the impact of different study techniques on exam scores, the independent variable might be the study method (e.g., flashcards, reading, or practice quizzes).
• Dependent Variable (DV) : The dependent variable is what you measure to assess the effect of the independent variable. It's the outcome variable affected by the manipulation of the independent variable. In our study example, the dependent variable would be the exam scores.
• Control Variables : These factors could influence the outcome but are kept constant or controlled to isolate the effect of the independent variable. Controlling variables helps ensure that any observed changes in the dependent variable can be attributed to manipulating the independent variable rather than other factors.
• Experimental Group : This group receives the treatment or intervention being tested. It's exposed to the manipulated independent variable. In contrast, the control group does not receive the treatment and serves as a baseline for comparison.

Types of Experimental Designs

Experimental designs can vary based on the research question, the nature of the variables, and the desired level of control. Here are some common types:

• Between-Subjects Design : In this design, different groups of participants are exposed to varying levels of the independent variable. Each group represents a different experimental condition, and participants are only exposed to one condition. For instance, in a study comparing the effectiveness of two teaching methods, one group of students would use Method A, while another would use Method B.
• Within-Subjects Design : Also known as repeated measures design , this approach involves exposing the same group of participants to all levels of the independent variable. Participants serve as their own controls, and the order of conditions is typically counterbalanced to control for order effects. For example, participants might be tested on their reaction times under different lighting conditions, with the order of conditions randomized to eliminate any research bias .
• Mixed Designs : Mixed designs combine elements of both between-subjects and within-subjects designs. This allows researchers to examine both between-group differences and within-group changes over time. Mixed designs help study complex phenomena that involve multiple variables and temporal dynamics.

Factors Influencing Experimental Design Choices

Several factors influence the selection of an appropriate experimental design:

• Research Question : The nature of your research question will guide your choice of experimental design. Some questions may be better suited to between-subjects designs, while others may require a within-subjects approach.
• Variables : Consider the number and type of variables involved in your study. A factorial design might be appropriate if you're interested in exploring multiple factors simultaneously. Conversely, if you're focused on investigating the effects of a single variable, a simpler design may suffice.
• Practical Considerations : Practical constraints such as time, resources, and access to participants can impact your choice of experimental design. Depending on your study's specific requirements, some designs may be more feasible or cost-effective   than others .
• Ethical Considerations : Ethical concerns, such as the potential risks to participants or the need to minimize harm, should also inform your experimental design choices. Ensure that your design adheres to ethical guidelines and safeguards the rights and well-being of participants.

By carefully considering these factors and selecting an appropriate experimental design, you can ensure that your study is well-designed and capable of yielding meaningful insights.

Experimental Research Elements

When conducting experimental research, understanding the key elements is crucial for designing and executing a robust study. Let's explore each of these elements in detail to ensure your experiment is well-planned and executed effectively.

Independent and Dependent Variables

In experimental research, the independent variable (IV) is the factor that the researcher manipulates or controls, while the dependent variable (DV) is the measured outcome or response. The independent variable is what you change in the experiment to observe its effect on the dependent variable.

For example, in a study investigating the effect of different fertilizers on plant growth, the type of fertilizer used would be the independent variable, while the plant growth (height, number of leaves, etc.) would be the dependent variable.

Control Groups and Experimental Groups

Control groups and experimental groups are essential components of experimental design. The control group serves as a baseline for comparison and does not receive the treatment or intervention being studied. Its purpose is to provide a reference point to assess the effects of the independent variable.

In contrast, the experimental group receives the treatment or intervention and is used to measure the impact of the independent variable. For example, in a drug trial, the control group would receive a placebo, while the experimental group would receive the actual medication.

Randomization and Random Sampling

Randomization is the process of randomly assigning participants to different experimental conditions to minimize biases and ensure that each participant has an equal chance of being assigned to any condition. Randomization helps control for extraneous variables and increases the study's internal validity .

Random sampling, on the other hand, involves selecting a representative sample from the population of interest to generalize the findings to the broader population. Random sampling ensures that each member of the population has an equal chance of being included in the sample, reducing the risk of sampling bias .

Replication and Reliability

Replication involves repeating the experiment to confirm the results and assess the reliability of the findings . It is essential for ensuring the validity of scientific findings and building confidence in the robustness of the results. A study that can be replicated consistently across different settings and by various researchers is considered more reliable. Researchers should strive to design experiments that are easily replicable and transparently report their methods to facilitate replication by others.

Validity: Internal, External, Construct, and Statistical Conclusion Validity

Validity refers to the degree to which an experiment measures what it intends to measure and the extent to which the results can be generalized to other populations or contexts. There are several types of validity that researchers should consider:

• Internal Validity : Internal validity refers to the extent to which the study accurately assesses the causal relationship between variables. Internal validity is threatened by factors such as confounding variables, selection bias, and experimenter effects. Researchers can enhance internal validity through careful experimental design and control procedures.
• External Validity : External validity refers to the extent to which the study's findings can be generalized to other populations or settings. External validity is influenced by factors such as the representativeness of the sample and the ecological validity of the experimental conditions. Researchers should consider the relevance and applicability of their findings to real-world situations.
• Construct Validity : Construct validity refers to the degree to which the study accurately measures the theoretical constructs of interest. Construct validity is concerned with whether the operational definitions of the variables align with the underlying theoretical concepts. Researchers can establish construct validity through careful measurement selection and validation procedures.
• Statistical Conclusion Validity : Statistical conclusion validity refers to the accuracy of the statistical analyses and conclusions drawn from the data. It ensures that the statistical tests used are appropriate for the data and that the conclusions drawn are warranted. Researchers should use robust statistical methods and report effect sizes and confidence intervals to enhance statistical conclusion validity.

By addressing these elements of experimental research and ensuring the validity and reliability of your study, you can conduct research that contributes meaningfully to the advancement of knowledge in your field.

How to Conduct Experimental Research?

Embarking on an experimental research journey involves a series of well-defined phases, each crucial for the success of your study. Let's explore the pre-experimental, experimental, and post-experimental phases to ensure you're equipped to conduct rigorous and insightful research.

Pre-Experimental Phase

The pre-experimental phase lays the foundation for your study, setting the stage for what's to come. Here's what you need to do:

• Formulating Research Questions and Hypotheses : Start by clearly defining your research questions and formulating testable hypotheses. Your research questions should be specific, relevant, and aligned with your research objectives. Hypotheses provide a framework for testing the relationships between variables and making predictions about the outcomes of your study.
• Reviewing Literature and Establishing Theoretical Framework : Dive into existing literature relevant to your research topic and establish a solid theoretical framework. Literature review helps you understand the current state of knowledge, identify research gaps, and build upon existing theories. A well-defined theoretical framework provides a conceptual basis for your study and guides your research design and analysis.

Experimental Phase

The experimental phase is where the magic happens – it's time to put your hypotheses to the test and gather data. Here's what you need to consider:

• Participant Recruitment and Sampling Techniques : Carefully recruit participants for your study using appropriate sampling techniques . The sample should be representative of the population you're studying to ensure the generalizability of your findings. Consider factors such as sample size , demographics , and inclusion criteria when recruiting participants.
• Implementing Experimental Procedures : Once you've recruited participants, it's time to implement your experimental procedures. Clearly outline the experimental protocol, including instructions for participants, procedures for administering treatments or interventions, and measures for controlling extraneous variables. Standardize your procedures to ensure consistency across participants and minimize sources of bias.
• Data Collection and Measurement : Collect data using reliable and valid measurement instruments. Depending on your research questions and variables of interest, data collection methods may include surveys , observations, physiological measurements, or experimental tasks. Ensure that your data collection procedures are ethical, respectful of participants' rights, and designed to minimize errors and biases.

Post-Experimental Phase

In the post-experimental phase, you make sense of your data, draw conclusions, and communicate your findings  to the world . Here's what you need to do:

• Data Analysis Techniques : Analyze your data using appropriate statistical techniques . Choose methods that are aligned with your research design and hypotheses. Standard statistical analyses include descriptive statistics, inferential statistics (e.g., t-tests, ANOVA), regression analysis , and correlation analysis. Interpret your findings in the context of your research questions and theoretical framework.
• Interpreting Results and Drawing Conclusions : Once you've analyzed your data, interpret the results and draw conclusions. Discuss the implications of your findings, including any theoretical, practical, or real-world implications. Consider alternative explanations and limitations of your study and propose avenues for future research. Be transparent about the strengths and weaknesses of your study to enhance the credibility of your conclusions.
• Reporting Findings : Finally, communicate your findings through research reports, academic papers, or presentations. Follow standard formatting guidelines and adhere to ethical standards for research reporting. Clearly articulate your research objectives, methods, results, and conclusions. Consider your target audience and choose appropriate channels for disseminating your findings to maximize impact and reach.

By meticulously planning and executing each experimental research phase, you can generate valuable insights, advance knowledge in your field, and contribute to scientific progress.

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Experimental Research Examples

Understanding how experimental research is applied in various contexts can provide valuable insights into its practical significance and effectiveness. Here are some examples illustrating the application of experimental research in different domains:

Market Research

Experimental studies are crucial in market research in testing hypotheses, evaluating marketing strategies, and understanding consumer behavior . For example, a company may conduct an experiment to determine the most effective advertising message for a new product. Participants could be exposed to different versions of an advertisement, each emphasizing different product features or appeals.

By measuring variables such as brand recall, purchase intent, and brand perception, researchers can assess the impact of each advertising message and identify the most persuasive approach.

Software as a Service (SaaS)

In the SaaS industry, experimental research is often used to optimize user interfaces, features, and pricing models to enhance user experience and drive engagement. For instance, a SaaS company may conduct A/B tests to compare two versions of its software interface, each with a different layout or navigation structure.

Researchers can identify design elements that lead to higher user satisfaction and retention by tracking user interactions, conversion rates, and customer feedback . Experimental research also enables SaaS companies to test new product features or pricing strategies before full-scale implementation, minimizing risks and maximizing return on investment.

Business Management

Experimental research is increasingly utilized in business management to inform decision-making, improve organizational processes, and drive innovation. For example, a business may conduct an experiment to evaluate the effectiveness of a new training program on employee productivity. Participants could be randomly assigned to either receive the training or serve as a control group.

By measuring performance metrics such as sales revenue, customer satisfaction, and employee turnover, researchers can assess the training program's impact and determine its return on investment. Experimental research in business management provides empirical evidence to support strategic initiatives and optimize resource allocation.

In healthcare , experimental research is instrumental in testing new treatments, interventions, and healthcare delivery models to improve patient outcomes and quality of care. For instance, a clinical trial may be conducted to evaluate the efficacy of a new drug in treating a specific medical condition. Participants are randomly assigned to either receive the experimental drug or a placebo, and their health outcomes are monitored over time.

By comparing the effectiveness of the treatment and placebo groups, researchers can determine the drug's efficacy, safety profile, and potential side effects. Experimental research in healthcare informs evidence-based practice and drives advancements in medical science and patient care.

These examples illustrate the versatility and applicability of experimental research across diverse domains, demonstrating its value in generating actionable insights, informing decision-making, and driving innovation. Whether in market research or healthcare, experimental research provides a rigorous and systematic approach to testing hypotheses, evaluating interventions, and advancing knowledge.

Experimental Research Challenges

Even with careful planning and execution, experimental research can present various challenges. Understanding these challenges and implementing effective solutions is crucial for ensuring the validity and reliability of your study. Here are some common challenges and strategies for addressing them.

Sample Size and Statistical Power

Challenge : Inadequate sample size can limit your study's generalizability and statistical power, making it difficult to detect meaningful effects. Small sample sizes increase the risk of Type II errors (false negatives) and reduce the reliability of your findings.

Solution : Increase your sample size to improve statistical power and enhance the robustness of your results. Conduct a power analysis before starting your study to determine the minimum sample size required to detect the effects of interest with sufficient power. Consider factors such as effect size, alpha level, and desired power when calculating sample size requirements. Additionally, consider using techniques such as bootstrapping or resampling to augment small sample sizes and improve the stability of your estimates.

To enhance the reliability of your experimental research findings, you can leverage our Sample Size Calculator . By determining the optimal sample size based on your desired margin of error, confidence level, and standard deviation, you can ensure the representativeness of your survey results. Don't let inadequate sample sizes hinder the validity of your study and unlock the power of precise research planning!

Confounding Variables and Bias

Challenge : Confounding variables are extraneous factors that co-vary with the independent variable and can distort the relationship between the independent and dependent variables. Confounding variables threaten the internal validity of your study and can lead to erroneous conclusions.

Solution : Implement control measures to minimize the influence of confounding variables on your results. Random assignment of participants to experimental conditions helps distribute confounding variables evenly across groups, reducing their impact on the dependent variable. Additionally, consider using matching or blocking techniques to ensure that groups are comparable on relevant variables. Conduct sensitivity analyses to assess the robustness of your findings to potential confounders and explore alternative explanations for your results.

Researcher Effects and Experimenter Bias

Challenge : Researcher effects and experimenter bias occur when the experimenter's expectations or actions inadvertently influence the study's outcomes. This bias can manifest through subtle cues, unintentional behaviors, or unconscious biases , leading to invalid conclusions.

Solution : Implement double-blind procedures whenever possible to mitigate researcher effects and experimenter bias. Double-blind designs conceal information about the experimental conditions from both the participants and the experimenters, minimizing the potential for bias. Standardize experimental procedures and instructions to ensure consistency across conditions and minimize experimenter variability. Additionally, consider using objective outcome measures or automated data collection procedures to reduce the influence of experimenter bias on subjective assessments.

External Validity and Generalizability

Challenge : External validity refers to the extent to which your study's findings can be generalized to other populations, settings, or conditions. Limited external validity restricts the applicability of your results and may hinder their relevance to real-world contexts.

Solution : Enhance external validity by designing studies closely resembling real-world conditions and populations of interest. Consider using diverse samples  that represent  the target population's demographic, cultural, and ecological variability. Conduct replication studies in different contexts or with different populations to assess the robustness and generalizability of your findings. Additionally, consider conducting meta-analyses or systematic reviews to synthesize evidence from multiple studies and enhance the external validity of your conclusions.

By proactively addressing these challenges and implementing effective solutions, you can strengthen the validity, reliability, and impact of your experimental research. Remember to remain vigilant for potential pitfalls throughout the research process and adapt your strategies as needed to ensure the integrity of your findings.

Advanced Topics in Experimental Research

As you delve deeper into experimental research, you'll encounter advanced topics and methodologies that offer greater complexity and nuance.

Quasi-Experimental Designs

Quasi-experimental designs resemble true experiments but lack random assignment to experimental conditions. They are often used when random assignment is impractical, unethical, or impossible. Quasi-experimental designs allow researchers to investigate cause-and-effect relationships in real-world settings where strict experimental control is challenging. Common examples include:

• Non-Equivalent Groups Design : This design compares two or more groups that were not created through random assignment. While similar to between-subjects designs, non-equivalent group designs lack the random assignment of participants, increasing the risk of confounding variables.
• Interrupted Time Series Design : In this design, multiple measurements are taken over time before and after an intervention is introduced. Changes in the dependent variable are assessed over time, allowing researchers to infer the impact of the intervention.
• Regression Discontinuity Design : This design involves assigning participants to different groups based on a cutoff score on a continuous variable. Participants just above and below the cutoff are treated as if they were randomly assigned to different conditions, allowing researchers to estimate causal effects.

Quasi-experimental designs offer valuable insights into real-world phenomena but require careful consideration of potential confounding variables and limitations inherent to non-random assignment.

Factorial Designs

Factorial designs involve manipulating two or more independent variables simultaneously to examine their main effects and interactions. By systematically varying multiple factors, factorial designs allow researchers to explore complex relationships between variables and identify how they interact to influence outcomes. Common types of factorial designs include:

• 2x2 Factorial Design : This design manipulates two independent variables, each with two levels. It allows researchers to examine the main effects of each variable as well as any interaction between them.
• Mixed Factorial Design : In this design, one independent variable is manipulated between subjects, while another is manipulated within subjects. Mixed factorial designs enable researchers to investigate both between-subjects and within-subjects effects simultaneously.

Factorial designs provide a comprehensive understanding of how multiple factors contribute to outcomes and offer greater statistical efficiency compared to studying variables in isolation.

Longitudinal and Cross-Sectional Studies

Longitudinal studies involve collecting data from the same participants over an extended period, allowing researchers to observe changes and trajectories over time. Cross-sectional studies , on the other hand, involve collecting data from different participants at a single point in time, providing a snapshot of the population at that moment. Both longitudinal and cross-sectional studies offer unique advantages and challenges:

• Longitudinal Studies : Longitudinal designs allow researchers to examine developmental processes, track changes over time, and identify causal relationships. However, longitudinal studies require long-term commitment, are susceptible to attrition and dropout, and may be subject to practice effects and cohort effects.
• Cross-Sectional Studies : Cross-sectional designs are relatively quick and cost-effective, provide a snapshot of population characteristics, and allow for comparisons across different groups. However, cross-sectional studies cannot assess changes over time or establish causal relationships between variables.

Researchers should carefully consider the research question, objectives, and constraints when choosing between longitudinal and cross-sectional designs.

Meta-Analysis and Systematic Reviews

Meta-analysis and systematic reviews are quantitative methods used to synthesize findings from multiple studies and draw robust conclusions. These methods offer several advantages:

• Meta-Analysis : Meta-analysis combines the results of multiple studies using statistical techniques to estimate overall effect sizes and assess the consistency of findings across studies. Meta-analysis increases statistical power, enhances generalizability, and provides more precise estimates of effect sizes.
• Systematic Reviews : Systematic reviews involve systematically searching, appraising, and synthesizing existing literature on a specific topic. Systematic reviews provide a comprehensive summary of the evidence, identify gaps and inconsistencies in the literature, and inform future research directions.

Meta-analysis and systematic reviews are valuable tools for evidence-based practice, guiding policy decisions, and advancing scientific knowledge by aggregating and synthesizing empirical evidence from diverse sources.

By exploring these advanced topics in experimental research, you can expand your methodological toolkit, tackle more complex research questions, and contribute to deeper insights and understanding in your field.

Experimental Research Ethical Considerations

When conducting experimental research, it's imperative to uphold ethical standards and prioritize the well-being and rights of participants. Here are some key ethical considerations to keep in mind throughout the research process:

• Informed Consent : Obtain informed consent from participants before they participate in your study. Ensure that participants understand the purpose of the study, the procedures involved, any potential risks or benefits, and their right to withdraw from the study at any time without penalty.
• Protection of Participants' Rights : Respect participants' autonomy, privacy, and confidentiality throughout the research process. Safeguard sensitive information and ensure that participants' identities are protected. Be transparent about how their data will be used and stored.
• Minimizing Harm and Risks : Take steps to mitigate any potential physical or psychological harm to participants. Conduct a risk assessment before starting your study and implement appropriate measures to reduce risks. Provide support services and resources for participants who may experience distress or adverse effects as a result of their participation.
• Confidentiality and Data Security : Protect participants' privacy and ensure the security of their data. Use encryption and secure storage methods to prevent unauthorized access to sensitive information. Anonymize data whenever possible to minimize the risk of data breaches or privacy violations.
• Avoiding Deception : Minimize the use of deception in your research and ensure that any deception is justified by the scientific objectives of the study. If deception is necessary, debrief participants fully at the end of the study and provide them with an opportunity to withdraw their data if they wish.
• Respecting Diversity and Cultural Sensitivity : Be mindful of participants' diverse backgrounds, cultural norms, and values. Avoid imposing your own cultural biases on participants and ensure that your research is conducted in a culturally sensitive manner. Seek input from diverse stakeholders to ensure your research is inclusive and respectful.
• Compliance with Ethical Guidelines : Familiarize yourself with relevant ethical guidelines and regulations governing research with human participants, such as those outlined by institutional review boards (IRBs) or ethics committees. Ensure that your research adheres to these guidelines and that any potential ethical concerns are addressed appropriately.
• Transparency and Openness : Be transparent about your research methods, procedures, and findings. Clearly communicate the purpose of your study, any potential risks or limitations, and how participants' data will be used. Share your research findings openly and responsibly, contributing to the collective body of knowledge in your field.

By prioritizing ethical considerations in your experimental research, you demonstrate integrity, respect, and responsibility as a researcher, fostering trust and credibility in the scientific community.

Conclusion for Experimental Research

Experimental research is a powerful tool for uncovering causal relationships and expanding our understanding of the world around us. By carefully designing experiments, collecting data, and analyzing results, researchers can make meaningful contributions to their fields and address pressing questions. However, conducting experimental research comes with responsibilities. Ethical considerations are paramount to ensure the well-being and rights of participants, as well as the integrity of the research process. Researchers can build trust and credibility in their work by upholding ethical standards and prioritizing participant safety and autonomy. Furthermore, as you continue to explore and innovate in experimental research, you must remain open to new ideas and methodologies. Embracing diversity in perspectives and approaches fosters creativity and innovation, leading to breakthrough discoveries and scientific advancements. By promoting collaboration and sharing findings openly, we can collectively push the boundaries of knowledge and tackle some of society's most pressing challenges.

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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.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

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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Experimental Research: Meaning And Examples Of Experimental Research

Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every…

Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every government knows that vaccines are a result of experimental research design and it takes years of collected data to make one. It takes a lot of time to compare formulas and combinations with an array of possibilities across different age groups, genders and physical conditions. With their efficiency and meticulousness, scientists redefined the meaning of experimental research when they discovered a vaccine in less than a year.

What Is Experimental Research?

Characteristics of experimental research design, types of experimental research design, advantages and disadvantages of experimental research, examples of experimental research.

Experimental research is a scientific method of conducting research using two variables: independent and dependent. Independent variables can be manipulated to apply to dependent variables and the effect is measured. This measurement usually happens over a significant period of time to establish conditions and conclusions about the relationship between these two variables.

Experimental research is widely implemented in education, psychology, social sciences and physical sciences. Experimental research is based on observation, calculation, comparison and logic. Researchers collect quantitative data and perform statistical analyses of two sets of variables. This method collects necessary data to focus on facts and support sound decisions. It’s a helpful approach when time is a factor in establishing cause-and-effect relationships or when an invariable behavior is seen between the two.  

Now that we know the meaning of experimental research, let’s look at its characteristics, types and advantages.

The hypothesis is at the core of an experimental research design. Researchers propose a tentative answer after defining the problem and then test the hypothesis to either confirm or disregard it. Here are a few characteristics of experimental research:

• Dependent variables are manipulated or treated while independent variables are exerted on dependent variables as an experimental treatment. Extraneous variables are variables generated from other factors that can affect the experiment and contribute to change. Researchers have to exercise control to reduce the influence of these variables by randomization, making homogeneous groups and applying statistical analysis techniques.
• Researchers deliberately operate independent variables on the subject of the experiment. This is known as manipulation.
• Once a variable is manipulated, researchers observe the effect an independent variable has on a dependent variable. This is key for interpreting results.
• A researcher may want multiple comparisons between different groups with equivalent subjects. They may replicate the process by conducting sub-experiments within the framework of the experimental design.

Experimental research is equally effective in non-laboratory settings as it is in labs. It helps in predicting events in an experimental setting. It generalizes variable relationships so that they can be implemented outside the experiment and applied to a wider interest group.

The way a researcher assigns subjects to different groups determines the types of experimental research design .

Pre-experimental Research Design

In a pre-experimental research design, researchers observe a group or various groups to see the effect an independent variable has on the dependent variable to cause change. There is no control group as it is a simple form of experimental research . It’s further divided into three categories:

• A one-shot case study research design is a study where one dependent variable is considered. It’s a posttest study as it’s carried out after treating what presumably caused the change.
• One-group pretest-posttest design is a study that combines both pretest and posttest studies by testing a single group before and after administering the treatment.
• Static-group comparison involves studying two groups by subjecting one to treatment while the other remains static. After post-testing all groups the differences are observed.

This design is practical but lacks in certain areas of true experimental criteria.

True Experimental Research Design

This design depends on statistical analysis to approve or disregard a hypothesis. It’s an accurate design that can be conducted with or without a pretest on a minimum of two dependent variables assigned randomly. It is further classified into three types:

• The posttest-only control group design involves randomly selecting and assigning subjects to two groups: experimental and control. Only the experimental group is treated, while both groups are observed and post-tested to draw a conclusion from the difference between the groups.
• In a pretest-posttest control group design, two groups are randomly assigned subjects. Both groups are presented, the experimental group is treated and both groups are post-tested to measure how much change happened in each group.
• Solomon four-group design is a combination of the previous two methods. Subjects are randomly selected and assigned to four groups. Two groups are tested using each of the previous methods.

True experimental research design should have a variable to manipulate, a control group and random distribution.

With experimental research, we can test ideas in a controlled environment before marketing. It acts as the best method to test a theory as it can help in making predictions about a subject and drawing conclusions. Let’s look at some of the advantages that make experimental research useful:

• It allows researchers to have a stronghold over variables and collect desired results.
• Results are usually specific.
• The effectiveness of the research isn’t affected by the subject.
• Findings from the results usually apply to similar situations and ideas.
• Cause and effect of a hypothesis can be identified, which can be further analyzed for in-depth ideas.
• It’s the ideal starting point to collect data and lay a foundation for conducting further research and building more ideas.
• Medical researchers can develop medicines and vaccines to treat diseases by collecting samples from patients and testing them under multiple conditions.
• It can be used to improve the standard of academics across institutions by testing student knowledge and teaching methods before analyzing the result to implement programs.
• Social scientists often use experimental research design to study and test behavior in humans and animals.
• Software development and testing heavily depend on experimental research to test programs by letting subjects use a beta version and analyzing their feedback.

Even though it’s a scientific method, it has a few drawbacks. Here are a few disadvantages of this research method:

• Human error is a concern because the method depends on controlling variables. Improper implementation nullifies the validity of the research and conclusion.
• Eliminating extraneous variables (real-life scenarios) produces inaccurate conclusions.
• The process is time-consuming and expensive
• In medical research, it can have ethical implications by affecting patients’ well-being.
• Results are not descriptive and subjects can contribute to response bias.

Experimental research design is a sophisticated method that investigates relationships or occurrences among people or phenomena under a controlled environment and identifies the conditions responsible for such relationships or occurrences

Experimental research can be used in any industry to anticipate responses, changes, causes and effects. Here are some examples of experimental research :

• This research method can be used to evaluate employees’ skills. Organizations ask candidates to take tests before filling a post. It is used to screen qualified candidates from a pool of applicants. This allows organizations to identify skills at the time of employment. After training employees on the job, organizations further evaluate them to test impact and improvement. This is a pretest-posttest control group research example where employees are ‘subjects’ and the training is ‘treatment’.
• Educational institutions follow the pre-experimental research design to administer exams and evaluate students at the end of a semester. Students are the dependent variables and lectures are independent. Since exams are conducted at the end and not the beginning of a semester, it’s easy to conclude that it’s a one-shot case study research.
• To evaluate the teaching methods of two teachers, they can be assigned two student groups. After teaching their respective groups on the same topic, a posttest can determine which group scored better and who is better at teaching. This method can have its drawbacks as certain human factors, such as attitudes of students and effectiveness to grasp a subject, may negatively influence results. 

Experimental research is considered a standard method that uses observations, simulations and surveys to collect data. One of its unique features is the ability to control extraneous variables and their effects. It’s a suitable method for those looking to examine the relationship between cause and effect in a field setting or in a laboratory. Although experimental research design is a scientific approach, research is not entirely a scientific process. As much as managers need to know what is experimental research , they have to apply the correct research method, depending on the aim of the study.

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Interrelatedness patterns of knowledge representation in extension concept mapping

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Extension concept mapping enhances existing maps by integrating new knowledge, yielding an additional map. This study focuses on two potential extension designs: Extended Kit-Build and Extended Scratch-Build methods. While prior research favored Extended Kit-Build for cognitive knowledge comprehension and map scores, it lacked insights into concept map-relatedness patterns. The tight connections among knowledge representations demonstrate a remarkable level of expertise and the profoundness of an individual’s understanding. This study reveals interrelatedness patterns in extension concept mapping activities that connect previous and new knowledge. The dataset was obtained from a database that accommodates the results of concept mapping activities of 55 university students on two material topics. The study employed a two-group design, wherein the experimental cohort embraced the Extended Kit-Build approach, contrasting with the control cohort’s utilization of the Extended Scratch-Build approach. Extension Relationship scores were utilized to evaluate the knowledge interrelatedness patterns in extension concept mapping. The scoring method assessed both the number and quality of concept map proposition relationships. The experimental group established a statistically more significant quantity and qualitative strength of extension relationships than those within the control group. In the experimental group, a statistically noteworthy positive correlation emerged between the scores of extension relationships and students’ comprehension.

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• Published: 15 May 2024

PAX6 promotes neuroendocrine phenotypes of prostate cancer via enhancing MET/STAT5A-mediated chromatin accessibility

• Nan Jing 1 , 2   na1 ,
• Xinxing Du 3   na1 ,
• Yu Liang 4   na1 ,
• ZhenKeke Tao 1 ,
• Shijia Bao 1 ,
• Huixiang Xiao 1 ,
• Baijun Dong 3 ,
• Wei-Qiang Gao 1 , 2 &
• Yu-Xiang Fang 1  

Journal of Experimental & Clinical Cancer Research volume  43 , Article number:  144 ( 2024 ) Cite this article

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Neuroendocrine prostate cancer (NEPC) is a lethal subset of prostate cancer which is characterized by neuroendocrine differentiation and loss of androgen receptor (AR) signaling. Growing evidence reveals that cell lineage plasticity is crucial in the failure of NEPC therapies. Although studies suggest the involvement of the neural transcription factor PAX6 in drug resistance, its specific role in NEPC remains unclear.

The expression of PAX6 in NEPC was identified via bioinformatics and immunohistochemistry. CCK8 assay, colony formation assay, tumorsphere formation assay and apoptosis assay were used to illustrate the key role of PAX6 in the progression of in vitro. ChIP and Dual-luciferase reporter assays were conducted to confirm the binding sequences of AR in the promoter region of PAX6 , as well as the binding sequences of PAX6 in the promoter regions of STAT5A and MET . For in vivo validation, the xenograft model representing NEPC subtype underwent pathological analysis to verify the significant role of PAX6 in disease progression. Complementary diagnoses were established through public clinical datasets and transcriptome sequencing of specific cell lines. ATAC-seq was used to detect the chromatin accessibility of specific cell lines.

PAX6 expression was significantly elevated in NEPC and negatively regulated by AR signaling. Activation of PAX6 in non-NEPC cells led to NE trans-differentiation, while knock-down of PAX6 in NEPC cells inhibited the development and progression of NEPC. Importantly, loss of AR resulted in an enhanced expression of PAX6, which reprogramed the lineage plasticity of prostate cancer cells to develop NE phenotypes through the MET/STAT5A signaling pathway. Through ATAC-seq, we found that a high expression level of PAX6 elicited enhanced chromatin accessibility, mainly through attenuation of H4K20me3, which typically causes chromatin silence in cancer cells.

This study reveals a novel neural transcription factor PAX6 could drive NEPC progression and suggest that it might serve as a potential therapeutic target for the management of NEPC.

Introduction

In recent years, studies have shown that although the majority of prostatic tumors exhibit an androgen-driven phenotype, a considerable subset of tumors transformed to an aggressive and second-generation androgen deprivation treatment (ADT) (e.g., enzalutamide (ENZ) and abiraterone) resistant form known as neuroendocrine prostate cancer (NEPC). The NEPC exhibits characteristics of loss of androgen receptor (AR) expression, increased expression of neuronal markers, such as synaptophysin (SYP), chromogranin A (CHGA), and neuron-specific enolase (NSE, encoded by ENO2 ), is highly aggressive and lacks effective clinical interventions [ 1 , 2 , 3 ]. By genomic profiling studies, recurrent alterations in several key signaling pathways have been identified as potential mechanisms for neuroendocrine (NE) trans-differentiation process, including the inactivation of tumor suppressor genes such as TP53 and RB1 [ 4 ], the activation of the MYCN [ 5 ] and Aurora kinase pathways [ 6 ], and the dysregulation of the PI3K/AKT/mTOR pathway [ 7 , 8 ]. However, Identification of additional key drivers and understanding of the related underlying molecular mechanisms for the development of NE trans-differentiation are still highly demanded so to develop novel therapeutic strategies to combat this formidable disease.

The transition from adenocarcinoma (Adeno) to NEPC is closely related to cells lineage plasticity. In fact, lineage plasticity is frequently harnessed by malignant cells to develop resistance against therapeutic interventions [ 9 ]. In this regard, prostate cancer (PCa) cells often undergo a transition towards the NE lineage after ADT, in which due to epigenetic influence, chromatin accessibility of the cells is augmented and the promoter/enhancer activity of the key driver genes for tumor progression are more active, thereby acquiring enhanced therapeutic resistance and aggressiveness [ 10 ]. NE differentiation may reflect a cell lineage transition to neural phenotypes, which mimics the neural differentiation process during embryogenesis. Addition to determinants of neuronal cell fates, many transcription factors (TFs) also show an important role in cell lineage plasticity in cancer, particularly after therapeutic treatment [ 11 , 12 ]. For example, neurogenic differentiation 1 ( NEUROD1 ) which plays a crucial role in the development and differentiation of nerve cells [ 13 ], has been shown to promote the progression and metastasis of small cell lung cancer (SCLC), which has NE characteristics, by regulating the receptor tyrosine kinase B (TrkB) and neural cell adhesion molecule (NCAM) in tumor cells [ 14 ]. In addition, it has been reported that BRN2, a neurodevelopment-related TF, promotes the lineage plasticity of PCa cells and facilitates NE differentiation [ 12 ]. Therefore, Identification of novel transcription factors related to neuronal differentiation during the NEPC formation and progression would benefits our understanding of the mechanism of NEPC development.

Of many neuronal TFs, PAX6 has long been recognized as a pivotal regulator of neurogenesis in the development of the central nervous system (CNS) during embryonic development, guiding the formation of neural tube, forebrain patterning and retinal cell differentiation [ 15 , 16 ]. In recent years, accumulating evidence has also shed light on the multifaceted role of PAX6 in tumorigenesis and tumor progression, revealing its remarkable contribution to the pathological processes [ 17 , 18 , 19 ]. For example, PAX6 acts as an oncogene responsible for inducing lung adenocarcinoma (LUAD) stem cell properties. The expression of PAX6 is positively correlated with the expression of GLI and SOX2 , driving cancer cells to a stem-like state [ 20 ]. However, whether or not PAX6 plays a role during the development of NEPC has not been determined.

In this study, we compare gene expression profiling of NEPC and non-NEPC specimens, including androgen-dependent prostate cancer (ADPC) and castration-resistant prostate cancer (CRPC), and provide evidence showing that PAX6 expression which is negatively regulated by AR signaling is elevated during the process of NE trans-differentiation. Our results suggest that PAX6 -induced activation of the MET/STAT5A pathway promotes NE trans-differentiation by attenuation of H4K20me3 for the lineage switch of PCa cells towards a NE phenotype.

Materials and methods

Cell lines and cell culture.

The human PCa cell lines LNCaP (ATCC; CRL-1740), 22Rv1(ATCC; CRL-2505), C42B (ATCC; CRL-3315), PC3 (ATCC; CRL-1435), and DU145 (ATCC; HTB-81) and human embryonic kidney 293T cell lines were obtained from the American Type Culture Collection (ATCC, Manassas, USA). 293T, 22Rv1, C42B, PC3, and DU145 cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Gibco, USA) supplemented with 10% fetal bovine serum (FBS; Sigma-Aldrich, St. Louis, Missouri, USA) and 1% penicillin/streptomycin (Corning, New York, USA). LNCaP cells were cultured in RPMI-1640 medium (Gibco) supplemented with 10% FBS (Sigma-Aldrich) and 1% penicillin/streptomycin (Gibco). LNCaP ENZ cell line was cultured further in the continuous presence of 20 µM ENZ (Med Chem Express, Shanghai, China) to maintain ENZ resistance. For the AR function assay, cells were maintained in androgen-depleted medium composed of phenol red-free RPMI-1640 medium, 5% charcoal/dextran-stripped serum (CSS; Gibco), and 1% penicillin/streptomycin (Gibco). All cell lines were cultured in a humidified incubator at 5%CO 2 and 95% air atmospheres at 37℃ and were routinely tested for mycoplasma (every ~ 6 weeks) using the MycoSEQTM Mycoplasma Detection Kit (Thermos Fisher Scientific, USA). Experiments were performed using fewer than 10 passages for each cell line.

A human PAX6 lentiviral expression construct containing a puromycin resistance gene was purchased from Genomeditech (Shanghai, China). A PAX6 P1 promoter androgen response element (ARE ) luciferase reporter construct ( PAX6 ARE-luc) was generated by inserting the PAX6 ARE-centric sequence, combined with a PAX6 minimal promoter into the upstream region of the firely luciferase gene in a pGL4.17 vector (Promega, E6721). Primer sequences for cloning the PAX6 P1 promoter sequence from LNCaP genomic DNA are provided in Supplementary Table S1 .

PAX6 short hairpin RNA (shRNA) expression constructs were purchased from Genomeditech. The STAT5A expression lentiviral vector was purchased from Miaoling Biology (Wuhan, China). Single-guide RNA (sgRNA) was designed using an online platform ( www.benchling.com ) and synthesized by Sangon Biotech Comp (Shanghai, China). The annealed DNA oligos were cloned into the pLenti-CRISPRv2 vector (Addgene_52961) for genome editing. Data from all shRNA and sgRNA sequencing methods used in this study are provided in Supplementary Table S1 .

Generation of stable knockdown and over-expression subclone cell lines

Stable PAX6 , STAT5A, AR, and MET knockdown subclone cell lines were achieved by infecting cells with lentiviral vectors expressing PAX6 shRNA (sh PAX6 -1#, sh PAX6 -2#), STAT5A shRNA (sh STAT5A -1#, sh STAT5A -2#), AR shRNA(sh AR -1#, sh AR -2#), and MET shRNA(sh MET ). A non-target control shRNA was used for construction of the control subclone cell line. LNCaP and C42B cells were infected PAX6 CDS-containing or STAT5A CDS-containing lentiviral vector for stably overexpressing PAX6 or STAT5A . Briefly, 293T cells were co-transfected with the lentiviral vector, psPAX2 (Addgene_12260) and pMD2G (Addgene_12259) at a 3:2:1 ratio using PEI (Thermo Fisher Scientific, MD, USA) following the manufacturer’s instructions. The medium was changed 6 h after transfection. The medium containing lentivirus was harvested 48 h after transfection. PCa cells were infected with lentivirus in the presence of polybrene (8 µg/mL) followed by 2 weeks puromycin selection (5 µg/mL).

Quantitative real-time PCR

Total RNA was extracted from the cells using the FastPure Cell/Tissue Total RNA Isolation Kit, following the manufacturer’s instructions (Vazyme, Shanghai, China). Subsequently, RNA was reverse-transcribed into cDNA using the HiScript III All-in-one RT SuperMix Perfect qPCR kit (Vazyme). qPCR was performed using the qPCR SYBR Green Master Mix (Vazyme). To ensure accuracy and reproducibility, β-actin was utilized as the internal control gene. All experimental data were obtained in triplicate and analyzed using the 2 − ΔΔCt method [ 21 ]. All primers used are available in Supplementary Table S1 .

Immunoblotting

Immunoblotting experiments were performed as described in our previous work [ 22 ]. Briefly, whole-cell lysates were prepared in radioimmunoprecipitation assay (RIPA) lysis buffer (Millipore, Bedford, MA, USA) supplemented with a protease inhibitor (Med Chem Express) and phosphatase inhibitor (Med Chem Express). After protein quantification using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific), 40 µg of total protein was separated via SDS-PAGE and transferred to a PVDF membrane (Millipore). The membrane was blocked with TBST containing 5% bovine serum albumin (BSA, Gibco) at 16–25 °C for 1 h and then incubated with the relevant primary antibodies at 4 °C overnight, followed by probing with a horseradish peroxidase (HRP)-conjugated secondary antibody at 16–25 °C for 1 h. The relevant proteins were visualized using an electrochemiluminescence detection instrument (Bio-Rad, California, USA) and HRP substrates. The following antibodies were used: PAX6 (Abcam, UK, ab195045), TP53(Cell Signaling Technology (CST, Danvers, MA, USA), 9282), RB1 (CST, 9313), AR (Abcam, ab133273), SYP (Proteintech, Chicago, USA, 17785-1-LG), NSE (Proteintech, 66150-1-Ap), CHGA (Proteintech, 10529-1-AP), STAT5A (CST, 94,205 and Santa Cruz Biotechnology, USA, 271,542), p-STAT5A (CST, 9359), MET (CST, 8198), p-MET (CST, 3077), Ki67 (Abcam, ab15580), KMT5C (Abclonal, Wuhan, China, A16235), and SMYD5 (Abclonal, A6191).

Hematoxylin-eosin (H&E) and immunohistochemical (IHC) staining assays

H&E and IHC staining of paraffin-embedded tissue sections were performed by Runnerbio Biotech (Shanghai, China). Briefly, the tissues were fixed in 4% paraformaldehyde overnight and embedded in paraffin. Paraffin-embedded tissue sections (4 μm) were dewaxed in xylene for 5 min and successively hydrated in 100%, 95%, 85%, and 70% ethanol. Following inactivation of endogenous peroxidase with disodium-hydrogen phosphate-2-hydrate, these sections were blocked using 10% donkey serum for 1 h at 16–25 °C for immunohistochemical staining. Next, the sections were incubated with primary antibody (1:200) at 4 °C overnight, washed three times (10 min each time) with PBS, and then incubated with horseradish peroxidase-conjugated secondary antibody (Vector Laboratories, Burlingame, CA, USA) for 1 h at 16–25 °C. Finally, after washing three times with PBS, the sections were visualized with diaminobenzidine (DAB) staining (Sangon Biotech) and hematoxylin counterstaining (Beyotime, Shanghai, China). Images were acquired using a microscope (DFC420C; Leica, Heerbrugg, Germany).

Immunofluorescence assay

Cells were seeded on cover slides, placed in a 24-well plate, and cultured in DMEM supplemented with 10% FBS at 5% CO 2 at 37 °C overnight. Adherent cells on the cover slides were fixed with 4% paraformaldehyde for 15 min at 16–25 °C. The cells were blocked with 10% normal donkey serum (GeneTex, Irvine CA, USA) for 1 h at 16–25 °C. After incubation with relevant primary antibody (diluted 1:200 in PBS containing 1% normal donkey serum) at 4 °C overnight, the cells were washed for 10 min three times with PBS buffer and then incubated with Alexa Fluor-594 conjugated secondary antibody (Thermo Fisher Scientific) at 16–25 °C for 1 h in the dark. Next, the cells were washed three times with PBS and stained with DAPI (Thermo Fisher Scientific). The immunofluorescence-stained slides were observed and photographed using a microscope (Leica).

The ChIP assay was performed using a SimpleChIP Enzymatic Chromatin IP Kit (CST, 9003) according to the manufacturer’s instructions. LNCaP cells were cultured in phenol red-free medium containing 5% CSS for 72 h, after which DHT (10 nM) or DMSO was added and the cells were cultured for another 24 h. For the assay, 2 × 10 7 cells were harvested. Briefly, chromatin was crosslinked with nuclear proteins, enzymatically digested with micrococcal nuclease, sonicated, and immunoprecipitated with anti-AR antibodies. Normal IgG included in the kit was used as the negative control for IP. Immunoprecipitates were pelleted with agarose beads, purified, and subjected to qPCR using primers specifically targeting the ARE-centric PAX6 genomic region or the PAX6-binding STAT5A and MET promoter region. The following antibodies were used: AR (Abcam, ab108341). Flag beads (Sigma-Aldrich, M8823) were used in the PAX6 chip experiment to pull down intracellular protein-DNA conjugates after over-expression of the PAX6 plasmid in cells. The ChIP primer sequences used in this study are listed in Supplementary Table S1 .

Cell proliferation assays

To determine cell proliferation, cells were seeded on 96-well plates at a density of 2,000 cells per well and were cultured in medium with or without ENZ (20 µM, Med Chem Express) for up to 6 days. Cell proliferation was assessed using the CellTiter96 Aqueous One Solution Cell Proliferation Assay (Biosharp, Shanghai, China) according to the manufacturer’s instructions. The absorbance values of CCK-8 were measured at 450 nm using a BioTek Synergy HT microplate reader (BioTek Inc., Vermont, USA). To assess the cell growth ability after the treatment of ENZ (Med Chem Express), 2000 cells in 96-wellplate were treated with the indicated concentrations of drug and then incubated for 72 h. CCK-8 assay was performed to measure cell viability at various time points. IC50 values were calculated using Graphpad Prism.

The MTT cell proliferation assay involved seeding 2000 cells per well in a 96-well cell culture plate. After cell adhesion, 10 µL of MTT solution (Beyotime) were added to each well, and the cells are further incubated in a cell culture incubator for 4 h. Subsequently, 100 µL of formazan solution (Beyotime) was added to each well, mixed appropriately, and incubated in the cell culture incubator until the formazan dissolved completely. The absorbance was measured at 570 nm.

Colony formation assay

For the colony formation assay, cells were seeded in 6-well plates at a density of 1,000 cells per well and cultured in medium with or without ENZ (20 µM, Med Chem Express) for up to 2 weeks. The cells were allowed to grow until visible colonies formed and were then stained with crystal violet.

Tumorsphere formation assay

To investigate tumor sphere formation, single PCa cells were suspended in a prostate sphere culture medium consisting of DMEM/RPMI-1640 medium supplemented with N2(Gibco), B27 (Gibco), epidermal growth factor (20 ng/mL, PeproTech, New Jersey, USA), and fibroblast growth factor (20 ng/mL, PeproTech). These cells were then seeded in 24-well low-attachment dishes (Corning) at a density of 1,000 cells per well in 500 µL of medium. The culture medium was supplemented every three days until cell spheres formed, which typically occurred after approximately 1–2 weeks of culturing. The number of colonies and spheres were counted under a light microscope.

Apoptosis assay

To detect apoptosis, cells were fixed and co-stained with propidium iodide (PI) and FITC-conjugated Annexin V using the FITC Annexin V Apoptosis Detection Kit (Yeasen, Shanghai, China), according to the manufacturer’s instructions. Briefly, 1 × 10 6 cells were collected and incubated with Annexin V-Alexa Fluor 647 and PI for flow cytometry. The stained samples were protected from light and subjected to flow cytometry within 1 h. The independent experiment was repeated three times. Data were collected using an Accuri C6 flow cytometer and analyzed using FlowJo software (BD Biosciences Inc., New Jersey, USA).

Luciferase reporter assay

For determining the effect of AR on PAX6 ARE recognition, LNCaP cells were co-transfected with firefly luciferase reporter vectors containing the PAX6 ARE together with the pRL-TK renilla luciferase vector (Addgene_11313) and were treated with R1881 (1 nM, Sigma-Aldrich) combined with or without ENZ (20 µM, Med Chem Express) for 24 h. The cells were then harvested and lysed. The cell lysates were assayed for relative luciferase activity using a Dual-Luciferase Reporter Assay Kit (Yeasen) according to the manufacturer’s instructions.

Whole transcriptome sequencing (RNA-seq) and ATAC seq

Total mRNA was reverse-transcribed into barcoded cDNA fragments using an oligo-dT primer with an attached adapter. Barcoded cDNA libraries were sequenced using an Illumina HiSeq 4000 PE150 platform (Illumina). Following quality assessment, RNA-seq reads were aligned to the reference genome (GRCh37/hg19) using HISAT2. StringTie was used to assemble and quantify the transcript abundance. DESeq2 (RRID: SCR_000154) was used to perform differential gene expression analysis of the normalized data. Three replicates for each cell line were used in the experiment. The full-gene list about gene expression profile change was shown in Supplementary Table S2 .

For the ATAC-seq assay, 50,000 cells were centrifuged at 500 g for 5 min at 4℃, and the supernatant was removed. Cells were washed once with cold PBS. Subsequently, the cells were again centrifuged at 500 g for 5 min at 4℃ and the supernatant was removed. The cells were then suspended in cold lysis buffer. Next, the cells were again centrifuged at 500 g for 10 min at 4℃ and the supernatant was removed. The transposition reaction system was configured using Tn5 transposase. The cell nuclear content was added to the transposing reaction system mixture, and the DNA were purified after incubation at 37℃ for 30 min. The PCR system was configured with purified DNA, and PCR amplification was performed. The final DNA libraries were run on an Illumina platform after the DNA was purified. We used an integrative genome browser (IGV) program for peak visualization. Two replicates for each cell line were used in this experiment.

Tumor xenograft experiment

Six-week-old male nude mice (SLAC, Shanghai, China) were housed and manipulated according to the protocols approved by the Renji Hospital Medical Experimental Animal Care Commission. All animals were euthanized before 20% body weight loss occurred. All mice were maintained in a pathogen-free facility at Ren Ji Hospital. Approximately 5 × 10 6 cells were suspended in 100 µL 50% Matrigel and injected into the right flank of nude mice. To evaluate the capacity for in vivo castration resistance, nude mice were castrated two weeks prior to subcutaneous tumor cell inoculation. ENZ (Med Chem Express, HY-70,002) 10 mg/kg or its vehicle (corn oil) was injected daily via intraperitoneal injection. The tumors were harvested, imaged, and weighed after the mice were euthanized. .

Bioinformatic analysis

Human PCa datasets used for correlation studies or for detecting profiling changes in PAX6 among different disease subtypes were downloaded from The Cancer Genome Atlas (TCGA) database, cBioPortal database ( https://www.cbioportal.org/ ), and Gene Expression Omnibus (GEO) datasets (GSE244024, GSE202299, GSE32967, GSE6752, GSE70380, GSE161167, GSE56288, GSE137829, GSE239593 and GSE116918, GSE21034, GSE35988, GSE3325, GSE66187, GSE40275, GSE43346, GSE16560, http://www.ncbi.nlm.nih.gov/geo/ ). The sequencing data from SU2C/PCF 2019 Cohort [ 23 ], Beltran 2016 Cohort [ 24 ], Gao, 2014 Cohort [ 25 ], MD Anderson 2023 Cohort [ 26 ], Fred Hutchinson 2016 Cohort [ 27 ] and Broad/Cornell 2012 Cohort [ 28 ] was downloaded from the cBioPortal database. In addition, CANCERTOOL( http://web.bioinformatics.cicbiogune.es/CANCERTOOL/index.html ) and PanCanSurvPlot ( https://smuonco.shinyapps.io/PanCanSurvPlot/ ) were used to evaluate the mRNA expression and conduct survival analysis of clinical patient samples. Correlations were determined using Pearson’s correlation coefficients. Detailed information about the analysis method of each of the datasets used was shown in Supplementary Table S3 .

Statistical analysis

All experiments were repeated at least three times and the mean and standard error (mean ± SD) values calculated. Statistically significant differences between two groups were analyzed using unpaired two-tailed Student’s t-tests, and differences between more than two groups were determined using one-way ANOVA. For all analysis, the results were considered statistically significant at * p  < 0.05, ** p  < 0.01, and *** p  < 0.001.

PAX6 expression is significantly elevated in NEPC

To screen potential candidates of NE trans-differentiation driver genes, we analyzed the expression profiling changes using data from 3 NEPC related datasets, including GSE239593 dataset (Bulk RNA-seq analysis data from a 3D-engineered PCa cell derived tissue (EPCaT) model), GSE244024 dataset (transcriptome profiling changes after over-expression of ONECUT2 ( OC2 ) in LNCaP cells) and GSE202299 dataset (transcriptome profiling changes after knockdown of TP53 and RB1 in C42B cells) (Fig.  1 a, and Supplementary Table S3 ). We took the intersection of the differentially expressed genes in these three datasets (total 154 genes) and identified eight neuron-related genes (Fig.  1 a). Among them, we found a novel neuron-related TF, PAX6, which exhibited an upregulation with the largest fold change in NEPC group (Fig. S1 a). To confirm the above findings, we further analyzed single-cell RNA-seq data of clinical NEPC specimens from GSE137829 dataset [ 29 ] and found that PAX6 expression was markedly high in the most advanced NEPC, accompanied with a low level of AR score (Fig.  1 b).

PAX6 expression is upregulated in NEPC. a Intersection of differentially expressed genes from the NEPC related datasets. b The expression of PAX6 in NEPC patients based on GSE137829 dataset (P2, patient 2; P4, patient 4; P5, patient 5; P6, patient 6). c Comparisons of PAX6 mRNA levels level in CRPC-Adeno and NEPC based on the GSE32967 dataset (CRPC-Adeno, n  = 8; NEPC, n  = 14). d Comparisons of PAX6 mRNA levels in CRPC-Adeno vs. CRPC-NE based on the Beltran-2016 dataset (CRPC-Adeno, n  = 35; CRPC-NE, n  = 15). e Comparisons of PAX6 mRNA levels in HSPC vs. HRPC based on GSE6752 dataset (HSPC, n  = 10; HRPC, n  = 21). f Correlation analysis of PAX6 with NE signature genes based on the Beltran 2016 Cohort. g Correlation analysis of PAX6 with NE signature genes based on the Broad/Cornell 2012 Cohort. h Correlation analysis of PAX6 with AR associated genes based on the Beltran 2016 Cohort. i Correlation analysis of PAX6 with AR associated genes based on the Broad/Cornell 2012 Cohort. j Representative H&E and IHC staining of PAX6, AR and SYP in tissues from patients with Primary PCa, CRPC or NEPC (Scale Bar: 100 μm). k Protein expression of PAX6 in PCa cell lines. l Protein and mRNA expression of PAX6 in LNCaP ENZ cells compared to the control cells. All the experiments were repeated for three times. Data represents the mean ± SD, *** p  < 0.001

To further study the relationship between the expression of PAX6 and the initiation of NEPC, we performed bioinformatics assays in other public datasets and found that PAX6 mRNA level was higher in NEPC patient-derived xenografts (PDXs) compared to Adeno PDXs (GSE32967), as well as in CRPC-NE samples compared to CRPC-Adeno samples (the Beltran 2016 Cohort [ 2 ]) (Fig.  1 c and d). Consistently, analysis of a published CRPC dataset (GSE6752) also confirmed a higher expression level of PAX6 in hormone-refractory prostate cancer (HRPC) compared to hormone-sensitive prostate cancer (HSPC) (Fig.  1 e). We also studied the PAX6 expression in both human and mouse tissue samples with various AR and NE markers profiling using the data from GSE66187 dataset. We observed an upregulation of PAX6 in AR − /NE + NEPC-like human as well as mouse samples, which indicated a positive relationship between elevated PAX6 expression and NEPC (Supplementary Fig. S1 b and S1c). Furthermore, we observed that PAX6 was positively correlated with the NE signature genes in the Beltran 2016 Cohort [ 2 ] (Fig.  1 f) and the Broad/Cornell 2012 Cohort [ 28 ] (Fig.  1 g). On the other hand, we also observed that PAX6 was negatively correlated with AR associated genes such as KLK3 in the Beltran 2016 Cohort and the Broad/Cornell 2012 Cohort (Fig.  1 h and i). For further confirmation, we analyzed and verified the negative correlation between PAX6 and AR expression levels in GSE32967 and GSE6752 datasets which we used above (Supplementary Fig. S1 d). Next we hypothesized that high expression of PAX6 might be generalized in other neuroendocrine cancers, and we examined expression of PAX6 in SCLC that is also a type of neuroendocrine and compared it to non-small cell lung cancer (NSCLC). We indeed observed that expression level of PAX6 was also significantly in SCLC higher than that in NSCLC, indicating that upregulation of PAX6 might play a general role on promotion of NE trans-differentiation in cancers (Supplementary Fig. S1 e and S1f).

To validate the elevated PAX6 expression levels in NEPC tissue samples, we performed IHC and H&E staining with tissues sections prepared from human CRPC and NEPC specimens. When compared to primary PCa tissues which never received ADT treatment, PAX6 levels were indeed higher in NEPC which exhibited NE histology than in CPRC and primary PCa tissues (Fig.  1 j and Supplementary Fig. S1 g and Supplementary Table S4 ). We further wondered whether the expression of PAX6 was also associated with other pathological characteristic such as gleason score stage and metastasis in PCa. To this end, we examined the relationship between PAX6 expression and gleason score stage in the GSE21034 dataset [ 30 ] and found that the PAX6 expression was markedly upregulated with the increase of gleason score (Supplementary Fig. S1 h). We also found that the PAX6 expression was significantly higher in metastatic PCa tissues than that in non-metastatic prostate carcinoma in the GSE35988 dataset [ 31 ] and the GSE3325 dataset [ 32 ] (Supplementary Fig. S1 i).

Consistently, in human PCa cell lines, we found that the PAX6 expression was significantly upregulated in the DU145 and PC3 cells, with characteristics of prostatic small-cell/NE carcinoma [ 33 ] (Fig.  1 k and Supplementary Fig. S1 j), compared to that in the LNCaP cells, a well-known non-NEPC cell line [ 34 ]. Moreover, we examined the drug-resistant growth ability of these cells following ENZ treatment (20 µM for 6 h) and found that the proliferation ability of LNCaP cells was weakened significantly after the ENZ treatment (Supplementary Fig. S1 k). Collectively, these results indicated that the expression of PAX6 was upregulated in NEPC as a response to the ENZ treatment.

Next, in order to examine the PAX6 expressional change in ADT-induced NEPC, LNCaP cells were selected from long-time cultures in the presence of ENZ (20 µM) to construct an ENZ-resistant LNCaP subcell line named LNCaP ENZ , which imitated the clinical transition to NEPC under ADT [ 35 ]. We found that LNCaP ENZ cells proliferated faster than parental LNCaP cells under the treatment of ENZ (Supplementary Fig. S1 l), and PAX6 mRNA and protein levels were upregulated as a response to the treatment (Fig.  1 l). Thus, these data together indicate that the expression of PAX6 is positively correlated with NE trans-differentiation in PCa.

PAX6 is necessary to maintain the NE traits and aggressive behavior of NEPC cells

In order to investigate the NE signature gene profiling changes in LNCaP ENZ cells, we performed transcriptome sequencing. As results shown in Fig.  2 a, downregulation of AR associated genes (e.g. KLK3 and TMPRSS2 ) and upregulation of NE signature genes (e.g. CHGA , SYP and CHGB ) were observed in LNCaP ENZ cells compared to the parental LNCaP cells. More importantly, we also performed Gene Set Enrichment Analysis (GSEA) with the RNA-seq data from the LNCaP ENZ vs. the parental LNCaP cells and demonstrated the significant enrichment of the gene signature related to “synapse_assembly” and “dendritic_cell_chemotaxis” pathway in both of which PAX6 played an activated role (Fig.  2 b). The two pathways are reported to be associated with the NE trans-differentiation of PCa cells [ 36 , 37 ] and to influence expression of NE markers, cell communication related genes and tumor microenvironment regulatory genes, contributing to the aggressive feature and poor prognosis [ 38 , 39 , 40 , 41 , 42 ]. In agreement with the results from the above profiling assay, we also confirmed that expression of PAX6, SYP and NSE was upregulated, and that expression of AR and KLK3 was downregulated in LNCaP ENZ cells compared to the parental LNCaP cells (Fig.  2 c - e).

Elevated expression of PAX6 is associated with the resistance to ENZ in PCa. a Relative mRNA expression of NE signature genes and AR associated genes in LNCaP ENZ cells compared with the control by RNA-seq.  b GSEA results of the indicated gene signatures for the comparisons of LNCaP ENZ and control cells. c mRNA expression of NE signature genes in LNCaP ENZ and control cells. d mRNA expression of AR associated genes in LNCaP ENZ and control cells. e Protein expression of SYP, NSE, KLK3, AR in LNCaP ENZ cells and control cells. f Protein expression of PAX6, NSE, CHGA, SYP and NCAM1 in LNCaP ENZ -sh PAX6 cells and control cells. g Cell proliferation assays in LNCaP ENZ -sh PAX6 cells and control cells. Data represent the fold change of OD value during an observation period of up to 4 days. Fold change on the day of cell seeding (day0) in each group was set as 1. h Representative image and quantification assay of colony numbers in LNCaP ENZ -sh PAX6 cells and control cells. i Representative image and quantification assay of tumorsphere formation in LNCaP ENZ -sh PAX6 cells and control cells. j Flow cytometric analysis for cell apoptosis by the percentage of Annexin V + cell population in LNCaP ENZ -sh PAX6 cells and control cells. All the experiments were repeated for three times. Data represents the mean ± SD. * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Since LNCaP ENZ cells displayed the characteristics of decreased expression of AR associated genes and increased expression of NE signature genes and PAX6 , we examined whether knocking down PAX6 in LNCaP ENZ cells could restore its sensitivity to ENZ. As expected, knockdown of PAX6 led to a decreased expression of NSE, CHGA, SYP and NCAM1 (Fig.  2 f), as well as downregulated the ability of proliferation, colony and tumorsphere formation following the treatment of ENZ in LNCaP ENZ cells (Fig.  2 g-i). Considering NEPC cells usually have anti-apoptotic properties [ 43 ], we wondered whether PAX6 had an effect on apoptosis of cells. To examine the effect of PAX6 on cell apoptosis, we performed the related assay and found that the proportion of apoptosis was increased in LNCaP ENZ cells after knockdown of PAX6 (Fig.  2 j). These results suggested that knockdown of PAX6 could repress the process of NE trans-differentiation and restore the sensitivity of PCa cells to ENZ.

Previous studies have reported that loss of TP53 and RB1 could promote NE trans-differentiation in LNCaP cells [ 4 ]. Therefore, we constructed the LNCaP-sh RB1/TP53 cell line as a NEPC cell model that represented more closely the clinical situations to evaluate the role of PAX6 in regulation of NE trans-differentiation (Supplementary Fig. S2 a). First, we detected an upregulation of PAX6 expression in LNCaP-sh RB1/TP53 cells compared to the control (Supplementary Fig. S2 b). To assess the necessity of PAX6 in maintaining the NE phenotype in PCa cells, we stably knocked down PAX6 in the LNCaP-sh RB1/TP53 cell line and found that the expression of NSE was downregulated compared to the control cells (Fig.  3 a and Supplementary Fig. S2 c). At the same time, the ability of cell proliferation, colony and tumorsphere formation was significantly reduced in the LNCaP-sh RB1/TP53 cells under the treatment of ENZ after PAX6 knockdown (Fig.  3 b-d). Similar to what we observed in the LNCaP ENZ cells, the effect of PAX6 knockdown led to a significant increase in Annexin V + cell populations, indicating an enhanced cell apoptosis at both early and late apoptotic stages in LNCaP-sh RB1/TP53 cells (Supplementary Fig. S2 d). As a further confirmation, we also downregulated PAX6 expression in DU145 (named as DU145-sh PAX6 , Fig.  3 e and Supplementary Fig. S2 e) and PC3 cells (named as PC3-sh PAX6 , Supplementary Fig. S2 h) and repeated the similar experiments described above. We found that knockdown of PAX6 could also attenuate NE phenotypes in both two cell lines (Fig.  3 f-h, and Supplementary Fig. S2 f-S2k).

Knockdown of PAX6 represses the phenotype of NEPC. a Protein expression of PAX6 and NSE after PAX6 knockdown in LNCaP-sh RB1/TP53 cells and control cells. b Cell proliferation assay after PAX6 knockdown in LNCaP-sh RB1/TP53 cells and control cells. c Representative image and quantification assay of tumorsphere formation after PAX6 knockdown in LNCaP-sh RB1/TP53 cells and control cells. d Representative image and quantification assay of colony formation after PAX6 knockdown in LNCaP-sh RB1/TP53 cells and control cells. e Protein expression of PAX6 and NSE in DU145-sh PAX6 cells and control cells. f Cell proliferation assays in DU145-sh PAX6 cells and control cells. g Representative image and quantification assay of tumorsphere formation after PAX6 knockdown in DU145-sh PAX6 cells and control cells. h Representative image and quantification assay of colony formation in DU145-sh PAX6 cells and control cells. i Graphic of the construction of the xenograft model in castrated nude mice. j Anatomic tumor image of DU145-sh PAX6 cells or control cells inoculated xenografts. k Tumor volume analysis of DU145-sh PAX6 cells and control cells inoculated xenografts at the end point. l Tumor weight analysis of DU145-sh PAX6 cells and control cells inoculated xenografts. m Representative H&E staining and IHC staining of Ki67, PAX6, SYP in xenograft samples (Scale Bar: 100 μm, with the boxed region enlarged and shown on the left). All the experiments were repeated for three times. Data represents the mean ± SD.* p  < 0.05, ** p  < 0.01, *** p  < 0.001

To verify the effect of PAX6 on tumor growth in vivo, we performed castration on 6–8 weeks old male nude mice. Two weeks after surgery, we inoculated 5 × 10 6 cells of DU145-sh PAX6 or PC3-sh PAX6 and their control cells subcutaneously and assessed the sizes and weights of tumor respectively (Fig.  3 i). The results showed that the tumor volumes and weights after knockdown of PAX6 were significantly lower than those in the control group (Fig.  3 j -l and Supplementary Fig. S2 l). IHC assay results showed that after knockdown PAX6 , the expression of SYP was significantly reduced (Fig.  3 m and Supplementary Fig. S2 m). Collectively, we concluded that PAX6 is essential for maintaining NE trans-differentiation and NEPC cell behaviors.

PAX6 promotes NE plasticity and inhibits AR signaling

To confirm PAX6 ’s role in induction of NE trans-differentiation in PCa cells, we stably overexpressed PAX6 in LNCaP and C42B cells, respectively (Fig.  4 a and Supplementary Fig. S3 a). We found that over-expression of PAX6 upregulated the expressional level of NE lineage markers such as SYP and NSE in LNCaP and C42B cells compared with controls (Fig.  4 b and Supplementary Fig. S3 b). Notably, over-expression of PAX6 in LNCaP and C42B cells accelerated cell proliferation, colony formation and tumor sphere formation in LNCaP and C42B cells after ENZ treatment (20 µM) (Fig.  4 c - e and Supplementary Fig. S3 c-3e). We also found that both LNCaP- PAX6 and control cells exhibited the dose–dependent response to the ENZ treatment, and the treatment sensitivity is lower in LNCaP-PAX6 cells (IC50: 76.85 µM) than in control cells (IC50: 33.65 µM) (Fig.  4 f). As expected, we observed similar results in C42B- PAX6 vs. control cells (Supplementary Fig. S3 f). Taken together, these results suggested that PAX6 acted as an important factor in promoting NE trans-differentiation in PCa cells.

Over-expression of PAX6 promotes the NE trans-differentiation in non-NEPC cells. a mRNA and protein expression of PAX6 in LNCaP- PAX6 cells and control cells. b mRNA and protein expression of SYP and ENO2 genes in LNCaP- PAX6 cells and control cells. c Cell proliferation assays in LNCaP- PAX6 cells and control cells after treatment of ENZ (20 µM). d Representative image and quantification assay of colony number in LNCaP- PAX6 cells and control cells. e Representative image and quantification assay of tumorsphere formation in LNCaP- PAX6 cells and control cells. f ENZ dose–response curves for LNCaP- PAX6 cells and control cells. All the experiments were repeated for three times. Data represents the mean ± SD. * p  < 0.05, ** p  < 0.01, *** p  < 0.001

PAX6 is suppressed by AR activation

It has been well observed that inhibition of AR signaling can negatively upregulate the expression of its target genes including NE trans-differentiation related genes [ 44 ]. Therefore, we next wondered whether PAX6 is regulated by AR during the process of NE trans-differentiation. We firstly detected a negative correlation of the expression of the two genes in the MD Anderson, 2023 Cohort [ 26 ] and the Gao, 2014 Cohort [ 25 ] (Fig.  5 a and b). In addition, data from the SU2C/PCF 2019 Cohort [ 23 ], the Broad/Cornell 2012 Cohort [ 28 ] and the Beltran 2016 Cohort [ 24 ] revealed a negative correlation between PAX6 and AR associated genes such as NKX3-1 , TMPRSS2 , PMEAP1 , KLK2 , ALDHA13 and KLK3 (Figs.  1 h and i and 5 c). Moreover, we interrogated two ChIP-seq datasets involving LNCaP cells (GSE161167) and human prostate tissues (GSE56288) and identified a consensus ARE within the PAX6 promoter region (Fig.  5 d). Thus, combining these data with our previous findings, it is plausible to suggest that PAX6 might undergo negative transcriptional regulation by AR.

The expression of PAX6 is negatively regulated by AR . a Correlation analysis of PAX6 with AR expression based on the MD Anderson, 2023 Cohort. b Correlation analysis of PAX6 with AR expression based on the Gao, 2014 Cohort. c Correlation analysis of PAX6 with AR expression based on the SU2C/PCF 2019 Cohort. d Genomic browser representation of AR binding in PAX6 promoter region encompassing an ARE by analysis of the data from GSE161167 (LNCaP cells) and GSE56288 (a cohort of normal and tumor human prostate tissues) datasets. e mRNA expression of PAX6 , SYP, ENO2 and STEAP4 in LNCaP cells after treatment with R1881 (1 nM) for 6 h. f mRNA and protein expression of PAX6 and AR after AR knockdown in LNCaP cells. g protein expression of PAX6 and AR after AR knockout in LNCaP cells. h ChIP assay of AR binding at region of the P1 promoter region of PAX6 after treatment with DHT (10 nM) in LNCaP cells. i Determination of PAX6 ARE-luc activity after treatment with R1881(1 nM, 6 h) or R1881 (1 nM, 6 h) + ENZ (20 µM, 6 h) in LNCaP cells. j mRNA and protein expression of AR and KLK3 in LNCaP- PAX6 and control cells. k mRNA and protein expression of AR and KLK3 in C42B- PAX6 and control cells. l ChIP assay of PAX6 binding at the promoter region of AR. All the experiments were repeated for three times. Data represents the mean ± SD. ns: no significance, ** p  < 0.01, *** p  < 0.001

Next, we further investigated whether AR signaling could also negatively regulate the expression of PAX6 . We found that after treatment with R1881(1 nM) in LNCaP cells, the PAX6 expression was reduced along with a decreased SYP , ENO2 expression and an increased STEAP4 expression (Fig.  5 e). Moreover, following steadily knockdown or knockout AR in LNCaP and we observed that the PAX6 expression level was increased as a response (Fig.  5 f and g). These results suggested that PAX6 expression might be transcriptionally inhibited by AR. For further confirmation, we identified one potential ARE on the P1 promoter of PAX6 and conducted ChIP-qPCR assay and revealed a DHT stimulation dependent binding of AR (Fig.  5 h). To verify whether AR signaling status affects PAX6 transcriptional activity, we incorporated the core fragment of PAX6 promoter sequence into a luciferase reporter construct and assessed luciferase activity upon AR activation or blockade. As compared to the control, a significant decrease in luciferase activity was observed after a 6-hour treatment with R1881 in LNCaP cells. In contrast, after addition of ENZ into the culture medium as an antagonist of R1881, a restoration of luciferase activity was observed (Fig.  5 i). Collectively, these results suggested that PAX6 is transcriptionally suppressed by AR, likely via binding to an ARE in the promoter region of PAX6 .

On the other hand, since a negative-loop feedback regulation between two genes was well-reported to be involved in the regulation of tumor progression [ 45 , 46 ], we herein investigated whether PAX6 could also regulate the transcription of AR as feedback. To verify our hypothesis, we studied mRNA and protein expression of AR and KLK3 in LNCaP- PAX6 and C42B- PAX6 cells and revealed an elevated expression of PAX6 along with the repressive AR expression (Fig.  5 j and k). Furthermore, we also identified a binding site of PAX6 (TTTACACAGGGCTT) in the AR promoter region by ChIP assay (Fig.  5 l). Taken together, these results consistently indicated that there was a negative feedback regulation loop between PAX6 and AR .

STAT5A is a major downstream effector of PAX6 for promoting NE trans-differentiation

To explore potential downstream effectors of PAX6 achieving the related aggressive behaviors in NEPC cells, we conducted RNA-seq analysis in DU145-sh PAX6 vs. DU145-scramble cells and found that TFs exhibited certain occupancy among all of the genes with a significantly differentiated expression, which indicated that TFs might be one of the important downstream effectors in response to the knockdown of PAX6 (Supplementary Fig. S4 a). Among these TFs with significant expression differences, STAT5A was observed with a significant downregulation after knockdown of PAX6 , which indicated that it might be a promising downstream TFs of PAX6 to promote NE trans-differentiation (Fig.  6 a).

PAX6 promotes NE characteristics via STAT5A . a The heatmap of candidate TFs with significant expressional difference in DU145-sh PAX6 cells and DU145-Scramble cells. b Comparisons of STAT5A mRNA expression in CRPC-Adeno vs. NEPC based on the GSE32967 dataset (CRPC-Adeno, n  = 8; NEPC, n  = 14). c Representative IHC staining of STAT5A in tissues from patient with Primary PCa, CRPC or NEPC (Scale Bar: 100 μm). d ChIP assay of PAX6 binding at the promoter region of STAT5A in LNCaP- PAX6 cells. e Protein expression of PAX6, STAT5A, SYP and NSE in DU145-sh PAX6 cells with or without STAT5A overexpression. f Cell proliferation assay in DU145-sh PAX6 cells with or without STAT5A overexpression. g Representative image and quantification assay of tumorsphere formation in DU145-sh PAX6 cells with or without STAT5A over-expression. h Anatomic tumor images and tumor weight analysis of DU145-sh PAX6 cells inoculated xenografts with or without STAT5A overexpression ( n  = 6). i Tumor volume analysis of DU145-Scramble, DU145-sh PAX6 or DU145-sh PAX6  +  STAT5A cells inoculated xenografts respectively ( n  = 6). j Tumor weights analysis of DU145-sh PAX6 and DU145-sh PAX6  +  STAT5A cells inoculated xenografts respectively ( n  = 6). k Representative staining H&E and IHC staining of PAX6, Ki67, SYP, NSE, NCAM1 in DU145-sh PAX6 and DU145-sh PAX6  +  STAT5A cells inoculated xenograft samples (Scale Bar: 100 μm, with the boxed region enlarged and shown on the left, n  = 6). All the experiments were repeated for three times. Data represents the mean ± SD. ns: no significance, * p  < 0.05, *** p  < 0.001

To provide supporting evidence for the above hypothesis, we performed bioinformatics analysis and observed a positive correlation between the expression of PAX6 and STAT5A in TCGA database and GSE35988 dataset [ 31 ] respectively (Supplementary Fig. S4 b and S4c). Similarly, the endogenous STAT5A expression was also higher in samples from the NEPC patients compared to those from the CRPC-Adeno patients (Fig.  6 b). More importantly, IHC assay revealed a higher expression of STAT5A in NEPC group than in either CRPC or primary PCa group (Fig.  6 c). In addition, data from GSE70380 dataset indicated that the expression of STAT5A and NCAM1 was increased following with the ENZ treatment, while the expression levels of NKX3.1 and KLK3 were decreased (Supplementary Fig. S4 d). Moreover, we also identified a positive correlation between the expression of STAT5A and that of NE signature genes by analyzing the data from Broad/Cornell 2012 Cohort (Supplementary Fig. S4 e). Furthermore, we analyzed data from two independent datasets (GSE16560 and GSE116918) and found that patients with high expression of the STAT5A had a poorer prognosis (Supplementary Fig. S4 f). Therefore, these data together indicated that STAT5A appeared to mediate the promoting role of PAX6 in NE trans-differentiation.

In order to confirm this possibility, we performed functional assays in vitro and in vivo to evaluate the effect of STAT5A expressional change on the NE trans-differentiation. First, an upregulation of STAT5A and phosphorylated STAT5A (p-STAT5A) expression was observed in LNCaP- PAX6 cells (Supplementary Fig. S5 a). We also detected a higher expression level of STAT5A and p-STAT5A in LNCaP ENZ cells compared to the control cells (Supplementary Fig. S5 b). Reversely, after knockdown of PAX6 in LNCaP ENZ cells, we observed a downregulation of both total STAT5A and p-STAT5A expression (Supplementary Fig. S5 c). Subsequently, we investigated whether PAX6 promoted STAT5A expression at a transcriptional level. To this end, we performed ChIP assay in LNCaP- PAX6 cells and identified two binding sites of PAX6 on the STAT5A promoter region (Fig.  6 d). Next, in order to further validate that PAX6 induced the NE characteristics through upregulation of STAT5A expression, we overexpressed STAT5A in DU145-sh PAX6 cells and PC3-sh PAX6 cells respectively as rescue assays. We found that the expression of NE marker genes decreased with the downregulation of PAX6 and was compensated with the over-expression of STAT5A in DU145 and PC3 cells (Fig.  6 e and Supplementary Fig. S5 d). We also found that the cell proliferation was significantly decreased following the PAX6 knockdown but was increased to a higher or a similar level compared to that in the control after STAT5A overexpression in DU145 cells (Fig.  6 f). In addition, we carried out tumorsphere formation assays using DU145-sh PAX6 and PC3-sh PAX6 cells after overexpression of STAT5A . We observed that the reduced sphere-forming ability due to PAX6 -knockdown in DU145 and PC3 cells could be rescued by upregulation of STAT5A expression (Fig.  6 g and Supplementary Fig. S5 e). Reversely, knockdown of STAT5A in LNCaP- PAX6 and C42B- PAX6 cells attenuated the cell proliferation which was previously enhanced by over-expression of PAX6 and downregulated the expression of SYP and NSE as well (Supplementary Fig. S5 f and S5g).

More importantly, we expanded the above in vitro findings to an in vivo setting. After subcutaneous ectopic inoculation of DU145-sh PAX6 or PC3-sh PAX6 cells with or without overexpression of STAT5A , we found that knockdown of PAX6 significantly inhibited tumor growth compared to the control cells, which was evidenced by the decreased tumor volume and tumor weight as well as the repressed expression of Ki67 (Fig.  6 h - k and Supplementary Fig. S5 h-S5k). As expected, the expression of SYP, NSE and NCAM1 was also downregulated after PAX6 knockdown by IHC assay, which indicated that NE trans-differentiation was repressed due to the inhibition of PAX6 expression. However, over-expression of STAT5A after knockdown of PAX6 made tumor cells restore their ability of tumor growth, which exhibited no significant difference to the control cells on both tumor volume and tumor weight (Fig.  6 h - k and Supplementary Fig. S5 h-S5k). Consistent with the observation in vitro, the expression of SYP, NSE and NCAM1 was upregulated by overexpression of STAT5A as a rescue to PAX6 knockdown (Fig.  6 k and Supplementary Fig. S5 k). Taken together, our findings in vitro and in vivo indicated that PAX6 promotes NE trans-differentiation by upregulation of STAT5A that acts as a major effector.

PAX6 induces NE trans-differentiation through the MET/STAT5A pathway

Given the fact that the STAT family members could be activated by the MET , a well-known receptor tyrosine kinase [ 47 ], and the elevated expression of MET has been reported in various cancers including PCa [ 48 , 49 , 50 ], we herein wondered whether phosphorylation of MET can activate STAT5A [ 51 , 52 ] for promotion of NE trans-differentiation [ 53 ]. Since hepatocyte growth factor (HGF) is the sole ligand for MET [ 54 ] and is enriched in the tumor microenvironment [ 55 ], we first evaluated the function of MET on phosphorylating and activating STAT5A with the treatment of HGF in both LNCaP and C42B cells. We observed that the expression of phosphorylated MET (p-MET) was enhanced along with the increase of HGF concentration in both cells, which indicated a dose-dependent activation of MET by HGF. As a response, the phosphorylation level of STAT5A was in turn increased (Fig.  7 a). Additionally, when we knocked down MET in LNCaP cells, STAT5A failed to be phosphorylated even under the stimulation of HGF, as a confirmation of MET mediated activation of STAT5A in PCa (Fig.  7 b). At the same time, by IHC assay, we observed the elevated expression of MET in tissues from NEPC patients compared to that from either CRPC or primary PCa patients, which exhibited a similar profiling to that of PAX6 (Fig.  7 c). In addition, data from the GSE116918 dataset indicated that patients with high MET expression showed a worse prognosis (Supplementary Fig. S6 a). These results gave us a hint that the expression of MET might also be regulated by PAX6. To verify this possibility, we first carried out bioinformatics assays to determine the relationship between PAX6 and MET expression based on the TCGA database, Fred Hutchinson, 2016 Cohort [ 27 ] and GSE21034 dataset respectively. We found that there was a positive correlation between PAX6 and MET expression (Fig.  7 d). In the Broad/Cornell 2012 Cohort, we also found a positive correlation between MET expression and the expression of NE signature genes (Fig.  7 e). In both DU145-sh PAX6 and PC3-sh PAX6 cells, MET expression was downregulated at both mRNA and protein levels compared to the control (Fig.  7 f and g). In sharp contrast, in both LNCaP- PAX6 and C42B- PAX6 cells, we observed a significant upregulation of MET expression after overexpression of PAX6 (Supplementary Fig. S6 b and S6c). Furthermore, we detected a significant decrease of the expression and phosphorylation levels of MET after knockdown of PAX6 compared to the control, further supporting the notion that MET might also be a potential downstream effector of PAX6 (Fig.  7 h). To study whether PAX6 could directly bind to the MET promoter region and promote its transcription, we performed ChIP assay using LNCaP- PAX6 cells, and we identified a binding site of PAX6 on the MET promoter region, indicating a direct regulation of the transcription of MET by PAX6 (Fig.  7 i). Thus, our findings indicated that the elevated expression of PAX6 promoted the expression of both MET and STAT5A as its downstream effectors to activate the MET/STAT5A pathway for the development of NE trans-differentiation.

PAX6 promotes the expression of MET to further phosphorylate STAT5A. a Protein expression of MET, p-MET, STAT5A and p-STAT5A after stimulation with different concentrations of HGF in LNCaP and C42B cells. b Protein expression of MET, p-MET, STAT5A and p-STAT5A after stimulation with different concentrations of HGF in MET -knockdown or the control LNCaP cells. c Representative IHC staining of MET in tissues from patients with Primary PCa, CRPC or NEPC. d Correlation analysis of MET with PAX6 expression based on the GSE21034 dataset, TCGA database and the Fred Hutchinson, 2016 Cohort. e Correlation analysis of the expression of MET and NE signature genes based on the Broad 2012 Cohort. f mRNA and protein expression of MET in DU145-sh PAX6 cells and control cells. g mRNA and protein expression of MET in PC3-sh PAX6 cells and control cells. h Representative IHC staining of MET and p-MET in DU145-sh PAX6 and PC3-sh PAX6 compared with control cells inoculated xenograft samples (Scale Bar: 100 μm, with the boxed region enlarged and shown on the left). i ChIP assay of PAX6 binding at regions of the MET promoter in LNCaP cells. All the experiments were repeated for three times. Data represents the mean ± SD. *** p  < 0.001

Over-expression of PAX6 enhances cell plasticity by inhibiting H4K20me3 through STAT5A

Lineage transition from Adeno to NEPC is relatively a common type of cancer cell plasticity in ADT-treated PCa [ 9 ]. It has been reported that STAT5A has a tightly correlation with lineage plasticity both in stem cells [ 56 ] and in tumors [ 57 , 58 ]. Therefore, we wondered whether PAX6 could promote NE trans-differentiation via STAT5A mediated changes of cells lineage plasticity. To this end, we performed ATAC-seq on LNCaP- PAX6 cells or LNCaP- STAT5A cells to evaluate the changes of chromatin accessibility. We observed that as a response to either PAX6 or STAT5A overexpression, the general chromatin accessibility was enhanced in LNCaP cells (Fig.  8 a). By cluster analysis of motifs with differential accessibility, we found that both PAX6 and STAT5A overexpression could enhance the chromatin accessibility and expression level of the NE markers or drivers including SYP , ENO2 , CHGA , NCAM1, MYCN and ASCL1 (Fig.  8 b). Moreover, our above RNA-seq analysis in DU145-sh PAX6 vs. Scramble cells also revealed that both synapse assembly and neurofilament bundle assembly associated genes were downregulated as a response to PAX6 knockdown, which again indicated the PAX6 induced profiling changes associated with the NE trans-differentiation (Supplementary Fig. S7a). Therefore, these data indicated that PAX6 -induced activation of the MET/STAT5A pathway promotes NE trans-differentiation by enhancing chromatin accessibility to alter the cells lineage plasticity.

PAX6 induced the change of lineage plasticity by attenuating the H4K20me3. a The heatmap showing the average ATAC-Seq signal centered on the TSS of the nearest genes in LNCaP- PAX6 , LNCaP- STAT5A and control cells. b Chromatin accessibility of ENO2, CHGA , SYP , NCAM1, MYCN and ASCL1 in LNCaP- PAX6 or LNCaP- STAT5A cells compared with that in the control cells. c GO analysis showing the top 5 increased and decreased biological process in LNCaP- PAX6 vs. the control cells. d Protein expression of SMYD5 and KMT5C in LNCaP- PAX6 cells with or without knockdown of STAT5A . e Protein expression of SMYD5 and KMT5C in DU145-sh PAX6 cells with or without overexpression of STAT5A . f Protein expression of KMT5C and STAT5A in DU145-sh PAX6 and PC3-sh PAX6 cells. g Graphic summary of this study

By Gene ontology (GO) enrichment analysis, we found that chromatin accessibility was increased in the region for positive regulation of TGF-beta1 production and the region for response to growth factor (Fig.  8 c). Interestingly, other than these regions with increased chromatin accessibility, we also observed several decreased regions of chromatin accessibility after overexpression of PAX6 . The top 2 significantly decreased regions of chromatin accessibility were “negative regulation of peptidyl-serine phosphorylation of STAT protein (p value < 0.03)” and “histone H4-K20 trimethylation (H4K20me3) (p value < 0.03)”, which indicated an inhibition on the process of negative regulation of STAT signaling and an attenuation of the tumor suppressive H4K20me3 after PAX6 overexpression (Fig.  8 c). These results were consistent with our above finding that PAX6 could upregulate the expression of STAT5A and also gave us a hint to focus our investigation on the H4K20me3, which was an important epigenetic modification for gene silencing or repression and was well-reported to be repressed in tumors [ 59 ]. Thus, we further investigated whether elevated expression of PAX6 could suppress H4K20me3 through activation of STAT5A . To this end, we detected the expression of two major methyltransferases for catalyzing the trimethylation of H4K20, KMT5C [ 60 ] and SMYD5 [ 61 ]. As expected, we observed a decreased expression of both KMT5C and SMYD5 following over-expression of PAX6 in LNCaP cells (Fig.  8 d). Next, when STAT5A expression was inhibited in LNCaP- PAX6 cells as a rescue assay, the expression of KMT5C and SMYD5 was increased, which indicated a negative regulation of KMT5C and SMYD5 by the PAX6/STAT5A axis (Fig.  8 d). For further confirmation, we observed that expression of KMT5C and SMYD5 was upregulated in DU145-sh PAX6 cells compared to the control. Moreover, when STAT5A was overexpressed under the condition of PAX6 knockdown, the expression of both two genes was again repressed (Fig.  8 e). In contrast, direct knockdown of STAT5A in either DU145 or PC3 cells significantly upregulated the expression of KMT5C and SMYD5 (Fig.  8 f and Supplementary Fig. S7b) along with the downregulation of the expression of SYP , ENO2 , CHGA (Supplementary Fig. S7c). Thus, these results together indicated that PAX6/STAT5A axis appears to change the lineage plasticity through inhibiting the expression of methyltransferases catalyzing the trimethylation of H4K20, such as KMT5C and SMYD5, to attenuate the H4K20me3, causing the NE trans-differentiation in PCa cells (Fig.  8 g).

Resistance to the second-generation ADT is the main challenge for the therapy in PCa. One of the regulatory mechanisms for the resistance is the development of NE trans-differentiation for tumor progression from primary PCa to NEPC. In this study, we found that PAX6, a neuron-related TF, is selectively upregulated in ADT-induced NEPC. Activated PAX6 signaling reprograms the chromatin accessibility via the MET/STAT5A axis, thereby enhancing the lineage plasticity. As a key downstream of PAX6 , STAT5A inhibits the expression of two major methyltransferases KMT5C and SMYD5, both of which mediate H4k20me3. Thus, activation of the PAX6/STAT5A axis leads to a global downregulation of H4K20me3, triggers cancer cells lineage changing and confers a NE transcriptional profile in PCa cells. Ablation of PAX6 in vitro and in vivo inhibits the development and progression of NEPC, and prevents the Adeno-to-NE phenotypic transition. Therefore, our study demonstrates that targeting PAX6 is an attractive therapeutic approach for NE malignancies.

It is worth emphasizing that we have identified a novel function of PAX6 in the regulation of NE cancer cells, which extends its role besides a coordinator of neural development in the CNS or as a key regulator of the development and maintenance of the eyes [ 62 ]. Firstly, our IHC analysis on the human NEPC samples reveals that PAX6 is highly expressed in NEPC. Secondly, knockdown of PAX6 in PCa cells exerts a profoundly repressive role during the progression of NEPC both in vitro and in vivo. Thirdly, in addition to PCa, we also detected a significantly higher expression of PAX6 in another NE tumor, SCLC than NSCLC. In agreement with current findings, it has been reported that PAX6 is critical for self-renewal of differentiation-competent radial glia-like neural stem cells [ 63 ] or acts as a transcriptional determinant in determining the transition from pluripotency to the neuroectoderm fate in human by differentially targeting pluripotent and neuroectoderm genes [ 16 ]. Finally, our sequencing results also show the enrichment of signaling pathways related to axons guidance and nerve filament development and assembly in PCa cells with a high expression of PAX6 . The last neuronal features might be related to additional potential function in tumor metastasis or possible interactions with nerve cells or other cells such as immune cells [ 64 , 65 ] in the tumor microenvironment to enhance the aggressiveness and therapy resistance, which is a subject of future studies.

Consistent with our findings, STAT family has been reported to be able to promote aggressive behavior and NE trans-differentiation in PCa cells [ 10 , 66 ]. Although the STAT5A pathway has been well-known for promoting cell proliferation, invasion and survival in various cancers [ 67 , 68 , 69 ], it has not been shown whether and how this pathway is also involved in the regulation of NE trans-differentiation. It is worth mentioned that in our study, we provide several lines of evidences to demonstrate that the MET/STAT5A pathway works as a major downstream signaling cascade of PAX6 for promotion of NE trans-differentiation in PCa. First, by bioinformatics and experimental assays, we revealed a positive correlation between the expression of PAX6 and the expression of STAT5A or MET . Second, overexpression of STAT5A upregulates the expression of NE signature genes such as SYP, CHGA, ENO2 and NCAM1 . Third, by rescue assays in vitro and in vivo, knockdown of STAT5A reverses the phenotype of NE trans-differentiation in PCa, even under the condition of PAX6 overexpression. Fourth, activation of the PAX6/STAT5A axis can change the lineage plasticity mainly by attenuation of H4K20me3 modification. Supporting for our findings comes from a previous report showing STAT3 as a key regulator of lineage plasticity to enhance the chromatin accessibility to promote NE trans-differentiation in PCa, during which STAT3 expression can be induced by multiple upstream TFs such as YIN YANG 1 [ 70 ]. Therefore, the current study adds a sub-member of STAT family, STAT5A, which attenuates H4K20me3 in PCa, as a new molecule to the list that regulates the lineage plasticity.

Moreover, our study uncovers altered epigenetic modulation of histone, which acts to orchestrate the Adeno-to-NE lineage transition. Due to the requirement of massive gene expressional changes during the lineage shift, epigenetic alteration has been proposed to be actively involved. However, the upstream signals and regulators that trigger the epigenetic reprogramming remain to be identified. Nevertheless, our results demonstrate that H4K20me3 is attenuated by the PAX6/STAT5A axis-induced inhibition of methyltransferases KMT5C and SMYD5 in PCa cells. Using ATAC-seq assay, we uncover that the PAX6 and STAT5A activation leads to a global change in transcriptional output, in particular, an increased NE lineage attribution, including enhanced expression of neuron-related genes such as SYP, CHGA, ENO2 , NCAM1 , axon guidance associated genes, synapse assembly and neurofilament bundle assembly associated genes.

In summary, our study demonstrates that elevated expression of PAX6 changes the lineage plasticity to promote NE trans-differentiation via activation of the downstream MET/STAT5A pathway. Although ADT-induced NEPC is a category of highly aggressive malignancies with an extremely poor prognosis and a lack of effective targeted therapies, our findings indicate that attenuation of PAX6 function or inhibiting its expression might be a potential therapeutic strategy to restore the sensitivity to the second-generation ADT in NEPC.

Data availability

RNA-seq and ATAC-seq data in this study is available in GEO database (GSE250422). Other data that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

Adenocarcinoma

Androgen-dependent prostate cancer

Androgen deprivation treatment

Androgen receptor

Androgen response element

Biochemical recurrence

Central nervous system

Castration-resistant prostate cancer

Disease free survival

Enzalutamide

Gene Expression Omnibus

Gene Set Enrichment Analysis

Hormone-refractory cancers

Hormone-sensitive cancers

Histone H4-K20 trimethylation

Immunohistochemical

Lung adenocarcinoma

Metastasis-free survival

Neuroendocrine

• Neuroendocrine prostate cancer

Non-neuroendocrine small cell lung cancer

Overall survival

Paired box 6

Prostate cancer

Patient-derived xenograft

Small cell lung cancer

Transcription factor

The Cancer Genome Atlas

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Acknowledgements

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This work was supported by the National Key Research and Development Program of China (2023YFC1404101 and 2022YFA1302704 to WQG), the National Natural Science Foundation of China (82072843 to YXF, U23A20441 to WQG and 82372698 to BD), the Science and Technology Commission of Shanghai Municipality (20JC1417600 and 21JC1404100 to WQG and 19411967400 to BD), the Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai to WQG, 111 Project (B21024) and KC Wong foundation to WQG. Summit Plateau Program, Research Physician Program, Shanghai Jiao Tong University School of Medicine to BD, Shanghai Municipal Health Commission (2019LJ11, 2020CXJQ03) to BD.

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Nan Jing, Xinxing Du and Yu Liang equal contribution and co-first authors.

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State Key Laboratory of Systems Medicine for Cancer, Renji-Med-X Stem Cell Research Center, Ren Ji Hospital, School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200127, China

Nan Jing, ZhenKeke Tao, Shijia Bao, Huixiang Xiao, Wei-Qiang Gao & Yu-Xiang Fang

Med-X Research Institutes, Shanghai Jiao Tong University, Shanghai, 200030, China

Nan Jing & Wei-Qiang Gao

Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China

Xinxing Du & Baijun Dong

State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China

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NJ, WQG and YXF conceptualized and designed the study. NJ, XD, YL, ZKT, SJB, HX and BD performed the experiments and analyzed the data. NJ, WQG and YXF wrote the manuscript. All authors read and approved the final manuscript.

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All animal experiments were conducted according to the protocols approved by the Committee on Animal Care of Ren Ji Hospital. The investigation was conducted in accordance with ethical standards, national and international guidelines, and the Committee for Ethical Review of Research Involving Animal Subjects at Ren Ji Hospital (approval number: RA-2021-192). Human PCa tissue samples used for IHC staining were obtained from the Department of Urology at the Ren Ji Hospital (Shanghai, China) and were approved by the Committee for Ethical Review of Research Involving Human Subjects at Ren Ji Hospital (approval number: KY2019-081) in accordance with national and international guidelines. Clinical data of the patients who participated in this study are summarized in Supplementary Table S4 .

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Jing, N., Du, X., Liang, Y. et al. PAX6 promotes neuroendocrine phenotypes of prostate cancer via enhancing MET/STAT5A-mediated chromatin accessibility. J Exp Clin Cancer Res 43 , 144 (2024). https://doi.org/10.1186/s13046-024-03064-1

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DOI : https://doi.org/10.1186/s13046-024-03064-1

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Background: Executive dysfunction is a core feature of frontotemporal dementia (FTD). Whilst there has been extensive research into such impairments in sporadic FTD, there has been little research in the familial forms. Methods: 752 individuals were recruited in total: 214 C9orf72, 205 GRN and 86 MAPT mutation carriers, stratified into asymptomatic, prodromal and fully symptomatic, and 247 mutation negative controls. Attention and executive function were measured using the Weschler Memory Scale-Revised (WMS-R) Digit Span Backwards (DSB), the Wechsler Adult Intelligence Scale-Revised Digit Symbol task, the Trail Making Test Parts A and B, the Delis-Kaplan Executive Function System Color Word Interference Test and verbal fluency tasks (letter and category). Linear regression models with bootstrapping were used to assess differences between groups. Correlation of task score with disease severity was also performed, as well an analysis of the neuroanatomical correlates of each task. Results: Fully symptomatic C9orf72, GRN and MAPT mutation carriers were significantly impaired on all tasks compared with controls (all p<0.001), except on the WMS-R DSB in the MAPT mutation carriers (p=0.147). Whilst asymptomatic and prodromal C9orf72 individuals also demonstrated deficits compared with controls, neither the GRN or MAPT asymptomatic or prodromal mutation carriers showed significant differences. All tasks significantly correlated with disease severity in each of the genetic groups (all p<0.001). Conclusions: Individuals with C9orf72 mutations show difficulties with executive function from very early on in the disease and this continues to deteriorate with disease severity. In contrast, similar difficulties occur only in the later stages of the disease in GRN and MAPT mutation carriers. This differential performance across the genetic groups will be important in neuropsychological task selection in upcoming clinical trials.

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The authors have declared no competing interest.

Funding Statement

We thank the research participants for their contribution to the study. The Dementia Research Centre is supported by Alzheimer's Research UK, Alzheimer's Society, Brain Research UK, and The Wolfson Foundation. This work was supported by the NIHR UCL/H Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility, and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. JDR is supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). This work was also supported by the MRC UK GENFI grant (MR/M023664/1), the Bluefield Project and the JPND GENFI-PROX grant (2019-02248). Several authors of this publication (JCvS, MS, RSV, AD, MO, RV, JDR) are members of the European Reference Network for Rare Neurological Diseases - Project ID No 739510. MB is supported by a Fellowship award from the Alzheimer's Society, UK (AS-JF-19a-004-517). MB is also supported by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. RC/CG are supported by a Frontotemporal Dementia Research Studentships in memory of David Blechner funded through The National Brain Appeal (RCN 290173). JBR is supported by the Wellcome Trust (103838), the Medical Research Council (MC_UU_00030/14; MR/T033371/1) and the NIHR Cambridge Biomedical Research Centre (NIHR203312). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. The GIF template database includes volumetric MRI scans from the University College London Genetic FTD Initiative (GENFI) study (www.genfi.org.uk) which is funded by the Medical Research Council UK GENFI grant (MR/M023664/1). The GIF template database includes volumetric MRI scans from the University College London Genetic FTD Initiative (GENFI) study (www.genfi.org.uk) which is funded by the Medical Research Council UK GENFI grant (MR/M023664/1).

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics committee/IRB of London Queen Square Research Ethics gave ethical approval for this work

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

All data produced in the present study are available upon reasonable request to the authors

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Supplementary Material

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