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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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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

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

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

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

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

Randomized Block Design

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

Factorial Design

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

Repeated Measures Design

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

Crossover Design

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

Split-plot Design

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

Nested Design

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

Laboratory Experiment

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

Field Experiment

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

Experimental Design Methods

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

Randomization

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

Control Group

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

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

Counterbalancing

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

Replication

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

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

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

Data Collection Method

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

Direct Observation

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

Self-report Measures

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

Behavioral Measures

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

Physiological Measures

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

Archival Data

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

Computerized Measures

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

Video Recording

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

Data Analysis Method

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

Descriptive Statistics

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

Inferential Statistics

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

Analysis of Variance (ANOVA)

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling (SEM)

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

Cluster Analysis

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Experimental Design 

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

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

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

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

When to use Experimental Research Design 

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

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

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

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

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

Purpose of Experimental Design 

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

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

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

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

Advantages of Experimental Design 

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

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

Limitations of Experimental Design

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

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Study/Experimental/Research Design: Much More Than Statistics

Kenneth l. knight.

Brigham Young University, Provo, UT

The purpose of study, experimental, or research design in scientific manuscripts has changed significantly over the years. It has evolved from an explanation of the design of the experiment (ie, data gathering or acquisition) to an explanation of the statistical analysis. This practice makes “Methods” sections hard to read and understand.

To clarify the difference between study design and statistical analysis, to show the advantages of a properly written study design on article comprehension, and to encourage authors to correctly describe study designs.

Description:

The role of study design is explored from the introduction of the concept by Fisher through modern-day scientists and the AMA Manual of Style . At one time, when experiments were simpler, the study design and statistical design were identical or very similar. With the complex research that is common today, which often includes manipulating variables to create new variables and the multiple (and different) analyses of a single data set, data collection is very different than statistical design. Thus, both a study design and a statistical design are necessary.

Advantages:

Scientific manuscripts will be much easier to read and comprehend. A proper experimental design serves as a road map to the study methods, helping readers to understand more clearly how the data were obtained and, therefore, assisting them in properly analyzing the results.

Study, experimental, or research design is the backbone of good research. It directs the experiment by orchestrating data collection, defines the statistical analysis of the resultant data, and guides the interpretation of the results. When properly described in the written report of the experiment, it serves as a road map to readers, 1 helping them negotiate the “Methods” section, and, thus, it improves the clarity of communication between authors and readers.

A growing trend is to equate study design with only the statistical analysis of the data. The design statement typically is placed at the end of the “Methods” section as a subsection called “Experimental Design” or as part of a subsection called “Data Analysis.” This placement, however, equates experimental design and statistical analysis, minimizing the effect of experimental design on the planning and reporting of an experiment. This linkage is inappropriate, because some of the elements of the study design that should be described at the beginning of the “Methods” section are instead placed in the “Statistical Analysis” section or, worse, are absent from the manuscript entirely.

Have you ever interrupted your reading of the “Methods” to sketch out the variables in the margins of the paper as you attempt to understand how they all fit together? Or have you jumped back and forth from the early paragraphs of the “Methods” section to the “Statistics” section to try to understand which variables were collected and when? These efforts would be unnecessary if a road map at the beginning of the “Methods” section outlined how the independent variables were related, which dependent variables were measured, and when they were measured. When they were measured is especially important if the variables used in the statistical analysis were a subset of the measured variables or were computed from measured variables (such as change scores).

The purpose of this Communications article is to clarify the purpose and placement of study design elements in an experimental manuscript. Adopting these ideas may improve your science and surely will enhance the communication of that science. These ideas will make experimental manuscripts easier to read and understand and, therefore, will allow them to become part of readers' clinical decision making.

WHAT IS A STUDY (OR EXPERIMENTAL OR RESEARCH) DESIGN?

The terms study design, experimental design, and research design are often thought to be synonymous and are sometimes used interchangeably in a single paper. Avoid doing so. Use the term that is preferred by the style manual of the journal for which you are writing. Study design is the preferred term in the AMA Manual of Style , 2 so I will use it here.

A study design is the architecture of an experimental study 3 and a description of how the study was conducted, 4 including all elements of how the data were obtained. 5 The study design should be the first subsection of the “Methods” section in an experimental manuscript (see the Table ). “Statistical Design” or, preferably, “Statistical Analysis” or “Data Analysis” should be the last subsection of the “Methods” section.

Table. Elements of a “Methods” Section

The “Study Design” subsection describes how the variables and participants interacted. It begins with a general statement of how the study was conducted (eg, crossover trials, parallel, or observational study). 2 The second element, which usually begins with the second sentence, details the number of independent variables or factors, the levels of each variable, and their names. A shorthand way of doing so is with a statement such as “A 2 × 4 × 8 factorial guided data collection.” This tells us that there were 3 independent variables (factors), with 2 levels of the first factor, 4 levels of the second factor, and 8 levels of the third factor. Following is a sentence that names the levels of each factor: for example, “The independent variables were sex (male or female), training program (eg, walking, running, weight lifting, or plyometrics), and time (2, 4, 6, 8, 10, 15, 20, or 30 weeks).” Such an approach clearly outlines for readers how the various procedures fit into the overall structure and, therefore, enhances their understanding of how the data were collected. Thus, the design statement is a road map of the methods.

The dependent (or measurement or outcome) variables are then named. Details of how they were measured are not given at this point in the manuscript but are explained later in the “Instruments” and “Procedures” subsections.

Next is a paragraph detailing who the participants were and how they were selected, placed into groups, and assigned to a particular treatment order, if the experiment was a repeated-measures design. And although not a part of the design per se, a statement about obtaining written informed consent from participants and institutional review board approval is usually included in this subsection.

The nuts and bolts of the “Methods” section follow, including such things as equipment, materials, protocols, etc. These are beyond the scope of this commentary, however, and so will not be discussed.

The last part of the “Methods” section and last part of the “Study Design” section is the “Data Analysis” subsection. It begins with an explanation of any data manipulation, such as how data were combined or how new variables (eg, ratios or differences between collected variables) were calculated. Next, readers are told of the statistical measures used to analyze the data, such as a mixed 2 × 4 × 8 analysis of variance (ANOVA) with 2 between-groups factors (sex and training program) and 1 within-groups factor (time of measurement). Researchers should state and reference the statistical package and procedure(s) within the package used to compute the statistics. (Various statistical packages perform analyses slightly differently, so it is important to know the package and specific procedure used.) This detail allows readers to judge the appropriateness of the statistical measures and the conclusions drawn from the data.

STATISTICAL DESIGN VERSUS STATISTICAL ANALYSIS

Avoid using the term statistical design . Statistical methods are only part of the overall design. The term gives too much emphasis to the statistics, which are important, but only one of many tools used in interpreting data and only part of the study design:

The most important issues in biostatistics are not expressed with statistical procedures. The issues are inherently scientific, rather than purely statistical, and relate to the architectural design of the research, not the numbers with which the data are cited and interpreted. 6

Stated another way, “The justification for the analysis lies not in the data collected but in the manner in which the data were collected.” 3 “Without the solid foundation of a good design, the edifice of statistical analysis is unsafe.” 7 (pp4–5)

The intertwining of study design and statistical analysis may have been caused (unintentionally) by R.A. Fisher, “… a genius who almost single-handedly created the foundations for modern statistical science.” 8 Most research did not involve statistics until Fisher invented the concepts and procedures of ANOVA (in 1921) 9 , 10 and experimental design (in 1935). 11 His books became standard references for scientists in many disciplines. As a result, many ANOVA books were titled Experimental Design (see, for example, Edwards 12 ), and ANOVA courses taught in psychology and education departments included the words experimental design in their course titles.

Before the widespread use of computers to analyze data, designs were much simpler, and often there was little difference between study design and statistical analysis. So combining the 2 elements did not cause serious problems. This is no longer true, however, for 3 reasons: (1) Research studies are becoming more complex, with multiple independent and dependent variables. The procedures sections of these complex studies can be difficult to understand if your only reference point is the statistical analysis and design. (2) Dependent variables are frequently measured at different times. (3) How the data were collected is often not directly correlated with the statistical design.

For example, assume the goal is to determine the strength gain in novice and experienced athletes as a result of 3 strength training programs. Rate of change in strength is not a measurable variable; rather, it is calculated from strength measurements taken at various time intervals during the training. So the study design would be a 2 × 2 × 3 factorial with independent variables of time (pretest or posttest), experience (novice or advanced), and training (isokinetic, isotonic, or isometric) and a dependent variable of strength. The statistical design , however, would be a 2 × 3 factorial with independent variables of experience (novice or advanced) and training (isokinetic, isotonic, or isometric) and a dependent variable of strength gain. Note that data were collected according to a 3-factor design but were analyzed according to a 2-factor design and that the dependent variables were different. So a single design statement, usually a statistical design statement, would not communicate which data were collected or how. Readers would be left to figure out on their own how the data were collected.

MULTIVARIATE RESEARCH AND THE NEED FOR STUDY DESIGNS

With the advent of electronic data gathering and computerized data handling and analysis, research projects have increased in complexity. Many projects involve multiple dependent variables measured at different times, and, therefore, multiple design statements may be needed for both data collection and statistical analysis. Consider, for example, a study of the effects of heat and cold on neural inhibition. The variables of H max and M max are measured 3 times each: before, immediately after, and 30 minutes after a 20-minute treatment with heat or cold. Muscle temperature might be measured each minute before, during, and after the treatment. Although the minute-by-minute data are important for graphing temperature fluctuations during the procedure, only 3 temperatures (time 0, time 20, and time 50) are used for statistical analysis. A single dependent variable H max :M max ratio is computed to illustrate neural inhibition. Again, a single statistical design statement would tell little about how the data were obtained. And in this example, separate design statements would be needed for temperature measurement and H max :M max measurements.

As stated earlier, drawing conclusions from the data depends more on how the data were measured than on how they were analyzed. 3 , 6 , 7 , 13 So a single study design statement (or multiple such statements) at the beginning of the “Methods” section acts as a road map to the study and, thus, increases scientists' and readers' comprehension of how the experiment was conducted (ie, how the data were collected). Appropriate study design statements also increase the accuracy of conclusions drawn from the study.

CONCLUSIONS

The goal of scientific writing, or any writing, for that matter, is to communicate information. Including 2 design statements or subsections in scientific papers—one to explain how the data were collected and another to explain how they were statistically analyzed—will improve the clarity of communication and bring praise from readers. To summarize:

• Purge from your thoughts and vocabulary the idea that experimental design and statistical design are synonymous.
• Study or experimental design plays a much broader role than simply defining and directing the statistical analysis of an experiment.
• A properly written study design serves as a road map to the “Methods” section of an experiment and, therefore, improves communication with the reader.
• Study design should include a description of the type of design used, each factor (and each level) involved in the experiment, and the time at which each measurement was made.
• Clarify when the variables involved in data collection and data analysis are different, such as when data analysis involves only a subset of a collected variable or a resultant variable from the mathematical manipulation of 2 or more collected variables.

Acknowledgments

Thanks to Thomas A. Cappaert, PhD, ATC, CSCS, CSE, for suggesting the link between R.A. Fisher and the melding of the concepts of research design and statistics.

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• A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

Published on 11 April 2022 by Rebecca Bevans . Revised on 5 December 2022.

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 means creating 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 if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

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, frequently asked questions about experimental design.

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 generalised 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 randomised design vs a randomised block design .
• A between-subjects design vs a within-subjects design .

Randomisation

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

• In a completely randomised design , every subject is assigned to a treatment group at random.
• In a randomised 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 randomisation 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 (randomising 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 minimise bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalised 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.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

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.

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Rebecca Bevans

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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10 Experimental research

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

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

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

Basic concepts

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

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

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

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

History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.

Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.

Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam.

Not conducting a pretest can help avoid this threat.

Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.

Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.

Regression threat —also called a regression to the mean—refers to the statistical tendency of a group’s overall performance to regress toward the mean during a posttest rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest were possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.

Two-group experimental designs

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

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

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

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

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

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

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

Factorial designs

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

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

Hybrid experimental designs

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

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

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

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

Quasi-experimental designs

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

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

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

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

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

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

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

Perils of experimental research

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

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

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

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Experimental design: Guide, steps, examples

Last updated

27 April 2023

Reviewed by

Miroslav Damyanov

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. 

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

• 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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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

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

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

Descriptive studies

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

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

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

Observational studies

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

Cross-sectional study:

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

Cohort study:

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

Case-Control Study:

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

Experimental Studies

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

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

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

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

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

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

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

References (pdf)

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

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

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

Very informative and easy understandable

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

Wow very erll explained and easy to understand

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

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

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

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

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

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

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

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

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

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

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

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

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

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

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

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Neag School of Education

Educational Research Basics by Del Siegle

Experimental research.

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

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

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

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

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

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

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

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

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• What is experimental research: Definition, types & examples

Defne Çobanoğlu

Life and its secrets can only be proven right or wrong with experimentation. You can speculate and theorize all you wish, but as William Blake once said, “ The true method of knowledge is experiment. ”

It may be a long process and time-consuming, but it is rewarding like no other. And there are multiple ways and methods of experimentation that can help shed light on matters. In this article, we explained the definition, types of experimental research, and some experimental research examples . Let us get started with the definition!

• What is experimental research?

Experimental research is the process of carrying out a study conducted with a scientific approach using two or more variables. In other words, it is when you gather two or more variables and compare and test them in controlled environments. 

With experimental research, researchers can also collect detailed information about the participants by doing pre-tests and post-tests to learn even more information about the process. With the result of this type of study, the researcher can make conscious decisions. 

The more control the researcher has over the internal and extraneous variables, the better it is for the results. There may be different circumstances when a balanced experiment is not possible to conduct. That is why are are different research designs to accommodate the needs of researchers.

• 3 Types of experimental research designs

There is more than one dividing point in experimental research designs that differentiates them from one another. These differences are about whether or not there are pre-tests or post-tests done and how the participants are divided into groups. These differences decide which experimental research design is used.

Types of experimental research designs

1 - Pre-experimental design

This is the most basic method of experimental study. The researcher doing pre-experimental research evaluates a group of dependent variables after changing the independent variables . The results of this scientific method are not satisfactory, and future studies are planned accordingly. The pre-experimental research can be divided into three types:

A. One shot case study research design

Only one variable is considered in this one-shot case study design. This research method is conducted in the post-test part of a study, and the aim is to observe the changes in the effect of the independent variable.

B. One group pre-test post-test research design

In this type of research, a single group is given a pre-test before a study is conducted and a post-test after the study is conducted. The aim of this one-group pre-test post-test research design is to combine and compare the data collected during these tests. 

C. Static-group comparison

In a static group comparison, 2 or more groups are included in a study where only a group of participants is subjected to a new treatment and the other group of participants is held static. After the study is done, both groups do a post-test evaluation, and the changes are seen as results.

2 - Quasi-experimental design

This research type is quite similar to the experimental design; however, it changes in a few aspects. Quasi-experimental research is done when experimentation is needed for accurate data, but it is not possible to do one because of some limitations. Because you can not deliberately deprive someone of medical treatment or give someone harm, some experiments are ethically impossible. In this experimentation method, the researcher can only manipulate some variables. There are three types of quasi-experimental design:

A. Nonequivalent group designs

A nonequivalent group design is used when participants can not be divided equally and randomly for ethical reasons. Because of this, different variables will be more than one, unlike true experimental research.

B. Regression discontinuity

In this type of research design, the researcher does not divide a group into two to make a study, instead, they make use of a natural threshold or pre-existing dividing point. Only participants below or above the threshold get the treatment, and as the divide is minimal, the difference would be minimal as well.

C. Natural Experiments

In natural experiments, random or irregular assignment of patients makes up control and study groups. And they exist in natural scenarios. Because of this reason, they do not qualify as true experiments as they are based on observation.

3 - True experimental design

In true experimental research, the variables, groups, and settings should be identical to the textbook definition. Grouping of the participant are divided randomly, and controlled variables are chosen carefully. Every aspect of a true experiment should be carefully designed and acted out. And only the results of a true experiment can really be fully accurate . A true experimental design can be divided into 3 parts:

A. Post-test only control group design

In this experimental design, the participants are divided into two groups randomly. They are called experimental and control groups. Only the experimental group gets the treatment, while the other one does not. After the experiment and observation, both groups are given a post-test, and a conclusion is drawn from the results.

B. Pre-test post-test control group

In this method, the participants are divided into two groups once again. Also, only the experimental group gets the treatment. And this time, they are given both pre-tests and post-tests with multiple research methods. Thanks to these multiple tests, the researchers can make sure the changes in the experimental group are directly related to the treatment.

C. Solomon four-group design

This is the most comprehensive method of experimentation. The participants are randomly divided into 4 groups. These four groups include all possible permutations by including both control and non-control groups and post-test or pre-test and post-test control groups. This method enhances the quality of the data.

• Advantages and disadvantages of experimental research

Just as with any other study, experimental research also has its positive and negative sides. It is up to the researchers to be mindful of these facts before starting their studies. Let us see some advantages and disadvantages of experimental research:

Advantages of experimental research:

• All the variables are in the researchers’ control, and that means the researcher can influence the experiment according to the research question’s requirements.
• As you can easily control the variables in the experiment, you can specify the results as much as possible.
• The results of the study identify a cause-and-effect relation .
• The results can be as specific as the researcher wants.
• The result of an experimental design opens the doors for future related studies.

Disadvantages of experimental research:

• Completing an experiment may take years and even decades, so the results will not be as immediate as some of the other research types.
• As it involves many steps, participants, and researchers, it may be too expensive for some groups.
• The possibility of researchers making mistakes and having a bias is high. It is important to stay impartial
• Human behavior and responses can be difficult to measure unless it is specifically experimental research in psychology.
• Examples of experimental research

When one does experimental research, that experiment can be about anything. As the variables and environments can be controlled by the researcher, it is possible to have experiments about pretty much any subject. It is especially crucial that it gives critical insight into the cause-and-effect relationships of various elements. Now let us see some important examples of experimental research:

An example of experimental research in science:

When scientists make new medicines or come up with a new type of treatment, they have to test those thoroughly to make sure the results will be unanimous and effective for every individual. In order to make sure of this, they can test the medicine on different people or creatures in different dosages and in different frequencies. They can double-check all the results and have crystal clear results.

An example of experimental research in marketing:

The ideal goal of a marketing product, advertisement, or campaign is to attract attention and create positive emotions in the target audience. Marketers can focus on different elements in different campaigns, change the packaging/outline, and have a different approach. Only then can they be sure about the effectiveness of their approaches. Some methods they can work with are A/B testing, online surveys , or focus groups .

• Frequently asked questions about experimental research

Is experimental research qualitative or quantitative?

Experimental research can be both qualitative and quantitative according to the nature of the study. Experimental research is quantitative when it provides numerical and provable data. The experiment is qualitative when it provides researchers with participants' experiences, attitudes, or the context in which the experiment is conducted.

What is the difference between quasi-experimental research and experimental research?

In true experimental research, the participants are divided into groups randomly and evenly so as to have an equal distinction. However, in quasi-experimental research, the participants can not be divided equally for ethical or practical reasons. They are chosen non-randomly or by using a pre-existing threshold.

• Wrapping it up

The experimentation process can be long and time-consuming but highly rewarding as it provides valuable as well as both qualitative and quantitative data. It is a valuable part of research methods and gives insight into the subjects to let people make conscious decisions.

In this article, we have gathered experimental research definition, experimental research types, examples, and pros & cons to work as a guide for your next study. You can also make a successful experiment using pre-test and post-test methods and analyze the findings. For further information on different research types and for all your research information, do not forget to visit our other articles!

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

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How the Experimental Method Works in Psychology

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

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

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The Experimental Process

Types of experiments, potential pitfalls of the experimental method.

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

At a Glance

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

What Is the Experimental Method in Psychology?

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

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

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

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

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

History of the Experimental Method

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

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

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

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

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

Key Terms to Know

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

Dependent Variable

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

Independent Variable

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

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

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

Extraneous Variables

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

Demand Characteristics

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

Intervening Variables

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

Confounding Variables

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

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

The five basic steps of the experimental process are:

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

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

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

Lab Experiments

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

Field Experiments

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

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

Quasi-Experiments

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

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

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

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

Examples of the Experimental Method in Use

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

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

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

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

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

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

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

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

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

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

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

What This Means For You

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

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

Colorado State University. Experimental and quasi-experimental research .

American Psychological Association. Experimental psychology studies human and animals .

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

Mandler G. A History of Modern Experimental Psychology .

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

Britannica. Gustav Fechner .

Britannica. Hermann von Helmholtz .

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

Britannica. Georg Elias Müller .

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

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

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

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

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

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

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

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

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

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

Experimental Research: Meaning And Examples Of Experimental Research

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

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

What Is Experimental Research?

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

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

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

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

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

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

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

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

Pre-experimental Research Design

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

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

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

True Experimental Research Design

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

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

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

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

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

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

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

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

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

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

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

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Explore Harappa Diaries to learn more about topics such as Main Objective Of Research , Definition Of Qualitative Research , Examples Of Experiential Learning and Collaborative Learning Strategies to upgrade your knowledge and skills.

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Organizing Your Social Sciences Research Paper

Glossary of research terms.

• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
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• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
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This glossary is intended to assist you in understanding commonly used terms and concepts when reading, interpreting, and evaluating scholarly research. Also included are common words and phrases defined within the context of how they apply to research in the social and behavioral sciences.

• Acculturation -- refers to the process of adapting to another culture, particularly in reference to blending in with the majority population [e.g., an immigrant adopting American customs]. However, acculturation also implies that both cultures add something to one another, but still remain distinct groups unto themselves.
• Accuracy -- a term used in survey research to refer to the match between the target population and the sample.
• Affective Measures -- procedures or devices used to obtain quantified descriptions of an individual's feelings, emotional states, or dispositions.
• Aggregate -- a total created from smaller units. For instance, the population of a county is an aggregate of the populations of the cities, rural areas, etc. that comprise the county. As a verb, it refers to total data from smaller units into a large unit.
• Anonymity -- a research condition in which no one, including the researcher, knows the identities of research participants.
• Baseline -- a control measurement carried out before an experimental treatment.
• Behaviorism -- school of psychological thought concerned with the observable, tangible, objective facts of behavior, rather than with subjective phenomena such as thoughts, emotions, or impulses. Contemporary behaviorism also emphasizes the study of mental states such as feelings and fantasies to the extent that they can be directly observed and measured.
• Beliefs -- ideas, doctrines, tenets, etc. that are accepted as true on grounds which are not immediately susceptible to rigorous proof.
• Benchmarking -- systematically measuring and comparing the operations and outcomes of organizations, systems, processes, etc., against agreed upon "best-in-class" frames of reference.
• Bias -- a loss of balance and accuracy in the use of research methods. It can appear in research via the sampling frame, random sampling, or non-response. It can also occur at other stages in research, such as while interviewing, in the design of questions, or in the way data are analyzed and presented. Bias means that the research findings will not be representative of, or generalizable to, a wider population.
• Case Study -- the collection and presentation of detailed information about a particular participant or small group, frequently including data derived from the subjects themselves.
• Causal Hypothesis -- a statement hypothesizing that the independent variable affects the dependent variable in some way.
• Causal Relationship -- the relationship established that shows that an independent variable, and nothing else, causes a change in a dependent variable. It also establishes how much of a change is shown in the dependent variable.
• Causality -- the relation between cause and effect.
• Central Tendency -- any way of describing or characterizing typical, average, or common values in some distribution.
• Chi-square Analysis -- a common non-parametric statistical test which compares an expected proportion or ratio to an actual proportion or ratio.
• Claim -- a statement, similar to a hypothesis, which is made in response to the research question and that is affirmed with evidence based on research.
• Classification -- ordering of related phenomena into categories, groups, or systems according to characteristics or attributes.
• Cluster Analysis -- a method of statistical analysis where data that share a common trait are grouped together. The data is collected in a way that allows the data collector to group data according to certain characteristics.
• Cohort Analysis -- group by group analytic treatment of individuals having a statistical factor in common to each group. Group members share a particular characteristic [e.g., born in a given year] or a common experience [e.g., entering a college at a given time].
• Confidentiality -- a research condition in which no one except the researcher(s) knows the identities of the participants in a study. It refers to the treatment of information that a participant has disclosed to the researcher in a relationship of trust and with the expectation that it will not be revealed to others in ways that violate the original consent agreement, unless permission is granted by the participant.
• Confirmability Objectivity -- the findings of the study could be confirmed by another person conducting the same study.
• Construct -- refers to any of the following: something that exists theoretically but is not directly observable; a concept developed [constructed] for describing relations among phenomena or for other research purposes; or, a theoretical definition in which concepts are defined in terms of other concepts. For example, intelligence cannot be directly observed or measured; it is a construct.
• Construct Validity -- seeks an agreement between a theoretical concept and a specific measuring device, such as observation.
• Constructivism -- the idea that reality is socially constructed. It is the view that reality cannot be understood outside of the way humans interact and that the idea that knowledge is constructed, not discovered. Constructivists believe that learning is more active and self-directed than either behaviorism or cognitive theory would postulate.
• Content Analysis -- the systematic, objective, and quantitative description of the manifest or latent content of print or nonprint communications.
• Context Sensitivity -- awareness by a qualitative researcher of factors such as values and beliefs that influence cultural behaviors.
• Control Group -- the group in an experimental design that receives either no treatment or a different treatment from the experimental group. This group can thus be compared to the experimental group.
• Controlled Experiment -- an experimental design with two or more randomly selected groups [an experimental group and control group] in which the researcher controls or introduces the independent variable and measures the dependent variable at least two times [pre- and post-test measurements].
• Correlation -- a common statistical analysis, usually abbreviated as r, that measures the degree of relationship between pairs of interval variables in a sample. The range of correlation is from -1.00 to zero to +1.00. Also, a non-cause and effect relationship between two variables.
• Covariate -- a product of the correlation of two related variables times their standard deviations. Used in true experiments to measure the difference of treatment between them.
• Credibility -- a researcher's ability to demonstrate that the object of a study is accurately identified and described based on the way in which the study was conducted.
• Critical Theory -- an evaluative approach to social science research, associated with Germany's neo-Marxist “Frankfurt School,” that aims to criticize as well as analyze society, opposing the political orthodoxy of modern communism. Its goal is to promote human emancipatory forces and to expose ideas and systems that impede them.
• Data -- factual information [as measurements or statistics] used as a basis for reasoning, discussion, or calculation.
• Data Mining -- the process of analyzing data from different perspectives and summarizing it into useful information, often to discover patterns and/or systematic relationships among variables.
• Data Quality -- this is the degree to which the collected data [results of measurement or observation] meet the standards of quality to be considered valid [trustworthy] and  reliable [dependable].
• Deductive -- a form of reasoning in which conclusions are formulated about particulars from general or universal premises.
• Dependability -- being able to account for changes in the design of the study and the changing conditions surrounding what was studied.
• Dependent Variable -- a variable that varies due, at least in part, to the impact of the independent variable. In other words, its value “depends” on the value of the independent variable. For example, in the variables “gender” and “academic major,” academic major is the dependent variable, meaning that your major cannot determine whether you are male or female, but your gender might indirectly lead you to favor one major over another.
• Deviation -- the distance between the mean and a particular data point in a given distribution.
• Discourse Community -- a community of scholars and researchers in a given field who respond to and communicate to each other through published articles in the community's journals and presentations at conventions. All members of the discourse community adhere to certain conventions for the presentation of their theories and research.
• Discrete Variable -- a variable that is measured solely in whole units, such as, gender and number of siblings.
• Distribution -- the range of values of a particular variable.
• Effect Size -- the amount of change in a dependent variable that can be attributed to manipulations of the independent variable. A large effect size exists when the value of the dependent variable is strongly influenced by the independent variable. It is the mean difference on a variable between experimental and control groups divided by the standard deviation on that variable of the pooled groups or of the control group alone.
• Emancipatory Research -- research is conducted on and with people from marginalized groups or communities. It is led by a researcher or research team who is either an indigenous or external insider; is interpreted within intellectual frameworks of that group; and, is conducted largely for the purpose of empowering members of that community and improving services for them. It also engages members of the community as co-constructors or validators of knowledge.
• Empirical Research -- the process of developing systematized knowledge gained from observations that are formulated to support insights and generalizations about the phenomena being researched.
• Epistemology -- concerns knowledge construction; asks what constitutes knowledge and how knowledge is validated.
• Ethnography -- method to study groups and/or cultures over a period of time. The goal of this type of research is to comprehend the particular group/culture through immersion into the culture or group. Research is completed through various methods but, since the researcher is immersed within the group for an extended period of time, more detailed information is usually collected during the research.
• Expectancy Effect -- any unconscious or conscious cues that convey to the participant in a study how the researcher wants them to respond. Expecting someone to behave in a particular way has been shown to promote the expected behavior. Expectancy effects can be minimized by using standardized interactions with subjects, automated data-gathering methods, and double blind protocols.
• External Validity -- the extent to which the results of a study are generalizable or transferable.
• Factor Analysis -- a statistical test that explores relationships among data. The test explores which variables in a data set are most related to each other. In a carefully constructed survey, for example, factor analysis can yield information on patterns of responses, not simply data on a single response. Larger tendencies may then be interpreted, indicating behavior trends rather than simply responses to specific questions.
• Field Studies -- academic or other investigative studies undertaken in a natural setting, rather than in laboratories, classrooms, or other structured environments.
• Focus Groups -- small, roundtable discussion groups charged with examining specific topics or problems, including possible options or solutions. Focus groups usually consist of 4-12 participants, guided by moderators to keep the discussion flowing and to collect and report the results.
• Framework -- the structure and support that may be used as both the launching point and the on-going guidelines for investigating a research problem.
• Generalizability -- the extent to which research findings and conclusions conducted on a specific study to groups or situations can be applied to the population at large.
• Grey Literature -- research produced by organizations outside of commercial and academic publishing that publish materials, such as, working papers, research reports, and briefing papers.
• Grounded Theory -- practice of developing other theories that emerge from observing a group. Theories are grounded in the group's observable experiences, but researchers add their own insight into why those experiences exist.
• Group Behavior -- behaviors of a group as a whole, as well as the behavior of an individual as influenced by his or her membership in a group.
• Hypothesis -- a tentative explanation based on theory to predict a causal relationship between variables.
• Independent Variable -- the conditions of an experiment that are systematically manipulated by the researcher. A variable that is not impacted by the dependent variable, and that itself impacts the dependent variable. In the earlier example of "gender" and "academic major," (see Dependent Variable) gender is the independent variable.
• Individualism -- a theory or policy having primary regard for the liberty, rights, or independent actions of individuals.
• Inductive -- a form of reasoning in which a generalized conclusion is formulated from particular instances.
• Inductive Analysis -- a form of analysis based on inductive reasoning; a researcher using inductive analysis starts with answers, but formulates questions throughout the research process.
• Insiderness -- a concept in qualitative research that refers to the degree to which a researcher has access to and an understanding of persons, places, or things within a group or community based on being a member of that group or community.
• Internal Consistency -- the extent to which all questions or items assess the same characteristic, skill, or quality.
• Internal Validity -- the rigor with which the study was conducted [e.g., the study's design, the care taken to conduct measurements, and decisions concerning what was and was not measured]. It is also the extent to which the designers of a study have taken into account alternative explanations for any causal relationships they explore. In studies that do not explore causal relationships, only the first of these definitions should be considered when assessing internal validity.
• Life History -- a record of an event/events in a respondent's life told [written down, but increasingly audio or video recorded] by the respondent from his/her own perspective in his/her own words. A life history is different from a "research story" in that it covers a longer time span, perhaps a complete life, or a significant period in a life.
• Margin of Error -- the permittable or acceptable deviation from the target or a specific value. The allowance for slight error or miscalculation or changing circumstances in a study.
• Measurement -- process of obtaining a numerical description of the extent to which persons, organizations, or things possess specified characteristics.
• Meta-Analysis -- an analysis combining the results of several studies that address a set of related hypotheses.
• Methodology -- a theory or analysis of how research does and should proceed.
• Methods -- systematic approaches to the conduct of an operation or process. It includes steps of procedure, application of techniques, systems of reasoning or analysis, and the modes of inquiry employed by a discipline.
• Mixed-Methods -- a research approach that uses two or more methods from both the quantitative and qualitative research categories. It is also referred to as blended methods, combined methods, or methodological triangulation.
• Modeling -- the creation of a physical or computer analogy to understand a particular phenomenon. Modeling helps in estimating the relative magnitude of various factors involved in a phenomenon. A successful model can be shown to account for unexpected behavior that has been observed, to predict certain behaviors, which can then be tested experimentally, and to demonstrate that a given theory cannot account for certain phenomenon.
• Models -- representations of objects, principles, processes, or ideas often used for imitation or emulation.
• Naturalistic Observation -- observation of behaviors and events in natural settings without experimental manipulation or other forms of interference.
• Norm -- the norm in statistics is the average or usual performance. For example, students usually complete their high school graduation requirements when they are 18 years old. Even though some students graduate when they are younger or older, the norm is that any given student will graduate when he or she is 18 years old.
• Null Hypothesis -- the proposition, to be tested statistically, that the experimental intervention has "no effect," meaning that the treatment and control groups will not differ as a result of the intervention. Investigators usually hope that the data will demonstrate some effect from the intervention, thus allowing the investigator to reject the null hypothesis.
• Ontology -- a discipline of philosophy that explores the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality.
• Panel Study -- a longitudinal study in which a group of individuals is interviewed at intervals over a period of time.
• Participant -- individuals whose physiological and/or behavioral characteristics and responses are the object of study in a research project.
• Peer-Review -- the process in which the author of a book, article, or other type of publication submits his or her work to experts in the field for critical evaluation, usually prior to publication. This is standard procedure in publishing scholarly research.
• Phenomenology -- a qualitative research approach concerned with understanding certain group behaviors from that group's point of view.
• Philosophy -- critical examination of the grounds for fundamental beliefs and analysis of the basic concepts, doctrines, or practices that express such beliefs.
• Phonology -- the study of the ways in which speech sounds form systems and patterns in language.
• Policy -- governing principles that serve as guidelines or rules for decision making and action in a given area.
• Policy Analysis -- systematic study of the nature, rationale, cost, impact, effectiveness, implications, etc., of existing or alternative policies, using the theories and methodologies of relevant social science disciplines.
• Population -- the target group under investigation. The population is the entire set under consideration. Samples are drawn from populations.
• Position Papers -- statements of official or organizational viewpoints, often recommending a particular course of action or response to a situation.
• Positivism -- a doctrine in the philosophy of science, positivism argues that science can only deal with observable entities known directly to experience. The positivist aims to construct general laws, or theories, which express relationships between phenomena. Observation and experiment is used to show whether the phenomena fit the theory.
• Predictive Measurement -- use of tests, inventories, or other measures to determine or estimate future events, conditions, outcomes, or trends.
• Principal Investigator -- the scientist or scholar with primary responsibility for the design and conduct of a research project.
• Probability -- the chance that a phenomenon will occur randomly. As a statistical measure, it is shown as p [the "p" factor].
• Questionnaire -- structured sets of questions on specified subjects that are used to gather information, attitudes, or opinions.
• Random Sampling -- a process used in research to draw a sample of a population strictly by chance, yielding no discernible pattern beyond chance. Random sampling can be accomplished by first numbering the population, then selecting the sample according to a table of random numbers or using a random-number computer generator. The sample is said to be random because there is no regular or discernible pattern or order. Random sample selection is used under the assumption that sufficiently large samples assigned randomly will exhibit a distribution comparable to that of the population from which the sample is drawn. The random assignment of participants increases the probability that differences observed between participant groups are the result of the experimental intervention.
• Reliability -- the degree to which a measure yields consistent results. If the measuring instrument [e.g., survey] is reliable, then administering it to similar groups would yield similar results. Reliability is a prerequisite for validity. An unreliable indicator cannot produce trustworthy results.
• Representative Sample -- sample in which the participants closely match the characteristics of the population, and thus, all segments of the population are represented in the sample. A representative sample allows results to be generalized from the sample to the population.
• Rigor -- degree to which research methods are scrupulously and meticulously carried out in order to recognize important influences occurring in an experimental study.
• Sample -- the population researched in a particular study. Usually, attempts are made to select a "sample population" that is considered representative of groups of people to whom results will be generalized or transferred. In studies that use inferential statistics to analyze results or which are designed to be generalizable, sample size is critical, generally the larger the number in the sample, the higher the likelihood of a representative distribution of the population.
• Sampling Error -- the degree to which the results from the sample deviate from those that would be obtained from the entire population, because of random error in the selection of respondent and the corresponding reduction in reliability.
• Saturation -- a situation in which data analysis begins to reveal repetition and redundancy and when new data tend to confirm existing findings rather than expand upon them.
• Semantics -- the relationship between symbols and meaning in a linguistic system. Also, the cuing system that connects what is written in the text to what is stored in the reader's prior knowledge.
• Social Theories -- theories about the structure, organization, and functioning of human societies.
• Sociolinguistics -- the study of language in society and, more specifically, the study of language varieties, their functions, and their speakers.
• Standard Deviation -- a measure of variation that indicates the typical distance between the scores of a distribution and the mean; it is determined by taking the square root of the average of the squared deviations in a given distribution. It can be used to indicate the proportion of data within certain ranges of scale values when the distribution conforms closely to the normal curve.
• Statistical Analysis -- application of statistical processes and theory to the compilation, presentation, discussion, and interpretation of numerical data.
• Statistical Bias -- characteristics of an experimental or sampling design, or the mathematical treatment of data, that systematically affects the results of a study so as to produce incorrect, unjustified, or inappropriate inferences or conclusions.
• Statistical Significance -- the probability that the difference between the outcomes of the control and experimental group are great enough that it is unlikely due solely to chance. The probability that the null hypothesis can be rejected at a predetermined significance level [0.05 or 0.01].
• Statistical Tests -- researchers use statistical tests to make quantitative decisions about whether a study's data indicate a significant effect from the intervention and allow the researcher to reject the null hypothesis. That is, statistical tests show whether the differences between the outcomes of the control and experimental groups are great enough to be statistically significant. If differences are found to be statistically significant, it means that the probability [likelihood] that these differences occurred solely due to chance is relatively low. Most researchers agree that a significance value of .05 or less [i.e., there is a 95% probability that the differences are real] sufficiently determines significance.
• Subcultures -- ethnic, regional, economic, or social groups exhibiting characteristic patterns of behavior sufficient to distinguish them from the larger society to which they belong.
• Testing -- the act of gathering and processing information about individuals' ability, skill, understanding, or knowledge under controlled conditions.
• Theory -- a general explanation about a specific behavior or set of events that is based on known principles and serves to organize related events in a meaningful way. A theory is not as specific as a hypothesis.
• Treatment -- the stimulus given to a dependent variable.
• Trend Samples -- method of sampling different groups of people at different points in time from the same population.
• Triangulation -- a multi-method or pluralistic approach, using different methods in order to focus on the research topic from different viewpoints and to produce a multi-faceted set of data. Also used to check the validity of findings from any one method.
• Unit of Analysis -- the basic observable entity or phenomenon being analyzed by a study and for which data are collected in the form of variables.
• Validity -- the degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure. A method can be reliable, consistently measuring the same thing, but not valid.
• Variable -- any characteristic or trait that can vary from one person to another [race, gender, academic major] or for one person over time [age, political beliefs].
• Weighted Scores -- scores in which the components are modified by different multipliers to reflect their relative importance.
• White Paper -- an authoritative report that often states the position or philosophy about a social, political, or other subject, or a general explanation of an architecture, framework, or product technology written by a group of researchers. A white paper seeks to contain unbiased information and analysis regarding a business or policy problem that the researchers may be facing.

Elliot, Mark, Fairweather, Ian, Olsen, Wendy Kay, and Pampaka, Maria. A Dictionary of Social Research Methods. Oxford, UK: Oxford University Press, 2016; Free Social Science Dictionary. Socialsciencedictionary.com [2008]. Glossary. Institutional Review Board. Colorado College; Glossary of Key Terms. Writing@CSU. Colorado State University; Glossary A-Z. Education.com; Glossary of Research Terms. Research Mindedness Virtual Learning Resource. Centre for Human Servive Technology. University of Southampton; Miller, Robert L. and Brewer, John D. The A-Z of Social Research: A Dictionary of Key Social Science Research Concepts London: SAGE, 2003; Jupp, Victor. The SAGE Dictionary of Social and Cultural Research Methods . London: Sage, 2006.

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[ ri- surch , ree -surch ]

recent research in medicine.

Synonyms: study , scrutiny

• a particular instance or piece of research.

verb (used without object)

• to make researches; investigate carefully.

verb (used with object)

to research a matter thoroughly.

Synonyms: scrutinize , examine , inquire , study

/ ˈriːsɜːtʃ; rɪˈsɜːtʃ /

• systematic investigation to establish facts or principles or to collect information on a subject
• to carry out investigations into (a subject, problem, etc)

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Derived forms.

• reˈsearcher , noun
• reˈsearchable , adjective

Other Words From

• re·searcha·ble adjective
• re·searcher re·searchist noun
• prore·search adjective
• under·re·search verb (used with object)

Word History and Origins

Origin of research 1

Synonym Study

Example sentences.

Have you tried to access the research that your tax dollars finance, almost all of which is kept behind a paywall?

Have a look at this telling research from Pew on blasphemy and apostasy laws around the world.

And Epstein continues to steer money toward universities to advance scientific research.

The research literature, too, asks these questions, and not without reason.

We also have a growing body of biological research showing that fathers, like mothers, are hard-wired to care for children.

We find by research that smoking was the most general mode of using tobacco in England when first introduced.

This class is composed frequently of persons of considerable learning, research and intelligence.

Speaking from recollection, it appears to be a work of some research; but I cannot say how far it is to be relied on.

Thomas Pope Blount died; an eminent English writer and a man of great learning and research.

That was long before invention became a research department full of engineers.

Related Words

• exploration
• investigation

Effects of temperature and moisture fluctuations for suitable use of raw-crushed wind-turbine blade in concrete

• Research Article
• Open access
• Published: 24 May 2024

Cite this article

You have full access to this open access article

• Víctor Revilla-Cuesta   ORCID: orcid.org/0000-0003-3337-6250 1 ,
• Nerea Hurtado-Alonso 2 ,
• Javier Manso-Morato 1 ,
• Roberto Serrano-López 1 &
• Juan M. Manso 1  

Raw-crushed wind-turbine blade (RCWTB), a waste from the recycling of wind-turbine blades, is used as a raw material in concrete in this research. It contains not only fiberglass-composite fibers that bridge the cementitious matrix but also polyurethane and balsa-wood particles. Therefore, concrete containing RCWTB can be notably affected by moisture and temperature fluctuations and by exposure to high temperatures. In this research, the performance of five concrete mixes with 0.0%, 1.5%, 3.0%, 4.5%, and 6.0% RCWTB, respectively, is studied under moist/dry, alternating-sign-temperature-shock, and high-temperature-shock tests. Two damage mechanisms of RCWTB within concrete were found through these tests: on the one hand, micro-cracking of the cementitious matrix, which was verified by microscopic analyses and was dependent on concrete porosity; on the other, damage and degradation of the RCWTB components, as the polyurethane melted, and the balsa-wood particles burned. Both phenomena led to larger remaining-strain levels and reduced concrete compressive strength by up to 25% under temperature and humidity variations, although the bridging effect of the fiberglass-composite fibers was effective when adding RCWTB amounts higher than 3.0%. The compressive-strength loss after the high-temperature-shock test increased with the RCWTB content, reaching maximum values of 8% after an exposure time of 7 days. Statistical analyses revealed that effect of the RCA amount in the concrete was conditioned by the exposure times in all the tests. The accurate definition of those times is therefore key to set an RCWTB content in concrete that ensures its suitable behavior under the environmental conditions analyzed.

Avoid common mistakes on your manuscript.

Introduction

A wind-turbine blade is aerodynamically designed to balance lightness and structural strength (Özkan and Genç 2023 ). Therefore, they combine materials with mechanical strength with other light-weight materials (Haselbach et al. 2022 ), which causes that their composition is quite complex (Joustra et al. 2021 ). Glass fiber-reinforced polymer (GFRP) composite is commonly used as the main blade material (Mallesh et al. 2019 ), as it has a high tensile strength (Ingersoll and Ning 2020 ). However, its high density is often mitigated with lighter materials such as balsa wood (Pan et al. 2018 ), which has an appearance similar to cork and a density of 0.25–0.35 kg/dm 3 (Jang and Kang 2022 ). Both materials are usually arranged in sandwich panels or one after the other on the blade wall, as shown in Fig.  1 . In addition, polyurethane is often added, which forms the inner stiffeners of the blade (Fig.  1 right) and is sometimes used in joining elements (Marín and Graciani 2022 ). Finally, a protective exterior gel coating is applied to the blade (Zhang et al. 2021 ).

Sandwich panel (left) and section (right) of a decommissioned wind-turbine blade

In view of the above, it is clear that wind-turbine blade designs were never conceived with recycling in mind, due to the very varied materials within one blade (Gennitsaris et al. 2023 ; Ozturk and Karipoglu 2023 ). So, today, one of the great challenges of the wind-energy sector is to address the issue of recycling these elements (Sommer and Walther 2021 ), whose urgency is pressing, as the first wind farms installed will reach the end of their useful life in the coming years (Liu and Barlow 2017 ; AEE 2022 ). Thus, varied solutions have been offered to give a second life to the materials that make up wind-turbine blades. First, blade components can be separated through chemical formulations such as solvolysis or pyrolysis (Fonte and Xydis 2021 ). Second, mechanical separation of blade components using cutting processes can also be conducted (Yazdanbakhsh et al. 2018 ). A third option involves crushing and then sieving to separate the blade materials (G. T. Xu et al. 2022a , b ).

One potential usage of the raw materials recovered after these treatments is the production of concrete (Baturkin et al. 2022 ). Recycled fibers from chemical treatments of GFRP composite could be used for the manufacture of fiber-reinforced concrete (Rodsin et al. 2022 ), thereby providing the concrete with a slightly higher load-bearing capacity (Barris et al. 2023 ). The GFRP composite separated from the other components after a cutting process can be machined to obtain aggregate-like particles with which to reduce natural-aggregate consumption (Yazdanbakhsh et al. 2018 ). Finally, the crushed GFRP composite, separated after sieving out all other components, can also be successfully added to the concrete in the form of fibers (G. T. Xu et al. 2022a , b ) or powder (Pławecka et al. 2021 ).

Nevertheless, all these processes have drawbacks. Chemical processes are energy intensive and lead to greenhouse gas emissions (Kawajiri and Kobayashi 2022 ; Al-Fatesh et al. 2023 ). Mechanical processes only recover the recycled GFRP composite, so the question of what to do with all the other materials remains unresolved (Liu and Barlow 2017 ). In this study, the selected approach was to crush the entire wind-turbine blade without component separation to produce raw-crushed wind-turbine blade (RCWTB). This material, composed of GFRP-composite fibers and small particles of balsa wood and polyurethane, can be used as an addition to concrete though a proper mix design (Baturkin et al. 2021 ; Revilla-Cuesta et al. 2023b ).

Despite the robust appearance of concrete, thermal fluctuations when in service can significantly affect its service life (Ferronato et al. 2023 ). Thermal stress due to temperature fluctuations must always be computed at the design stage of any concrete component (EC-2, 2010 ; ACI 2014 ). Moreover, the presence of alternative materials within the concrete to improve its sustainability (J. Xu et al. 2022a , b ; Çeçen et al. 2023 ; Li et al. 2023 ), such as recycled concrete, slag, and plastic aggregates, will always affect its response to temperature fluctuations. Recycled concrete aggregate exposed to cyclic thermal fluctuations causes micro-cracking within the interfacial transition zones that reduces strength (Revilla-Cuesta et al. 2023a ). The high density of slag aggregate increases the capability of the concrete to withstand increasing temperatures with less loss of strength (Beaucour et al. 2020 ). Finally, the application of high temperatures to a concrete made with plastic aggregates causes a high loss of strength, due to the melting of plastic particles (Tariq et al. 2021 ).

RCWTB incorporates balsa wood and polyurethane particles in its composition (Revilla-Cuesta et al. 2023b ). These particles act as aggregate in the concrete, partially replacing natural aggregate. However, both materials are sensitive to the application of high temperatures, at which point they may melt (polyurethane) or burned (balsa wood) (Tariq et al. 2021 ). Moreover, if these temperatures occur under conditions of high humidity, balsa wood can be deteriorated more pronouncedly (Wang et al. 2021 ). Given this situation, the behavior of concrete-containing additions of RCWTB when exposed to high temperatures and thermal fluctuations in a humid atmosphere is studied in this research. Thus, the aim is to evaluate the levels of deterioration of the performance of concrete-containing RCWTB under those environments. Clearly defining this behavior will allow determining the suitability of the concrete with this waste in applications where concrete is exposed to similar conditions, such as pavements, bridge decks, or cooling towers (Beaucour et al. 2020 ; Santamaría et al. 2020 ; Tamayo et al. 2022 ).

Materials and methods

Raw materials.

The mixes for this study were prepared with conventional raw materials, as the purpose was to study the effects of additions of RCWTB. The concrete was therefore prepared with ordinary Portland cement CEM II/A-L 42.5 R, with a limestone addition of between 6 and 20% in weight according to EN 197–1 (EN-Euronorm) to improve sustainability; tap water; and two plasticizers (a polycarboxylate-based intended to guarantee suitable hydration of all the cement grains and a high-range water reducer) in order to maximize concrete strength while reaching proper workability for a suitable period of time (Shanahan et al. 2016 ); and three fractions of crushed siliceous aggregate sized 0/2 mm, 2/6 mm, and 6/22 mm. Their granulometry and the physical properties are shown in Fig.  2 and Table  1 , respectively, in which their continuous gradations and conventional densities and water-absorption levels can be seen.

Individual and joint gradation of crushed siliceous aggregate fractions

RCWTB (Fig.  3 ) was obtained by crushing sandwich panels similar to the one shown to the left of Fig.  1 using a knife mill designed for crushing plastic containers. Subsequently, it was passed through a 10-mm-aperture sieve. All the material that was retained in the sieve was once again crushed and sieved. The resulting RCWTB material was composed of GFRP-composite fibers (66.8% wt. of RCWTB; average length of 13.1 mm); approximately spherical balsa-wood particles (6.3% wt. of RCWTB; density of 0.33 kg/dm 3 ); polyurethane particles (8.3% wt. of RCWTB); and a mixture of glass fibers disintegrated from the epoxy matrix and small particles of balsa wood and polyurethane that were not separable by mechanical sieving (18.6% wt. of RCWTB). RCWTB had a real density of 1.63 kg/dm 3 . More details of this material can be found in another paper (Revilla-Cuesta et al. 2023b ). The RCWTB was used in this research without any previous surface treatment in order to analyze the suitability of the material composing wind-turbine blades in its original state (Tao et al. 2023 ).

• Raw-crushed wind-turbine blade

First, the reference mix (0% RCWTB) was designed and produced. The design had two objectives: on the one hand, a slump between 10.0 and 15.0 cm (slump class S3, EN 206 as per (EN-Euronorm)); on the other hand, a minimum cube compressive strength of 45 MPa. A concrete mix that satisfies both requirements is generally suitable for all types of applications (EC- 2 , 2010 ; ACI 2014 ). For this purpose, the proportioning specifications of Eurocode 2 (EC- 2 , 2010 ) were followed in the design phase, adjusting the amount of each aggregate fraction by optimizing the fit of the joint gradation to the Fuller’s curve by least squares (Fig.  2 ). Furthermore, concrete mixes with varying contents of both water and plasticizers were prepared previously to the definitive ones until the slump target was reached, thus accurately defining the amounts of water and plasticizers to be added.

Next, the RCWTB mixes, with the overall additions of the waste, were designed. The balsa-wood and polyurethane particles and the GFRP-composite fibers served as aggregate and fibers, respectively. These additions also reduced the cement content per m 3 of the concrete and its carbon footprint. Quantities of 1.5%, 3.0%, 4.5%, and 6.0% in volume of RCWTB were added, adjusting the content of water and plasticizer, so that the slump class remained constant. Thus, all the mixes were of class S3, guaranteeing the comparability of the results.

The five mixes prepared in that way were labeled with the letter M followed by the RCWTB percentage. So, for example, the M3.0 mix incorporated 3.0% RCWTB. The mix compositions are shown in Table  2 .

Production of concrete and specimens

A five-stage mixing process was implemented to ensure the highest possible level of concrete workability. The stages were intended to maximize aggregate water absorption during mixing (Güneyisi et al. 2014 ), to compensate for the expected loss of workability when RCWTB was added, due to the presence of GFRP-composite fibers (Ortega-López et al. 2022 ), and to ensure a uniform distribution of RCWTB in the concrete mass. These stages involved: (1) the addition of all the aggregates and 30% of the mix water shown in Table  2 , and mixing during 3 min; (2) the addition of the cement together with the remaining mix water, and mixing during 3 min; (3) the addition of half of the admixtures dissolved in a quarter liter of water, and mixing during 2 min; (4) the addition of RCWTB, and mixing during 2 min; (5) the addition of the other half of the admixtures dissolved in another quarter liter of water, and concrete mixing for 5 min.

After mixing, the slump test as specified in EN 12350–2 (EN-Euronorm) was performed on a fresh-concrete sample and then 10 × 10 × 10-cm cubic specimens were produced for all the other tests. All the tests were performed on three specimens; the results presented in this paper showing the average and standard deviation of the three individual results. The specimens were demolded 24 h after concrete manufacture and stored in a standardized humid chamber at a temperature of 20 ± 2 °C and a humidity level of 90 ± 5% until an age of 90 days. The strength was considered to have stabilized at this age (Ortega-López et al. 2022 ) and, therefore, all tests were conducted when the concrete was 90-day old.

Experimental procedure

Reference values.

First, tests were performed to define the reference values of hardened density as per EN 12390–7 (EN-Euronorm) and compressive strength according to EN 12390–3 (EN-Euronorm) with which to compare the results obtained after the temperature-moisture-fluctuation tests. Cubic specimens that had not previously been subjected to any test were used for that purpose. Moreover, the effective porosity of the concrete mixes was evaluated through the capillary-water-absorption test according to UNE 83982 ( 2008 ). To do so, the moisture content of the specimens was controlled by exposing them to a temperature of 20 ± 2 °C and a relative humidity of 60 ± 5% in a laboratory environment over 24 h as per UNE 83966 ( 2008 ). Then, and after having removed the skin from the opposite face to concrete pouring, the specimens were subsequently immersed in a 2-mm-thick layer of water until saturation. The effective porosity of the concrete was determined as the quotient between the difference in weight of the concrete at the beginning of the test and before saturation (water volume) and the geometric volume of the concrete specimen.

Behavior under simultaneous changes in humidity and temperature

After suitable conditioning of the concrete specimens as per UNE 83966 ( 2008 ), the analyses of concrete behavior under changes in humidity and temperature were conducted through moist/dry and alternating-sign-temperature-shock tests:

The moist/dry test was performed by adapting the instructions of ASTM D559 (ASTM-International). The cubic concrete specimens were subjected to 30 moist/dry cycles. The moist phase for each cycle lasted 16 h and consisted of immersing the specimens in water at an indoor temperature (20 ± 2 °C). The dry phase involved oven drying the specimens at a temperature of 70 ± 2 °C for 8 h. In this way, extreme environmental conditions of exposure to rain and solar radiation were simulated, which can be found, for instance, on pavements (Ortega-López et al. 2022 ).

The alternating-sign-temperature-shock test was also performed by exposing the concrete to ambient cycles. In each cycle, the specimens were placed for 8 h in a freezer at a temperature of – 20 ± 1 °C and then immersed in water at a temperature of + 70 ± 2 °C for 16 h. The freezing conditions were defined according to the freeze/thaw test, standard UNE 12390–9 EX (2008), while the heating phase was defined according to the moist/dry test, standard ASTM D559 (ASTM-International), although it was performed in a moist environment, to analyze possible deterioration of the balsa-wood particles within the RCWTB (Hirschmüller et al. 2018 ). The test was intended to simulate extreme environmental conditions of rain, solar heating, and potential frost, as can occur on pavements and bridge decks, for example, Santamaría et al. ( 2020 ) and Revilla-Cuesta et al. ( 2023a ). Two different durations, 10 and 20 cycles, were considered to evaluate the impact of the number of cycles in the strength performance of concrete.

The weight and ultrasonic pulse velocity (UPV) were recorded every five cycles in the moist/dry test and every cycle in the alternating-sign-temperature-shock test to evaluate the micro-structural damage. This damage was verified when the tests ended through scanning electron microscope (SEM) analyses performed on portions from within different concrete specimens. In addition, the length of the specimen edges (8 edge lengths per specimen) was measured before and after these tests to evaluate the dimensional variation of concrete under those environmental conditions (Ortega-López et al. 2022 ). Finally, hardened density and compressive strength were also determined at the end of these tests, to study the deterioration of those properties in relation to the reference values. Compressive strength was the mechanical property analyzed since it is considered the reference property by standards (EC- 2 , 2010 ; ACI 2014 ), and because the addition of RCWTB is usually more detrimental for it (Revilla-Cuesta et al. 2024 ), so the damage to the RCWTB components caused by the variations in humidity and temperature could be quite negative for it.

Behavior under high-temperature shock

The high-temperature-shock test consisted of exposing the concrete specimens to a temperature of 200 ± 5 °C for both 3 and 7 days. An oven was used to achieve this temperature in a sustained manner, which simulated the working conditions in concrete constructions where high temperatures are reached, such as cooling towers and nuclear-power plants, and even a possible fire (Beaucour et al. 2020 ; Tamayo et al. 2022 ). After the high-temperature-shock test, the compressive strength was evaluated for the purposes of a comparison with the reference values. This mechanical property was chosen due to the same reasons as for the moist/dry and alternating-sign-temperature-shock tests. UPV variations before and after the test were also analyzed.

Statistical and environmental analysis

An ANOVA with a 95% confidence level was conducted to evaluate the significance of the behavioral changes caused by the temperature and moisture fluctuations. In addition, the influence of the different factors on the compressive-strength variations of the RCWTB concrete was further investigated through a multiple-regression predictive analysis. Finally, an environmental analysis of the mixes was also performed. For this purpose, the carbon footprint per cubic meter and per unit of compressive strength of the different mixes after each test conducted was calculated.

Results and discussion

Reference properties.

The reference values of hardened density and compressive strength, measured on cubic concrete specimens not previously subjected to moisture and temperature variations, are shown in Fig.  4 . The effective-porosity values are also depicted in the same figure.

Reference 90-day hardened properties of concrete mixes: a density and cubic-specimen compressive strength; b effective porosity

As can be seen in Fig.  4 a, the hardened density decreased in an approximately linear manner as the RCWTB content increased, falling from 2.40 kg/dm 3 in the M0.0 reference mix to 2.27 kg/dm 3 in the M6.0 mix. This decrease was due to the lower density of RCWTB compared to aggregate and cement (Baturkin et al. 2021 ), and to the increased content of both water and plasticizers, both even less dense than the RCWTB, as the amount of that waste material increased (Andreu and Miren 2014 ). The increase in concrete porosity when adding RCWTB also contributed to lowering the hardened density (Ouyang et al. 2020 ).

From a general perspective, the compressive strength (Fig.  4 a) decreased as the RCWTB content increased. However, the lowest recorded strength was 57.8 MPa, which points to the quality of the concrete strength, despite the addition of large amounts of RCWTB (EC- 2 , 2010 ; ACI 2014 ). Two mixes disrupted this general trend: on the one hand, the M1.5 mix, with a 12.4% higher strength than that of the M0.0 mix, possibly due to the positive effect of the GFRP-composite fibers in terms of cementitious-matrix bridging (G. T. Xu et al. 2022a , b ); on the other hand, the M6.0 mix, almost identical to the M4.5 mix in terms of strength, which highlights the bridging effect of the GFRP-composite fibers when adding high amounts of RCWTB that counteracted the negative effect of the particles of balsa wood and polyurethane.

The trend exhibited by the effective porosity (Fig.  4 b), increasing with the RCWTB from a general approach, was closely linked to that of the compressive strength. Decreased porosity therefore implied an increase in strength, while a reduction in strength occurred in the more porous mixes, as was also found in other concrete types (Revilla-Cuesta et al. 2021 ). It is important to emphasize that the effective porosity of those mixes not only reflected the percentile volume of accessible pores in the cementitious matrix but might also be partially due to the volume of balsa-wood particles that can absorb water due to their high porosity (Jang and Kang 2022 ).

• Moist/dry test

The moist/dry test is a relevant durability test for construction materials (Ortega-López et al. 2018 ). As described in the “ Experimental procedure ” section, this test consisted of 30 cycles alternating water immersion at 20 ± 2 °C with oven drying at 70 ± 2 °C by adapting the ASTM D559 standard (ASTM-International). It involved simulating environmental conditions of humid and rainy climates (water-immersion phase), alternating with high temperatures (oven-drying phase) (Ortega-López et al. 2022 ). Water absorption and thermal shock due to sudden oven drying can affect a cement-based material internally, causing micro-cracking, due to the different thermal deformability of the materials that compose it (Revilla-Cuesta et al. 2023a ). The recurrent presence of excessive humidity and its rapid decrease during oven drying can also damage the interfacial transition zones (Sun et al. 2022 ). Finally, it should be noted that the moist/dry test is a laboratory test which takes the environmental conditions described to an extreme situation, i.e., humidity conditions (water immersion) and (oven) drying at 70 °C, which merely simulate real climatic conditions. Nevertheless, the test results offer a useful approximation of how cement-based materials may behave under such conditions (Ortega-López et al. 2022 ).

Cubic specimens of the five concrete mixes were subjected to a moist/dry test as described in the “ Experimental procedure ” section. At the end of the test, the concrete specimens showed no visible signs of damage, as shown in Fig.  5 , except for some dark colored marks due perhaps to deterioration of the specimen skin (Santamaría et al. 2020 ) and a slight rounding of the corners. However, the computation of the specimen weight and the UPV readings every five cycles pointed to progressive damage to the concrete throughout the test:

On the one hand, the concrete specimens progressively increased in weight, due to their increased water absorption throughout the test (Fig.  6 a). Thus, water-absorption levels were between 0.05 and 0.30% wt. after five cycles and between 0.25 and 0.80% wt. at the end of the test. This continuous increase in the amount of absorbed water may be attributed to the appearance of micro-cracking within the cementitious matrix, due to temperature changes and the different thermal deformability of the concrete components (Revilla-Cuesta et al. 2022 ), which was favored by the presence of GFRP-composite fibers in the RCWTB that disrupted the continuity of the cementitious matrix (Ortega-López et al. 2022 ). The higher or lower initial water absorption of the mixes was in accordance with the effective-porosity values (Fig.  4 b) (Hamada et al. 2023 ), which were not linearly proportional to the RCWTB content. The most porous mixes also experienced the highest increase in water-absorption levels throughout the test, due perhaps to their larger pore volume, which may have favored micro-cracking (Revilla-Cuesta et al. 2023a ).

On the other hand, the UPV readings showed a continuous decrease throughout the whole test (Fig.  6 b and Table  3 ). Thus, the UPV reading for the M6.0 mix was 6.0% lower at the end the test. A result that underlines the continuous deterioration within the concrete following the application of moist/dry cycles (Ortega-López et al. 2022 ) was also noted for the evolution of sample weight. However, unlike water absorption, the UPV readings were lower as the RCWTB content increased, so rather than dependent on porosity. Micro-cracking of the cementitious matrix was found in this test, although the RCWTB components, all sensitive to high temperatures, might also have been negatively affected (Rani et al. 2021 ; Tariq et al. 2021 ; Jang and Kang 2022 ).

SEM analyses performed after completion of the test (Fig.  7 ) on concrete fragments of the M4.5 and M6.0 mixes, the ones with the highest damage, verified the existence of micro-cracking that appeared primarily in the interfacial transition zones. Furthermore, it seemed that these micro-cracks were subsequently easily propagated through the pores. This reinforced the conclusions on internal damage derived from the analysis of weight and UPV evolutions throughout the moist/dry test.

External appearance of two representative specimens per concrete mix after the moist/dry test

Results of the moist/dry test: a weight evolution; b UPV variation; c dimensional stability; d variation of compressive strength and density

SEM analysis after the moist/dry test (micro-cracks encircled in red): M4.5 mix (left) and M6.0 mix (right)

The temperature fluctuations to which the concrete was exposed caused its dimensions to alter, due either to expansion or to contraction (Smith and Tighe 2009 ). In addition, the repeated application of these thermal changes can cause remaining strain to appear at the end of the test (Revilla-Cuesta et al. 2022 ), as shown in Fig.  6 c. On the one hand, the M0.0 mix specimens (0% RCWTB) shortened by 0.003%, which shows that the micro-cracking experienced by the reference mix was very limited and in no way affected its dimensional stability (Santamaría et al. 2020 ). However, remaining strain appeared in all the mixes with RCWTB that increased with the content of this waste, reaching length increases of 0.025% in the M6.0 mix. The application of high temperatures may have permanently increased the volume of the polyurethane and balsa-wood particles (Tariq et al. 2021 ), and the latter may also have been affected by the notable moisture changes during this test (Wang et al. 2021 ). In addition, the GFRP-composite fibers could have been permanently increased in length by thermal expansion of the epoxy resin (Rani et al. 2021 ). These aspects could have led to the emergence of a remaining strain within the concrete.

At the end of the moist/dry test, the density and compressive strength of the mixes were measured (Table  3 ) and compared with the reference values (Fig.  4 a). A comparison is depicted in Fig.  6 d.

Density levels increased due to the increased amount of water within the concrete after the test that resulted in weight gain (Fig.  6 a). On the contrary, density decreased due to the larger specimen dimensions after the test (Fig.  6 c). Combining both aspects resulted in increased density (Fig.  6 d), as the increase in weight was more relevant. However, any increase in density was minimal (less than 1% in all cases), which reflects the previously discussed question of micro-cracking (Ortega-López et al. 2018 ), evidence of which is the increased water absorption levels of the concrete (Santamaría et al. 2018 ). No clear trend was observed following the addition of RCWTB.

Compressive strength decreased by 15% to 25% after the moist/dry test (Fig.  6 d). Clearly, the evident micro-cracking of the concrete revealed by all the non-destructive properties under evaluation resulted in a strength decrease. However, this decrease showed no clear trends with regard to either the RCWTB content or the porosity of the mixes. In fact, the M1.5 mix suffered the greatest loss of compressive strength (− 25%) despite being the least porous and having a very low RCWTB content. It is thought that the GFRP-composite fibers contained in the RCWTB, although they may have slightly deteriorated due to epoxy-resin damage after exposure to high temperatures (Rani et al. 2021 ), exercised a bridging effect within the cementitious matrix (Baturkin et al. 2022 ), meaning that the negative effects on concrete strength of micro-cracking and damage to the particles of balsa wood and polyurethane were less noticeable (Ortega-López et al. 2022 ). Thus, the GFRP-composite fibers compensated for the negative effect of balsa wood and polyurethane particles under moist/dry cycling, leading to a strength behavior similar to that of conventional concrete without RCWTB.

Alternating-sign-temperature-shock test

The alternating-sign-temperature-shock test was intended to expose the concrete to more extreme environmental conditions than in the moist/dry test. For this purpose, cubic concrete specimens were subjected to either 10 or to 20 cycles of two phases, under wet and dry (freezing) environments, respectively, as described in the “ Experimental procedure ” section. The wet environment involved immersion of the specimens in water at + 70 ± 2 °C, thereby combining the effects of exposure to high temperatures and moisture described for the moist/dry test (Revilla-Cuesta et al. 2022 ; Sun et al. 2022 ). The dry environment was obtained by placing the specimens in a freezer at – 20 ± 1 °C, so that the water contained inside the concrete froze and increased in volume, which usually favors the internal micro-cracking of concrete (Güneyisi et al. 2014 ). In that way, environmental conditions corresponding to warm and rainy climates and harsh frosts were simultaneously simulated (Ortega-López et al. 2018 ). In short, very extreme climatic conditions were simulated through a single laboratory test to validate the concrete-containing RCWTB under a wide range of climatic conditions (Ortega-López et al. 2022 ).

At the end of the test, the specimens showed no external signs of deterioration (Fig. 8 ), but did internal damage (Fig. 9 ). The increased weight of the specimens (Figs. 10 a and 11 a) and their lower UPV readings (Table  4 and Figs. 10 b and 11 b) throughout the test did show the appearance of that internal damage in the concrete (Jones 1963 ), similar to what was found in the moist/dry test. The micro-cracking that appeared followed the same pattern as in the moist/dry test, although in this case cracking was also detected in some areas of the bond between the cementitious matrix and the GFRP-composite fibers. This phenomenon can be seen in Fig.  9 , an image obtained through SEM analysis of a fragment of the M4.5 mix after the 20-cycle alternating-sign-temperature-shock test, and may be due to the application of negative temperatures (Revilla-Cuesta et al. 2023a ).

External appearance of a representative specimen after the 20-cycle alternating-sign-temperature-shock test

SEM image of the M4.5 mix after the 20-cycle alternating-sign-temperature-shock test (micro-cracks encircled in red)

Results of the alternating-sign-temperature-shock test with a duration of 10 cycles: a weight evolution; b UPV variation; c dimensional stability; d variation of compressive strength and density

Results of the alternating-sign-temperature-shock test with a duration of 20 cycles: a weight evolution; b UPV variation; c dimensional stability; d variation of compressive strength and density

The variations of weight and UPV throughout the alternating-sign-temperature-shock test occurred more abruptly than in the moist/dry test. Thus, the main variation of both magnitudes took place in the first 2–3 cycles, increasing very slightly in the rest of the test. It appears that the micro-cracking damage and the deterioration of the RCWTB components caused by the freezing phase were concentrated within the initial exposure time of the concrete to these conditions, as was also found with regard to other concretes produced with alternative raw materials (Güneyisi et al. 2014 ; Fiol et al. 2020 ). The increased damage that occurred during the rest of the test is thought to have been due to the thermal shock (Ortega-López et al. 2018 ), identical to what was encountered in the moist/dry test, but it was much less noticeable than the damage during the first cycles. Thus, the extensive damage to the concrete at the start of the test hardly differed after the concrete had been when exposed to either 10 cycles (Fig. 10 a and b) or to 20 cycles (Fig. 11 a and b). The differences in weight gains and the lower UPV readings between both numbers of the cycles was 0.10–0.15% and 1.50–2.00% in absolute values, respectively. Finally, as in the moist/dry test, the micro-cracking that propagated due to mix porosity (Fig.  4 b) appeared to have controlled the weight increase due to water absorption (Revilla-Cuesta et al. 2023a ), while the lower UPV readings were attributable to the deterioration of the RCWTB components with cycled aging (Rani et al. 2021 ; Tariq et al. 2021 ; Jang and Kang 2022 ). The variations of both magnitudes were greater than in the moist/dry test, although the difference was more notable regarding UPV (approximately four times higher), which might point to further deterioration of the polyurethane and balsa-wood particles, and the GFRP-composite fibers, under freezing conditions.

The specimens underwent remaining strain resulting from the internal damage described above (Ortega-López et al. 2022 ), which was detected by measuring their side lengths. The percentile variations of side length after the alternating-sign-temperature-shock test are shown in Figs. 10 c and 11 c for the tests with durations of 10 and 20 cycles, respectively. Several aspects can be highlighted in relation to the dimensional stability of the mixes during this test:

First, the openings of the micro-cracks that occurred in the cementitious matrix as a consequence of the application of sub-zero temperatures appear to have been greater than the openings that occurred as a consequence of the thermal shock in the moist/dry test (Revilla-Cuesta et al. 2022 ). It could also be linked to the lower UPV readings during this test (Figs. 10 b and 11 b) (Jones 1963 ). All the concrete mixes therefore experienced permanent expansion. In fact, the specimens of the M0.0 reference mix, whose dimensional stability was not affected in the moist/dry test, showed an increase in their side length of 0.046% after 10 cycles and 0.055% after 20 cycles.

Second, the higher the RCWTB content, the greater the increase in side length, which could be due to the higher micro-cracking in these mixes and the deterioration that the RCWTB components underwent during freezing (Rani et al. 2021 ; Wang et al. 2021 ). Thus, the side-length increase of the M6.0 mix was 0.098% after 20 cycles, 0.40% more in absolute terms than the reference mix. However, it is also true that the higher the number of cycles, the more pronounced the trend, due to the greater damage to the concrete (Santamaría et al. 2018 ).

The higher the number of cycles, the greater the remaining strain of the specimens, but the difference was small. In absolute terms, the increase in the side length after 20 cycles was 0.10% higher than after 10 cycles. It once again shows that the main damage to the concrete occurred during the initial cycles when exposed to freezing conditions (Ortega-López et al. 2018 ).

The density and compressive strength of the mixes at the end of the test were used to verify the internal damage to the mixes (Table  4 ). Both properties varied in comparison with the reference values (Fig.  4 a), as shown by the alternating-sign-temperature-shock test results, depicted in Figs. 10 d and 11 d, for 10 and 20 cycles, respectively.

The concrete mixes underwent greater expansion in this test than in the moist/dry test. It meant that, despite the increase in weight due to water absorption, the density of all the mixes decreased after 10 cycles, due to the increased volume of the specimens (Santamaría et al. 2020 ). Internal damage to the specimens following micro-cracking due to thermal shock was greater after 20 cycles (Tariq et al. 2021 ), which caused the mixes with contents higher than 3.0% RCWTB to present an increase in density, due to their higher levels of water absorption (Ortega-López et al. 2022 ), with a trend similar to that observed in the moist/dry test. In all cases the variations were small, in the order of 1.0%, although the increase in RCWTB content led to a greater increase in density after the application of 20 cycles, due perhaps to the damage to the RCWTB components during the test (Fonte and Xydis 2021 ; Rani et al. 2021 ).

Internal damage to the concrete during the test resulted in a reduction in compressive strength. This behavior was observed in all the mixes, including the M0.0 reference mix, but increased with the additions of RCWTB. It was due to the fact that RCWTB favored internal damage, because the GFRP-composite fibers interrupted the continuity of the cementitious matrix (G. T. Xu et al. 2022a , b ), promoting micro-cracking, and the polyurethane and balsa-wood particles were damaged during the test (Jang and Kang 2022 ). The negative effect of RCWTB was mainly observed when increasing the number of cycles, as no clear trend of the effect of RCWTB was detected after the application of 10 cycles (Fig.  10 d), but the loss of compressive strength increased to 3.0% RCWTB and then stabilized after the application of 20 cycles (Fig.  11 d). The increased damage with cycling is thought to be due to increased deterioration of the GFRP-composite fibers, which stitched the cementitious matrix less effectively as the number of cycles increased (Rani et al. 2021 ; Ortega-López et al. 2022 ). The lower level of damage to the GFRP-composite fibers with fewer cycles may have also caused the decrease in compressive strength to be greater after the moist/dry test (Fig.  6 d) (Jones 1963 ), in which 30 cycles were conducted.

High-temperature-shock test

The high-temperature-shock test was the last test, in which the cubic concrete specimens were exposed to temperatures of 200 ± 5 °C, over 3 and 7 days. The test conditions were defined according to the available laboratory equipment. The objective was to evaluate the deterioration of the balsa-wood and polyurethane particles contained in the RCWTB because of high temperatures (Tariq et al. 2021 ; Jang and Kang 2022 ). In addition, the GFRP-composite fibers might have also been affected during the test, due to the thermal sensitivity of both the epoxy resin and the glass of their composition (Rani et al. 2021 ), although there was less expected damage, due to their lower individual volume (Alshahrani et al. 2023 ). Finally, it should not be forgotten that partial decomposition of ettringite also occurs at these temperatures, which weakens the cementitious matrix (Revilla-Cuesta et al. 2022 ).

The first aspect to be discussed is the visual analysis of the specimens conducted after the 7-day high-temperature-shock test. For this purpose, the skin was removed from the underside of some specimens before testing them under compression. In addition, the specimens were visually evaluated after breakage. No alteration of the characteristics of the GFRP-composite fibers was visible to the naked eye, but the other two RCWTB components were clearly affected, as shown in Fig.  12 . Firstly, the polyurethane particles located in the outermost area of the specimens melted, leading to an increase in the macro-porosity of the concrete specimens (Ozturk et al. 2023 ). Then, it was observed that the high temperatures had burned the initially brown-colored balsa-wood particles (Fig.  3 ) that had turned black. Together with the expected decomposition of ettringite, the test conditions might have weakened the adhesion between the balsa-wood particles and the cementitious matrix within the interfacial transition zones (Revilla-Cuesta et al. 2023a ). A reduction in concrete strength was therefore linked to both the melting of the polyurethane particles and the burning of the balsa-wood particles.

Visual analysis of the effects of the high-temperature-shock test on the components of RCWTB

Regarding the concrete properties measured during the high-temperature-shock test, UPV was recorded before the test, and UPV and compressive strength were measured after the test. All the results are detailed in Table  5 . The compressive strengths were compared with the reference values (Fig.  4 a), and the UPV values before and after the test were also compared. The variations of both properties throughout the test are depicted in Fig.  13 . The main points arising from the analysis of these results can be grouped under three points:

Firstly, in view of the test results, both UPV and compressive strength decreased during the high-temperature-shock test. The decrease in UPV was due to the increase in the porosity of the mix and the weakening of the interfacial transition zones (Jones 1963 ), which in turn reduced the compressive strength by weakening the concrete as a whole (Revilla-Cuesta et al. 2022 ). However, it should be noted that the M0.0 mix, with 0% RCWTB, also experienced a slight decrease in UPV and strength, which proves the aforementioned decomposition of ettringite (Ortega-López et al. 2018 ).

Secondly, the decrease in these properties was greater after exposure to high temperatures for 7 days. Overall, the maximum decreases after 3 days were around 20% for UPV and 5% for compressive strength, while after 7 days they were around 25% and 8%, respectively. Logically, longer exposure times aggravated both forms of deterioration (Beaucour et al. 2020 ), further worsening concrete performance. It should also be noted that the compressive-strength losses were very similar to those obtained after the 10-cycle alternating-sign-temperature-shock test, showing that both environmental conditions caused the same level of damage to concrete after similar exposure times.

Finally, the effect of RCWTB can be analyzed. The increase in the added amount of this recycled waste material increased the concrete damage more clearly than in the other tests. Thus, the decrease in compressive strength for the M0.0 mix after exposure over 7 days was 0.4%, while it reached 8.1% and 7.5% for the M4.5 and M6.0 mixes, respectively. However, it should also be noted that, regardless of the test duration, the decrease in strength was always slightly greater in the M4.5 mix than in the M6.0 mix (difference of 0.5–0.6% in absolute terms). A difference could be due to the beneficial bridging effect of the GFRP-composite fibers within the cementitious matrix (G. T. Xu et al. 2022a , b ), although the higher porosity of the M4.5 mix might have also influenced the result (Fig.  4 b) (Pławecka et al. 2021 ). This particularity between these mixes was not evident in the UPV readings, which were conditioned by the damage caused to the polyurethane and balsa-wood particles, especially after the 7-day duration test, as was also found in another study (Santamaría et al. 2018 ). Thus, the lower UPV readings were linked to higher RCWTB contents.

Variation of UPV and compressive strength after the high-temperature-shock test: a 3-day duration; b 7-day duration

Statistical analysis

Analysis of variance (anova).

The analyses performed so far in this study have revealed the effect of each factor on the behavior of concrete. However, it is necessary to evaluate whether these behavioral changes were sufficiently large to be significant (Ma et al. 2023 ). An ANOVA was conducted at a confidence level of 95% by considering all the individual results in the concrete specimens, to analyze the effects of both factors (RCWTB content and test duration) on concrete performance and their significance. The moist/dry test was analyzed with a one-way ANOVA, while a two-way ANOVA was implemented for the other two tests to consider the interaction between the RCWTB content and the test duration (Feng et al. 2022 ). Both the p -value and the homogeneous groups for each property of each test are detailed in Table  6 .

This statistical analysis showed that the effect of RCWTB on all properties was generally significant, except for the variation of specimen length in the moist/dry and alternating-sign-temperature-shock tests. Thus, it appears that the alteration in the dimensional stability of the concrete was minimal as the content of the recycled waste was increased. In addition, the duration of the alternating-sign-temperature-shock test was not significant in terms of dimensional stability variations, in the same way as the interaction in terms of dimensional stability and compressive strength between both test factors. All factors and the interactions between them had a significant effect on the high-temperature-shock test, which simulated the environmental conditions with the highest effect on the RCWTB concrete.

Linear multiple-regression adjustment

Among all the aspects described in this paper, the clearest effect of RCWTB was noted in the high-temperature-shock test: the higher the content of RCWTB, the higher the reduction in concrete compressive strength. In addition, the ANOVA revealed that the decrease in strength with the addition of certain amounts of the waste depended on the test duration, thereby underlining the interaction between both factors (Ma et al. 2023 ). This interaction is key to the thermal behavior of RCWTB concrete, as it shows the negative effect of RCWTB under high temperatures that is aggravated at longer exposure times.

A linear multiple-regression model was developed to corroborate the existence of this interaction and to predict the variation of the compressive strength ( ∆C , in %) from the exposure time ( t , in days) at high temperatures, in this case 200 ± 5 °C, and the RCWTB content of the concrete ( c RCWTB , in %). This model is detailed in Eq. ( 1 ), which presented an R 2 coefficient of 84.24%. It can be noted that the prediction of the effect of thermal exposure on the RCWTB concrete depended not only on the two aforementioned factors but also on the interaction between them, as shown by the last term of Eq. ( 1 ). It shows that the behavior of the RCWTB when exposed to high temperatures can be predicted by a simple linear model, but that any modification of the effect of this waste with the variation of the exposure times must also be considered (Revilla-Cuesta et al. 2021 ). If those factors are considered, then the model developed in this case can be sufficiently accurate at estimating compressive-strength decreases over 1% with a maximum deviation of ± 20%, as shown in Fig.  14 .

Comparison of experimental and estimated losses of compressive strength during the high-temperature-shock test

Environmental analysis

The carbon footprint per m 3 of all the concrete mixes ( CF c , in kgCO 2 eq/m 3 ) was calculated according to Eq. ( 2 ). In this formula, CF rm is the carbon footprint of each raw material in kgCO 2 eq/kg, obtained from the available scientific literature (Yang et al. 2015 ; Hossain et al. 2016 ; Rebello et al. 2019 ; Revilla-Cuesta et al. 2024 ), and Q is the content of each raw material in the concrete in kg/m 3 , values that are depicted in Table  2 . The carbon footprint was always reduced when increasing the RCWTB content (Revilla-Cuesta et al. 2024 ). Based on this, the carbon footprint per unit of compressive strength of the concrete mixes ( CF c,CS , in kgCO 2 eq/(MPa·m 3 )) was obtained through Eq. ( 3 ), in which CS is the compressive strength in MPa (Hossein et al. 2022 ). The residual compressive strength after testing was used in this research. Figure  15 shows the unit carbon footprint for each test and mix.

Carbon footprint per unit of compressive strength after each test

The unit carbon footprint followed a similar trend in all the tests, decreasing when 1.5% RCWTB was added. This was possible due to the decrease in cement content and the improvement of the concrete behavior under moisture and temperature fluctuations thanks to the GFRP-composite fibers (G. T. Xu et al. 2022a , b ; Hamada et al. 2023 ). The increased content of balsa-wood and polyurethane particles with the addition of higher percentages of RCWTB led to a larger loss of compressive strength after testing, so that the M0.0 and M3.0 mixes had the same unit carbon footprint, which was even higher in the M4.5 mix, despite the decrease of cement content per m 3 in the concrete. The bridging effect of the GFRP-composite fibers was more successful in the M6.0 mix, which led to a reduction in the unit carbon footprint. Finally, concrete exhibited a better environmental performance when exclusively exposed to high temperatures, as the combination of high temperatures and large moisture variations caused the highest deterioration of compressive strength.

Conclusions

RCWTB is a waste material from the non-selective crushing of wind-turbine blades, i.e., without prior separation of their components. Its use as a global addition in concrete has the key advantages of GFRP-composite fibers, but it also introduces balsa-wood and polyurethane particles within the concrete, which are in principle sensitive to fluctuating humidity and temperature. Therefore, the behavior of a concrete with up to 6% RCWTB as an overall addition to moisture and temperature changes has been analyzed in this paper by simulating them through moist/dry, alternating-sign-temperature-shock, and high-temperature-shock tests. The following conclusions can be drawn from the results of these tests:

The application of cyclic moisture and thermal variations (moist/dry and alternating-sign-temperature-shock tests) caused micro-cracking in the cementitious matrix of concrete. This micro-cracking was conditioned by the porosity of the concrete mix. Thus, the micro-cracking within the concrete throughout these tests was greater the higher its porosity, which meant that increasing the RCWTB content of the concrete was not always associated with increased micro-cracking, as the addition of this waste did not necessarily lead to an increase in concrete porosity.

The cyclic application of moisture and temperature variations also caused deterioration of the RCWTB components within the concrete, which was evident in the lower ultrasonic pulse velocity (UPV) readings throughout the test. The higher the RCWTB amount, the lower the UPV readings, due to the higher proportions of GFRP-composite fibers, polyurethane, and balsa wood within the concrete.

The application of sub-zero temperatures (− 20 °C) and high-temperature moisture conditions (+ 70 °C) in the alternating-sign-temperature-shock test caused micro-cracking and the deterioration of the RCWTB components to occur earlier than when applying moisture and drying cycles at less extreme temperatures, + 20 °C and + 70 °C, respectively. So, there was no noticeable increase in damage to the RCWTB concrete with the number of cycles in the alternating-sign-temperature-shock test, as the damage mainly occurred in the first few cycles of the test.

Both micro-cracking and damage to the RCWTB components caused the appearance of remaining strain in the concrete after the tests. Any remaining strain was higher whenever the RCWTB content was higher, and three times greater after the alternating-sign-temperature-shock test, possibly due to the application of sub-zero temperatures. However, the length variation was less than 0.10% in all cases.

The compressive strength of the concrete decreased due to both micro-cracking and damage to RCWTB components. However, this reduction in strength in both the moist/dry and alternating-sign-temperature-shock tests was globally greater for mixes containing 1.5–3.0% RCWTB than for those with higher RCWTB contents, for which the strength decrease stabilized. Although they could be damaged during the tests due to the thermal fluctuations, GFRP-composite fibers could also effectively maintain their bridging effect within the cementitious matrix after the tests, thereby compensating the greater deterioration of the balsa-wood and polyurethane particles following the addition of RCWTB in large amounts.

Exposure of RCWTB concrete to sustained high temperatures (high-temperature-shock test) clearly revealed the mode of deterioration of both the polyurethane and the balsa-wood particles during these tests. The polyurethane particles melted, while the balsa-wood particles burned. Those reactions increased concrete porosity and weakened the interfacial transition zones; so, the higher the RCWTB content, the higher the losses of compressive strength. However, the bridging effect of the GFRP-composite fibers was maintained when 6.0% RCWTB was added.

Statistical analyses of the test results showed that the added content of RCWTB significantly affected the response of concrete exposed to moisture and temperature fluctuations. The interaction of RCWTB content and exposure time to moisture and high temperatures also played a fundamental role in predicting the behavior of RCWTB concrete under those conditions, as the effects of different RCWTB contents varied according to the exposure time.

Overall, the performance of concrete containing RCWTB under temperature and humidity variations and high temperatures was suitable regarding the features analyzed in this paper. Furthermore, this waste improved concrete sustainability. Nevertheless, precise estimation of the exposure time is essential, in order to define adequate amounts of the RCWTB that will ensure proper behavior of concrete in real applications in which concrete is exposed to such environmental conditions.

Limitations of the study and future research lines

Notwithstanding the conclusions reached, the study presents different limitations that should be noted and addressed in future research aimed at utilizing concrete with RCWTB in applications exposed to high temperatures or variations in temperature and humidity. They are detailed below:

First, further research regarding the bending behavior of concrete containing RCWTB after exposure to simultaneous variations in humidity and temperature and high temperatures could be useful. The objective should be to evaluate the effectiveness of the bridging effect of GFRP-composite fibers under such environmental conditions. This performance should be analyzed both in specimens and in full-scale structural elements, such as slabs or beams, to simulate real applications.

Second, balsa wood and polyurethane are the most sensitive RCWTB components to temperature and humidity variations and high-temperature exposure, so they suffer the greatest deterioration. Therefore, tests precisely only on these RCWTB components should be conducted. Furthermore, possible surface treatments of balsa wood to improve its performance should be examined.

Third, the fire resistance of concrete with RCWTB should be further investigated, since balsa-wood and polyurethane particles can be very sensitive to these exposure conditions.

Finally, the durability performance of RCWTB concrete in other environmental conditions, such as freeze/thaw, marine environment, or gas-rich industrial atmospheres, should be evaluated. In this way, the range of applications of RCWTB concrete could be extended.

Data availability

All the data generated in this research are contained within the article.

ACI (2014) Building code requirements for structural concrete: Farmington Hills, MI: American Concrete Institute

AEE (2022) Statistics of the Spanish wind-energy sector. Asociación Empresarial Eólica

Al-Fatesh AS, Al-Garadi NYA, Osman AI, Al-Mubaddel FS, Ibrahim AA, Khan WU, Alanazi YM, Alrashed MM, Alothman OY (2023) From plastic waste pyrolysis to fuel: impact of process parameters and material selection on hydrogen production. Fuel 344:128107. https://doi.org/10.1016/j.fuel.2023.128107

Article   CAS   Google Scholar  

Alshahrani A, Kulasegaram S, Kundu A (2023) Elastic modulus of self-compacting fibre reinforced concrete: experimental approach and multi-scale simulation. Case Stud Constr Mater 18:e01723. https://doi.org/10.1016/j.cscm.2022.e01723

Article   Google Scholar  

Andreu G, Miren E (2014) Experimental analysis of properties of high performance recycled aggregate concrete. Constr Build Mater 52:227–235. https://doi.org/10.1016/j.conbuildmat.2013.11.054

ASTM-International Book Annual of ASTM Standars, West Conshohocken, 19429–2959 2008 USA PA.

Barris C, Baena M, Jahani Y, Codina A, Torres L (2023) Experimental study on flexural cracking and deformation of reinforced-concrete beams strengthened with NSM FRP reinforcement. J Compos Constr 27(2):04023006. https://doi.org/10.1061/JCCOF2.CCENG-3907

Baturkin D, Hisseine OA, Masmoudi R, Tagnit-Hamou A, Massicotte L (2021) Valorization of recycled FRP materials from wind turbine blades in concrete. Resour Conserv Recycl 174:105807. https://doi.org/10.1016/j.resconrec.2021.105807

Baturkin D, Masmoudi R, Tagnit-Hamou A, Metiche S, Massicotte L (2022) Feasibility study on the recycling of FRP materials from wind turbine blades in concrete. Lect Not Civ Eng 198 LNCE:1729–1742. https://doi.org/10.1007/978-3-030-88166-5_150

Beaucour AL, Pliya P, Faleschini F, Njinwoua R, Pellegrino C, Noumowé A (2020) Influence of elevated temperature on properties of radiation shielding concrete with electric arc furnace slag as coarse aggregate. Constr Build Mater 256:119385. https://doi.org/10.1016/j.conbuildmat.2020.119385

Çeçen F, Aktaş B, Özbayrak A (2023) Decarbonization of the concrete railway sleeper production: bringing the low-dosage pozzolanic cement usage in the sleeper production via novel laminated CFRPU reinforcement technique. Mater Today Sustain 23:100455. https://doi.org/10.1016/j.mtsust.2023.100455

EC-2 (2010) Eurocode 2: design of concrete structures. Part 1–1: general rules and rules for buildings. CEN (European Committee for Standardization). EN-Euronorm Rue de stassart, 36. Belgium-1050 Brussels, European Committee for Standardization.

Feng LY, Chen AJ, Liu HD (2022) Experimental study on the property and mechanism of the bonding between rubberized concrete and normal concrete. Ind J Concr Struct Mater 16(1):25. https://doi.org/10.1186/s40069-022-00513-z

Ferronato N, Fuentes Sirpa RC, GuisbertLizarazu EG, Conti F, Torretta V (2023) Construction and demolition waste recycling in developing cities: management and cost analysis. Environ Sci Pollut Res 30(9):24377–24397. https://doi.org/10.1007/s11356-022-23502-x

Fiol F, Thomas C, Manso JM, López I (2020) Influence of recycled precast concrete aggregate on durability of concrete’s physical processes. Appl Sci 10(20):7348. https://doi.org/10.3390/app10207348

Fonte R, Xydis G (2021) Wind turbine blade recycling: an evaluation of the European market potential for recycled composite materials. J Environ Manage 287:112269. https://doi.org/10.1016/j.jenvman.2021.112269

Gennitsaris S, Sagani A, Sofianopoulou S, Dedoussis V (2023) Integrated LCA and DEA approach for circular economy-driven performance evaluation of wind turbine end-of-life treatment options. Appl Energy 339:120951. https://doi.org/10.1016/j.apenergy.2023.120951

Güneyisi E, Gesoǧlu M, Algin Z, Yazici H (2014) Effect of surface treatment methods on the properties of self-compacting concrete with recycled aggregates. Constr Build Mater 64:172–183. https://doi.org/10.1016/j.conbuildmat.2014.04.090

Hamada HM, Shi J, Al Jawahery MS, Majdi A, Yousif ST, Kaplan G (2023) Application of natural fibres in cement concrete: a critical review. Mater Today Commun 35:105833. https://doi.org/10.1016/j.mtcomm.2023.105833

Haselbach PU, Chen X, Berring P (2022) Place smart, load hard - structural reinforcement of the trailing edge regions of a wind turbine blade strengthening the buckling resistance. Compos Struct 300:116068. https://doi.org/10.1016/j.compstruct.2022.116068

Hirschmüller S, Marte R, Pravida J, Flach M (2018) Inhibited wood degradation of cement-coated beech Laminated Veneer Lumber (LVL) for temporary in-ground applications. Eur J Wood Wood Prod 76(5):1483–1494. https://doi.org/10.1007/s00107-018-1325-9

Hossain MU, Poon CS, Lo IMC, Cheng JCP (2016) Comparative environmental evaluation of aggregate production from recycled waste materials and virgin sources by LCA. Resour Conserv Recycl 109:67–77. https://doi.org/10.1016/j.resconrec.2016.02.009

Hossein AH, AzariJafari H, Khoshnazar R (2022) The role of performance metrics in comparative LCA of concrete mixtures incorporating solid wastes: a critical review and guideline proposal. Waste Manage 140:40–54. https://doi.org/10.1016/j.wasman.2022.01.010

Ingersoll B, Ning A (2020) Efficient incorporation of fatigue damage constraints in wind turbine blade optimization. Wind Energy 23(4):1063–1076. https://doi.org/10.1002/we.2473

Jang ES, Kang CW (2022) Porosity analysis of three types of balsa (Ochroma pyramidale) wood depending on density. J Wood Sci 68(1):31. https://doi.org/10.1186/s10086-022-02037-2

Jones R (1963) The ultrasonic testing of concrete. Ultrasonics 1(2):78–82. https://doi.org/10.1016/0041-624X(63)90058-1

Joustra J, Flipsen B, Balkenende R (2021) Structural reuse of wind turbine blades through segmentation. Composite Part C Open Access 5:100137. https://doi.org/10.1016/j.jcomc.2021.100137

Kawajiri K, Kobayashi M (2022) Cradle-to-Gate life cycle assessment of recycling processes for carbon fibers: a case study of ex-ante life cycle assessment for commercially feasible pyrolysis and solvolysis approaches. J Clean Prod 378:134581. https://doi.org/10.1016/j.jclepro.2022.134581

Li L, Yu H, Zhou S, Dao V, Chen M, Ji L, Benhelal E (2023) Activation and utilization of tailings as CO2 mineralization feedstock and supplementary cementitious materials: a critical review. Mater Today Sustain 24:100530. https://doi.org/10.1016/j.mtsust.2023.100530

Liu P, Barlow CY (2017) Wind turbine blade waste in 2050. Waste Manage 62:229–240. https://doi.org/10.1016/j.wasman.2017.02.007

Ma W, Wang Y, Huang L, Yan L, Kasal B (2023) Natural and recycled aggregate concrete containing rice husk ash as replacement of cement: mechanical properties, microstructure, strength model and statistical analysis. J Build Eng 66:105917. https://doi.org/10.1016/j.jobe.2023.105917

Mallesh G, Paramesha M, Pradeep Kumar VG, Pavankumar R (2019) Mechanical characteristics of csp filled glass-epoxy composites. Int J Recent Technol Eng 8(3):896–900. https://doi.org/10.35940/ijrte.C4079.098319

Marín JC, Graciani E (2022) Normal stress flow evaluation in composite aircraft wing sections by strength of material models. Compos Struct 282:115088. https://doi.org/10.1016/j.compstruct.2021.115088

Ortega-López V, Fuente-Alonso JA, Santamaría A, San-José JT, Aragón Á (2018) Durability studies on fiber-reinforced EAF slag concrete for pavements. Constr Build Mater 163:471–481. https://doi.org/10.1016/j.conbuildmat.2017.12.121

Ortega-López V, Faleschini F, Pellegrino C, Revilla-Cuesta V, Manso JM (2022) Validation of slag-binder fiber-reinforced self-compacting concrete with slag aggregate under field conditions: durability and real strength development. Constr Build Mater 320:126280. https://doi.org/10.1016/j.conbuildmat.2021.126280

Ouyang K, Shi C, Chu H, Guo H, Song B, Ding Y, Guan X, Zhu J, Zhang H, Wang Y, Zheng J (2020) An overview on the efficiency of different pretreatment techniques for recycled concrete aggregate. J Clean Prod 263:121264. https://doi.org/10.1016/j.jclepro.2020.121264

Özkan R, Genç MS (2023) Aerodynamic design and optimization of a small-scale wind turbine blade using a novel artificial bee colony algorithm based on blade element momentum (ABC-BEM) theory. Energy Convers Manage 283:116937. https://doi.org/10.1016/j.enconman.2023.116937

Ozturk S, Karipoglu F (2023) Investigation of the best possible methods for wind turbine blade waste management by using GIS and FAHP: Turkey case. Environ Sci Pollut Res 30(6):15020–15033. https://doi.org/10.1007/s11356-022-23256-6

Ozturk M, BalcikanliBankir M, Sevim UK (2023) High-temperature effect on mechanical properties of fiber reinforced concretes including waste tire rubber. Struct Concr 24(1):1521–1530. https://doi.org/10.1002/suco.202200151

Pan Z, Wu J, Liu J, Zhao X (2018) Fatigue failure of a composite wind turbine blade at the trailing edge. Defect Diffus Forum 382 DDF:191–195. https://doi.org/10.4028/www.scientific.net/DDF.382.191

Pławecka K, Przybyła J, Korniejenko K, Lin WT, Cheng A, Łach M (2021) Recycling of mechanically ground wind turbine blades as filler in geopolymer composite. Materials 14(21):6539. https://doi.org/10.3390/ma14216539

Rani M, Choudhary P, Krishnan V, Zafar S (2021) A review on recycling and reuse methods for carbon fiber/glass fiber composites waste from wind turbine blades. Compos Part B: Eng 215:108768. https://doi.org/10.1016/j.compositesb.2021.108768

Rebello TA, Zulcão R, Calmon JL, Gonçalves RF (2019) Comparative life cycle assessment of ornamental stone processing waste recycling, sand, clay and limestone filler. Waste Manage Res 37(2):186–195. https://doi.org/10.1177/0734242X18819976

Revilla-Cuesta V, Faleschini F, Zanini MA, Skaf M, Ortega-López V (2021) Porosity-based models for estimating the mechanical properties of self-compacting concrete with coarse and fine recycled concrete aggregate. J Build Eng 44:103425. https://doi.org/10.1016/j.jobe.2021.103425

Revilla-Cuesta V, Skaf M, Santamaría A, Espinosa AB, Ortega-López V (2022) Self-compacting concrete with recycled concrete aggregate subjected to alternating-sign temperature variations: thermal strain and damage. Case Stud Constr Mater 17:e01204. https://doi.org/10.1016/j.cscm.2022.e01204

Revilla-Cuesta V, Skaf M, Chica JA, Ortega-López V, Manso JM (2023) Quantification and characterization of the microstructural damage of recycled aggregate self-compacting concrete under cyclic temperature changes. Mater Lett 333:133628. https://doi.org/10.1016/j.matlet.2022.133628

Revilla-Cuesta V, Skaf M, Ortega-López V, Manso JM (2023) Raw-crushed wind-turbine blade: waste characterization and suitability for use in concrete production. Resour Conserv Recycl 198:107160. https://doi.org/10.1016/j.resconrec.2023.107160

Revilla-Cuesta V, Manso-Morato J, Hurtado-Alonso N, Skaf M, Ortega-López V (2024) Mechanical and environmental advantages of the revaluation of raw-crushed wind-turbine blades as a concrete component. J Build Eng 82:108383. https://doi.org/10.1016/j.jobe.2023.108383

Rodsin K, Ali N, Joyklad P, Chaiyasarn K, Al Zand AW, Hussain Q (2022) Improving stress-strain behavior of waste aggregate concrete using affordable glass fiber reinforced polymer (GFRP) composites. Sustainability 14(11):6611. https://doi.org/10.3390/su14116611

Santamaría A, Orbe A, San José JT, González JJ (2018) A study on the durability of structural concrete incorporating electric steelmaking slags. Constr Build Mater 161:94–111. https://doi.org/10.1016/j.conbuildmat.2017.11.121

Santamaría A, González JJ, Losáñez MM, Skaf M, Ortega-López V (2020) The design of self-compacting structural mortar containing steelmaking slags as aggregate. Cem Concr Compos 111:103627. https://doi.org/10.1016/j.cemconcomp.2020.103627

Shanahan N, Bien-Aime A, Buidens D, Meagher T, Sedaghat A, Riding K, Zayed A (2016) Combined effect of water reducer-retarder and variable chloride-based accelerator dosage on rapid repair concrete mixtures for jointed plain concrete pavement. J Mater Civ Eng 28(7):04016036. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001544

Smith JT, Tighe SL (2009) Recycled concrete aggregate coefficient of thermal expansion: characterization, variability, and impacts on pavement performance. Transp Res Rec 2113(1):53–61. https://doi.org/10.3141/2113-07

Sommer V, Walther G (2021) Recycling and recovery infrastructures for glass and carbon fiber reinforced plastic waste from wind energy industry: a European case study. Waste Manage 121:265–275. https://doi.org/10.1016/j.wasman.2020.12.021

Sun D, Huang W, Liu K, Ma R, Wang A, Guan Y, Shen S (2022) Effect of the moisture content of recycled aggregate on the mechanical performance and durability of concrete. Materials 15(18):6299. https://doi.org/10.3390/ma15186299

Tamayo P, Thomas C, Rico J, Pérez S, Mañanes A (2022) Radiation shielding properties of siderurgical aggregate concrete. Constr Build Mater 319:126098. https://doi.org/10.1016/j.conbuildmat.2021.126098

Tao Y, Hadigheh SA, Wei Y (2023) Recycling of glass fibre reinforced polymer (GFRP) composite wastes in concrete: a critical review and cost benefit analysis. Structures 53:1540–1556. https://doi.org/10.1016/j.istruc.2023.05.018

Tariq KA, Rehman MU, Ghafran R, Kamran I, Haroon M (2021) Physio-mechanical and thermal properties of concrete produced by partial replacement of sand with plastic fines. Proc PAS: A 57(3):61–68

Google Scholar  

UNE 12390–9 EX (2008) Testing hardened concrete. Part 9: Freeze-thaw resistance. Scaling

UNE 83966 (2008) Concrete durability. Test methods. Conditioning of concrete test pieces for the purpose of gas permeability and capilar suction tests

UNE 83982 (2008) Concrete durability. Test methods. Determination of the capillar suction in hardened concrete. Fagerlund method.

Wang Z, Lin S, Li X, Zou H, Zhuo B, Ti P, Yuan Q (2021) Optimization and absorption performance of wood sponge. J Mater Sci 56(14):8479–8496. https://doi.org/10.1007/s10853-020-05547-w

Xu GT, Liu MJ, Xiang Y, Fu B (2022) Valorization of macro fibers recycled from decommissioned turbine blades as discrete reinforcement in concrete. J Clean Prod 379:134550. https://doi.org/10.1016/j.jclepro.2022.134550

Xu J, Xiong W, Guo X, Lai T, Liu Y, Ying W (2022) Properties of using excavated soil waste as fine and coarse aggregates in unfired clay bricks after dry-wet cycles. Case Stud Constr Mater 17:e01471. https://doi.org/10.1016/j.cscm.2022.e01471

Yang KH, Jung YB, Cho MS, Tae SH (2015) Effect of supplementary cementitious materials on reduction of CO2 emissions from concrete. J Clean Prod 103:774–783. https://doi.org/10.1016/j.jclepro.2014.03.018

Yazdanbakhsh A, Bank LC, Rieder KA, Tian Y, Chen C (2018) Concrete with discrete slender elements from mechanically recycled wind turbine blades. Resour Conserv Recycl 128:11–21. https://doi.org/10.1016/j.resconrec.2017.08.005

Zhang X, Wang Z, Li W (2021) Structural optimization of H-type VAWT blade under fluid-structure interaction conditions. J Vibroeng 23(5):1207–1218. https://doi.org/10.21595/jve.2021.21766

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Víctor Revilla-Cuesta: conceptualization, methodology, investigation, formal analysis, data curation, and writing—original draft; Nerea Hurtado-Alonso: investigation, software, formal analysis, data curation, and writing—review and editing; Javier Manso-Morato: investigation, software, formal analysis, data curation, and writing—review and editing; Roberto Serrano-López: conceptualization, methodology, supervision, validation, and writing—review and editing; Juan M. Manso: supervision, validation, resources, project administration, and funding acquisition.

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Revilla-Cuesta, V., Hurtado-Alonso, N., Manso-Morato, J. et al. Effects of temperature and moisture fluctuations for suitable use of raw-crushed wind-turbine blade in concrete. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33720-0

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Received : 12 February 2024

Accepted : 14 May 2024

Published : 24 May 2024

DOI : https://doi.org/10.1007/s11356-024-33720-0

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