true experimental research characteristics

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True Experimental Design - Types & How to Conduct

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True-experimental research is often considered the most accurate research. A researcher has complete control over the process which helps reduce any error in the result. This also increases the confidence level of the research outcome. 

In this blog, we will explore in detail what it is, its various types, and how to conduct it in 7 steps.

What is a true experimental design?

True experimental design is a statistical approach to establishing a cause-and-effect relationship between variables. This research method is the most accurate forms which provides substantial backing to support the existence of relationships.

There are three elements in this study that you need to fulfill in order to perform this type of research:

1. The existence of a control group:  The sample of participants is subdivided into 2 groups – one that is subjected to the experiment and so, undergoes changes and the other that does not. 

2. The presence of an independent variable:  Independent variables that influence the working of other variables must be there for the researcher to control and observe changes.

3.   Random assignment:  Participants must be randomly distributed within the groups.

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An example of true experimental design

A study to observe the effects of physical exercise on productivity levels can be conducted using a true experimental design.

Suppose a group of 300 people volunteer for a study involving office workers in their 20s. These 300 participants are randomly distributed into 3 groups. 

• 1st Group:  A control group that does not participate in exercising and has to carry on with their everyday schedule. 
• 2nd Group:  Asked to indulge in home workouts for 30-45 minutes every day for one month. 
• 3rd Group:  Has to work out 2 hours every day for a month. Both groups have to take one rest day per week.

In this research, the  level of physical exercise acts  as an  independent variable  while the  performance at the workplace  is a  dependent variable  that varies with the change in exercise levels.

Before initiating the true experimental research, each participant’s current performance at the workplace is evaluated and documented. As the study goes on, a progress report is generated for each of the 300 participants to monitor how their physical activity has impacted their workplace functioning.

At the end of two weeks, participants from the 2nd and 3rd groups that are able to endure their current level of workout, are asked to increase their daily exercise time by half an hour. While those that aren’t able to endure, are suggested to either continue with the same timing or fix the timing to a level that is half an hour lower. 

So, in this true experimental design a participant who at the end of two weeks is not able to put up with 2 hours of workout, will now workout for 1 hour and 30 minutes for the remaining tenure of two weeks while someone who can endure the 2 hours, will now push themselves towards 2 hours and 30 minutes.

In this manner, the researcher notes the timings of each member from the two active groups for the first two weeks and the remaining two weeks after the change in timings and also monitors their corresponding performance levels at work.

The above example can be categorized as true experiment research since now we have:

• Control group:  Group 1 carries on with their schedule without being conditioned to exercise.
• Independent variable : The duration of exercise each day.
• Random assignment:  300 participants are randomly distributed into 3 groups and as such, there are no criteria for the assignment.

What is the purpose of conducting true experimental research?

Both the primary usage and purpose of a true experimental design lie in establishing meaningful relationships based on quantitative surveillance. 

True experiments focus on connecting the dots between two or more variables by displaying how the change in one variable brings about a change in another variable. It can be as small a change as having enough sleep improves retention or as large scale as geographical differences affect consumer behavior. 

The main idea is to ensure the presence of different sets of variables to study with some shared commonality.

Beyond this, the research is used when the three criteria of random distribution, a control group, and an independent variable to be manipulated by the researcher, are met.

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What are the advantages of true experimental design?

Let’s take a look at some advantages that make this research design conclusive and accurate research.

Concrete method of research:

The statistical nature of the experimental design makes it highly credible and accurate. The data collected from the research is subjected to statistical tools. 

This makes the results easy to understand, objective and actionable. This makes it a better alternative to observation-based studies that are subjective and difficult to make inferences from.

Easy to understand and replicate:

Since the research provides hard figures and a precise representation of the entire process, the results presented become easily comprehensible for any stakeholder. 

Further, it becomes easier for future researchers conducting studies around the same subject to get a grasp of prior takes on the same and replicate its results to supplement their own research.

Establishes comparison:

The presence of a control group in true experimental research allows researchers to compare and contrast. The degree to which a methodology is applied to a group can be studied with respect to the end result as a frame of reference.

Conclusive:

The research combines observational and statistical analysis to generate informed conclusions. This directs the flow of follow-up actions in a definite direction, thus, making the research process fruitful.

What are the disadvantages of true experimental design?

We should also learn about the disadvantages it can pose in research to help you determine when and how you should use this type of research. 

This research design is costly. It takes a lot of investment in recruiting and managing a large number of participants which is necessary for the sample to be representative. 

The high resource investment makes it highly important for the researcher to plan each aspect of the process to its minute details.

Too idealistic:

The research takes place in a completely controlled environment. Such a scenario is not representative of real-world situations and so the results may not be authentic. 

T his is one of the main limitation why open-field research is preferred over lab research, wherein the researcher can influence the study.

Time-consuming:

Setting up and conducting a true experiment is highly time-consuming. This is because of the processes like recruiting a large enough sample, gathering respondent data, random distribution into groups, monitoring the process over a span of time, tracking changes, and making adjustments. 

The amount of processes, although essential to the entire model, is not a feasible option to go for when the results are required in the near future.

Now that we’ve learned about the advantages and disadvantages let’s look at its types.

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What are the 3 types of true experimental design?

The research design is categorized into three types based on the way you should conduct the research. Each type has its own procedure and guidelines, which you should be aware of to achieve reliable data.  

The three types are: 

1) Post-test-only control group design. 

2) Pre-test post-test control group design.

3) Solomon four group control design.

Let’s see how these three types differ. 

1) Post-test-only control group design:

In this type of true experimental research, the control as well as the experimental group that has been formed using random allocation, are not tested before applying the experimental methodology. This is so as to avoid affecting the quality of the study.

The participants are always on the lookout to identify the purpose and criteria for assessment. Pre-test conveys to them the basis on which they are being judged which can allow them to modify their end responses, compromising the quality of the entire research process. 

However, this can hinder your ability to establish a comparison between the pre-experiment and post-experiment conditions which weighs in on the changes that have taken place over the course of the research.

2) Pre-test post-test control group design:

It is a modification of the post-test control group design with an additional test carried out before the implementation of the experimental methodology. 

This two-way testing method can help in noticing significant changes brought in the research groups as a result of the experimental intervention. There is no guarantee that the results present the true picture as post-testing can be affected due to the exposure of the respondents to the pre-test.

3) Solomon four group control design:

This type of true experimental design involves the random distribution of sample members into 4 groups. These groups consist of 2 control groups that are not subjected to the experiments and changes and 2 experimental groups that the experimental methodology applies to.

Out of these 4 groups, one control and one experimental group is used for pre-testing while all four groups are subjected to post-tests.

This way researcher gets to establish pre-test post-test contrast while there remains another set of respondents that have not been exposed to pre-tests and so, provide genuine post-test responses, thus, accounting for testing effects.

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What is the difference between pre-experimental & true experimental research design.

Pre-experimental research helps determine the researchers’ intervention on a group of people. It is a step where you design the proper experiment to address a research question. 

True experiment defines that you are conducting the research. It helps establish a cause-and-effect relationship between the variables. 

We’ll discuss the differences between the two based on four categories, which are: 

• Observatory Vs. Statistical. 
• Absence Vs. Presence of control groups. 
• Non-randomization Vs. Randomization. 
• Feasibility test Vs. Conclusive test.

Let’s find the differences to better understand the two experiments. 

Observatory vs Statistical:

Pre-experimental research  is an observation-based model i.e. it is highly subjective and qualitative in nature. 

The true experimental design  offers an accurate analysis of the data collected using statistical data analysis tools.

Absence vs Presence of control groups:

Pre-experimental research  designs do not usually employ a control group which makes it difficult to establish contrast. 

While all three types of  true experiments  employ control groups.

Non-randomization vs Randomization:

Pre-experimental research  doesn’t use randomization in certain cases whereas 

True experimental research  always adheres to a randomization approach to group distribution.

Feasibility test vs Conclusive test:

Pre-tests  are used as a feasibility mechanism to see if the methodology being applied is actually suitable for the research purpose and whether it will have an impact or not.

While  true experiments  are conclusive in nature.

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7 Steps to conduct a true experimental research

It’s important to understand the steps/guidelines of research in order to maintain research integrity and gather valid and reliable data.  

We have explained 7 steps to conducting this research in detail. The TL;DR version of it is: 

1) Identify the research objective.

2) Identify independent and dependent variables.

3) Define and group the population.

4) Conduct Pre-tests.

5) Conduct the research.

6) Conduct post-tests.

7) Analyse the collected data. 

Now let’s explore these seven steps in true experimental design. 

1) Identify the research objective:

Identify the variables which you need to analyze for a cause-and-effect relationship. Deliberate which particular relationship study will help you make effective decisions and frame this research objective in one of the following manners:

• Determination of the impact of X on Y
• Studying how the usage/application of X causes Y

2) Identify independent and dependent variables:

Establish clarity as to what would be your controlling/independent variable and what variable would change and would be observed by the researcher. In the above samples, for research purposes, X is an independent variable & Y is a dependent variable.

3) Define and group the population:

Define the targeted audience for the true experimental design. It is out of this target audience that a sample needs to be selected for accurate research to be carried out. It is imperative that the target population gets defined in as much detail as possible.

To narrow the field of view, a random selection of individuals from the population is carried out. These are the selected respondents that help the researcher in answering their research questions. Post their selection, this sample of individuals gets randomly subdivided into control and experimental groups.

4) Conduct Pre-tests:

Before commencing with the actual study, pre-tests are to be carried out wherever necessary. These pre-tests take an assessment of the condition of the respondent so that an effective comparison between the pre and post-tests reveals the change brought about by the research.

5) Conduct the research:

Implement your experimental procedure with the experimental group created in the previous step in the true experimental design. Provide the necessary instructions and solve any doubts or queries that the participants might have. Monitor their practices and track their progress. Ensure that the intervention is being properly complied with, otherwise, the results can be tainted.

6) Conduct post-tests:

Gauge the impact that the intervention has had on the experimental group and compare it with the pre-tests. This is particularly important since the pre-test serves as a starting point from where all the changes that have been measured in the post-test, are the effect of the experimental intervention. 

So for example: If the pre-test in the above example shows that a particular customer service employee was able to solve 10 customer problems in two hours and the post-test conducted after a month of 2-hour workouts every day shows a boost of 5 additional customer problems being solved within those 2 hours, the additional 5 customer service calls that the employee makes is the result of the additional productivity gained by the employee as a result of putting in the requisite time

7) Analyse the collected data:

Use appropriate statistical tools to derive inferences from the data observed and collected. Correlational data analysis tools and tests of significance are highly effective relationship-based studies and so are highly applicable for true experimental research.

This step also includes differentiating between the pre and the post-tests for scoping in on the impact that the independent variable has had on the dependent variable. A contrast between the control group and the experimental groups sheds light on the change brought about within the span of the experiment and how much change is brought intentionally and is not caused by chance.

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Wrapping up;

This sums up everything about true experimental design. While it’s often considered complex and expensive, it is also one of the most accurate research.

The true experiment uses statistical analysis which ensures that your data is reliable and has a high confidence level. Curious to learn how you can use  survey software  to conduct your experimental research,  book a meeting with us .

• What is true experimental research design?

True experimental research design helps investigate the cause-and-effect relationships between the variables under study. The research method requires manipulating an independent variable, random assignment of participants to different groups, and measuring the dependent variable. 

• How does true experiment research differ from other research designs?

The true experiment uses random selection/assignment of participants in the group to minimize preexisting differences between groups. It allows researchers to make causal inferences about the influence of independent variables. This is the factor that makes it different from other research designs like correlational research. 

• What are the key components of true experimental research designs?

The following are the important factors of a true experimental design: 

• Manipulation of the independent variable. 
• Control groups. 
• Experiment groups. 
• Dependent variable. 
• Random assignment. 
• What are some advantages of true experiment design?

It enables you to establish causal relationships between variables and offers control over the confounding variables. Moreover, you can generalize the research findings to the target population. 

• What ethical considerations are important in a true experimental research design?

When conducting this research method, you must obtain informed consent from the participants. It’s important to ensure the confidentiality and privacy of the participants to minimize any risk or harm. 

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True Experimental Design

True experimental design is regarded as the most accurate form of experimental research, in that it tries to prove or disprove a hypothesis mathematically, with statistical analysis.

This article is a part of the guide:

• Research Designs
• Quantitative and Qualitative Research
• Literature Review
• Quantitative Research Design

Browse Full Outline

• 1 Research Designs
• 2.1 Pilot Study
• 2.2 Quantitative Research Design
• 2.3 Qualitative Research Design
• 2.4 Quantitative and Qualitative Research
• 3.1 Case Study
• 3.2 Naturalistic Observation
• 3.3 Survey Research Design
• 3.4 Observational Study
• 4.1 Case-Control Study
• 4.2 Cohort Study
• 4.3 Longitudinal Study
• 4.4 Cross Sectional Study
• 4.5 Correlational Study
• 5.1 Field Experiments
• 5.2 Quasi-Experimental Design
• 5.3 Identical Twins Study
• 6.1 Experimental Design
• 6.2 True Experimental Design
• 6.3 Double Blind Experiment
• 6.4 Factorial Design
• 7.1 Literature Review
• 7.2 Systematic Reviews
• 7.3 Meta Analysis

For some of the physical sciences, such as physics, chemistry and geology, they are standard and commonly used. For social sciences, psychology and biology, they can be a little more difficult to set up.

For an experiment to be classed as a true experimental design, it must fit all of the following criteria.

• The sample groups must be assigned randomly .
• There must be a viable control group .
• Only one variable can be manipulated and tested. It is possible to test more than one, but such experiments and their statistical analysis tend to be cumbersome and difficult.
• The tested subjects must be randomly assigned to either control or experimental groups.

The results of a true experimental design can be statistically analyzed and so there can be little argument about the results .

It is also much easier for other researchers to replicate the experiment and validate the results.

For physical sciences working with mainly numerical data, it is much easier to manipulate one variable, so true experimental design usually gives a yes or no answer.

Disadvantages

Whilst perfect in principle, there are a number of problems with this type of design. Firstly, they can be almost too perfect, with the conditions being under complete control and not being representative of real world conditions.

For psychologists and behavioral biologists, for example, there can never be any guarantee that a human or living organism will exhibit ‘normal’ behavior under experimental conditions.

True experiments can be too accurate and it is very difficult to obtain a complete rejection or acceptance of a hypothesis because the standards of proof required are so difficult to reach.

True experiments are also difficult and expensive to set up. They can also be very impractical.

While for some fields, like physics, there are not as many variables so the design is easy, for social sciences and biological sciences, where variations are not so clearly defined it is much more difficult to exclude other factors that may be affecting the manipulated variable.

True experimental design is an integral part of science, usually acting as a final test of a hypothesis . Whilst they can be cumbersome and expensive to set up, literature reviews , qualitative research and descriptive research can serve as a good precursor to generate a testable hypothesis, saving time and money.

Whilst they can be a little artificial and restrictive, they are the only type of research that is accepted by all disciplines as statistically provable.

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Martyn Shuttleworth (Mar 24, 2008). True Experimental Design. Retrieved Sep 09, 2024 from Explorable.com: https://explorable.com/true-experimental-design

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

Guide to Experimental Design | Overview, 5 steps & Examples

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

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

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

There are five key steps in designing an experiment:

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

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

Table of contents

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

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

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

Start by simply listing the independent and dependent variables .

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Randomization

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

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

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

Between-subjects vs. within-subjects

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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

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

When designing the experiment, you decide:

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

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

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

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

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

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

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

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

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

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

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Experimental design: Guide, steps, examples

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Experimental research design is a scientific framework that allows you to manipulate one or more variables while controlling the test environment. 

When testing a theory or new product, it can be helpful to have a certain level of control and manipulate variables to discover different outcomes. You can use these experiments to determine cause and effect or study variable associations. 

This guide explores the types of experimental design, the steps in designing an experiment, and the advantages and limitations of experimental design. 

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• What is experimental research design?

You can determine the relationship between each of the variables by: 

Manipulating one or more independent variables (i.e., stimuli or treatments)

Applying the changes to one or more dependent variables (i.e., test groups or outcomes)

With the ability to analyze the relationship between variables and using measurable data, you can increase the accuracy of the result. 

What is a good experimental design?

A good experimental design requires: 

Significant planning to ensure control over the testing environment

Sound experimental treatments

Properly assigning subjects to treatment groups

Without proper planning, unexpected external variables can alter an experiment's outcome. 

To meet your research goals, your experimental design should include these characteristics:

Provide unbiased estimates of inputs and associated uncertainties

Enable the researcher to detect differences caused by independent variables

Include a plan for analysis and reporting of the results

Provide easily interpretable results with specific conclusions

What's the difference between experimental and quasi-experimental design?

The major difference between experimental and quasi-experimental design is the random assignment of subjects to groups. 

A true experiment relies on certain controls. Typically, the researcher designs the treatment and randomly assigns subjects to control and treatment groups. 

However, these conditions are unethical or impossible to achieve in some situations.

When it's unethical or impractical to assign participants randomly, that’s when a quasi-experimental design comes in. 

This design allows researchers to conduct a similar experiment by assigning subjects to groups based on non-random criteria. 

Another type of quasi-experimental design might occur when the researcher doesn't have control over the treatment but studies pre-existing groups after they receive different treatments.

When can a researcher conduct experimental research?

Various settings and professions can use experimental research to gather information and observe behavior in controlled settings. 

Basically, a researcher can conduct experimental research any time they want to test a theory with variable and dependent controls. 

Experimental research is an option when the project includes an independent variable and a desire to understand the relationship between cause and effect. 

• The importance of experimental research design

Experimental research enables researchers to conduct studies that provide specific, definitive answers to questions and hypotheses. 

Researchers can test Independent variables in controlled settings to:

Test the effectiveness of a new medication

Design better products for consumers

Answer questions about human health and behavior

Developing a quality research plan means a researcher can accurately answer vital research questions with minimal error. As a result, definitive conclusions can influence the future of the independent variable. 

Types of experimental research designs

There are three main types of experimental research design. The research type you use will depend on the criteria of your experiment, your research budget, and environmental limitations. 

Pre-experimental research design

A pre-experimental research study is a basic observational study that monitors independent variables’ effects. 

During research, you observe one or more groups after applying a treatment to test whether the treatment causes any change. 

The three subtypes of pre-experimental research design are:

One-shot case study research design

This research method introduces a single test group to a single stimulus to study the results at the end of the application. 

After researchers presume the stimulus or treatment has caused changes, they gather results to determine how it affects the test subjects. 

One-group pretest-posttest design

This method uses a single test group but includes a pretest study as a benchmark. The researcher applies a test before and after the group’s exposure to a specific stimulus. 

Static group comparison design

This method includes two or more groups, enabling the researcher to use one group as a control. They apply a stimulus to one group and leave the other group static. 

A posttest study compares the results among groups. 

True experimental research design

A true experiment is the most common research method. It involves statistical analysis to prove or disprove a specific hypothesis . 

Under completely experimental conditions, researchers expose participants in two or more randomized groups to different stimuli. 

Random selection removes any potential for bias, providing more reliable results. 

These are the three main sub-groups of true experimental research design:

Posttest-only control group design

This structure requires the researcher to divide participants into two random groups. One group receives no stimuli and acts as a control while the other group experiences stimuli.

Researchers perform a test at the end of the experiment to observe the stimuli exposure results.

Pretest-posttest control group design

This test also requires two groups. It includes a pretest as a benchmark before introducing the stimulus. 

The pretest introduces multiple ways to test subjects. For instance, if the control group also experiences a change, it reveals that taking the test twice changes the results.

Solomon four-group design

This structure divides subjects into two groups, with two as control groups. Researchers assign the first control group a posttest only and the second control group a pretest and a posttest. 

The two variable groups mirror the control groups, but researchers expose them to stimuli. The ability to differentiate between groups in multiple ways provides researchers with more testing approaches for data-based conclusions. 

Quasi-experimental research design

Although closely related to a true experiment, quasi-experimental research design differs in approach and scope. 

Quasi-experimental research design doesn’t have randomly selected participants. Researchers typically divide the groups in this research by pre-existing differences. 

Quasi-experimental research is more common in educational studies, nursing, or other research projects where it's not ethical or practical to use randomized subject groups.

• 5 steps for designing an experiment

Experimental research requires a clearly defined plan to outline the research parameters and expected goals. 

Here are five key steps in designing a successful experiment:

Step 1: Define variables and their relationship

Your experiment should begin with a question: What are you hoping to learn through your experiment? 

The relationship between variables in your study will determine your answer.

Define the independent variable (the intended stimuli) and the dependent variable (the expected effect of the stimuli). After identifying these groups, consider how you might control them in your experiment. 

Could natural variations affect your research? If so, your experiment should include a pretest and posttest. 

Step 2: Develop a specific, testable hypothesis

With a firm understanding of the system you intend to study, you can write a specific, testable hypothesis. 

What is the expected outcome of your study? 

Develop a prediction about how the independent variable will affect the dependent variable. 

How will the stimuli in your experiment affect your test subjects? 

Your hypothesis should provide a prediction of the answer to your research question . 

Step 3: Design experimental treatments to manipulate your independent variable

Depending on your experiment, your variable may be a fixed stimulus (like a medical treatment) or a variable stimulus (like a period during which an activity occurs). 

Determine which type of stimulus meets your experiment’s needs and how widely or finely to vary your stimuli. 

Step 4: Assign subjects to groups

When you have a clear idea of how to carry out your experiment, you can determine how to assemble test groups for an accurate study. 

When choosing your study groups, consider: 

The size of your experiment

Whether you can select groups randomly

Your target audience for the outcome of the study

You should be able to create groups with an equal number of subjects and include subjects that match your target audience. Remember, you should assign one group as a control and use one or more groups to study the effects of variables. 

Step 5: Plan how to measure your dependent variable

This step determines how you'll collect data to determine the study's outcome. You should seek reliable and valid measurements that minimize research bias or error. 

You can measure some data with scientific tools, while you’ll need to operationalize other forms to turn them into measurable observations.

• Advantages of experimental research

Experimental research is an integral part of our world. It allows researchers to conduct experiments that answer specific questions. 

While researchers use many methods to conduct different experiments, experimental research offers these distinct benefits:

Researchers can determine cause and effect by manipulating variables.

It gives researchers a high level of control.

Researchers can test multiple variables within a single experiment.

All industries and fields of knowledge can use it. 

Researchers can duplicate results to promote the validity of the study .

Replicating natural settings rapidly means immediate research.

Researchers can combine it with other research methods.

It provides specific conclusions about the validity of a product, theory, or idea.

• Disadvantages (or limitations) of experimental research

Unfortunately, no research type yields ideal conditions or perfect results. 

While experimental research might be the right choice for some studies, certain conditions could render experiments useless or even dangerous. 

Before conducting experimental research, consider these disadvantages and limitations:

Required professional qualification

Only competent professionals with an academic degree and specific training are qualified to conduct rigorous experimental research. This ensures results are unbiased and valid. 

Limited scope

Experimental research may not capture the complexity of some phenomena, such as social interactions or cultural norms. These are difficult to control in a laboratory setting.

Resource-intensive

Experimental research can be expensive, time-consuming, and require significant resources, such as specialized equipment or trained personnel.

Limited generalizability

The controlled nature means the research findings may not fully apply to real-world situations or people outside the experimental setting.

Practical or ethical concerns

Some experiments may involve manipulating variables that could harm participants or violate ethical guidelines . 

Researchers must ensure their experiments do not cause harm or discomfort to participants. 

Sometimes, recruiting a sample of people to randomly assign may be difficult. 

• Experimental research design example

Experiments across all industries and research realms provide scientists, developers, and other researchers with definitive answers. These experiments can solve problems, create inventions, and heal illnesses. 

Product design testing is an excellent example of experimental research. 

A company in the product development phase creates multiple prototypes for testing. With a randomized selection, researchers introduce each test group to a different prototype. 

When groups experience different product designs , the company can assess which option most appeals to potential customers. 

Experimental research design provides researchers with a controlled environment to conduct experiments that evaluate cause and effect. 

Using the five steps to develop a research plan ensures you anticipate and eliminate external variables while answering life’s crucial questions.

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Module 2: Research Design - Section 2

• Section 1 Discussion
• Section 2 Discussion

Section 2: Experimental Studies

Unlike a descriptive study, an experiment is a study in which a treatment, procedure, or program is intentionally introduced and a result or outcome is observed. The American Heritage Dictionary of the English Language defines an experiment as "A test under controlled conditions that is made to demonstrate a known truth, to examine the validity of a hypothesis, or to determine the efficacy of something previously untried."

This means that no matter who the participant is, he/she has an equal chance of getting into all of the groups or treatments in an experiment. This process helps to ensure that the groups or treatments are similar at the beginning of the study so that there is more confidence that the manipulation (group or treatment) "caused" the outcome. More information about random assignment may be found in section Random assignment.

Definition : An experiment is a study in which a treatment, procedure, or program is intentionally introduced and a result or outcome is observed.

Case Example for Experimental Study

Experimental studies — example 1.

Experimental Studies — Example 2

A fitness instructor wants to test the effectiveness of a performance-enhancing herbal supplement on students in her exercise class. To create experimental groups that are similar at the beginning of the study, the students are assigned into two groups at random (they can not choose which group they are in). Students in both groups are given a pill to take every day, but they do not know whether the pill is a placebo (sugar pill) or the herbal supplement. The instructor gives Group A the herbal supplement and Group B receives the placebo (sugar pill). The students' fitness level is compared before and after six weeks of consuming the supplement or the sugar pill. No differences in performance ability were found between the two groups suggesting that the herbal supplement was not effective.

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21 13. Experimental design

Chapter outline.

• What is an experiment and when should you use one? (8 minute read)
• True experimental designs (7 minute read)
• Quasi-experimental designs (8 minute read)
• Non-experimental designs (5 minute read)
• Critical, ethical, and critical considerations  (5 minute read)

Content warning : examples in this chapter contain references to non-consensual research in Western history, including experiments conducted during the Holocaust and on African Americans (section 13.6).

13.1 What is an experiment and when should you use one?

Learning objectives.

Learners will be able to…

• Identify the characteristics of a basic experiment
• Describe causality in experimental design
• Discuss the relationship between dependent and independent variables in experiments
• Explain the links between experiments and generalizability of results
• Describe advantages and disadvantages of experimental designs

The basics of experiments

The first experiment I can remember using was for my fourth grade science fair. I wondered if latex- or oil-based paint would hold up to sunlight better. So, I went to the hardware store and got a few small cans of paint and two sets of wooden paint sticks. I painted one with oil-based paint and the other with latex-based paint of different colors and put them in a sunny spot in the back yard. My hypothesis was that the oil-based paint would fade the most and that more fading would happen the longer I left the paint sticks out. (I know, it’s obvious, but I was only 10.)

I checked in on the paint sticks every few days for a month and wrote down my observations. The first part of my hypothesis ended up being wrong—it was actually the latex-based paint that faded the most. But the second part was right, and the paint faded more and more over time. This is a simple example, of course—experiments get a heck of a lot more complex than this when we’re talking about real research.

Merriam-Webster defines an experiment   as “an operation or procedure carried out under controlled conditions in order to discover an unknown effect or law, to test or establish a hypothesis, or to illustrate a known law.” Each of these three components of the definition will come in handy as we go through the different types of experimental design in this chapter. Most of us probably think of the physical sciences when we think of experiments, and for good reason—these experiments can be pretty flashy! But social science and psychological research follow the same scientific methods, as we’ve discussed in this book.

As the video discusses, experiments can be used in social sciences just like they can in physical sciences. It makes sense to use an experiment when you want to determine the cause of a phenomenon with as much accuracy as possible. Some types of experimental designs do this more precisely than others, as we’ll see throughout the chapter. If you’ll remember back to Chapter 11  and the discussion of validity, experiments are the best way to ensure internal validity, or the extent to which a change in your independent variable causes a change in your dependent variable.

Experimental designs for research projects are most appropriate when trying to uncover or test a hypothesis about the cause of a phenomenon, so they are best for explanatory research questions. As we’ll learn throughout this chapter, different circumstances are appropriate for different types of experimental designs. Each type of experimental design has advantages and disadvantages, and some are better at controlling the effect of extraneous variables —those variables and characteristics that have an effect on your dependent variable, but aren’t the primary variable whose influence you’re interested in testing. For example, in a study that tries to determine whether aspirin lowers a person’s risk of a fatal heart attack, a person’s race would likely be an extraneous variable because you primarily want to know the effect of aspirin.

In practice, many types of experimental designs can be logistically challenging and resource-intensive. As practitioners, the likelihood that we will be involved in some of the types of experimental designs discussed in this chapter is fairly low. However, it’s important to learn about these methods, even if we might not ever use them, so that we can be thoughtful consumers of research that uses experimental designs.

While we might not use all of these types of experimental designs, many of us will engage in evidence-based practice during our time as social workers. A lot of research developing evidence-based practice, which has a strong emphasis on generalizability, will use experimental designs. You’ve undoubtedly seen one or two in your literature search so far.

The logic of experimental design

How do we know that one phenomenon causes another? The complexity of the social world in which we practice and conduct research means that causes of social problems are rarely cut and dry. Uncovering explanations for social problems is key to helping clients address them, and experimental research designs are one road to finding answers.

As you read about in Chapter 8 (and as we’ll discuss again in Chapter 15 ), just because two phenomena are related in some way doesn’t mean that one causes the other. Ice cream sales increase in the summer, and so does the rate of violent crime; does that mean that eating ice cream is going to make me murder someone? Obviously not, because ice cream is great. The reality of that relationship is far more complex—it could be that hot weather makes people more irritable and, at times, violent, while also making people want ice cream. More likely, though, there are other social factors not accounted for in the way we just described this relationship.

Experimental designs can help clear up at least some of this fog by allowing researchers to isolate the effect of interventions on dependent variables by controlling extraneous variables . In true experimental design (discussed in the next section) and some quasi-experimental designs, researchers accomplish this w ith the control group and the experimental group . (The experimental group is sometimes called the “treatment group,” but we will call it the experimental group in this chapter.) The control group does not receive the intervention you are testing (they may receive no intervention or what is known as “treatment as usual”), while the experimental group does. (You will hopefully remember our earlier discussion of control variables in Chapter 8 —conceptually, the use of the word “control” here is the same.)

In a well-designed experiment, your control group should look almost identical to your experimental group in terms of demographics and other relevant factors. What if we want to know the effect of CBT on social anxiety, but we have learned in prior research that men tend to have a more difficult time overcoming social anxiety? We would want our control and experimental groups to have a similar gender mix because it would limit the effect of gender on our results, since ostensibly, both groups’ results would be affected by gender in the same way. If your control group has 5 women, 6 men, and 4 non-binary people, then your experimental group should be made up of roughly the same gender balance to help control for the influence of gender on the outcome of your intervention. (In reality, the groups should be similar along other dimensions, as well, and your group will likely be much larger.) The researcher will use the same outcome measures for both groups and compare them, and assuming the experiment was designed correctly, get a pretty good answer about whether the intervention had an effect on social anxiety.

You will also hear people talk about comparison groups , which are similar to control groups. The primary difference between the two is that a control group is populated using random assignment, but a comparison group is not. Random assignment entails using a random process to decide which participants are put into the control or experimental group (which participants receive an intervention and which do not). By randomly assigning participants to a group, you can reduce the effect of extraneous variables on your research because there won’t be a systematic difference between the groups.

Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other related fields. Random sampling also helps a great deal with generalizability , whereas random assignment increases internal validity .

We have already learned about internal validity in Chapter 11 . The use of an experimental design will bolster internal validity since it works to isolate causal relationships. As we will see in the coming sections, some types of experimental design do this more effectively than others. It’s also worth considering that true experiments, which most effectively show causality , are often difficult and expensive to implement. Although other experimental designs aren’t perfect, they still produce useful, valid evidence and may be more feasible to carry out.

Key Takeaways

• Experimental designs are useful for establishing causality, but some types of experimental design do this better than others.
• Experiments help researchers isolate the effect of the independent variable on the dependent variable by controlling for the effect of extraneous variables .
• Experiments use a control/comparison group and an experimental group to test the effects of interventions. These groups should be as similar to each other as possible in terms of demographics and other relevant factors.
• True experiments have control groups with randomly assigned participants, while other types of experiments have comparison groups to which participants are not randomly assigned.
• Think about the research project you’ve been designing so far. How might you use a basic experiment to answer your question? If your question isn’t explanatory, try to formulate a new explanatory question and consider the usefulness of an experiment.
• Why is establishing a simple relationship between two variables not indicative of one causing the other?

13.2 True experimental design

• Describe a true experimental design in social work research
• Understand the different types of true experimental designs
• Determine what kinds of research questions true experimental designs are suited for
• Discuss advantages and disadvantages of true experimental designs

True experimental design , often considered to be the “gold standard” in research designs, is thought of as 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 and its ability to establish ( causality ) through treatment manipulation, while controlling for the effects of extraneous variable. Sometimes the treatment level is no treatment, while other times it is simply a different treatment than that which we are trying to evaluate. For example, we might have a control group that is made up of people who will not receive any treatment for a particular condition. Or, a control group could consist of people who consent to treatment with DBT when we are testing the effectiveness of CBT.

As we discussed in the previous section, a true experiment has a control group with participants randomly assigned , and an experimental group . This is the most basic element of a true experiment. The next decision a researcher must make is when they need to gather data during their experiment. Do they take a baseline measurement and then a measurement after treatment, or just a measurement after treatment, or do they handle measurement another way? Below, we’ll discuss the three main types of true experimental designs. There are sub-types of each of these designs, but here, we just want to get you started with some of the basics.

Using a true experiment in social work research is often pretty difficult, since as I mentioned earlier, true experiments can be quite resource intensive. True experiments work best with relatively large sample sizes, and random assignment, a key criterion for a true experimental design, is hard (and unethical) to execute in practice when you have people in dire need of an intervention. Nonetheless, some of the strongest evidence bases are built on true experiments.

For the purposes of this section, let’s bring back the example of CBT for the treatment of social anxiety. We have a group of 500 individuals who have agreed to participate in our study, and we have randomly assigned them to the control and experimental groups. The folks in the experimental group will receive CBT, while the folks in the control group will receive more unstructured, basic talk therapy. These designs, as we talked about above, are best suited for explanatory research questions.

Before we get started, take a look at the table below. When explaining experimental research designs, we often use diagrams with abbreviations to visually represent the experiment. Table 13.1 starts us off by laying out what each of the abbreviations mean.

Pretest and post-test control group design

In pretest and post-test control group design , participants are given a pretest of some kind to measure their baseline state before their participation in an intervention. In our social anxiety experiment, we would have participants in both the experimental and control groups complete some measure of social anxiety—most likely an established scale and/or a structured interview—before they start their treatment. As part of the experiment, we would have a defined time period during which the treatment would take place (let’s say 12 weeks, just for illustration). At the end of 12 weeks, we would give both groups the same measure as a post-test .

In the diagram, RA (random assignment group A) is the experimental group and RB is the control group. O 1 denotes the pre-test, X e denotes the experimental intervention, and O 2 denotes the post-test. Let’s look at this diagram another way, using the example of CBT for social anxiety that we’ve been talking about.

In a situation where the control group received treatment as usual instead of no intervention, the diagram would look this way, with X i denoting treatment as usual (Figure 13.3).

Hopefully, these diagrams provide you a visualization of how this type of experiment establishes time order , a key component of a causal relationship. Did the change occur after the intervention? Assuming there is a change in the scores between the pretest and post-test, we would be able to say that yes, the change did occur after the intervention. Causality can’t exist if the change happened before the intervention—this would mean that something else led to the change, not our intervention.

Post-test only control group design

Post-test only control group design involves only giving participants a post-test, just like it sounds (Figure 13.4).

But why would you use this design instead of using a pretest/post-test design? One reason could be the testing effect that can happen when research participants take a pretest. In research, the testing effect refers to “measurement error related to how a test is given; the conditions of the testing, including environmental conditions; and acclimation to the test itself” (Engel & Schutt, 2017, p. 444) [1] (When we say “measurement error,” all we mean is the accuracy of the way we measure the dependent variable.) Figure 13.4 is a visualization of this type of experiment. The testing effect isn’t always bad in practice—our initial assessments might help clients identify or put into words feelings or experiences they are having when they haven’t been able to do that before. In research, however, we might want to control its effects to isolate a cleaner causal relationship between intervention and outcome.

Going back to our CBT for social anxiety example, we might be concerned that participants would learn about social anxiety symptoms by virtue of taking a pretest. They might then identify that they have those symptoms on the post-test, even though they are not new symptoms for them. That could make our intervention look less effective than it actually is.

However, without a baseline measurement establishing causality can be more difficult. If we don’t know someone’s state of mind before our intervention, how do we know our intervention did anything at all? Establishing time order is thus a little more difficult. You must balance this consideration with the benefits of this type of design.

Solomon four group design

One way we can possibly measure how much the testing effect might change the results of the experiment is with the Solomon four group design. Basically, as part of this experiment, you have two control groups and two experimental groups. The first pair of groups receives both a pretest and a post-test. The other pair of groups receives only a post-test (Figure 13.5). This design helps address the problem of establishing time order in post-test only control group designs.

For our CBT project, we would randomly assign people to four different groups instead of just two. Groups A and B would take our pretest measures and our post-test measures, and groups C and D would take only our post-test measures. We could then compare the results among these groups and see if they’re significantly different between the folks in A and B, and C and D. If they are, we may have identified some kind of testing effect, which enables us to put our results into full context. We don’t want to draw a strong causal conclusion about our intervention when we have major concerns about testing effects without trying to determine the extent of those effects.

Solomon four group designs are less common in social work research, primarily because of the logistics and resource needs involved. Nonetheless, this is an important experimental design to consider when we want to address major concerns about testing effects.

• True experimental design is best suited for explanatory research questions.
• True experiments require random assignment of participants to control and experimental groups.
• Pretest/post-test research design involves two points of measurement—one pre-intervention and one post-intervention.
• Post-test only research design involves only one point of measurement—post-intervention. It is a useful design to minimize the effect of testing effects on our results.
• Solomon four group research design involves both of the above types of designs, using 2 pairs of control and experimental groups. One group receives both a pretest and a post-test, while the other receives only a post-test. This can help uncover the influence of testing effects.
• Think about a true experiment you might conduct for your research project. Which design would be best for your research, and why?
• What challenges or limitations might make it unrealistic (or at least very complicated!) for you to carry your true experimental design in the real-world as a student researcher?
• What hypothesis(es) would you test using this true experiment?

13.4 Quasi-experimental designs

• Describe a quasi-experimental design in social work research
• Understand the different types of quasi-experimental designs
• Determine what kinds of research questions quasi-experimental designs are suited for
• Discuss advantages and disadvantages of quasi-experimental designs

Quasi-experimental designs are a lot more common in social work research than true experimental designs. Although quasi-experiments don’t do as good a job of giving us robust proof of causality , they still allow us to establish time order , which is a key element of causality. The prefix quasi means “resembling,” so quasi-experimental research is research that resembles experimental research, but is not true experimental research. Nonetheless, given proper research design, quasi-experiments can still provide extremely rigorous and useful results.

There are a few key differences between true experimental and quasi-experimental research. The primary difference between quasi-experimental research and true experimental research is that quasi-experimental research does not involve random assignment to control and experimental groups. Instead, we talk about comparison groups in quasi-experimental research instead. As a result, these types of experiments don’t control the effect of extraneous variables as well as a true experiment.

Quasi-experiments are most likely to be conducted in field settings in which random assignment is difficult or impossible. They are often conducted to evaluate the effectiveness of a treatment—perhaps a type of psychotherapy or an educational intervention.  We’re able to eliminate some threats to internal validity, but we can’t do this as effectively as we can with a true experiment.  Realistically, our CBT-social anxiety project is likely to be a quasi experiment, based on the resources and participant pool we’re likely to have available. 

It’s important to note that not all quasi-experimental designs have a comparison group.  There are many different kinds of quasi-experiments, but we will discuss the three main types below: nonequivalent comparison group designs, time series designs, and ex post facto comparison group designs.

Nonequivalent comparison group design

You will notice that this type of design looks extremely similar to the pretest/post-test design that we discussed in section 13.3. But instead of random assignment to control and experimental groups, researchers use other methods to construct their comparison and experimental groups. A diagram of this design will also look very similar to pretest/post-test design, but you’ll notice we’ve removed the “R” from our groups, since they are not randomly assigned (Figure 13.6).

Researchers using this design select a comparison group that’s as close as possible based on relevant factors to their experimental group. Engel and Schutt (2017) [2] identify two different selection methods:

• Individual matching : Researchers take the time to match individual cases in the experimental group to similar cases in the comparison group. It can be difficult, however, to match participants on all the variables you want to control for.
• Aggregate matching : Instead of trying to match individual participants to each other, researchers try to match the population profile of the comparison and experimental groups. For example, researchers would try to match the groups on average age, gender balance, or median income. This is a less resource-intensive matching method, but researchers have to ensure that participants aren’t choosing which group (comparison or experimental) they are a part of.

As we’ve already talked about, this kind of design provides weaker evidence that the intervention itself leads to a change in outcome. Nonetheless, we are still able to establish time order using this method, and can thereby show an association between the intervention and the outcome. Like true experimental designs, this type of quasi-experimental design is useful for explanatory research questions.

What might this look like in a practice setting? Let’s say you’re working at an agency that provides CBT and other types of interventions, and you have identified a group of clients who are seeking help for social anxiety, as in our earlier example. Once you’ve obtained consent from your clients, you can create a comparison group using one of the matching methods we just discussed. If the group is small, you might match using individual matching, but if it’s larger, you’ll probably sort people by demographics to try to get similar population profiles. (You can do aggregate matching more easily when your agency has some kind of electronic records or database, but it’s still possible to do manually.)

Time series design

Another type of quasi-experimental design is a time series design. Unlike other types of experimental design, time series designs do not have a comparison group. A time series is a set of measurements taken at intervals over a period of time (Figure 13.7). Proper time series design should include at least three pre- and post-intervention measurement points. While there are a few types of time series designs, we’re going to focus on the most common: interrupted time series design.

But why use this method? Here’s an example. Let’s think about elementary student behavior throughout the school year. As anyone with children or who is a teacher knows, kids get very excited and animated around holidays, days off, or even just on a Friday afternoon. This fact might mean that around those times of year, there are more reports of disruptive behavior in classrooms. What if we took our one and only measurement in mid-December? It’s possible we’d see a higher-than-average rate of disruptive behavior reports, which could bias our results if our next measurement is around a time of year students are in a different, less excitable frame of mind. When we take multiple measurements throughout the first half of the school year, we can establish a more accurate baseline for the rate of these reports by looking at the trend over time.

We may want to test the effect of extended recess times in elementary school on reports of disruptive behavior in classrooms. When students come back after the winter break, the school extends recess by 10 minutes each day (the intervention), and the researchers start tracking the monthly reports of disruptive behavior again. These reports could be subject to the same fluctuations as the pre-intervention reports, and so we once again take multiple measurements over time to try to control for those fluctuations.

This method improves the extent to which we can establish causality because we are accounting for a major extraneous variable in the equation—the passage of time. On its own, it does not allow us to account for other extraneous variables, but it does establish time order and association between the intervention and the trend in reports of disruptive behavior. Finding a stable condition before the treatment that changes after the treatment is evidence for causality between treatment and outcome.

Ex post facto comparison group design

Ex post facto (Latin for “after the fact”) designs are extremely similar to nonequivalent comparison group designs. There are still comparison and experimental groups, pretest and post-test measurements, and an intervention. But in ex post facto designs, participants are assigned to the comparison and experimental groups once the intervention has already happened. This type of design often occurs when interventions are already up and running at an agency and the agency wants to assess effectiveness based on people who have already completed treatment.

In most clinical agency environments, social workers conduct both initial and exit assessments, so there are usually some kind of pretest and post-test measures available. We also typically collect demographic information about our clients, which could allow us to try to use some kind of matching to construct comparison and experimental groups.

In terms of internal validity and establishing causality, ex post facto designs are a bit of a mixed bag. The ability to establish causality depends partially on the ability to construct comparison and experimental groups that are demographically similar so we can control for these extraneous variables .

Quasi-experimental designs are common in social work intervention research because, when designed correctly, they balance the intense resource needs of true experiments with the realities of research in practice. They still offer researchers tools to gather robust evidence about whether interventions are having positive effects for clients.

• Quasi-experimental designs are similar to true experiments, but do not require random assignment to experimental and control groups.
• In quasi-experimental projects, the group not receiving the treatment is called the comparison group, not the control group.
• Nonequivalent comparison group design is nearly identical to pretest/post-test experimental design, but participants are not randomly assigned to the experimental and control groups. As a result, this design provides slightly less robust evidence for causality.
• Nonequivalent groups can be constructed by individual matching or aggregate matching .
• Time series design does not have a control or experimental group, and instead compares the condition of participants before and after the intervention by measuring relevant factors at multiple points in time. This allows researchers to mitigate the error introduced by the passage of time.
• Ex post facto comparison group designs are also similar to true experiments, but experimental and comparison groups are constructed after the intervention is over. This makes it more difficult to control for the effect of extraneous variables, but still provides useful evidence for causality because it maintains the time order[ /pb_glossary] of the experiment.
• Think back to the experiment you considered for your research project in Section 13.3. Now that you know more about quasi-experimental designs, do you still think it's a true experiment? Why or why not?
• What should you consider when deciding whether an experimental or quasi-experimental design would be more feasible or fit your research question better?

13.5 Non-experimental designs

Learners will be able to...

• Describe non-experimental designs in social work research
• Discuss how non-experimental research differs from true and quasi-experimental research
• Demonstrate an understanding the different types of non-experimental designs
• Determine what kinds of research questions non-experimental designs are suited for
• Discuss advantages and disadvantages of non-experimental designs

The previous sections have laid out the basics of some rigorous approaches to establish that an intervention is responsible for changes we observe in research participants. This type of evidence is extremely important to build an evidence base for social work interventions, but it's not the only type of evidence to consider. We will discuss qualitative methods, which provide us with rich, contextual information, in Part 4 of this text. The designs we'll talk about in this section are sometimes used in [pb_glossary id="851"] qualitative research, but in keeping with our discussion of experimental design so far, we're going to stay in the quantitative research realm for now. Non-experimental is also often a stepping stone for more rigorous experimental design in the future, as it can help test the feasibility of your research.

In general, non-experimental designs do not strongly support causality and don't address threats to internal validity. However, that's not really what they're intended for. Non-experimental designs are useful for a few different types of research, including explanatory questions in program evaluation. Certain types of non-experimental design are also helpful for researchers when they are trying to develop a new assessment or scale. Other times, researchers or agency staff did not get a chance to gather any assessment information before an intervention began, so a pretest/post-test design is not possible.

A significant benefit of these types of designs is that they're pretty easy to execute in a practice or agency setting. They don't require a comparison or control group, and as Engel and Schutt (2017) [3] point out, they "flow from a typical practice model of assessment, intervention, and evaluating the impact of the intervention" (p. 177). Thus, these designs are fairly intuitive for social workers, even when they aren't expert researchers. Below, we will go into some detail about the different types of non-experimental design.

One group pretest/post-test design

Also known as a before-after one-group design, this type of research design does not have a comparison group and everyone who participates in the research receives the intervention (Figure 13.8). This is a common type of design in program evaluation in the practice world. Controlling for extraneous variables is difficult or impossible in this design, but given that it is still possible to establish some measure of time order, it does provide weak support for causality.

Imagine, for example, a researcher who is interested in the effectiveness of an anti-drug education program on elementary school students’ attitudes toward illegal drugs. The researcher could assess students' attitudes about illegal drugs (O 1 ), implement the anti-drug program (X), and then immediately after the program ends, the researcher could once again measure students’ attitudes toward illegal drugs (O 2 ). You can see how this would be relatively simple to do in practice, and have probably been involved in this type of research design yourself, even if informally. But hopefully, you can also see that this design would not provide us with much evidence for causality because we have no way of controlling for the effect of extraneous variables. A lot of things could have affected any change in students' attitudes—maybe girls already had different attitudes about illegal drugs than children of other genders, and when we look at the class's results as a whole, we couldn't account for that influence using this design.

All of that doesn't mean these results aren't useful, however. If we find that children's attitudes didn't change at all after the drug education program, then we need to think seriously about how to make it more effective or whether we should be using it at all. (This immediate, practical application of our results highlights a key difference between program evaluation and research, which we will discuss in Chapter 23 .)

After-only design

As the name suggests, this type of non-experimental design involves measurement only after an intervention. There is no comparison or control group, and everyone receives the intervention. I have seen this design repeatedly in my time as a program evaluation consultant for nonprofit organizations, because often these organizations realize too late that they would like to or need to have some sort of measure of what effect their programs are having.

Because there is no pretest and no comparison group, this design is not useful for supporting causality since we can't establish the time order and we can't control for extraneous variables. However, that doesn't mean it's not useful at all! Sometimes, agencies need to gather information about how their programs are functioning. A classic example of this design is satisfaction surveys—realistically, these can only be administered after a program or intervention. Questions regarding satisfaction, ease of use or engagement, or other questions that don't involve comparisons are best suited for this type of design.

Static-group design

A final type of non-experimental research is the static-group design. In this type of research, there are both comparison and experimental groups, which are not randomly assigned. There is no pretest, only a post-test, and the comparison group has to be constructed by the researcher. Sometimes, researchers will use matching techniques to construct the groups, but often, the groups are constructed by convenience of who is being served at the agency.

Non-experimental research designs are easy to execute in practice, but we must be cautious about drawing causal conclusions from the results. A positive result may still suggest that we should continue using a particular intervention (and no result or a negative result should make us reconsider whether we should use that intervention at all). You have likely seen non-experimental research in your daily life or at your agency, and knowing the basics of how to structure such a project will help you ensure you are providing clients with the best care possible.

• Non-experimental designs are useful for describing phenomena, but cannot demonstrate causality.
• After-only designs are often used in agency and practice settings because practitioners are often not able to set up pre-test/post-test designs.
• Non-experimental designs are useful for explanatory questions in program evaluation and are helpful for researchers when they are trying to develop a new assessment or scale.
• Non-experimental designs are well-suited to qualitative methods.
• If you were to use a non-experimental design for your research project, which would you choose? Why?
• Have you conducted non-experimental research in your practice or professional life? Which type of non-experimental design was it?

13.6 Critical, ethical, and cultural considerations

• Describe critiques of experimental design
• Identify ethical issues in the design and execution of experiments
• Identify cultural considerations in experimental design

As I said at the outset, experiments, and especially true experiments, have long been seen as the gold standard to gather scientific evidence. When it comes to research in the biomedical field and other physical sciences, true experiments are subject to far less nuance than experiments in the social world. This doesn't mean they are easier—just subject to different forces. However, as a society, we have placed the most value on quantitative evidence obtained through empirical observation and especially experimentation.

Major critiques of experimental designs tend to focus on true experiments, especially randomized controlled trials (RCTs), but many of these critiques can be applied to quasi-experimental designs, too. Some researchers, even in the biomedical sciences, question the view that RCTs are inherently superior to other types of quantitative research designs. RCTs are far less flexible and have much more stringent requirements than other types of research. One seemingly small issue, like incorrect information about a research participant, can derail an entire RCT. RCTs also cost a great deal of money to implement and don't reflect “real world” conditions. The cost of true experimental research or RCTs also means that some communities are unlikely to ever have access to these research methods. It is then easy for people to dismiss their research findings because their methods are seen as "not rigorous."

Obviously, controlling outside influences is important for researchers to draw strong conclusions, but what if those outside influences are actually important for how an intervention works? Are we missing really important information by focusing solely on control in our research? Is a treatment going to work the same for white women as it does for indigenous women? With the myriad effects of our societal structures, you should be very careful ever assuming this will be the case. This doesn't mean that cultural differences will negate the effect of an intervention; instead, it means that you should remember to practice cultural humility implementing all interventions, even when we "know" they work.

How we build evidence through experimental research reveals a lot about our values and biases, and historically, much experimental research has been conducted on white people, and especially white men. [4] This makes sense when we consider the extent to which the sciences and academia have historically been dominated by white patriarchy. This is especially important for marginalized groups that have long been ignored in research literature, meaning they have also been ignored in the development of interventions and treatments that are accepted as "effective." There are examples of marginalized groups being experimented on without their consent, like the Tuskegee Experiment or Nazi experiments on Jewish people during World War II. We cannot ignore the collective consciousness situations like this can create about experimental research for marginalized groups.

None of this is to say that experimental research is inherently bad or that you shouldn't use it. Quite the opposite—use it when you can, because there are a lot of benefits, as we learned throughout this chapter. As a social work researcher, you are uniquely positioned to conduct experimental research while applying social work values and ethics to the process and be a leader for others to conduct research in the same framework. It can conflict with our professional ethics, especially respect for persons and beneficence, if we do not engage in experimental research with our eyes wide open. We also have the benefit of a great deal of practice knowledge that researchers in other fields have not had the opportunity to get. As with all your research, always be sure you are fully exploring the limitations of the research.

• While true experimental research gathers strong evidence, it can also be inflexible, expensive, and overly simplistic in terms of important social forces that affect the resources.
• Marginalized communities' past experiences with experimental research can affect how they respond to research participation.
• Social work researchers should use both their values and ethics, and their practice experiences, to inform research and push other researchers to do the same.
• Think back to the true experiment you sketched out in the exercises for Section 13.3. Are there cultural or historical considerations you hadn't thought of with your participant group? What are they? Does this change the type of experiment you would want to do?
• How can you as a social work researcher encourage researchers in other fields to consider social work ethics and values in their experimental research?
• Engel, R. & Schutt, R. (2016). The practice of research in social work. Thousand Oaks, CA: SAGE Publications, Inc. ↵
• Sullivan, G. M. (2011). Getting off the “gold standard”: Randomized controlled trials and education research. Journal of Graduate Medical Education ,  3 (3), 285-289. ↵

an operation or procedure carried out under controlled conditions in order to discover an unknown effect or law, to test or establish a hypothesis, or to illustrate a known law.

explains why particular phenomena work in the way that they do; answers “why” questions

variables and characteristics that have an effect on your outcome, but aren't the primary variable whose influence you're interested in testing.

the group of participants in our study who do not receive the intervention we are researching in experiments with random assignment

in experimental design, the group of participants in our study who do receive the intervention we are researching

the group of participants in our study who do not receive the intervention we are researching in experiments without random assignment

using a random process to decide which participants are tested in which conditions

The ability to apply research findings beyond the study sample to some broader population,

Ability to say that one variable "causes" something to happen to another variable. Very important to assess when thinking about studies that examine causation such as experimental or quasi-experimental designs.

the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief

An experimental design in which 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

a type of experimental design in which participants are randomly assigned to control and experimental groups, one group receives an intervention, and both groups receive pre- and post-test assessments

A measure of a participant's condition before they receive an intervention or treatment.

A measure of a participant's condition after an intervention or, if they are part of the control/comparison group, at the end of an experiment.

A demonstration that a change occurred after an intervention. An important criterion for establishing causality.

an experimental design in which participants are randomly assigned to control and treatment groups, one group receives an intervention, and both groups receive only a post-test assessment

The measurement error related to how a test is given; the conditions of the testing, including environmental conditions; and acclimation to the test itself

a subtype of experimental design that is similar to a true experiment, but does not have randomly assigned control and treatment groups

In nonequivalent comparison group designs, the process by which researchers match individual cases in the experimental group to similar cases in the comparison group.

In nonequivalent comparison group designs, the process in which researchers match the population profile of the comparison and experimental groups.

a set of measurements taken at intervals over a period of time

Graduate research methods in social work Copyright © 2021 by Matthew DeCarlo, Cory Cummings, Kate Agnelli is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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30 8.1 Experimental design: What is it and when should it be used?

Learning objectives.

• Define experiment
• Identify the core features of true experimental designs
• Describe the difference between an experimental group and a control group
• Identify and describe the various types of true experimental designs

Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.

Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.

Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:

• random assignment of participants into experimental and control groups
• a “treatment” (or intervention) provided to the experimental group
• measurement of the effects of the treatment in a post-test administered to both groups

Some true experiments are more complex.  Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment.

Experimental and control groups

In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.

Treatment or intervention

In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.

In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.

The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test .  In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.

Types of experimental design

Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.

An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.

In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963).  The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects , in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.

Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.

Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.

Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we  will discuss in the next section–can be used.  However, the differences in rigor from true experimental designs leave their conclusions more open to critique.

Experimental design in macro-level research

You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals.  For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change.  There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013).  Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments.  For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.

Key Takeaways

• True experimental designs require random assignment.
• Control groups do not receive an intervention, and experimental groups receive an intervention.
• The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
• Testing effects may cause researchers to use variations on the classic experimental design.
• Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
• Control group- the group in an experiment that does not receive the intervention
• Experiment- a method of data collection designed to test hypotheses under controlled conditions
• Experimental group- the group in an experiment that receives the intervention
• Posttest- a measurement taken after the intervention
• Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
• Pretest- a measurement taken prior to the intervention
• Random assignment-using a random process to assign people into experimental and control groups
• Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
• Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
• True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups

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Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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 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.

Experimental Research vs. Alternatives and When to Use Them

1. experimental research vs causal comparative.

Experimental research enables you to control variables and identify how the independent variable affects the dependent variable. Causal-comparative find out the cause-and-effect relationship between the variables by comparing already existing groups that are affected differently by the independent variable.

For example, in an experiment to see how K-12 education affects children and teenager development. An experimental research would split the children into groups, some would get formal K-12 education, while others won’t. This is not ethically right because every child has the right to education. So, what we do instead would be to compare already existing groups of children who are getting formal education with those who due to some circumstances can not.

Pros and Cons of Experimental vs Causal-Comparative Research

• Causal-Comparative:   Strengths:  More realistic than experiments, can be conducted in real-world settings.  Weaknesses:  Establishing causality can be weaker due to the lack of manipulation.

2. Experimental Research vs Correlational Research

When experimenting, you are trying to establish a cause-and-effect relationship between different variables. For example, you are trying to establish the effect of heat on water, the temperature keeps changing (independent variable) and you see how it affects the water (dependent variable).

For correlational research, you are not necessarily interested in the why or the cause-and-effect relationship between the variables, you are focusing on the relationship. Using the same water and temperature example, you are only interested in the fact that they change, you are not investigating which of the variables or other variables causes them to change.

Pros and Cons of Experimental vs Correlational Research

3. experimental research vs descriptive research.

With experimental research, you alter the independent variable to see how it affects the dependent variable, but with descriptive research you are simply studying the characteristics of the variable you are studying.

So, in an experiment to see how blown glass reacts to temperature, experimental research would keep altering the temperature to varying levels of high and low to see how it affects the dependent variable (glass). But descriptive research would investigate the glass properties.

Pros and Cons of Experimental vs Descriptive Research

4. experimental research vs action research.

Experimental research tests for causal relationships by focusing on one independent variable vs the dependent variable and keeps other variables constant. So, you are testing hypotheses and using the information from the research to contribute to knowledge.

However, with action research, you are using a real-world setting which means you are not controlling variables. You are also performing the research to solve actual problems and improve already established practices.

For example, if you are testing for how long commutes affect workers’ productivity. With experimental research, you would vary the length of commute to see how the time affects work. But with action research, you would account for other factors such as weather, commute route, nutrition, etc. Also, experimental research helps know the relationship between commute time and productivity, while action research helps you look for ways to improve productivity

Pros and Cons of Experimental vs Action 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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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

Experimental research: characteristics, definition, examples

The experimental research is the alteration of an experimental variable or several at the same time, in an environment strictly monitored by the person who performs the experiment.

In this way, the researcher can evaluate in what way or for what reason something particular happens. East kind of investigation it is provoked, which allows variables to be modified in intensity, being able to evaluate the causes and consequences of the results.

The objective of the manipulation of variables is to see the changes in the dependent variable in an environment or context strictly controlled by the researcher.

On the contrary, in a non-experimental investigation the person validates the characteristics and factors, and observes the results without modifying or manipulating these characteristics.

In contrast, in experimental research the researcher manipulates the characteristics, intensity and frequency to vary the results.

Experimental research differs from other types of research because the objective of the study and its method depend on the researcher and the decisions he establishes to carry out the experiment.

In the experiment, the variables are manipulated voluntarily and the results are observed in a controlled environment.

Repeats of the experiments are performed to verify certain hypotheses made by the researcher. This can be done in a laboratory or in the field.

• 1.1 Santa Palella and Feliberto Martins
• 1.2 Phidias Arias
• 1.3 Douglas Montgomery
• 2.1 The variables or experimental factors are manipulated
• 2.2 Control groups are established
• 2.3 It is assigned randomly
• 3.1 Study on improving the social climate in the classroom
• 3.2 Possible cure for breast and prostate cancer
• 3.3 Sleeping badly can cause problems in the couple
• 3.4 Discoveries about the regeneration of cancer cells
• 3.5 Prevention of volcanic action in Mexico
• 4 References

Definition according to different authors

Santa palella and feliberto martins.

Santa Palella and Feliberto Martins (2010), authors of the book Methodology of quantitative research , define the experimental design as the experiment in which the researcher manipulates an experimental variable not proven.

According to these researchers, the conditions must be strictly controlled, in order to describe in what way and for what cause a phenomenon is produced or can occur.

Phidias Arias

On the other hand, according to Fidias Arias, author of the book The Research Project, "Experimental research is a process that involves subjecting an object or group of individuals under certain conditions, stimuli or treatment (independent variable), to observe the effects or reactions that occur (dependent variable)".

Douglas Montgomery

Douglas Montgomery, specialist in designs of experiments and professor of the University of Arizona in the United States, defines the experiment as an experiment in which one or more variables are deliberately manipulated.

Characteristics of experimental research

The variables or experimental factors are manipulated.

The researcher intervenes by modifying variables or factors that affect the experiment and observes the reactions that are generated.

Several factors can be altered simultaneously. However, the ideal is to alter one by one and then alter several, in order to observe the results independently and see how each variation affects the results.

Control groups are established

There must be two groups. One in which the factors and variables are not modified and another in which the manipulation is carried out.

In this way it will be possible to observe the results in both groups and be able to identify the differences. This allows to quantify the change induced by the experimental treatment and guarantees the possibility of verifying the variations in the groups of the variables

It is assigned randomly

With two equivalent groups, the application of the experiment is established in a random way, in order to be able to make the valid relationships from the experimental data. This must be done in two moments:

Since the groups are initially equal in their variables, the differences found after each treatment will be due to the treatment.

Examples of experimental investigations

Study on improving the social climate in the classroom.

In a public institute of the community of Valencia called Castellar-Oliveral, an investigation was carried out whose general objective was to improve the social climate of the classroom.

This was intended to be achieved through the application of an education program for coexistence, in which participation and cooperation, conflict resolution and learning standards were promoted.

The fundamental idea of ​​this research was to improve the perception that each student had about the classroom.

In this research, two groups of students were selected. One of the groups was the experimental one; that is, the one who was exposed to the influence of the pedagogical program.

The other group was the control group, which was the one that remained free from the influence of the experiment.

The study is field because it is performed under normal conditions of daily life. In this case, it is in a classroom at school.

Both groups were quite homogeneous, because they studied in the same course (in different sections) and their classrooms were similar, since they had the same conditions.

After the experiment it was found that there was indeed a noticeable improvement in the social climate of the classroom.

These results allowed to consider the application of said education program for coexistence in a generalized manner in both classrooms.

Possible cure for breast and prostate cancer

Julio César Cárdenas, principal scientist of the Laboratory of Cellular Metabolism and Bioenergetics of the University of Chile, carried out an experiment through which he discovered a possible cure for breast and prostate cancer.

The results of this research were generated after 7 years of studies. During that time, Cardenas was investigating with cells of human beings ( in vitro ) and with mice.

The data that his study showed reflect that there was a 50% decrease in the reproduction of the tumor in prostate and breast cancers.

Although these results are quite encouraging, the researcher states that it is not yet possible to do tests in humans. He estimates that this will happen in about 10 years.

Sleeping badly can cause problems in the couple

According to a study by the University of California, Berkeley, it has been estimated that poor sleep can generate selfish attitudes in people and produce problems in couples .

The research is led by the psychologist Amie Gordon, who states that, in addition to selfish attitudes, it is possible that a bad dream produces a rather negative vision towards life.

The study was based on data collected from 60 couples, who were between 18 and 56 years of age. The aspects that they took into account were the way in which they solved their daily problems and the feelings that they said they had towards their partners.

People who said they had problems with sleep effectively showed much less appreciation towards their partners and were more inconsiderate, showing very little recognition towards their partners.

Discoveries about the regeneration of cancer cells

Scientists at the National Center for Scientific Research in France announced a revolutionary discovery.

It deals with the possibility of regeneration of cells affected by UVA rays used in chemotherapy and radiotherapy procedures.

The method to carry out this discovery was nanotechnology. What these scientists achieved, after many experiments, was to record in real time the enzymes while they were repairing the cancer cells.

The scope of this research is that it may be possible to inhibit the performance of these enzymes when they are preparing to repair cells affected by ultraviolet rays.

Prevention of volcanic action in Mexico

Donald Bruce Dingwell is a scientist at the Ludwig-Maximilians University in Munich, Germany.

He carried out an experimental investigation through which he studied the natural processes that are generated when eruptions occur explosively in volcanoes.

What this scientist did was to recreate in a laboratory conditions similar to those experienced before eruptions of volcanoes. Dingwell's intention is to identify possible risks and elements that can be predicted.

The advantage of this research is that it will allow people living near volcanoes to have a normal life.

This will be so because it will be possible to identify elements that can predict the proximity of a volcanic activity, and allow the inhabitants of these areas to have the possibility to act in time.

One of the main beneficiaries of this will be Mexico. The Institute of Geophysics of the National Autonomous University of Mexico hosted a conference given by Dingwell, in which he spoke about his findings.

Among the specific elements explored by this researcher, the texture of magma, the quality of volcanic ash and the concentration of gases stand out. All these are primordial elements to predict volcanic activity.

• Types of research. Retrieved Eumed: eumed.net
• Scientists discover new repair mechanism of cancer cells. Recovered from Excelsior: excelsior.com.mx
• Chilean scientist tests possible cure for cancer in the United States after years of research. Recovered from El Dinamo: eldinamo.cl
• German research would help to make maps of volcano risks. Recovered 20 minutes: 20minutos.com.mx
• Research methods of experimental approach. Recovered from the Graduate School of the National University of Education Enrique Guzman y Valle: postgradoune.edu.ve

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• Published: 10 September 2024

Elastoplastic analysis on deformation and failure characteristics of surrounding rock of soft-coal roadway based on true triaxial loading and unloading tests

• Chongyang Jiang 1 ,
• Lianguo Wang 1 ,
• Jiaxing Guo 1 &
• Shuai Wang 1  

Scientific Reports volume  14 , Article number:  21103 ( 2024 ) Cite this article

Metrics details

• Civil engineering

Since accidents such as roof caving, rock fragmentation, and severe deformation are particularly likely to occur during roadway excavation in soft and thick coal seams, grasping the range and distribution of deformation and fracturing of surrounding rock is of crucial for evaluating roadway stability and optimizing support design in such coal seams. In this study, based on the stress paths encountered during roadway excavation, true triaxial loading and unloading tests were carried out on soft coal, and the deformation and strength evolutions of soft coal under different intermediate principal stress conditions were analyzed. The test results show that the stress–strain relationship in the pre-peak plasticity-strengthening and post-peak plasticity-weakening stages follows a quadratic function, and the strengeth evolution conforms to the Mogi–Coulomb criterion. Moreover, analytical solutions for the displacement of surrounding rock, the radius of the broken zone, and the radius of the plastic zone of soft-coal roadways under excavation stress paths were derived after taking the nonlinear hardening and softening characteristics of the strain of soft coal, the Mogi–Coulomb criterion, the intermediate principal stress, and the dilatancy characteristics of surrounding rock into comprehensive consideration. Finally, in accordance with a practical engineering case, the influences of the intermediate principal stress coefficient, the lateral pressure coefficient, and the support force on the deformation and failure characteristics of the soft-coal roadway were analyzed. The analysis reveals that an increase in intermediate principal stress aggravates the deformation of surrounding rock and enlarges the plastic and broken zones; variations in the lateral pressure coefficient alter the shape of the broken zone and the distribution of surface displacement; and an increase in the support force effectively reduces the plastic zone, broken zone, and surface displacement of the roadway. The research results can provide valuable theoretical basis for the stability evaluation and support design of soft-coal roadways.

Introduction

With the depletion of shallow coal resources, deep mining has become the norm in the coal industry 1 , 2 . Affected by high ground stress in the deep, coal seams tend to become soft and fractured. During the excavation of a roadway in such soft and thick coal seams, large broken and plastic zones will emerge in the surrounding rock, posing safety hazards such as roof caving, rock fragmentation, and severe deformation 3 . Given the above fact, grasping the range and distribution of deformation, broken zone, and plastic zone in the surrounding rock is crucial for evaluating roadway stability and optimizing support design in such coal seams 4 . However, different constitutive models and strength criteria can lead to significant differences in the theoretical calculation results of the deformation and failure characteristics of the surrounding rock. Therefore, aming at accurately calculating the deformation and failure characteristics of the surrounding rock, it is meaningful to select a constitutive model and a strength criterion that are consistent with actual engineering.

Roadway excavation is actually a loading and unloading process for coal in a true triaxial stress state 5 , 6 . In this process, the stress of the surrounding rock of a roadway gradually transitions from being triaxial to being biaxial or unidirectional 7 , 8 , and the surrounding rock undergoes changes of vertical stress loading and horizontal stress unloading 9 . Numerous scholars have conducted plentiful loading and unloading tests on coal and rock according to the excavation and unloading process. Feng et al. 10 , 11 , 12 carried out loading and unloading tests on hard rock (e.g., marble) under different stress paths and explored the strength, deformation, and failure mechanisms of hard rock under these paths. Li et al. 13 conducted true triaxial loading and unloading tests on sandstone with different intermediate principal stresses and revealed the influences of the intermediate principal stress and the unloading action on sandstone dilatancy. Through true triaxial loading and unloading tests on columnar jointed rock specimens with different inclinations, Que et al. 14 uncovered the impact of excavation and unloading on the anisotropy strength and energy evolution of columnar jointed rock. Liu et al. 15 studied the changes in acoustic emission parameters of red sandstone during its true triaxial unloading failure and established the relationship between the intermediate principal stress and the acoustic emission information. Wang et al. 16 performed unloading tests on coal samples with the aid of a true triaxial fluid–structure interaction testing system, and obtained their deformation, damage, and failure characteristics. Liang et al. 17 conducted true triaxial loading and unloading tests on coal under different stress paths and analyzed the changes in deformation parameters, energy distribution, and fracture characteristics of coal under these paths. Currently, experimental research on true triaxial loading and unloading is mainly carried out on marble, granite, sandstone, hard coal, etc., while that on soft and fractured coal is scarcely reported.

In terms of elastoplastic analysis, relavent scholars have conducted extensive research on the deformation and failure characteristics of surrounding rock of roadways using different constitutive models and yield criteria of coal and rock. Sharan 18 , Sofianos and Nomikos 19 , Lee and Pietruszczak 20 , and Lv et al. 21 considered different constitutive models of rock based on the Hoek–Brown strength criterion and the non-correlated flow rule, and made elastoplastic analysis on the surrounding rock of roadways. Moreover, they derived analytical solutions for the deformation and failure range of surrounding rock. Zareifard and Fahimifar 22 , Park 23 , and Ranjbarnia et al. 24 derived analytical solutions for the stress and displacement of surrounding rock under hydrostatic pressure fields based on the linear strain softening model and the Mohr–Coulomb criterion. They also analyzed the influence of softening parameters on the stress and displacement of surrounding rock through numerical examples. Ghorbani and Hasanzadehshooiili 25 , Zhang et al. 26 , Yuan et al. 27 , and Wang et al. 28 integrated the intermediate principal stress, strain softening characteristics, rock shear dilation parameters, and Young’s modulus variations. Based on the Drucker–Prager criterion or the unified strength theory, they derived analytical solutions for the stress, displacement, and plastic zone of surrounding rock of circular roadways. Based on the Mohr–Coulomb and Drucker–Prager strength criteria, Jing et al. 29 derived the elastoplastic solution for the surrounding rock by combining rock rheology and long-term strength tests, and verified it with engineering examples. Overall, scholars have conducted abundant research on the analytical solutions for deformation and failure of roadways. However, most of the existing elastoplastic solutions for roadways are theoretically derived based on the constitutive relationship and strength criteria of coal and rock masses under compressive loading conditions, which differ from the stress changes encountered during roadway excavation and have certain limitations. The above research status nessesiates performing true triaxial loading and unloading tests based on the stress change characteristics of surrounding rock during the excavation of soft-coal roadways and establishing corresponding elastoplastic analysis models.

In this study, the evolutions of deformation and strength characteristics of soft coal under true triaxial loading and unloading conditions were investigated, and the constitutive relationship and strength criterion of soft coal under real stress paths during roadway excavation were determined. On this basis, the analytical solutions for the displacement of surrounding rock, the radius of the broken zone, and the radius of the plastic zone of soft-coal roadways under excavation stress paths were derived by taking the intermediate principal stress and the dilatancy characteristics of surrounding rock into account. Finally, in accordance with a practical engineering case, the effects of different influencing factors on the deformation and failure characteristics of the soft-coal roadway were analyzed. The research results can provide essential theoretical basis for the stability evaluation and support design of soft-coal roadways.

True triaxial loading and unloading tests on soft coal

Sample preparation.

The coal samples used in this experiment were collected from #8 coal seam in Huaibei Mining Area, China. This coal seam is of an extremely low strength and basically appears in a fragmented and loose state. Since such soft and fractured coal is difficult to be sampled and directly used for tests, it was processed into equivalent soft coal samples after being collected on site. The loose raw coal has a moisture content of approximately 3.25%. Prior to sample preparation, the raw coal particles were screened using sieves with pore sizes of 0.075 mm, 0.25 mm, 0.5 mm, 1 mm, 2 mm, 5 mm, 10 mm, and 15 mm, respectivly, based on which the raw coal samples were then classified. The mass proportions and particle size distribution curves for the raw coal were calculated (Fig.  1 ). As illustarted in Fig.  1 , the particle size of the raw coal predominantly ranges from 0 to 15 mm. Particles within this size range account for 97.92% of the total mass, while those larger than 15 mm, mostly consisting of small gangue, account for only 2.08% and are considered unrepresentative. Therefore, raw coal particles within the size range of 0–15 mm were selected for testing, and samples were prepared based on their proportional distribution. This approach not only replicates the grading composition of the raw coal but also avoids test errors arising from uneven packing density and particle arrangement, thereby ensuring sample uniformity.

Size distribution of raw coal particle.

Considering that the actual buried depth of the coal seam was 850 m, the forming pressure of the coal samples was set to 22 MPa. Following several preliminary tests, it was determined that 1480 g of loose raw coal was required for preparing a coal sample. The coal particles were weighed according to their respective size proportions, uniformly mixed (Fig.  2 a), and then added to the briquette pressing mold shown in Fig.  2 b, where it was subjected to slowly increasing loads through a testing machine until the forming pressure was reached. Afterwards, the pressure was maintained for 20 min before the cubic soft coal sample with a size of 100 mm × 100 mm × 100 mm was taken out (Fig.  2 c). Finally, the obtained samples were wrapped with cling films for later use. To ensure homogeneity of the samples, wave velocity tests were carried out to remove highly dispersed ones. The test results show that the wave velocities of the selected samples range from 0.279 to 0.364 km/s, with an average of 0.331 km/s.

Sample mold and soft coal samples: ( a ) raw coal: ( b ) sample mold; ( c ) part of coal samples.

Experimental equipment

The true triaxial loading and unloading tests were carried out with the aid of a true triaxial electro-hydraulic servo loading test system from China University of Mining and Technology. This system mainly consists of a three-way servo control loading system, an automatic acquisition system, a true triaxial pressure chamber, and an acoustic emission monitoring system 30 . As displayed in Fig.  3 a, the three-way servo control loading system comprises three mutually perpendicular and independent loading subsystems \(\sigma_{1}\) , \(\sigma_{2}\) , and \(\sigma_{3}\) , which can achieve independent servo loading control and simulate the real stress state of rock masses in underground engineering. The maximum servo loading pressures in the three directions are 1,600 kN, 500 kN, and 300 kN, respectively. The measurement accuracies of the test system for force and deformation are 0.01 kN and 0.002 mm, respectively. As shown in Fig.  3 b, the true triaxial pressure chamber, located at the center of the three-way servo control loading system, is composed of a pressure box, a base, and loading plates. The sample and the loading plates are interlocked, which allows for loading on the sample in three main stress directions.

True triaxial electro-hydraulic servo loading test system: ( a ) true triaxial testing machine; ( b ) true triaxial pressure chamber.

Experimental scheme

According to field measurements, the maximum principal stress ( \(\sigma_{1}\) ) is approximately 22 MPa, the intermediate principal stress ( \(\sigma_{2}\) ) ranges from 13.57 MPa to 21 MPa, and the minimum principal stress ( \(\sigma_{3}\) ) is around 10 MPa. Before roadway excavation, the surrounding rock is in a three-dimensional stress equilibrium state. During excavation, the stress state of the surrounding rock changes. To be specific, stress concentration and stress unloading occur, typically characterized by an increase in \(\sigma_{1}\) and a gradual decrease in \(\sigma_{2}\) and \(\sigma_{3}\) . Affected by stress changes, soft coal fractures and becomes unstable, which results in deformation and failure of the surrounding rock. In addition, the intermediate principal stress significantly influences the deformation and failure of the surrounding rock. Therefore, in the hope of exploring the deformation and failure characteristics of soft coal in the presence of excavation-dincued disturbance, five sets of true triaxial loading and unloading tests under different intermediate principal stress conditions were designed in this experiment. The test scheme is disclosed in Table 1 , and the specific stress path is illustrated in Fig.  4 . Considering the discreteness of the experimental results, each group of tests was repeated three times.

Schematic diagram of the stress path in the true triaxial loading and unloading tests.

As illustrated in Fig.  4 , a tested coal sample was first loaded to the preset initial true triaxial stress state. Then, \(\sigma_{1}\) , \(\sigma_{2}\) , and \(\sigma_{3}\) were synchronously applied to the sample at a rate of 0.2 MPa/s until the target hydrostatic pressure state (10 MPa) was reached. Subsequently, \(\sigma_{3}\) remained unchanged, and \(\sigma_{2}\) was loaded to the preset value at a rate of 0.2 MPa/s. After that, \(\sigma_{2}\) and \(\sigma_{3}\) remained constant, and \(\sigma_{1}\) was loaded to the preset value at a rate of 0.2 MPa/s and then remained unchanged. At this time, the initial true triaxial stress state was reached. Based on relevant studies 31 , 32 and multiple pre-experiments, it was concluded that a lower loading and unloading rate conduces to effectively preventing sudden failure or instability of the sample, allowing for a more accurate capture of the stress–strain relationship in soft coal. Acorddingly, \(\sigma_{1}\) was loaded at a rate of 0.1 MPa/s, while \(\sigma_{2}\) and \(\sigma_{3}\) were simultaneously unloaded at a rate of 0.1 MPa/s until the specimen failed.

Analysis and discussion of test results

Deformation characteristics of soft coal.

To facilitate comparison, the deformation of the samples in the initial stress state after loading was taken as the starting point, and only the data of subsequent loading and unloading processes were analyzed. Figure  5 presents the variation curves of axial stress \(\sigma_{1}\) of soft coal with axial strain \(\varepsilon_{1}\) , lateral strains \(\varepsilon_{2}\) and \(\varepsilon_{3}\) , and volumetric strain \(\varepsilon_{v}\) under true triaxial loading and unloading conditions, and Fig.  6 displays the variation curves of peak strains with the intermediate principal stress when the peak stress of soft coal is reached.

Stress–strain curves of soft coal under true triaxial loading and unloading conditions: ( a ) \(\sigma_{2}\)  = 14 MPa; ( b ) \(\sigma_{2}\)  = 16 MPa; ( c ) \(\sigma_{2}\)  = 18 MPa; ( d ) \(\sigma_{2}\)  = 20 MPa; ( e ) \(\sigma_{2}\)  = 22 MPa.

Variation curves of peak strains of soft coal under different intermediate principal stress conditions: ( a ) axial strain ε 1 ; ( b ) lateral strains ε 2 and ε 3.

It can be observed from Fig.  5 that the stress–strain curves of soft coal under these five intermediate principal stress conditions can be divided into four stages, namely the elastic stage (Stage I), the pre-peak plasticity-strengthening (hardening) stage (Stage II), the post-peak plasticity-weakening (softening) stage (Stage III), and the instability failure stage (Stage IV). In Stage I, the stress of coal changes roughly linearly with the strain. As the loading and unloading test proceeds, the coal enters Stage II where the axial stress \(\sigma_{1}\) rises nonlinearly at a gradually decelerated rate. After reaching the peak, it starts to decline progressively. In Stage III, the axial stress \(\sigma_{1}\) decreases nonlinearly, while the strains in the \(\sigma_{2}\) and \(\sigma_{3}\) unloading directions surge. The dilatancy and deformation in the \(\sigma_{3}\) direction are more notable than those in the \(\sigma_{2}\) direction. Eventually, the coal loses its bearing capacity in Stage IV. In Stages II and III, the stress–strain relationship basically follows a quadratic function, whose maximum value is exactly the peak stress of coal. Besides, the volumetric strain tends to increase first and then decrease throughout the loading and unloading process, indicating that the deformation pattern of coal transitions from axial compression in the initial stage to lateral dilatancy later stage.

It can be found from Fig.  6 that at the initial \(\sigma_{2}\) of 14 MPa, the values of \(\sigma_{2}\) and \(\sigma_{3}\) differ slightly, so do the values of \(\varepsilon_{2}\) and \(\varepsilon_{3}\) when the peak strength is reached. At this time, the lateral deformation of coal remains coordinated, and its bearing capacity is relatively strong. As the initial \(\sigma_{2}\) increases, the difference between values of \(\sigma_{2}\) and \(\sigma_{3}\) enlarges gradually. When the peak strength coal is reached, \(\varepsilon_{1}\) gradually decreases; \(\varepsilon_{2}\) gradually declines and tends to level off; and \(\varepsilon_{3}\) progressively rises. That is, the difference between \(\varepsilon_{2}\) and \(\varepsilon_{3}\) enlarges correspondingly. These results indicate that an increase in \(\sigma_{2}\) can accelerate the failure of coal, which makes the coal more prone to dilatancy and deformation in the \(\sigma_{3}\) direction.

Discussion on the applicability of strength criteria

The variation of the peak strength of coal with the intermediate principal stress under true triaxial loading and unloading conditions is exhibited in Fig.  7 . Clearly, the peak strength gradually decreases with the increase in initial \(\sigma_{2}\) . Specifically, it decreases from 35.37 MPa at the \(\sigma_{2}\) of 14 MPa to 26.3 MPa at the \(\sigma_{2}\) of 22 MPa, a decrease of 25.64%. The reason for this phenomenon is that an increase in \(\sigma_{2}\) restrains dilatancy in the \(\sigma_{2}\) direction and promotes dilatancy in the \(\sigma_{3}\) direction, thereby accelerating the failure of coal.

Variations of the peak strength of coal under true triaxial loading and unloading conditions.

The Mohr–Coulomb criterion, Drucker–Prager criterion, and Mogi–Coulomb criterion, three common criteria for coal strength, were selected for investigating the applicability of different strength criteria in true triaxial loading and unloading tests on soft coal.

According to the Mohr–Coulomb criterion, the principal stress can be written as:

where \(\phi\) is the friction angle of coal; and c is the cohesion.

Based on the Drucker–Prager criterion, the principal stress can be expressed by:

where \(I_{1}\) is the first invariant of stress; \(J_{2}\) is the second invariant of stress bias; and \(\alpha\) and \(K\) are experimental constants related to the friction angle \(\phi\) and the cohesive force c , which can be calculated as:

The Mogi–Coulomb criterion is an empirical criterion based on plentiful true triaxial test results 33 . According to this criterion, a specimen has yielded or failed if the octahedral shear stress \(\tau_{{{\text{oct}}}}\) on any side of it reaches the limit value, as expressed by:

where \(\tau_{{{\text{oct}}}}\) is the octahedral shear stress; \(\sigma_{m,2}\) is the average stress; and \(a_{1}\) and \(a_{2}\) are experimental constants related to the friction angle \(\phi\) and the cohesion c , which can be calculated as:

The strength of soft coal under the true triaxial loading and unloading path was fitted based on the above three strength criteria, and the fitting results are given in Fig.  8 .

Fitting results based on the three strength criteria: ( a ) Mohr–Coulomb criterion; ( b ) Drucker–Prager criterion; ( c ) Mogi–Coulomb criterion.

As can be seen from the fitting results (Fig.  8 ), the coefficient of determination ( R 2 ) of the Mohr–Coulomb criterion is 0.76, which represents a low fitting degree. As this criterion does not consider the influence of the intermediate principal stress, it fails to accurately reflect the strength characteristics of soft coal under the true triaxial loading and unloading path. Meanwhile, the R 2 of the Drucker–Prager criterion is only 0.73, which represents a low fitting degree, so this criterion is also not suitable for describing the strength characteristics of soft coal under loading and unloading conditions. In contrast, the R 2 of the Mogi–Coulomb criterion reaches 0.92, demonstrating its excellent fitting effect. Hence, the Mogi–Coulomb criterion is the most effective in describing the strength characteristics of soft coal under the true triaxial loading and unloading path. Finally, the fitting parameters were utilized to calculate the cohesion and internal friction angle of soft coal, which turned to be 0.858 MPa and 38.95°, respectively.

Elastoplastic analysis on surrounding rock of soft-coal roadway during its deformation and failure

Mechanical model of soft-coal roadway.

The geological conditions of roadways are complex in practical engineering. To facilitate the theoretical elastoplastic analysis on soft-coal roadways, the “equal-area method” was adopted when constructing the mechanical model, and the circular arch-shaped roadway was equivalent to a circle with a radius of \(R_{0}\) . As displayed in Fig.  9 , the surrounding rock of the roadway is divided into a broken zone, a plastic zone, and an elastic zone. In practical engineering, the roadway excavation direction is typically parallel to the horizontal principal stress direction in order to minimize roadway deformation and damage. In view of this fact, this paper assumes that the three principal stress directions are either orthogonal or parallel to the roadway axis. The vertical and horizontal stresses applied to the roadway model are p 0 and λp 0 , respectively; \(\lambda\) is the lateral pressure coefficient; and \(p_{i}\) is the support force of the roadway.

Mechanical model of the roadway.

It has been uncovered from the stress–strain curves of soft coal in the true triaxial loading and unloading tests that coal stays in an elastic state in the early stage of loading and unloading. When the limit elastic strength is reached, the coal enters the plastic stage, and its strain strengthens nonlinearly. After the peak stress is reached, the strain tends to weaken nonlinearly. Finally, the coal enters the failure stage. As depicted in Fig.  10 , the stress is linearly correlated with the strain in both the elastic stage and the failure stage, while their correlation follows a quadratic function in the plastic phase (i.e., the pre-peak hardening and post-peak softening stages). In Fig.  10 , \(\varepsilon_{E}\) is the ultimate elastic strain of coal; \(\varepsilon_{0}\) is the peak strain of coal; \(\varepsilon_{b}\) is the failure strain of coal; \(\sigma_{E}\) is the ultimate elastic stress of coal; \(\sigma_{c}\) is the peak stress of coal; and \(\sigma_{b}\) is the failure stress of coal.

Stress–strain relationships in different stages.

It is assumed that the stress \(\sigma_{p}\) and the strain \(\varepsilon_{p}\) of coal in the plastic phase (both hardening and softening stages) satisfy the quadratic function:

where D 1 , D 2 , and D 3 are constants.

At the peak strain \((\varepsilon_{0} ,\sigma_{c} )\) in Fig.  10 ,

In Fig.  10 , the elastic and plastic curves are smoothly connected and have the same slope E . At the ultimate elastic strain \((\varepsilon_{E} ,\sigma_{E} )\) , the stress and strain in the plastic stage follow the quadratic relationship:

By combining Eqs. ( 11 – 13 ), this quadratic relationship can be rewritten as:

It has been verified in the above section that the Mogi–Coulomb criterion can effectively describe the strength characteristics of soft coal under true triaxial loading and unloading conditions. Thus, this criterion is employed for identifying surrounding rock failure in theoretical calculation here. In practical engineering, the intermediate principal stress coefficient b is often introduced to denote the relationship between the three principal stresses, which is defined as:

The elastoplastic problem of the surrounding rock can be solved as a plane strain problem. Then, the stress state of the surrounding rock follows:

where \(\sigma_{\theta }\) , \(\sigma_{z}\) , and \(\sigma_{r}\) are the tangential stress, axial stress, and radial stress of the surrounding rock, respectively.

By combining Eqs. ( 6 ), ( 7 ), ( 8 ), ( 15 ), and ( 16 ), the Mogi–Coulomb criterion can be expressed as:

where \(M_{i} = \frac{{3a_{2} + 2\sqrt {2b^{2} - 2b + 2} }}{{2\sqrt {2b^{2} - 2b + 2} - 3a_{2} }}\) ; \(N_{i} = \frac{{6a_{1} }}{{2\sqrt {2b^{2} - 2b + 2} - 3a_{2} }}\) ; the subscript i can be replaced by “ p ” and “ b ”, which represent the plastic zone and the broken zone of the surrounding rock, respectively.

The surrounding rock will fracture and dilate when it fails. Based on the experimental results, the relationship between the dilatancy coefficient and the strain was plotted (Fig.  11 ).

Dilatancy model of surrounding rock.

In Fig.  11 , the tangential and radial strains in the plastic and broken zones of the surrounding rock satisfy the non-correlated flow rule:

where \(\eta_{1}\) and \(\eta_{2}\) are the dilatancy coefficients of these two zones, respectively.

Elastoplastic mechanical analysis on soft-coal roadway

Basic equations

According to elastoplastic mechanics, the equilibrium differential equation of the surrounding rock in various zones of the roadway can be described as:

where the subscript “ i ” can be replaced by “ e ”, “ p ”, and “ b ” which represent the elastic zone, the plastic zone, and the broken zone, respectively.

The geometric equations can be written as:

The physical equations are given:

The boundary conditions for various zones of the roadway are:

Analysis on the elastic zone

As depicted in Fig.  12 , the non-isobaric stress field of the circular roadway can be regarded as a superposition of two situations, namely a uniform compressive stress field and a stress field that is tensile on two sides and compressive on the other two sides 34 , 35 .

Analysis on stress fields of the surrounding rock.

In Fig.  12 ,

By using elastic mechanics to solve the stress fields in two different situations and combining their solutions, the stress of the surrounding rock in the non-isobaric stress field can be obtained:

where \(\sigma_{R}\) is the radial stress at the elastic–plastic interface. When \(r = R_{p}\) , \(\sigma_{re} = \sigma_{R}\) , and the radial and tangential stresses of the surrounding rock meet the Mogi–Coulomb criterion:

The stress at \(r = R_{p}\) is:

By substituting Eq. ( 25 ) into the physical equations, the strain in the elastic zone can be calculated:

Based on the geometric equations, the displacement of the elastic zone can be obtained:

Analysis on the plastic zone.

By combining the equilibrium equation and the Mogi–Coulomb criterion, the integral solution is calculated:

where C is an undetermined coefficient. When \(r = R_{p}\) , \(\sigma_{rp} = \sigma_{re}\) , and then the expression for C can be obtained:

Thus, the stress in the plastic zone can be expressed by:

The strain in the plastic zone consists of two parts, namely the elastic strain and the plastic strain:

By combining Eqs. ( 18 ) and ( 33 ) with the geometric equations and utilizing the continuous displacement conditions at the elastic–plastic interface, the displacement of the softening zone can be obtained:

where \(A_{1} = \varepsilon_{re} \left| {_{{r = R_{p} }} = } \right.\frac{1 + \mu }{E}[p_{1} (1 - 2\mu ) - p_{1} + \sigma_{R} + 4p_{2} \cos 2\theta (\mu - 2)]\)

According to the geometric equations, the strain in the plastic zone is:

Analysis on the broken zone.

Likewise, in the broken zone, \(\sigma_{r}\) and \(\sigma_{\theta }\) satisfy the equilibrium differential equation and the Mogi–Coulomb criterion, and when \(r = R_{0}\) , \(\sigma_{rb} = p_{i}\) . The stress in the broken zone is calculated as:

The displacement of the broken zone can be obtained in the same manner as the plastic zone:

where \(A_{2} = \varepsilon_{rp} \left| {_{{r = R_{b} }} } \right.\) , \(B_{2} = \varepsilon_{\theta p} \left| {_{{r = R_{b} }} } \right.\) , and \(C_{2} = u_{p} \left| {_{{r = R_{b} }} } \right.\) .

Determination of radii of the plastic zone and the broken zone.

In the plastic zone, Eq. ( 38 ) can be obtained on the basis of Eq. ( 35 ):

As mentioned earlier, in the plastic zone, \(\sigma_{p}\) and \(\varepsilon_{p}\) satisfy Eq. ( 14 ), then

When \(r = R_{b}\) , by substituting Eq. ( 38 ) into Eq. ( 39 ), the ratio of the radius of the broken zone to that of the plastic zone can be calculated by:

Additionally, when \(r = R_{b}\) , \(\sigma_{rp} = \sigma_{rb}\) . By combining Eqs. ( 32 ), ( 36 ), and ( 40 ), the calculation formulas for the radii of the plastic zone and the broken zone is obtained:

Analysis on influencing factors on deformation and failure of soft-coal roadway

The practical engineering of the #842 ventilating roadway in Guobei Coal Mine in Huaibei Mining Area was investigated as a case study. The surrounding rock of this roadway was identified as soft coal, and its geological conditions were obtained: the buried depth around 850 m, the stress of original rock \(p_{0}\)  = 22 MPa, the lateral pressure coefficient λ  = 1.2, the equivalent radius R 0  = 2.5 m, and the support force \(p_{i}\)  = 0.4 MPa. According to the true triaxial loading and unloading tests, the mechanical parameters of soft coal were measured: the elastic modulus E  = 270 MPa, Poisson’s ratio μ  = 0.31, the internal friction angle φ  = 38.95°, the cohesion c  = 0.858 MPa, the plastic-zone dilatancy coefficient η 1  = 1.18, and the broken-zone dilatancy coefficient η 2  = 1.32. The control variable method was adopted to successively analyze the effects of the intermediate principal stress coefficient b , the lateral pressure coefficient λ , and the support force \(p_{i}\) on the surface deformation of the surrounding rock, the radius of the plastic zone, and the radius of the broken zone.

Influence of intermediate principal stress coefficient on roadway failure

To investigate the influence of the intermediate principal stress coefficient b on the radius of the plastic zone R p , the radius of the broken zone R b , and the surface displacement of the surrounding rock \(u_{b}\) , based on the true triaxial loading and unloading test scheme, the values of b were determined to be 1/3, 1/2, 2/3, 5/6, and 1, respectively, while the other parameters remained unchanged. With these settings, the values of R p , R b , and \(u_{b}\) under different intermediate principal stress conditions were calculated and plotted (Figs. 13 , 14 and 15 ).

Radius of the plastic zone R p under different intermediate principal stresses.

Radius of the broken zone R b under different intermediate principal stresses.

Surface displacement of the surrounding rock under different intermediate principal stresses.

In Figs. 13 , 14 and 15 , under different intermediate principal stress coefficients, the plastic zones are ellipse; the broken zones are elongated ellipse; and the surface displacements are distributed in the shape of a spindle. The maximum values of R p , R b , and \(u_{b}\) all appear at the roof of the roadway ( θ  = 90°). As b increases, R p , R b , and \(u_{b}\) at the roof tend to slowly increase. Specifically, when b  = 1/3, R p , R b , and \(u_{b}\) at the roof are 5.28 m, 3.92 m, and 73.6 mm, respectively. When b increases to 2/3, their values rise to 5.69 m, 4.17 m, and 85.3 mm, a rise of 7.76%, 6.37%, and 15.90%, respectively. When b  > 2/3, their values start to surge. As b increases to 5/6, their values are 6.14 m, 4.34 m, and 95.5 mm, which are 7.90%, 4.07%, and 11.96% higher than those at b  = 2/3, respectively. When b increases to 1, their values grow to 6.68 m, 4.58 m, and 113.7 mm, 8.79%, 5.53%, and 19.06% higher than those at b  = 5/6, respectively. These results indicate that during excavation-induced unloading of surrounding rock, an increase in the intermediate principal stress will exacerbate the deformation of the surrounding rock and the dilatancy of the plastic and broken zones, exerting a noticeable impact on roadway failure.

Influence of lateral pressure coefficient on roadway failure

The distributions of R p , R b , and \(u_{b}\) under different lateral pressure coefficients are presented in Figs. 16 , 17 , and 18 , respectively. When λ  = 0.9, the plastic zone, the broken zone, and the surface displacement distribution are all ellipse. The minimum values of R p , R b , and \(u_{b}\) all appear at the roof of the roadway ( θ  = 90°), which are 4.98 m, 2.94 m, and 64.3 mm, respectively. Their maximum values all appear on the sides of the roadway ( θ  = 0°), which are 5.20 m, 3.52 m, and 70.4 mm, being 4.41%, 19.7%, and 9.5% higher than those at the roof, respectively. When λ  = 1, the plastic zone, the broken zone, and the surface displacement distribution are all circular, R p , R b , and \(u_{b}\) being 5.15 m, 3.32 m, and 65.1 mm, respectively. When λ  > 1, the shapes of the broken zone and the displacement distribution begin to change with the increase in λ . The broken zone gradually changes from an ellipse to an elongated ellipse, while the displacement distribution gradually transitions from an ellipse to a spindle. The maximum values of R p , R b , and \(u_{b}\) appear at the roof and gradually increase, while their values on the sides gradually decrease. When λ  = 1.1, their values at the roof are 5.30 m, 3.69 m, and 70.0 mm, respectively. When λ  = 1.3, these values grow to 5.60 m, 4.36 m, and 94.6 mm, a growth of 5.67%, 18.2%, and 35.1%, respectively. The radius of the broken zone and the surface displacement of the surrounding rock increase notably at the roof, suggesting that an increase in the lateral pressure coefficient will lead to pronounced deformation and failure of the roadway roof.

Radius of the plastic zone R p under different lateral pressure coefficients.

Radius of the broken zone R b under different lateral pressure coefficients.

Surface displacement of the surrounding rock under different lateral pressure coefficients.

Influence of support force on roadway failure

The distributions of R p , R b , and \(u_{b}\) under different support force conditions are displayed in Figs. 19 , 20 , and 21 , respectively. On the whole, the plastic zone, the broken zone, and the surface displacement distribution are in the shape of an ellipse, an elongated ellipse, and a spindle, respectively. The maximum values of R p , R b , and \(u_{b}\) all appear at the roof, and the minimum values all appear on the sides. As the support force p i increases, the plastic zone, the broken zone, and the surface displacement of the roadway gradually shrink. When p i  = 0.1 MPa, the R p , R b , and \(u_{b}\) at the roof are 5.91 m, 4.37 m, and 104.2 mm, respectively. As p i increases to 0.3 MPa, their values at the roof drop to 5.59 m, 4.13 m, and 84.8 mm, by 5.41%, 5.49%, and 18.6%, respectively. When p i increases to 0.5 MPa, these values decline to 5.32 m, 3.93 m, and 71.1 mm, a further decline of 4.83%, 4.85%, and 16.2%, respectively. Hence, it can be concluded that during the excavation of a soft-coal roadway, the deformation and failure of its surrounding rock can be controlled by increasing the support force.

Radius of the plastic zone R p under different support forces.

Radius of the broken zone R b under different support forces.

Surface displacement of the surrounding rock under different support forces.

Conclusions

The deformation and strength characteristics of soft coal under true triaxial loading and unloading conditions were investigated, and the constitutive relationship and strength criterion of soft coal under true stress paths in roadway excavation were determined. On this basis, analytical solutions for the displacement of the surrounding rock, the radius of the broken zone, and the radius of the plastic zone were derived. Finally, in accordance with a practical engineering case, the effects of different influencing factors on the deformation and failure characteristics of a soft-coal roadway were analyzed. The following main conclusions were drawn.

The stress–strain curves of soft coal in the true triaxial loading and unloading tests can be divided into four stages, namely the elastic stage, the pre-peak plasticity-strengthening (hardening) stage, the post-peak plasticity-weakening (softening) stage, and the instability failure stage. The stress–strain relationship in the pre-peak hardening and post-peak softening stages follows a quadratic function. As the initial \(\sigma_{2}\) increases, the difference between strains in the two unloading directions gradually enlarges, making the coal more prone to dilatancy and deformation in the \(\sigma_{3}\) direction and thereby decreasing its peak strength.

Compared to the Mohr–Coulomb criterion and the Drucker–Prager criterion, the Mogi–Coulomb criterion can more accurately describe the strength characteristics of soft coal under true triaxial loading and unloading paths.

Analytical solutions for the displacement of the surrounding rock, the radius of the broken zone, and the radius of the plastic zone in soft-coal roadways under excavation stress paths were derived after taking the nonlinear strain strengthening and softening characteristics of soft coal, the Mogi–Coulomb criterion, the intermediate principal stress, and the surrounding rock dilatancy characteristics into account.

An increase in the intermediate principal stress coefficient b will aggravate the deformation of the surrounding rock and the dilatancy of the plastic and broken zones. Meanwhile, an increase in the lateral pressure coefficient λ can bring about a gradual increase in the deformation degree of the plastic and broken zones at the roof and a decrease on the sides. The shape of the broken zone and surface displacement distribution will change correspondingly. Moreover, as the support force p i increases, the plastic zone, broken zone, and the surface displacement of the roadway all gradually shrink.

Data availability

The data used to support the findings and results of this study are available from the corresponding author upon request.

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Chongyang Jiang, Lianguo Wang, Jiaxing Guo & Shuai Wang

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Jiang, C., Wang, L., Guo, J. et al. Elastoplastic analysis on deformation and failure characteristics of surrounding rock of soft-coal roadway based on true triaxial loading and unloading tests. Sci Rep 14 , 21103 (2024). https://doi.org/10.1038/s41598-024-72052-4

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8.1 Experimental design: What is it and when should it be used?

Learning objectives.

• Define experiment
• Identify the core features of true experimental designs
• Describe the difference between an experimental group and a control group
• Identify and describe the various types of true experimental designs

Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.

Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.

Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:

• random assignment of participants into experimental and control groups
• a “treatment” (or intervention) provided to the experimental group
• measurement of the effects of the treatment in a post-test administered to both groups

Some true experiments are more complex.  Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment.

Experimental and control groups

In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.

Treatment or intervention

In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.

In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.

The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test .  In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.

Types of experimental design

Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.

An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.

In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963).  The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects , in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.

Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.

Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.

Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we  will discuss in the next section–can be used.  However, the differences in rigor from true experimental designs leave their conclusions more open to critique.

Experimental design in macro-level research

You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals.  For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change.  There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013).  Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments.  For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.

Key Takeaways

• True experimental designs require random assignment.
• Control groups do not receive an intervention, and experimental groups receive an intervention.
• The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
• Testing effects may cause researchers to use variations on the classic experimental design.
• Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
• Control group- the group in an experiment that does not receive the intervention
• Experiment- a method of data collection designed to test hypotheses under controlled conditions
• Experimental group- the group in an experiment that receives the intervention
• Posttest- a measurement taken after the intervention
• Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
• Pretest- a measurement taken prior to the intervention
• Random assignment-using a random process to assign people into experimental and control groups
• Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
• Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
• True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups

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exam scientific experiment by mohamed_hassan CC-0

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Experimental cell models for investigating neurodegenerative diseases.

1. Introduction

2. main text, 2.1. primary patient cell lines, 2.2. human induced pluripotent stem cells (ipscs), 2.3. organoids, 3. current challenges and future directions, 4. discussion, 5. conclusions, author contributions, conflicts of interest.

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Evangelisti, C.; Ramadan, S.; Orlacchio, A.; Panza, E. Experimental Cell Models for Investigating Neurodegenerative Diseases. Int. J. Mol. Sci. 2024 , 25 , 9747. https://doi.org/10.3390/ijms25179747

Evangelisti C, Ramadan S, Orlacchio A, Panza E. Experimental Cell Models for Investigating Neurodegenerative Diseases. International Journal of Molecular Sciences . 2024; 25(17):9747. https://doi.org/10.3390/ijms25179747

Evangelisti, Cecilia, Sherin Ramadan, Antonio Orlacchio, and Emanuele Panza. 2024. "Experimental Cell Models for Investigating Neurodegenerative Diseases" International Journal of Molecular Sciences 25, no. 17: 9747. https://doi.org/10.3390/ijms25179747

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Experimental Investigation Into the Propagation Law of Supercritical Carbon Dioxide Fracturing Cracks Induced by Directional Grooving of Hard Roof

• Original Paper
• Published: 03 September 2024

Cite this article

• Hao Yan 1 , 2 ,
• Weihang Mao 1 , 2 ,
• Jixiong Zhang 1 , 2 ,
• Wenlong Wang 1 , 2 ,
• Peitao Shi 1 , 2 &
• Dan Ma 1 , 2  

Directional fracturing technology for coal and rock is crucial for preventing accidents resulting from the rupture of the hard roof in coal mines. Supercritical carbon dioxide (SC-CO 2 ) fracturing, a green and waterless technology, has garnered significant research interest due to its ability to form complex crack networks. This technology holds promise for mitigating the risk of hard roof collapse in coal mines. Understanding the mechanisms to induce optimal crack propagation through SC-CO 2 fracturing is highly valuable. This study employs the true triaxial test platform for SC-CO 2 fracturing to examine the propagation law of cracks induced by directional grooving. A series of SC-CO 2 fracturing tests were performed under varying in-situ stress differentials, groove numbers, and spacing between them, and a method for characterizing the directional fracturing effects in terms of inducing cracks was proposed. The morphology of crack propagation in fractured samples was photographed, and the types, lengths, and directions of surface cracks were statistically analyzed. A three-dimensional model of cracks in the fractured samples was reconstructed, revealing the evolution characteristics of hole-line spacing and the main crack-induced angle of deflection. The influence of grooves on crack propagation during SC-CO 2 fracturing under various conditions was determined, and the interaction mechanism of the stress field at the crack tip within the grooved area was analyzed. These results provide theoretical guidance for applying SC-CO 2 fracturing technology in coal mines.

The true triaxial test platform for SC-CO 2 fracturing was utilized to examine the propagation law of cracks induced by directional grooving.

The method for characterizing the directional fracturing effects in terms of inducing cracks was proposed.

The effect of the grooves on directional crack propagation when using SC-CO 2 fracturing technology was obtained.

The interaction mechanism of the stress field at the tip of the cracks in the area of the groove was discussed.

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A Study of Variation in the Initiation Pressure and Fracture Distribution Patterns of Raw Coal in SC-CO 2 Fracturing Under the True Tri-axial System

Experimental Study of Supercritical CO 2 Fracturing Across Coal–Rock Interfaces

Experimental Study on Stress Transfer and Fracture Law During the Hydraulic Fracturing of Coal Seams

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Data will be made available on request.

Abbreviations

Supercritical carbon dioxide

The hole–line distance

The angle of deflection

The mean value of the deflection angles on the left and right surfaces

The mean value of the deflection angles on the top and bottom surfaces

The angle of deflection on the left surface

The angle of deflection on the right surface

The angle of deflection on the top surface

The angle of deflection on the bottom surface

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Acknowledgements

This research was supported by the National Key R&D Program of China (2022YFC2905600), the National Natural Science Foundation of China (52304158), the Key R&D Program of Xinjiang Uygur Autonomous Region (2023B01009), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX23_2781).

The National Key R&D Program of China, 2022YFC2905600, Dan Ma, the National Natural Science Foundation of China, 52304158, Hao Yan, the Key R&D Program of Xinjiang Uygur Autonomous Region, 2023B01009, Jixiong Zhang, the Postgraduate Research & Practice Innovation Program of Jiangsu Province, KYCX23_2781, Peitao Shi.

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Key Laboratory of Deep Coal Resource Mining (CUMT), Ministry of Education of China, School of Mines, China University of Mining & Technology, Xuzhou, 221116, Jiangsu, China

Hao Yan, Weihang Mao, Jixiong Zhang, Wenlong Wang, Peitao Shi & Dan Ma

State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining & Technology, Xuzhou, 221116, Jiangsu, China

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Yan, H., Mao, W., Zhang, J. et al. Experimental Investigation Into the Propagation Law of Supercritical Carbon Dioxide Fracturing Cracks Induced by Directional Grooving of Hard Roof. Rock Mech Rock Eng (2024). https://doi.org/10.1007/s00603-024-04151-7

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Received : 23 May 2024

Accepted : 18 August 2024

Published : 03 September 2024

DOI : https://doi.org/10.1007/s00603-024-04151-7

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