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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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What is Experimental Research: Definition, Types & Examples

Understand how experimental research enables researchers to confidently identify causal relationships between variables and validate findings, enhancing credibility.

June 16, 2024

Experimental research is crucial for companies because it allows them to precisely control and measure key factors, identify dependent and independent elements, and set conditions to observe their effects. By changing one variable systematically, it is possible to determine possible cause-and-effect relations and analyze how specific observed effects depend on them.

Read this blog to learn more about how experimental research design can drive business success and provide practical examples of its application in various industries.

What is Experimental Research?

Experimental research is a systematic and scientific approach in which the researcher manipulates one or more independent variables and observes the effect on a dependent variable while controlling for extraneous variables. This method allows for the establishment of cause-and-effect relationships between variables.

Experimental research involves using control groups, random assignment, and standardized procedures to ensure the reliability and validity of the results. It is commonly used in psychology, medicine, and the social sciences to test hypotheses and theories under controlled conditions.

Example of Experimental Research

An experimental research example scenario can be a clinical trial for a new medication. This scenario aims to determine whether the new type of drug applies to the patient. Accordingly, patients with hypertension diagnosed by a medical practitioner are randomly assigned to two groups.

The experimental group is subjected to the new medication the research treatment facility delivers. In contrast, the control group is treated with either a placebo or the medical drugs previously used by the patients. The data will be both quantitative and qualitative .

Quantitative data will include blood pressure levels or symptom severity scores. Qualitative data will include symptoms reported by the patient or symptoms observed by the practitioner and side effects experienced by the patients. Consequently, the research on which type of drug is effective is tested, and results are obtained by comparing the patient's conditions in the two groups.

Researchers believe a new medication works if the experimental group shows significant symptom improvements compared to a control group and has no immediate side effects. Testing many patients increases confidence that the effects are due to the medication and not a placebo effect.

What Are The Different Types of Experimental Research?

The following are the different types of experimental research: Pre-Experimental Research

One-Shot Case Study: A single group is exposed to a treatment and then observed for outcomes. There is no control group for comparison.
One-Group Pretest-Posttest Design: A single group is measured before and after treatment to observe changes.

True Experimental Research

Randomized Controlled Trials (RCT): Participants are randomly assigned to experimental and control groups to ensure comparability and reduce bias. This design is considered the gold standard in experimental research.
Pretest-Posttest Control Group Design: Both the experimental and control groups are measured before and after the treatment. The experimental group receives the treatment, while the control group does not.
Posttest-Only Control Group Design: Participants are randomly assigned to experimental and control groups, but measurements are taken only after the treatment is administered to the experimental group.

Quasi-Experimental Research

Non-Equivalent Groups Design: Similar to the pretest-posttest control group design, participants are not randomly assigned to groups. This design is often used when random assignment is not feasible.
Interrupted Time Series Design: Multiple measurements are taken before and after a treatment to observe changes over time. This design helps control time-related variables.
Matched Groups Design: Participants are matched based on certain characteristics before being assigned to experimental and control groups, ensuring comparable groups.

Factorial Design

Full Factorial Design: Involves manipulating two or more independent variables simultaneously to observe their interaction effects on the dependent variable. All possible combinations of the independent variables are tested.
Fractional Factorial Design: A subset of the possible combinations of independent variables is tested, making it more practical when dealing with many variables.

What is the Importance of Experimental Research?

Establishing Causality

Experimental research is essential for establishing correlations between variables of interest and demonstrating causality. It allows researchers to manipulate one or more independent variables considered the cause and record changes in the dependent variable, the effect.

Controlling Variables

One of the strengths of this type of research is that it allows for controlling the effect of extraneous variables. This means that experimental research reduces alternative explanations of effects. Using control groups and random assignment to conditions, the experimental method can accurately determine whether the observed group differences resulted from manipulating the independent variable or other factors.

Providing Reliable and Valid Results

A structured and rigorous methodology while conducting experimental research minimizes the possibility of measurement errors and biases. In addition, randomized controlled trials are generally accepted as the gold standard in research. Because of these features, the data’s reliability can be confirmed in advance by similar findings, and the results will also be more replicable and generalizable to the broader population.

Informing Decision-Making

Experimental research provides empirical evidence and data to support important organizational decisions, such as product testing and experimentation, marketing strategies, or improving operational processes and activities.

Driving Innovation

Experimental research drives innovation by systematically testing new ideas and interventions. It allows companies and researchers to experiment with novel concepts in a controlled environment, identify successful innovations, and confidently scale them up.

What Are The Disadvantages of Experimental Research?

Ethical concerns.

Experimental research implies ethical dilemmas, especially when human subjects are concerned. Generally speaking, ethical principles prohibit manipulating variables, specifically intentionally causing harm to, offending, and inducing psychological or physical pressure. Ethics guidelines and review boards are expected to curb risks in an experiment, but they could also somewhat restrain findings.

Artificial Settings

Most experimental studies are conducted in highly controlled artificial conditions, such as laboratories, where external variables are properly controlled and isolated. Thus, the conclusions of the findings might only sometimes be extended to the real world, so they will only sometimes be applicable. The main type of validity under which this problem falls is external validity. Some variables cannot be controlled or do not appear in artificial conditions.

High Costs and Time Consumption

Experimental research is expensive and time-consuming. There are various reasons for this statement. First, such a type of research requires specialized equipment, controlled conditions of measurement, and large sample sizes, which means increased costs. Second, designing an experiment, preparing all the necessary information and tools for its implementation, running it, and analyzing the data received is usually time-consuming, even in the simplest cases.

Practical and Logistical Constraints

Some variables or phenomena cannot be either manipulated or controlled. Experimental studies are impractical if processes are complex, large-scale, or long-term. For example, a lab cannot treat anything related to the environment or societal changes. Therefore, due to the inability to conduct experiments based on such phenomena, some questions can only be studied by other experimental research methods, such as observational or correlational.

Participant Behavior and Bias

Experimental studies may be biased based on the participants’ awareness of being observed during the process. Also called the Hawthorne effect, another issue that can hurt the study’s validity, especially in medical research, is using control groups. Although they are necessary to measure the efficiency of a certain treatment, such research may involve not providing some groups with potentially beneficial treatment.

These two problems may affect the results and make them unethical. In either case, corrective steps should be taken to address this issue and ensure that the results have been obtained properly.

How Businesses Can Leverage Experimental Research?

Product development and testing.

Businesses can use experimental research to test new products or features before launching them. By creating controlled experiments, such as A/B testing , companies can compare different versions of a product to see which one performs better in terms of customer satisfaction, usability, and sales. This approach allows businesses to refine their products based on empirical evidence, reducing the risk of failure upon release.

Marketing Strategy Optimization

Experimental research is invaluable for optimizing marketing strategies. Businesses can test different marketing messages, channels, and tactics to determine which are most effective in engaging their target audience and driving conversions. For example, they can conduct randomized controlled trials to compare the impact of various advertising campaigns on consumer behavior , enabling data-driven decisions that enhance marketing ROI.

Customer Experience Enhancement

Customer experience is increasingly more critical for retention and loyalty. Companies use experimental research to determine the best practices for customer service, website design, and in-store experience. Through experimenting and measuring responses, companies can identify what promotes satisfaction and loyalty and apply these results to enhance customer experience.

Pricing Strategies

Experimental research helps businesses determine optimal pricing strategies. Companies can analyze consumer reactions and willingness to pay by testing different price points in controlled settings. This approach enables businesses to find the price that maximizes revenue without deterring customers, balancing profitability with market competitiveness.

Operational Efficiency

Businesses can use experimental research to enhance operational efficiency. For instance, they can test various processes, workflows, or technologies to identify which ones improve productivity, reduce costs, or enhance quality. Companies can implement the most effective strategies and practices by systematically experimenting with different operational changes, leading to better overall performance.

Final Words

Experimental research has become a powerful instrument for modern business development. It systematically tests assumptions and variables associated with various activities, from product development, marketing strategies, and customer experiences to pricing and operational efficiencies.

Get your hands on Decode , an AI-powered market research tool that can help you test hypotheses about consumer behavior and preferences. Companies can determine cause-and-effect relationships by manipulating specific variables, such as pricing or advertising methods, and observing the effects on consumer responses using Decode diary studies .

This research method collects qualitative data on user behaviors, activities, and experiences over time. This helps them make informed decisions about product development, marketing strategies, and overall business operations.

Frequently Asked Questions (FAQs)

Question 1: what are examples of experimental research.

Answer: Examples of experimental research include drug trials, psychology experiments, and studies testing new teaching methods. These experiments involve manipulating variables and comparing outcomes to establish causal relationships.

Question 2: What is the meaning of experimental design in research?

Answer: Experimental design in research refers to the methodical planning of experiments to control variables, minimize bias, and draw valid conclusions. It involves carefully considering factors like sample size, randomization, and control groups.

Question 3: What are the characteristics of experimental research?

Answer: Characteristics of experimental research include manipulation of variables, random assignment, control groups, and measurement of outcomes. These features ensure that researchers can isolate the effects of specific variables and draw reliable conclusions.

Question 4: Where is experimental research used?

Answer: Experimental research is used in medicine, psychology, education, and natural sciences to investigate cause-and-effect relationships and validate hypotheses. It provides a systematic approach to testing theories and informing evidence-based practices.

Frequently Asked Questions

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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:

$E = (O_{1} - O_{2})\,.$

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

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).

$2 \times 2$

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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Statistics By Jim

Making statistics intuitive

Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Scientist who developed an experimental design for her research.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

Understanding the relationships between variables.
Identifying the variables that have the largest impact on the outcomes.
Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

Lots of preplanning.
Developing experimental treatments.
Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

A literature review is crucial for the design of experiments.

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

Null hypothesis : The jumping exercise intervention does not affect bone density.
Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .


Assigned to one experimental condition	Participates in all experimental conditions
Requires more subjects	Fewer subjects
Differences between subjects in the groups can affect the results	Uses same subjects in all conditions.
No order of treatment effects.	Order of treatments can affect results.

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

Reader Interactions

March 23, 2024 at 2:35 pm

Dear Jim You wrote a superb document, I will use it in my Buistatistics course, along with your three books. Thank you very much! Miguel

March 23, 2024 at 5:43 pm

Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

April 10, 2023 at 4:36 am

What are the purpose and uses of experimental research design?

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Experimental Research Designs: Types, Examples & Methods

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

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

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

What is Experimental Research?

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

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

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

What are The Types of Experimental Research Design?

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

Pre-experimental Research Design

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

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

One-shot Case Study Research Design

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

One-group Pretest-posttest Research Design:

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

Static-group Comparison:

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

Quasi-experimental Research Design

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

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

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

True Experimental Research Design

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

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

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

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

Examples of Experimental Research

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

Administering Exams After The End of Semester

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

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

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

Employee Skill Evaluation

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

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

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

Evaluation of Teaching Method

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

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

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

What are the Characteristics of Experimental Research?

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

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

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

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

Multivariable

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

Why Use Experimental Research Design?

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

Some uses of experimental research design are highlighted below.

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

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

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

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

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

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

What are the Disadvantages of Experimental Research?

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

What are the Data Collection Methods in Experimental Research?

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

1. Observational Study

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

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

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

2. Simulations

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

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

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

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

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

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

Differences between Experimental and Non-Experimental Research

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

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

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

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

Experimental Research vs. Alternatives and When to Use Them

1. experimental research vs causal comparative.

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

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

Pros and Cons of Experimental vs Causal-Comparative Research

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

2. Experimental Research vs Correlational Research

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

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

Pros and Cons of Experimental vs Correlational Research

3. experimental research vs descriptive research.

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

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

Pros and Cons of Experimental vs Descriptive Research

4. experimental research vs action research.

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

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

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

Pros and Cons of Experimental vs Action Research

Conclusion .

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

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

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

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Experimental Research

First Online: 25 February 2021

Cite this chapter

C. George Thomas 2

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

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

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

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Experimental Research Design — 6 mistakes you should never make!

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

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

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

Table of Contents

What Is Experimental Research Design?

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

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

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

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

Importance of Experimental Research Design

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

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

Types of Experimental Research Designs

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

1. Pre-experimental Research Design

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

Pre-experimental research is of three types —

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

2. True Experimental Research Design

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

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

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

3. Quasi-experimental Research Design

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

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

Advantages of Experimental Research

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

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

6 Mistakes to Avoid While Designing Your Research

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

1. Invalid Theoretical Framework

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

2. Inadequate Literature Study

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

3. Insufficient or Incorrect Statistical Analysis

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

4. Undefined Research Problem

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

5. Research Limitations

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

6. Ethical Implications

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

Experimental Research Design Example

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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What is experimental research: Definition, types & examples

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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Experimental Research

Experimental research is commonly used in sciences such as sociology and psychology, physics, chemistry, biology and medicine etc.

This article is a part of the guide:

Pretest-Posttest
Third Variable
Research Bias
Independent Variable
Between Subjects

Browse Full Outline

1 Experimental Research
2.1 Independent Variable
2.2 Dependent Variable
2.3 Controlled Variables
2.4 Third Variable
3.1 Control Group
3.2 Research Bias
3.3.1 Placebo Effect
3.3.2 Double Blind Method
4.1 Randomized Controlled Trials
4.2 Pretest-Posttest
4.3 Solomon Four Group
4.4 Between Subjects
4.5 Within Subject
4.6 Repeated Measures
4.7 Counterbalanced Measures
4.8 Matched Subjects

It is a collection of research designs which use manipulation and controlled testing to understand causal processes. Generally, one or more variables are manipulated to determine their effect on a dependent variable.

The experimental method is a systematic and scientific approach to research in which the researcher manipulates one or more variables, and controls and measures any change in other variables.

Experimental Research is often used where:

There is time priority in a causal relationship ( cause precedes effect )
There is consistency in a causal relationship (a cause will always lead to the same effect)
The magnitude of the correlation is great.

(Reference: en.wikipedia.org)

The word experimental research has a range of definitions. In the strict sense, experimental research is what we call a true experiment .

This is an experiment where the researcher manipulates one variable, and control/randomizes the rest of the variables. It has a control group , the subjects have been randomly assigned between the groups, and the researcher only tests one effect at a time. It is also important to know what variable(s) you want to test and measure.

A very wide definition of experimental research, or a quasi experiment , is research where the scientist actively influences something to observe the consequences. Most experiments tend to fall in between the strict and the wide definition.

A rule of thumb is that physical sciences, such as physics, chemistry and geology tend to define experiments more narrowly than social sciences, such as sociology and psychology, which conduct experiments closer to the wider definition.

Aims of Experimental Research

Experiments are conducted to be able to predict phenomenons. Typically, an experiment is constructed to be able to explain some kind of causation . Experimental research is important to society - it helps us to improve our everyday lives.

Identifying the Research Problem

After deciding the topic of interest, the researcher tries to define the research problem . This helps the researcher to focus on a more narrow research area to be able to study it appropriately. Defining the research problem helps you to formulate a research hypothesis , which is tested against the null hypothesis .

The research problem is often operationalizationed , to define how to measure the research problem. The results will depend on the exact measurements that the researcher chooses and may be operationalized differently in another study to test the main conclusions of the study.

An ad hoc analysis is a hypothesis invented after testing is done, to try to explain why the contrary evidence. A poor ad hoc analysis may be seen as the researcher's inability to accept that his/her hypothesis is wrong, while a great ad hoc analysis may lead to more testing and possibly a significant discovery.

Constructing the Experiment

There are various aspects to remember when constructing an experiment. Planning ahead ensures that the experiment is carried out properly and that the results reflect the real world, in the best possible way.

Sampling Groups to Study

Sampling groups correctly is especially important when we have more than one condition in the experiment. One sample group often serves as a control group , whilst others are tested under the experimental conditions.

Deciding the sample groups can be done in using many different sampling techniques. Population sampling may chosen by a number of methods, such as randomization , "quasi-randomization" and pairing.

Reducing sampling errors is vital for getting valid results from experiments. Researchers often adjust the sample size to minimize chances of random errors .

Here are some common sampling techniques :

probability sampling
non-probability sampling
simple random sampling
convenience sampling
stratified sampling
systematic sampling
cluster sampling
sequential sampling
disproportional sampling
judgmental sampling
snowball sampling
quota sampling

Creating the Design

The research design is chosen based on a range of factors. Important factors when choosing the design are feasibility, time, cost, ethics, measurement problems and what you would like to test. The design of the experiment is critical for the validity of the results.

Typical Designs and Features in Experimental Design

Pretest-Posttest Design Check whether the groups are different before the manipulation starts and the effect of the manipulation. Pretests sometimes influence the effect.
Control Group Control groups are designed to measure research bias and measurement effects, such as the Hawthorne Effect or the Placebo Effect . A control group is a group not receiving the same manipulation as the experimental group. Experiments frequently have 2 conditions, but rarely more than 3 conditions at the same time.
Randomized Controlled Trials Randomized Sampling, comparison between an Experimental Group and a Control Group and strict control/randomization of all other variables
Solomon Four-Group Design With two control groups and two experimental groups. Half the groups have a pretest and half do not have a pretest. This to test both the effect itself and the effect of the pretest.
Between Subjects Design Grouping Participants to Different Conditions
Within Subject Design Participants Take Part in the Different Conditions - See also: Repeated Measures Design
Counterbalanced Measures Design Testing the effect of the order of treatments when no control group is available/ethical
Matched Subjects Design Matching Participants to Create Similar Experimental- and Control-Groups
Double-Blind Experiment Neither the researcher, nor the participants, know which is the control group. The results can be affected if the researcher or participants know this.
Bayesian Probability Using bayesian probability to "interact" with participants is a more "advanced" experimental design. It can be used for settings were there are many variables which are hard to isolate. The researcher starts with a set of initial beliefs, and tries to adjust them to how participants have responded

Pilot Study

It may be wise to first conduct a pilot-study or two before you do the real experiment. This ensures that the experiment measures what it should, and that everything is set up right.

Minor errors, which could potentially destroy the experiment, are often found during this process. With a pilot study, you can get information about errors and problems, and improve the design, before putting a lot of effort into the real experiment.

If the experiments involve humans, a common strategy is to first have a pilot study with someone involved in the research, but not too closely, and then arrange a pilot with a person who resembles the subject(s) . Those two different pilots are likely to give the researcher good information about any problems in the experiment.

Conducting the Experiment

An experiment is typically carried out by manipulating a variable, called the independent variable , affecting the experimental group. The effect that the researcher is interested in, the dependent variable(s) , is measured.

Identifying and controlling non-experimental factors which the researcher does not want to influence the effects, is crucial to drawing a valid conclusion. This is often done by controlling variables , if possible, or randomizing variables to minimize effects that can be traced back to third variables . Researchers only want to measure the effect of the independent variable(s) when conducting an experiment , allowing them to conclude that this was the reason for the effect.

Analysis and Conclusions

In quantitative research , the amount of data measured can be enormous. Data not prepared to be analyzed is called "raw data". The raw data is often summarized as something called "output data", which typically consists of one line per subject (or item). A cell of the output data is, for example, an average of an effect in many trials for a subject. The output data is used for statistical analysis, e.g. significance tests, to see if there really is an effect.

The aim of an analysis is to draw a conclusion , together with other observations. The researcher might generalize the results to a wider phenomenon, if there is no indication of confounding variables "polluting" the results.

If the researcher suspects that the effect stems from a different variable than the independent variable, further investigation is needed to gauge the validity of the results. An experiment is often conducted because the scientist wants to know if the independent variable is having any effect upon the dependent variable. Variables correlating are not proof that there is causation .

Experiments are more often of quantitative nature than qualitative nature, although it happens.

Examples of Experiments

This website contains many examples of experiments. Some are not true experiments , but involve some kind of manipulation to investigate a phenomenon. Others fulfill most or all criteria of true experiments.

Here are some examples of scientific experiments:

Social Psychology

Stanley Milgram Experiment - Will people obey orders, even if clearly dangerous?
Asch Experiment - Will people conform to group behavior?
Stanford Prison Experiment - How do people react to roles? Will you behave differently?
Good Samaritan Experiment - Would You Help a Stranger? - Explaining Helping Behavior
Law Of Segregation - The Mendel Pea Plant Experiment
Transforming Principle - Griffith's Experiment about Genetics
Ben Franklin Kite Experiment - Struck by Lightning
J J Thomson Cathode Ray Experiment
Psychology 101
Flags and Countries
Capitals and Countries

Oskar Blakstad (Jul 10, 2008). Experimental Research. Retrieved Aug 08, 2024 from Explorable.com: https://explorable.com/experimental-research

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A Complete Guide to Experimental Research

Published by Carmen Troy at August 14th, 2021 , Revised On August 25, 2023

A Quick Guide to Experimental Research

Experimental research refers to the experiments conducted in the laboratory or observation under controlled conditions. Researchers try to find out the cause-and-effect relationship between two or more variables.

The subjects/participants in the experiment are selected and observed. They receive treatments such as changes in room temperature, diet, atmosphere, or given a new drug to observe the changes. Experiments can vary from personal and informal natural comparisons. It includes three types of variables ;

Independent variable
Dependent variable
Controlled variable

Before conducting experimental research, you need to have a clear understanding of the experimental design. A true experimental design includes identifying a problem , formulating a hypothesis , determining the number of variables, selecting and assigning the participants, types of research designs , meeting ethical values, etc.

There are many types of research methods that can be classified based on:

The nature of the problem to be studied
Number of participants (individual or groups)
Number of groups involved (Single group or multiple groups)
Types of data collection methods (Qualitative/Quantitative/Mixed methods)
Number of variables (single independent variable/ factorial two independent variables)
The experimental design

Types of Experimental Research

Laboratory Experiment

It is also called experimental research. This type of research is conducted in the laboratory. A researcher can manipulate and control the variables of the experiment.

Example: Milgram’s experiment on obedience.

Pros	Cons
The researcher has control over variables. Easy to establish the relationship between cause and effect. Inexpensive and convenient. Easy to replicate.	The artificial environment may impact the behaviour of the participants. Inaccurate results The short duration of the lab experiment may not be enough to get the desired results.

Field Experiment

Field experiments are conducted in the participants’ open field and the environment by incorporating a few artificial changes. Researchers do not have control over variables under measurement. Participants know that they are taking part in the experiment.

Pros	Cons
Participants are observed in the natural environment. Participants are more likely to behave naturally. Useful to study complex social issues.	It doesn’t allow control over the variables. It may raise ethical issues. Lack of internal validity

Natural Experiments

The experiment is conducted in the natural environment of the participants. The participants are generally not informed about the experiment being conducted on them.

Examples: Estimating the health condition of the population. Did the increase in tobacco prices decrease the sale of tobacco? Did the usage of helmets decrease the number of head injuries of the bikers?

Pros	Cons
The source of variation is clear. It’s carried out in a natural setting. There is no restriction on the number of participants.	The results obtained may be questionable. It does not find out the external validity. The researcher does not have control over the variables.

Quasi-Experiments

A quasi-experiment is an experiment that takes advantage of natural occurrences. Researchers cannot assign random participants to groups.

Example: Comparing the academic performance of the two schools.

Pros	Cons
Quasi-experiments are widely conducted as they are convenient and practical for a large sample size. It is suitable for real-world natural settings rather than true experimental research design. A researcher can analyse the effect of independent variables occurring in natural conditions.	It cannot the influence of independent variables on the dependent variables. Due to the absence of a control group, it becomes difficult to establish the relationship between dependent and independent variables.

Does your Research Methodology Have the Following?

Great Research/Sources
Perfect Language
Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

How to Conduct Experimental Research?

Step 1. identify and define the problem.

You need to identify a problem as per your field of study and describe your research question .

Example: You want to know about the effects of social media on the behavior of youngsters. It would help if you found out how much time students spend on the internet daily.

Example: You want to find out the adverse effects of junk food on human health. It would help if you found out how junk food frequent consumption can affect an individual’s health.

Step 2. Determine the Number of Levels of Variables

You need to determine the number of variables . The independent variable is the predictor and manipulated by the researcher. At the same time, the dependent variable is the result of the independent variable.

Independent variables	Dependent variables	Confounding Variable
The number of hours youngsters spend on social media daily.	The overuse of social media among the youngsters and negative impact on their behaviour.	Measure the difference between youngsters’ behaviour with the minimum social media usage and maximum social media utilisation. You can control and minimise the number of hours of using the social media of the participants.
The overconsumption of junk food.	Adverse effects of junk food on human health like obesity, indigestion, constipation, high cholesterol, etc.	Identify the difference between people’s health with a healthy diet and people eating junk food regularly. You can divide the participants into two groups, one with a healthy diet and one with junk food.

In the first example, we predicted that increased social media usage negatively correlates with youngsters’ negative behaviour.

In the second example, we predicted the positive correlation between a balanced diet and a good healthy and negative relationship between junk food consumption and multiple health issues.

Step 3. Formulate the Hypothesis

One of the essential aspects of experimental research is formulating a hypothesis . A researcher studies the cause and effect between the independent and dependent variables and eliminates the confounding variables. A null hypothesis is when there is no significant relationship between the dependent variable and the participants’ independent variables. A researcher aims to disprove the theory. H0 denotes it. The Alternative hypothesis is the theory that a researcher seeks to prove. H1or HA denotes it.

Null hypothesis
The usage of social media does not correlate with the negative behaviour of youngsters.	Over-usage of social media affects the behaviour of youngsters adversely.
There is no relationship between the consumption of junk food and the health issues of the people.	The over-consumption of junk food leads to multiple health issues.

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Step 4. Selection and Assignment of the Subjects

It’s an essential feature that differentiates the experimental design from other research designs . You need to select the number of participants based on the requirements of your experiment. Then the participants are assigned to the treatment group. There should be a control group without any treatment to study the outcomes without applying any changes compared to the experimental group.

Randomisation: The participants are selected randomly and assigned to the experimental group. It is known as probability sampling. If the selection is not random, it’s considered non-probability sampling.

Stratified sampling : It’s a type of random selection of the participants by dividing them into strata and randomly selecting them from each level.

Randomisation	Stratified sampling
Participants are randomly selected and assigned a specific number of hours to spend on social media.	Participants are divided into groups as per their age and then assigned a specific number of hours to spend on social media.
Participants are randomly selected and assigned a balanced diet.	Participants are divided into various groups based on their age, gender, and health conditions and assigned to each group’s treatment group.

Matching: Even though participants are selected randomly, they can be assigned to the various comparison groups. Another procedure for selecting the participants is ‘matching.’ The participants are selected from the controlled group to match the experimental groups’ participants in all aspects based on the dependent variables.

What is Replicability?

When a researcher uses the same methodology and subject groups to carry out the experiments, it’s called ‘replicability.’ The results will be similar each time. Researchers usually replicate their own work to strengthen external validity.

Step 5. Select a Research Design

You need to select a research design according to the requirements of your experiment. There are many types of experimental designs as follows.

Type of Research Design	Definition
Two-group Post-test only	It includes a control group and an experimental group selected randomly or through matching. This experimental design is used when the sample of subjects is large. It is carried out outside the laboratory. Group’s dependent variables are compared after the experiment.
Two-group pre-test post-test only.	It includes two groups selected randomly. It involves pre-test and post-test measurements in both groups. It is conducted in a controlled environment.
Soloman 4 group design	It includes both post-test-only group and pre-test-post-test control group design with good internal and external validity.
Factorial design	Factorial design involves studying the effects of two or more factors with various possible values or levels. Example: Factorial design applied in optimisation technique.
Randomised block design	It is one of the most widely used experimental designs in forestry research. It aims to decrease the experimental error by using blocks and excluding the known sources of variation among the experimental group.
Cross over design	In this type of experimental design, the subjects receive various treatments during various periods.
Repeated measures design	The same group of participants is measured for one dependant variable at various times or for various dependant variables. Each individual receives experimental treatment consistently. It needs a minimum number of participants. It uses counterbalancing (randomising and reversing the order of subjects and treatment) and increases the treatments/measurements’ time interval.

Step 6. Meet Ethical and Legal Requirements

Participants of the research should not be harmed.
The dignity and confidentiality of the research should be maintained.
The consent of the participants should be taken before experimenting.
The privacy of the participants should be ensured.
Research data should remain confidential.
The anonymity of the participants should be ensured.
The rules and objectives of the experiments should be followed strictly.
Any wrong information or data should be avoided.

Tips for Meeting the Ethical Considerations

To meet the ethical considerations, you need to ensure that.

Participants have the right to withdraw from the experiment.
They should be aware of the required information about the experiment.
It would help if you avoided offensive or unacceptable language while framing the questions of interviews, questionnaires, or Focus groups.
You should ensure the privacy and anonymity of the participants.
You should acknowledge the sources and authors in your dissertation using any referencing styles such as APA/MLA/Harvard referencing style.

Step 7. Collect and Analyse Data.

Collect the data by using suitable data collection according to your experiment’s requirement, such as observations, case studies , surveys , interviews , questionnaires, etc. Analyse the obtained information.

Step 8. Present and Conclude the Findings of the Study.

Write the report of your research. Present, conclude, and explain the outcomes of your study .

Frequently Asked Questions

What is the first step in conducting an experimental research.

The first step in conducting experimental research is to define your research question or hypothesis. Clearly outline the purpose and expectations of your experiment to guide the entire research process.

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

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

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

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

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

Randomized Block Design

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

Factorial Design

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

Repeated Measures Design

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

Crossover Design

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

Split-plot Design

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

Nested Design

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

Laboratory Experiment

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

Field Experiment

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

Experimental Design Methods

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

Randomization

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

Control Group

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

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

Counterbalancing

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

Replication

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

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

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

Data Collection Method

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

Direct Observation

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

Self-report Measures

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

Behavioral Measures

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

Physiological Measures

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

Archival Data

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

Computerized Measures

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

Video Recording

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

Data Analysis Method

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

Descriptive Statistics

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

Inferential Statistics

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

Analysis of Variance (ANOVA)

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling (SEM)

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

Cluster Analysis

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Experimental Design

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

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

Examples of Experimental Design

Here are some examples of experimental design in different fields:

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

When to use Experimental Research Design

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

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

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

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

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

Purpose of Experimental Design

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

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

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

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

Advantages of Experimental Design

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

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

Limitations of Experimental Design

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

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

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Editor-in-Chief for Simply Psychology

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups. For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable. This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only. If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background. These differences are known as participant variables (i.e., a type of extraneous variable ).
Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition. This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior. Performance in the second condition may be better because the participants know what to do (i.e., practice effect). Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants. Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B). For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

Con : If one participant drops out, you lose 2 PPs’ data.
Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
Con : Very time-consuming trying to find closely matched pairs.
Pro : It avoids order effects, so counterbalancing is not necessary.
Con : Impossible to match people exactly unless they are identical twins!
Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

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

Experimenter effects

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

Demand characteristics

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

Independent variable (IV)

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

Dependent variable (DV)

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

Extraneous variables (EV)

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

Confounding variables

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

Random Allocation

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

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

Order effects

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

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

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

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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

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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v.45(1); Jan-Feb 2010

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

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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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Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs. quantitative : Will your data take the form of words or numbers?
Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .


Qualitative		to broader populations. .
Quantitative		.

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.


Primary	.	methods.
Secondary

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.


Descriptive		. .
Experimental

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Research methods for collecting data
Research method	Primary or secondary?	Qualitative or quantitative?	When to use
	Primary	Quantitative	To test cause-and-effect relationships.
	Primary	Quantitative	To understand general characteristics of a population.
Interview/focus group	Primary	Qualitative	To gain more in-depth understanding of a topic.
Observation	Primary	Either	To understand how something occurs in its natural setting.
	Secondary	Either	To situate your research in an existing body of work, or to evaluate trends within a research topic.
	Either	Either	To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study.

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

During an experiment .
Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Research methods for analyzing data
Research method	Qualitative or quantitative?	When to use
	Quantitative	To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations).
Meta-analysis	Quantitative	To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner.
	Qualitative	To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated.
	Either	To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words).

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

Chi square test of independence
Statistical power
Descriptive statistics
Degrees of freedom
Pearson correlation
Null hypothesis
Double-blind study
Case-control study
Research ethics
Data collection
Hypothesis testing
Structured interviews

Research bias

Hawthorne effect
Unconscious bias
Recall bias
Halo effect
Self-serving bias
Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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

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

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Meaning of field experiment in English

The field experiments were carried out at three sites over three consecutive summer growing seasons .
Field experiments are to be conducted to explore the factors that lead executives to accept or reject investment project proposals .
The field experiments were conducted at the end of the dry season on dry soil profiles .
as an experiment
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put someone/something through their/its paces idiom
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Celebrating the Milestone of Fusion Ignition

In 2022, Lawrence Livermore National Laboratory made history by demonstrating fusion ignition for the first time in a laboratory setting. Read about the people, facilities, capabilities and decades of tenacity that made this achievement possible.

Read about our fusion breakthrough

Research confirms importance of symmetry in pre-ignition fusion experiments

Joe Ralph, co-lead author and inertial confinement fusion research physicist at Lawrence Livermore National Laboratory, discusses the critical role of implosion symmetry in achieving a burning plasma state at the National Ignition Facility. (Photo: Blaise Douros/LLNL)

Time-integrated X-ray images of the “hot spot” used to infer mode-2 symmetry and mix fraction.

Researchers at Lawrence Livermore National Laboratory (LLNL) have retrospectively confirmed that implosion asymmetry was a major aspect for fusion experiments before achieving ignition for the first time at the Lab’s National Ignition Facility (NIF), the world’s most energetic laser.

The findings were recently detailed in a Nature Communications paper titled “The impact of low-mode symmetry on inertial fusion energy output in the burning plasma state.” The study was co-led by LLNL inertial confinement fusion (ICF) research physicists Joe Ralph, Steven Ross and Alex Zylstra, the former lead of the Hybrid-E ICF campaign.

In 2021, indirect drive ICF experiments achieved a burning plasma state with neutron yields exceeding 170 kJ, roughly three times the record in 2019 and a necessary stage for igniting plasmas. The results were achieved despite multiple sources of degradations — including asymmetries — that lead to high variability in performance. This milestone was a critical step toward achieving ignition on Dec. 5, 2022, Ralph said.

The significance of symmetry in ICF experiments, Ralph said, is like trying to fly an airplane with a heavy left wing. The relative wing weight doesn’t matter much while you are still on the ground, but it makes a big difference when you try to lift off. Achieving a burning plasma is like lifting off.

“Reaching a burning plasma state was a pivotal moment for us,” Ralph said. “It validated years of theoretical and experimental work and provided a solid foundation for future advancements.”

For the first time, the paper presents an empirical degradation factor for mode-2 asymmetry in the burning plasma regime, in addition to previously determined degradations of radiative mix and mode-1 asymmetry. The analysis demonstrates that incorporating these three degradations into the theoretical fusion yield scaling developed in 2017-2018 accounts for the measured fusion performance variability in the two highest-performing experimental campaigns on the NIF to within error.

“In our fusion experiments, achieving symmetry is crucial,” Ralph said. “If the plasma is not uniformly compressed, the energy is not efficiently contained, then the performance suffers. By understanding and correcting these asymmetries, we can ensure that the conditions are just right for ignition, much like making sure your airplane is properly balanced before taking off.”

The paper highlights how the team quantified the performance sensitivity to mode-2 asymmetry in the burning plasma regime and applied the results, in the form of an empirical degradation factor, to a 1D fusion performance model. Additionally, the team determined through a series of integrated 2D radiation hydrodynamic simulations that the sensitivity to mode-2 was consistent with the experimentally determined sensitivity only when including alpha-heating.

“By isolating and quantifying the mode-2 degradation, we were able to refine our models and improve the accuracy of our predictions,” Ralph said. “These findings underscore the importance of continuous refinement and understanding of the variables affecting fusion performance. By identifying and accounting for these degradation factors, we have been better able to assess the performance of our experiments and make more informed decisions. This was a significant step toward achieving ignition.”

Click here for a complete list of authors.

The regulatory effects of water probiotic supplementation on the blood physiology, reproductive performance, and its related genes in Red Tilapia ( Oreochromis niloticus X O. mossambicus )

El-Sayed Hemdan Eissa ORCID: orcid.org/0000-0002-7229-2175 1 ,
Abdel-Fattah M. El-Sayed 2 ,
Basma M. Hendam 3 ,
Sara F. Ghanem 4 ,
Heba E. Abd Elnabi 5 ,
Yasmin M. Abd El-Aziz ORCID: orcid.org/0000-0002-4369-8221 6 ,
Sameh A. Abdelnour 7 ,
Moaheda E.H. Eissa ORCID: orcid.org/0000-0002-1733-6892 8 &
Hagar Sedeek Dighiesh ORCID: orcid.org/0000-0002-1669-5850 9

BMC Veterinary Research volume 20 , Article number: 351 ( 2024 ) Cite this article

Metrics details

Probiotics are becoming increasingly popular as eco-friendly alternatives in aquaculture. However, there is limited research on their impacts on the reproductive efficiency of Red Tilapia ( Oreochromis niloticus x O. mossambicus ) broodstock. Therefore, this experiment aimed to explore the combined effects of selective probiotics Bacillus subtilis and B. licheniformis (BSL; 1:1) added to water on blood hematology, serum metabolites, gonadal histology, reproductive performance, and reproductive associated genes in Red Tilapia broodstock. Tilapia broodfish weighing 140–160 g were stocked in four treatment groups: control (T0), and the other three groups were added different levels of BSL to the water as follows: T1 (0.01 g/m 3 ), T2 (0.02 g/m 3 ), and T3 (0.03 g/m 3 ), respectively. Results indicate that BSL administration significantly improved RBCs, hemoglobin, hematocrit, MCH, and MCHC, with the highest improvement seen in the T3 group ( P < 0.05). BSL added to the fish water significantly enhanced serum protein fractions (total protein, albumin, and globulins), while AST, ALT, ALP, creatinine, uric acid, and glucose were significantly diminished in a dose-dependent way ( P < 0.05). Adding 0.02–0.03 g/ m 3 of BSL resulted in higher antioxidant status (superoxide dismutase and catalase) compared to other groups ( P < 0.05). Testosterone levels were higher in T3 than in other groups ( P < 0.05). All female hormones (LH, FSH, estradiol, and progesterone) were substantially augmented by the addition of BSL. Additionally, the BSL groups exhibited higher GSI, HSI, VSI (male only), egg diameter (mm), mean number of fry/fish, and mean fry weight (g) compared to the control group ( P < 0.05). Expression of reproductive-associated genes ( vasa , nanos1a , nanos2 , dnd1 , pum1 , AMH , and vtg ) were significantly up-regulated in the gonads of fish in the 0.03 g/m 3 treatment. The histological gonadal structure exhibited that BSL improved gonad maturation in both genders of Tilapia fish. Overall, adding a mixture of B. subtilis and B. licheniformis (0.03 g/m 3 water) can accelerate reproductive performance in Red Tilapia through up-regulation of reproductive genes and enhance the health profile.

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Introduction

Aquaculture’s sustainability depends on the effective utilization of aquafeeds and the implementation of robust aquaculture health management practices. Aquaculture contributes to approximately 50% of the world’s total fish production, solidifying its position as the fastest-growing sector within the industry [ 1 , 2 ]. Moreover, it plays a significant part in providing sustainable income opportunities and contributing to global food security [ 3 ]. In Egypt, there are numerous fish species that inhabit its water resources. The country’s diverse aquatic ecosystems support a wide variety of fish, including Nile tilapia, catfish, and mullet [ 4 ]. These fish play a crucial role in the local economy and provide a vital source of protein for the population. The aquaculture industry in Egypt is an important sector that contributes to the country’s food security and economic development. However, local production rarely meets domestic demand, leading the country to rely on imports to cover the shortage [ 2 ]. Tilapias are a globally farmed group of fish, with a production of approximately 6.7 million tons in 2023. This industry is valued at over 14.1 billion US dollars [ 5 ]. As omnivorous fish, tilapias can host both beneficial and harmful bacteria in their gastrointestinal tract, culture water, and sediment. Some examples of bacteria found in the gastrointestinal tract of Nile Tilapia fish include Lactobacillus farciminis , Lactobacillus coryniformis , Lactobacillus brevis , Lactobacillus collinoides , Bacillus sp., and others. Bacillus sp., P. Fluorescens , L. brevis , and L. collinoides are commonly abundant in the fish’s gut [ 3 , 6 ].

Probiotics have emerged as a promising alternative strategy for preventing infectious diseases [ 7 , 8 ]. In aquaculture, probiotics offer numerous benefits, such as improving water quality, enhancing digestion, and boosting fish growth and immune response [ 9 , 10 , 11 ]. Probiotics can enhance feed efficiency in aquatic animals by increasing the activity of digestive enzymes and maintaining a healthy balance of intestinal microbes. This leads to better nutrient absorption and utilization, as well as improved reproductive system function [ 12 , 13 ]. Probiotic supplementation also increases appetite and the digestibility of organisms [ 9 ]. B. subtilis and B. licheniformis bacteria are important probiotic additives for maintaining the normal growth and functions of aquatic animals’ by providing vitamins, nutrients, and producing digestive enzymes. These factors positively effects on feed utilization, nutritional absorption, and growth performance [ 14 ]. Bacillus spp. have various positive ways, such as promoting better nutrient utilization, production and secretion of exogenous enzymes, and enhancing gut microbiota to support intestinal physiological functions [ 10 , 15 , 16 ]. Therefore, fishes fed with different Bacillus species have shown improve growth indices [ 17 ]. Additionally, altering the harmful intestinal microbiota composition to favour a greater proportion of beneficial bacterial communities can support adaptive and innate functions and promote intestinal integrity in the host [ 3 ].

Probiotic mechanisms include actions to inhibit pathogen growth, production of various ingredients (e.g., organic acids, bacteriocins, and volatile compounds), competition for adherence sites and nutrients [ 18 , 19 ], and enhancement of innate immune responses (e.g., respiratory burst activities, lysozyme enhancement) and interactions with natural killer cells, leukocytes, and phagocytes [ 11 , 19 ]. An appropriate and balanced diet not only provides the principal and necessary components for better fish growth but also commonly includes feed supplements such as herbal extracts, probiotics, and symbiotics to boost the immune system and growth rate [ 17 ]. Probiotic addition has been shown to enhance antioxidant capacity, digestive enzymes, and immune function in Nile tilapia fish [ 6 , 8 , 20 ]. Additionally, both serum and mucosal surfaces’ immunoglobulin M (IgM) levels have been found to play an essential role in defending against numerous pathogenic organisms that infect cultured fish [ 21 , 22 ]. One important attribute of Bacillus species is their ability to form spores, which allows them to withstand the heat generated during feed palletization [ 16 , 17 , 23 ]. These spores also enable the bacteria to survive the adverse environment of the fish’s stomach and colonize the intestines, where they can multiply and produce various beneficial digestive enzymes such as amylase, lipase, and protease [ 23 , 24 , 25 ]. Additionally, probiotics’ molecular mechanism of action involves influencing the expression and regulation of different genes [ 15 , 26 , 27 ]. Therefore, the authors of the current study discovered that increasing levels of B. subtilis and B. licheniformis have many substantially beneficial consequences on the physiology, blood health and reproductive performance of red tilapia. With this backdrop, the experiment was conducted to determine the effect of graded levels of water probiotics, B. subtilis and B. licheniformis on hematological variables, reproductive ability, and expression of reproductive-related genes in Red Tilapia broodstock ( O. niloticus x O. mossambicus ).

Materials and methods

Fish and experimental design.

This experiment was performed at the Fish Research Centre, Arish University, North Sinai, Egypt. Adult male and female hybrid red tilapia ( O. niloticus x O. mossambicus ) weighing 140–160 g were housed in concrete tanks with a volume of 3 × 4 × 0.8 m³. A total of 480 fish were used in this experiment. The fish were divided into four groups, with each treatment consisting of 120 fish (three tanks, 40 fish in each tank). The fish were stocked in triplicates at a ratio of 1 male to 3 females per cubic meter, with a total density of forty fish per tank (around 860 g/m 3 ). The fish were acclimated to the trial culture conditions for fifteen days. Air stones were provided in the tanks throughout the trial, and a light cycle of 12 h light and 12 h dark was maintained. Four treatments were included: a control group (T0), and three groups (T1, T2 and T3) with varying levels of B. subtilis and B. licheniformis added to the water. The treatment fish groups (2.5% of the total biomass) were labeled as follows: T0 (0 g/m 3 ), T1 (0.01 g/m 3 ), T2 (0.02 g/m 3 ), and T3 (0.03 g/m 3 ). The fish were fed an extruded diet from ALLER AQUA FEED ( https://www.aller-aqua.com/ ) with 30% crude protein, 5.2% crude fat, 5.8% total ash, and 4.8% crude fiber. Each morning, before the first feeding, the fish feces and waste were siphoned, and approximately 10% of the pond water was replaced with dechlorinated water of similar temperature. Every day, the doses of B. subtilis and B. licheniformis were adjusted according to the rate of water changes. At the end of the experiment, the fish from each tank were collected, tallied, weighed, and the weights and survival rate were documented. The fishpond was then cleaned, and the fish were prepared for the spawning period. Thirty ripe females and ten ripe males were placed in the culture tanks for 20 days. During this period, the reproduction capability and spawning performance were measured.

Water quality parameters

The water elements such as salinity (g/L), temperature (°C), pH, dissolved oxygen (DO, mg/L), total ammonia nitrogen (TAN, mg/L), and ammonia (mg/L) were monitored twice a week employing the YSI-556 multi-parameter method (YSI Inc., Yellow Springs, OH, USA) to assess water quality.

Blood sampling

Before the final harvest, all fish were fasted for approximately 12 h. Subsequently,5 fish per tank were anesthetized using amino-benzoic acid (120 mg/L, Sigma-Aldrich, Germany) for blood sample collection. The blood samples were obtained for hemato-biochemical and other physiological parameters. Blood samples were captured from the caudal vein using sterilized needles and separated into subsamples. The first part was stored in heparinized tubes for the hematology parameters analysis. While, the second part was stored in non-heparinized tubes and left to coagulate at room temperature, following the method described by [ 25 ] for serum separation. The blood was then centrifuged at 4000 rpm for 10 min to separate the serum, which was subsequently stored at -20 °C for further analysis.

Blood hematology assessment

Red blood cells (RBCs) were counted using the method described by [ 28 ] with a Bright-Line Hemocytometer (Neubauer enhanced, Germany). Hemoglobin (Hb) levels were measured calorimetrically, as outlined by [ 29 ]. Hematocrit (Hct) was calculated following the method of [ 30 ]. The levels of MCV (mean corpuscular volume), MCHC (mean corpuscular hemoglobin concentration), and MCH (mean corpuscular hemoglobin) were determined according to [ 28 ].

Serum metabolites assays

The serum total protein fraction (total protein and albumin) was determined using kits provided by Diamond Diagnostics Company. Globulin concentration was calculated using the difference method between total protein and albumin. Kidney related biomarkers such as uric acid, creatinine, and urea were assess according to the method of [ 31 ] using kits provided by Biocompare company (South San Francisco, United States). Glucose levels were determined by the colorimetric glucose oxidase technique of [ 32 ]. The activities of ALT (alanine aminotransferase), AST (aspartate aminotransferase), and ALP (alkaline phosphatase) were measured using an automated analyzer (Abbott Alcyon 300, USA) in accordance with the Pars Azmon Kit’s protocol (Pars Azmon, Iran). The “hydroxylamine method” was used to determine superoxide dismutase (SOD) activity [ 33 ], while the “visible light method” used for catalase (CAT) activity [ 34 ]. Steroid female hormones such as estradiol (E2, MBS700179), progesterone (P, MBS2602842), luteinizing (LH, MBS283097), and follicle-stimulating (FSH, MBS281137) hormones were determined using commercial ELISA kits as explained by [ 35 ]. Testosterone (T, MBS933475) hormone was assessed using quantitative competitive method by ELISA kit. All kits used for steroids hormones were provided by the MyBiosource company (San Diego, USA).

Organosomatic indices

The total body length (T.L) in centimeters and weight (W) in grams were recorded for 30 fish in each group (15 males and 15 females). The liver, gut, and gonads of 30 fish (5 males and 5 females/ tank) per group were removed and weighed. The hepatosomatic index (HSI), viscerasomatic index (VSI), and gonadosomatic index (GSI) were calculated using the following equations:

Egg diameter, mean number of fry/fish and mean fry weight

For 20 days, the spawning performance was monitored. Five gravid, spawn-ready females were eliminated from all tanks, gently stripped, and then subsamples of around ten eggs were randomly selected for determining the diameter of eggs (mm) [ 36 ]. Each female was returned to the appropriate tank after stripping until the end of the trial. Females in each tank were checked daily to find eggs or fry. The eggs were left in the females’ mouths until hatching and complete yolk sac absorption. The fry were then gathered from their respective females, counted, and weighed; the averages were evaluated following the method described by [ 37 ] method. By distributing the total quantity of fry in the tank by the number of female spawns, the mean number of fry per spawning was determined.

Genes expression

Cdna production and total rna extraction.

Samples of testes and ovaries were collected and frozen using liquid nitrogen to analyze the expression of various reproduction-related genes. Each 50 mg of ovarian and testicular tissues was used for RNA extraction with Trizol reagent (iNtRON Biotechnology, Inc., South Korea). The RNA concentration was determined using a NanoDrop method (UV-Vis spectrophotometer, USA). The cDNA was synthesized with the Fast HiSenScript TM RH RT PreMix cDNA synthesis kit (iNtRON Biotechnology, South Korea), and the samples were kept at -20 °C for further analysis.

Real time qPCR (RT-PCR)

The specific primer sequences, product sizes, and GenBank accession numbers of reproduction-associated genes, namely vasa , nanos1a , nanos2a , dnd1 , pum1 , AMH , and vtg for both males and females, are listed in Table 1 . The Elf1α gene served as a housekeeping (reference) gene for normalizing mRNA expressions. RT-PCR was performed using the SYBR Green PCR Master Mix to quantify the mRNA expression of the target genes (SensiFast™ SYBR Lo-Rox kit, Bioline). The thermocycling settings were as follows: 95 °C for 10 min, followed by 40 cycles at 94 °C for 15s, 60 °C for 1 min, and 72 °C for 20 s. The mRNA expression levels of each gene were normalized and standardized to the mRNA of elf1α transcripts using the 2 −ΔΔCT approach [ 38 ].

Histological analysis

The testes and ovaries of males and females were freshly removed, fixed in neutral formaldehyde (10%) for 24 h, then dehydrated with graded ethanol, and immersed in methyl benzoate for 24 h. They were then cleared in xylene, embedded in purified paraffin wax, and sectioned to a thickness of 5–7 μm using an automated microtome. The sectioned tissues were stained with hematoxylin and eosin and examined under a light microscope (Zeiss) using the method described by [ 39 ].

Statistical analysis

Results are presented as means ± standard error (S.E.). All numerical data were checked for homogeneity of variance using Levene’s test and for normality of distribution using the Shapiro-Wilk test. The data were analyzed using SPSS software (Version 26.0; SPSS, Chicago, IL, USA) through a one-way analysis of variance (ANOVA) to determine statistical significance at a 95% confidence level. If the F values from the ANOVA test were found to be significant ( P < 0.05), Duncan’s multiple range test was also used to compare means.

Water quality

The administration of BSL significantly improved water quality variables ( P < 0.05; Table 2 ). Total ammonia nitrogen (TAN) and NH 3 values were reduced in a dose-dependent manner ( P < 0.05) with the most significant decrease observed in T3. The pH values were significantly lower in the T2 and T3 treatments compared to other treatments ( P < 0.05). Salinity levels did not vary among the groups ( P > 0.05). T3 revealed lower dissolved oxygen (DO) levels compared to other groups ( P < 0.05).

Hematological and biochemical parameters

The impact of various doses of BSL (0.01, 0.02, and 0.03 g/m 3 ) on hemato-biochemical parameters is shown in Table 3 . The results show a significant increase in RBC counts, Hb, and Hct in T3 ( P < 0.05) compared to other groups. MCV did not show a significant change ( P > 0.05) with the addition of probiotics. In contrast, MCH and MCHC values were substantially increased ( P < 0.05) in T3, with MCHC reaching its maximum value in this treatment. Besides, the highest values of albumin, total protein, and globulin ( P < 0.05) were obtained in the B. subtilis and B. licheniformis (0.03 g/m 3 ) treatment.

The hepatic function enzymes ALT, AST, and ALP were notably affected ( P < 0.05) by the addition of BSL, with higher levels observed in the untreated group compared to other treatments. The probiotics-treated groups shown lower values for ALP, AST, and ALT than the control group ( P < 0.05), indicating improved liver function. Similar trends were observed for creatinine, urea, and uric acid ( P < 0.05), suggesting that probiotics enhanced overall fish health. In terms of antioxidant enzymes CAT and SOD, there was a significant increase ( P < 0.05) with higher levels of B. subtilis and B. licheniformis. Both T2 and T3 groups exhibited superior values of SOD and CAT compared to other groups ( P < 0.05).

Reproductive hormones

Table 4 shows that the treatment with three levels of B. licheniformis and B. subtilis had a significantly higher effect ( P < 0.05) on Red Tilapia reproductive hormones compared to the control group. Specifically, the probiotic treatment at level 3 (T3, 0.03 g/m 3 ) showed significant results ( P < 0.05) in increasing the concentrations of the hormones FSH, LH, E2, and progesterone compared to the other treatment groups. Regarding testosterone hormone parameters, the highest concentration increase was observed in the T3 treatment, while there was no significant difference among T1, T2, and the control group.

Organosomatic indexes

The findings suggest that the levels of B. subtilis and B. licheniformis positively influenced the organosomatic indexes and reproductive functions (Table 5 ). Both B. subtilis and B. licheniformis levels contributed to hepatic and gonadal development in both sexes compared to the control group ( P < 0.05). The hepatosomatic index (HSI) ranged from 3.07 to 3.55% for males and 3.09–3.34% for females. The viscerosomatic index (VSI) was significantly impacted by the addition of various doses of B. subtilis and B. licheniformis in all treatments ( P < 0.05), ranging from 9.99 to 11.06%. However, the VSI for females showed no significant effect with the addition of different levels of B. subtilis and B. licheniformis in all treatments ( P > 0.05). The gonadosomatic index (GSI) significantly improved in all probiotic treatments ( P < 0.05), ranging from 3.36 to 4.95% for males and 4.06–5.05% for females.

The inclusion of varying levels of B. subtilis and B. licheniformis resulted in a notable improvement in egg diameter, the average number of fry (spawning efficiency and larval production), and the average fry weight. Egg diameter varied from 1.17 mm to 1.69 mm, the mean number of fries ranged from 1130 to 1478, and the average fry weight ranged from 16.05 g to 16.89 g, as presented in Table 5 .

Reproductive development associated gene expression

The current findings show the expression of genes associated with development and reproduction, including Vasa , nanos1a , nanos2 , dnd1 , pum1 , AMH , and VTG in testicular (Fig. 1 ) and ovarian (Fig. 2 ) tissues of Red Tilapia. It was noted that the expressions of Vasa , nanos1a , nanos2 , dnd1 , pum1 , AMH , and VTG genes in testicular tissues were significantly upregulated in response to different graded levels of B. subtilis and B. licheniformis ( P < 0.05) compared to the control group (Fig. 1 ). Additionally, the expressions of Vasa , nanos1a , nanos2 , dnd1 , pum1 , AMH , and VTG genes in the ovarian tissues followed the same pattern (Fig. 2 ). This upregulation increased in a dose-dependent manner, with levels of 0.03 g/m 3 of B. subtilis and B. licheniformis being the most effective (Figs. 1 and 2 ).

Effect of the B. subtilis and B. licheniformis (T0; 0, T1; 0.01, T2; 0.02, T3; 0.03, g/m 3 ) added to the water on expression of reproduction-associated genes in the testis of Red Tilapia

Effect of the B. subtilis and B. licheniformis (T0; 0, T1; 0.01, T2; 0.02, T3; 0.03, g/m 3 ) added to the water on mRNA of reproduction-related genes in the ovaries of Red Tilapia

Histological changes in testicular tissue

Sections of fish testicular tissue from the control group (T0; Fig. 3 A) showed the typical anatomy of interstitial cells (It), spermatocytes (Sp), spermatids (St), spermatozoa (Sz), and testicular lobules (T). The testes treated with B. subtilis and B. licheniformis contained all stages of spermatogenesis. In the T1 group (Fig. 3 B), which received 0.01 g/m 3 of B. subtilis and B. licheniformis , we noticed normal and healthy architectures of seminiferous tubules containing spermatocytes, spermatids, and spermatozoa. There was a noticeable increase in the abundance of spermatogenetic cells and growth of testicular tubules, particularly in both the T2 (0.02 g/m 3 ; Fig. 3 C) and T3 groups (0.03 g/m 3 ; Fig. 3 D) treated with B. subtilis and B. licheniformis . The T3 group showed an increase in spermatogenic cells, particularly spermatids and mature spermatozoa (Fig. 3 D).

Photomicrographs of transverse sections of mature testis of Red Tilapia kept in various levels of B. subtilis and B. licheniformis {0 (Fig. 3 A ), 0.01(Fig. 3 B ), 0.02 (Fig. 3 C ), 0.03 (Fig. 3 D , g/m 3 } added to the water. Interstitial cells (It), spermatocytes (Sp), spermatids (St), spermatozoa (Sz), testicular lobules (T). [H&E stain was used, 100 μm]

Histological changes in ovaries

The control group (T0) fish ovaries (Fig. 4 A) displayed a slightly normal ovarian structure containing normal chromatin nucleolar oocytes (C), vitellogenic oocytes, cortical alveoli (CA), ripe oocytes (R), yolk globules (Y), and previtellogenic stage (Pr). Fish fed on different levels of B. subtilis and B. licheniformis (0.01, 0.02 and 0.03 mg/m 3 ) exhibited normal development in all types of oocytes, including chromatin nucleolar oocytes (C), previtellogenic stage (Pr), vitellogenic oocytes, cortical alveoli (CA), ripe oocytes (R), yolk globules (Y), postvitellogenic stage (Po), and postspawning ova (PSo) (Fig. 4 B). This development was most pronounced in the T2 (0.02 g/m 3 ) and T3 (0.03 g/m 3 ) groups. Compared to the control group (T0), the T2 (Fig. 4 C) and T3 (Fig. 4 D) groups showed an improvement in oocytes with post-ovulation luteinization and demonstrated superiority in oogonia and oocyte occurrence at various developmental stages.

Photomicrographs of transverse sections of mature ovaries of Red Tilapia kept in water supplemented with various levels of B. subtilis and B. licheniformis {0 (Fig. 4 A ), 0.01 (Fig. 4 B ), 0.02 (Fig. 4 C ), 0.03 (Fig. 4 D ), g/m 3 }. Arrows: stroma that around the vitellogenic oocytes’ follicles, growing oocytes at different developmental stages, normal chromatin nucleolar oocyte (C), vitellogenic oocytes with cortical alveoli (CA), ripe oocytes (R), yolk globules (Y), previtellogenic stage (Pr), postvitellogenic stage (Po), postspawning ova are collapsed (PSo), asterisk: degeneration of some tissues around the oogonia follicles. [H&E stain was used, 100 μm]

Aquaculture has recently played a significant role as a vital food source, supplying humans with excellent protein and easily absorbable minerals, particularly in developing nations such as Egypt [ 20 , 40 , 41 ]. Enhancing fish broodstock reproductive capacity with probiotic supplements can benefit the industry’s sustainability. While most studies focus on probiotics’ role in growth stages, their impact on reproductive capacity is less explored. In this study, we investigated the effects of adding BSL to water on red Tilapia’s reproductive variables. The results show that supplementing water with BSL significantly improved hematobiochemical parameters, reproductive hormones, organosomatic attributes, and reproductive capacity in red Tilapia. Gene expression analysis revealed upregulation of reproductive-related genes in testicular and ovarian tissues in response to varying levels of B. subtilis and B. licheniformis compared to fish on a basal diet. Various reports have documented the positive impacts of different additives on fish. Among these additives, probiotics, especially Bacillus strains, have become the most widely used and popular in aquaculture [ 23 , 42 ]. The production of fish in aquaculture heavily depends on water conditions. To achieve optimal reproductive capacity, survivability, and production, it is crucial to enhance the aquatic environment by reducing aquatic pathogens and improving water quality. This will lead to successful reproduction [ 6 ]. The findings of the current trial demonstrate a noticeable improvement in water quality, supported by a considerable decrease in total hazardous and toxic degrees of ammonia in the probiotics-treated groups, especially the T3 group. Many previous studies have found similar results [ 9 , 43 ].

According to the findings of a research conducted by [ 44 ], the addition of B. licheniformis as denitrifying bacteria to rearing water decreases the levels of toxic components (TAN and NH 3 ) and improves the breakdown of protein and starch in leftover feeds. The quality of water is enhanced by the biodegradation of nitrogenous wastes by Bacillus species, resulting in waste mineralization [ 44 ]. Maintaining good water quality is crucial for the survival of aquatic organisms especially in Broadstock fish, with ammonia nitrogen and nitrite nitrogen being key indicators in aquaculture. High levels of these compounds can be toxic to farmed species. Effective water quality management is essential in aquaculture production. Enriching water with efficient microbial communities can enhance organic matter recycling and maintain a clean water environment for farmed fish [ 45 ]. Previous studies have found that the addition of B. subtilis (10 9 CFU/mL) significantly decreased the total nitrogen and ammonia nitrogen concentrations in water. Additionally, Cha et al. [ 46 ], performed that B. subtilis (0.5% of the diet) effectively reduced the concentration of ammonia nitrogen in the Japanese flounder ( Paralichthys olivaceus ) culture system. The authors suggested that probiotics play a crucial role in water quality by breaking down organic matter and converting NH 4+ to NO 3 . Furthermore, probiotics have been shown to eliminate pathogenic bacteria from water. Improving water quality can enhance fish health, leading to increased production and reduced susceptibility to disease.

Haemato-biochemical parameters are considered valuable indicators for evaluating the health profile of fish [ 47 , 48 ]. According to our findings, the use of B. subtilis and B. licheniformis improved the hematopoietic state of red tilapia. Hematological parameters in the current study, such as HB, mean MHC, MCHC, and HCT in the treated groups with the addition of probiotics also significantly increased compared to the control group, indicating a high capacity for oxygen carrying in the blood [ 49 ]. On the contrary, the MCV was not significantly affected in all treatments. The addition of B . subtilis in water demonstrated a significant improvement in albumin, total protein, and globulin values compared to the control group [ 50 , 51 ]. Glucose levels in our study exhibited a gradual decline in all treated groups, which is in line with the results of a previous study [ 52 ]. The reduction in glucose levels was attributed to the probiotic’s treatment altering the expression of genes involved in glucose uptake and lowering overall glucose levels in zebrafish larvae [ 52 ]. Significant differences were observed in the blood serum composition of red tilapia that received supplementation with B. licheniformis and B. subtilis . Components in the blood serum indicate the physiological performance of the fish body, especially in relation to the functions of vital organs such as the liver, kidneys, and the circulatory system. Hepatic function enzymes, AST, and ALT are biochemical indices of liver function and health. These indicators are used to evaluate how additives can influence the metabolic activities and overall health of fish [ 41 ]. In our trial, a significant reduction in liver activities was observed in all groups treated with B. subtilis and B. licheniformis , which is consistent with findings reported in Nile tilapia showing the same effect of these enzymes when probiotics are added to the rearing water.

The part of probiotics in controlling metabolic enzymes has also been explored and studied in a scarce other aquatic fish species. Studies by [ 53 ] and [ 54 ] suggested that feeding Nile tilapia a diet supplemented with B. subtilis may reduce ALT and AST activities.

In the current research, we noticed a significant decrease in blood levels of creatinine, urea, and uric acid levels showed a significant decrease with the increase in probiotic levels. This data was in contrast to the findings by [ 55 ] and [ 20 ], who indicated that no notable changes in creatinine and urea levels among supplemented groups with probiotics. Additionally, the activities of antioxidants SOD and CAT were increased with the addition of graded levels of B. subtilis and B. licheniformis ( p < 0.05), consistent with the results of study by [ 20 ]. Prebiotics and probiotics have been stated to improve the reproductive capability of certain fish species. For instance, Zebrafish ( Danio rerio ) fed a diet enriched either bacillus spp or lactobacillus spp showed improvements in gonad development, fecundity, egg production, GSI, and the number of viable fries produced [ 56 , 57 ].

As a secondary effect of increased absorption and utilization of nutrients in aquatic animals receiving probiotic supplementation, there is an increased availability of nutrients essential for reproductive system function, including the production of hormones important for reproductive function. Pituitary gonadotropins (GnRH) such as LH and FSH are the main regulators of gametogenesis in teleost fish [ 58 ].

The data of the current experiment is consistent with the findings by [ 27 ], indicating that red tilapia receiving probiotic supplementation will experience an increase in the production of hormones such as testosterone, FSH, LH, estrogen, and progesterone compared to the control group.When using probiotics for aquatic animals, the type of probiotic bacteria and the dosage of probiotics play a significant role in the outcomes. In this study, the T3 treatment group (dose 0.03 g/m 3 ) exhibited a greater increase in hormone concentrations compared to the T2 and T1 treatment groups [ 27 ]. The results of this experiment showed that the treatment with three levels of probiotics containing B. subtilis and B. licheniformis had a significantly greater effect on the reproductive hormones of red tilapia compared to the fish fed the basal diet without any treatment.

Specifically, the probiotic treatment at level 3 (T3) produced highly significant results in increasing the concentration of the hormones FSH, LH, E2, and P compared to the other treatment groups. Regarding testosterone hormone, the highest level was found in the T3 treatment, while for T1 and T2, they did not differ significantly different from the control group (T0).

Studies by several researchers [ 48 , 59 , 60 ] have documented that beneficial microbes can lead organisms utilize energy sources more efficiently, leading to improved growth and reproductive performance in zebrafish [ 61 ]. In our study, the inclusion of levels of B. licheniformis and B. subtilis notably enhanced the growth performance of red tilapia compared to the control group.

Furthermore, higher levels of these probiotics in red tilapia groups reared in water treated with B. subtilis and B. licheniformis can be directly contributed to the improvement of water quality. There was a substantial variation ( P < 0.05) between the treatment groups in the GSI percentage, mean number of fries, and mean fry weight parameters. Only the group receiving probiotic supplementation showed a significant difference in the HIS percentage and egg diameter parameters compared to the control group. Body indices, including GSI, VSI, and HSI, which indicating dietary value, growth, and feed utilization, can be improved by adding feed with a mixture of B. subtilis and B. licheniformis [ 62 ]. Another study of [ 62 ] reported that all doses of probiotics had a substantial valuable consequence on the GSI and HSI indices compared to the control treatment. This suggests that adding B. licheniformis and B. subtilis can boost the reproductive capability of zebrafish [ 56 , 57 ].

Dead-end (dnd) , Nanos , pumilio (pum) , piwil-like (piwil) vasa , and genes are known to be implicated in translational repression of germ cells [ 63 ], which is believed to be essential for the preservation of germline integrity across animal phyla, containing mice [ 64 ], zebrafish [ 65 ], and Xenopus [ 66 ]. Recently, four nanos’ genes [ 67 ], two piwil genes [ 68 ] and three vasa genes [ 69 ] have been identified in tilapias. Additionally, in silico examination of public databases by NCBI revealed anticipated sequences for three pum genes and one dnd1a . Vitellogenin (Vtg) is a reproductive protein found in females, that is broken down into yolk proteins. Lipovitellin (Lv), and phosvitin (Pv), which are deposited in eggs to provide essential nutrients for early-stage embryos. Several studies have confirmed that probiotics can improve the reproductive capacity in Nile tilapia by supporting reproductive-related genes, as observed in this study. A study by [ 70 ] clarified that probiotics (0.5 g/kg) added to Nile tilapia feed during the breeding season improved reproductive performance and profitability.

In the extant work, the transcript of development-reproduction-related genes in red tilapia fed with B. subtilis and B. licemiformis were significantly upregulated compared to fish reared in the control group. This highlights the beneficial effects of B. subtilis and B. licemiformis on fish reproduction, in addition to the previously reported improvements in hematology profile, blood metabolites, and reproductive parameters such as GSI, egg diameter, and fry production. Male zebrafish fed a diet with a containing probiotic P. acidilactici exhibited higher expression of fertility markers ( lepa , dmrt , and bdnf ) compared to the control group [ 71 ]. This indicates that P. acidilactici could be a promising probiotic supplement to enhance molecular parameters in testicular cells of male zebrafish, potentially leading to improve the reproductive performance, sperm quality.Probiotics have been shown to prevent apoptosis and enhance survivability in fish during the growth period [ 72 ]. They also stimulate the intratubular and tubular sections, which are known to enhance sperm production [ 71 ]. Certain probiotics have been shown to activate various cell types, including neuronal, connective tissue, blood/lymphatic vessels, mast cells, macrophages, and steroidogenic Leydig cells. Additionally, probiotic supplementation in feed has been found to improve fish reproductive health and feed utilization, particularly with lactic acid bacteria [ 24 ].

Probiotics have the potential to modulate gene expression patterns or hormone levels that regulate fish reproduction [ 73 ], thereby enhancing reproductive functions and activating reproductive genes to address reproductive disorders when added to the diet or water. Histological investigations revealed that the addition of B. subtilis and B. licemiformis enhanced gonadal development in red tilapia, particularly in spermatogenic cells, including spermatids and mature spermatozoa. Female fish reared in 0.02 and 0.03 g/m 3 showed different stages of oocyte development, with the best gonadal development observed in the 0.03 g/m 3 group, which had a higher number of mature and ripe oocytes. These findings are consistent with those reported by [ 74 ] in Nile tilapia. Further studies are needed to confirm these results, as there is a lack of research on the potential effects of probiotics on reproductive performance in fish species, especially using omics tools.

The study showed that adding B. subtilis and B. licheniformis at a concentration of 0.03 g/m 3 can enhance fish blood profile and reproductive health. This experiment demonstrated that probiotics in water can improve water quality, hematological and biochemical parameters in red tilapia broodfish, and support gonad maturation, gametogenesis production, gene expression, and overall reproductive performance. Additional research is required to validate these findings, as there is a dearth of studies examining the potential impacts of probiotics on reproductive performance in fish species.

Data availability

No datasets were generated or analysed during the current study.

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Eissa, ES., El-Sayed, AF.M., Hendam, B.M. et al. The regulatory effects of water probiotic supplementation on the blood physiology, reproductive performance, and its related genes in Red Tilapia ( Oreochromis niloticus X O. mossambicus ). BMC Vet Res 20 , 351 (2024). https://doi.org/10.1186/s12917-024-04190-w

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Effects of increasing tidal volume and end-expiratory lung volume on induced bronchoconstriction in healthy humans

Alessandro Gobbi 1 , 2 ,
Andrea Antonelli 3 ,
Raffaele Dellaca 1 ,
Giulia M. Pellegrino 4 ,
Riccardo Pellegrino 5 ,
Jeffrey J. Fredberg 6 ,
Julian Solway 7 &
Vito Brusasco 8

Respiratory Research volume 25 , Article number: 298 ( 2024 ) Cite this article

Metrics details

Increasing functional residual capacity (FRC) or tidal volume (V T ) reduces airway resistance and attenuates the response to bronchoconstrictor stimuli in animals and humans. What is unknown is which one of the above mechanisms is more effective in modulating airway caliber and whether their combination yields additive or synergistic effects. To address this question, we investigated the effects of increased FRC and increased V T in attenuating the bronchoconstriction induced by inhaled methacholine (MCh) in healthy humans.

Nineteen healthy volunteers were challenged with a single-dose of MCh and forced oscillation was used to measure inspiratory resistance at 5 and 19 Hz (R 5 and R 19 ), their difference (R 5-19 ), and reactance at 5 Hz (X 5 ) during spontaneous breathing and during imposed breathing patterns with increased FRC, or V T , or both. Importantly, in our experimental design we held the product of V T and breathing frequency (BF), i.e , minute ventilation (V E ) fixed so as to better isolate the effects of changes in V T alone.

Tripling V T from baseline FRC significantly attenuated the effects of MCh on R 5 , R 19 , R 5-19 and X 5 . Doubling V T while halving BF had insignificant effects. Increasing FRC by either one or two V T significantly attenuated the effects of MCh on R 5, R 19 , R 5-19 and X 5 . Increasing both V T and FRC had additive effects on R 5 , R 19 , R 5-19 and X 5 , but the effect of increasing FRC was more consistent than increasing V T thus suggesting larger bronchodilation. When compared at iso-volume, there were no differences among breathing patterns with the exception of when V T was three times larger than during spontaneous breathing.

Conclusions

These data show that increasing FRC and V T can attenuate induced bronchoconstriction in healthy humans by additive effects that are mainly related to an increase of mean operational lung volume. We suggest that static stretching as with increasing FRC is more effective than tidal stretching at constant V E , possibly through a combination of effects on airway geometry and airway smooth muscle dynamics.

Introduction

Studies in animals and humans have brought clear evidence that increasing the operating lung volume, i.e., the end-expiratory lung volume above normal functional residual capacity (FRC) or the tidal volume (V T ), reduces airway resistance [ 1 , 2 ] and can attenuate [ 3 ] or reverse [ 4 ] the response to bronchoconstrictor stimuli. These effects of breathing at increased lung volume can be explained by either static or dynamic mechanisms. Since airways and lung parenchyma are interdependent, a static increase of lung volume is associated with an increase of airway caliber by the action of tethering forces opposing both the passive elastic recoil of the airway wall and the active contractile forces of airway smooth muscle. On the other hand, studies in-vitro have shown that dynamic swings can blunt the response of airway smooth muscle to contractile stimuli by mechanisms that reduce its force generation capacity [ 5 , 6 ], though in bronchial segments this effect was observed only when pressure oscillations were raised to twice of those corresponding to normal V T [ 7 ]. In vivo, increasing V T [ 4 ], or breathing frequency (BF), or both [ 8 ] have a bronchodilator effect.

Therefore, it can be expected that increasing FRC or V T, or their combinations, have beneficial effects in counteracting bronchoconstriction in vivo. However, in porcine bronchial segments, static hyper-distension reduced the maximal response to acetylcholine but blunted the relaxant effect of superimposed pressure oscillations of amplitude corresponding to twice the baseline V T [ 9 ], raising the possibility that lung hyperinflation may compete with the bronchodilator effects of increasing V T in vivo. In humans, the relative efficacy of physiologically relevant static hyperinflation and increased dynamic swings in countering airway narrowing has not been studied, but it can be hypothesized that they differ, owing to different underlying mechanisms.

To test this hypothesis, we designed the present study to evaluate whether the bronchodilator effect of breathing at increased lung volumes differs depending on whether attained by increasing FRC or V T . Moreover, we investigated whether the bronchodilator effects of increasing FRC and V T were additive.

Nineteen healthy volunteers (13 males/6 females) with no history respiratory/cardiovascular diseases participated in the study. No one was obese. Main anthropometric and respiratory functional data are reported in Table 1 . Data were collected at Santa Croce and Carle Hospital (Cuneo, Italy), the protocol was approved by the local Ethical Committee, and each subject gave a written informed consent before participation.

Measurements

Spirometry was measured by a mass flowmeter (SensorMedics Inc., CA, USA) following the ATS/ERS recommendations [ 10 ]. Respiratory impedance was measured by a forced oscillation technique (FOT) as previously described [ 11 , 12 ]. Briefly, sinusoidal pressure oscillations (5 and 19 Hz; ~ 2 cmH 2 O peak-to-peak) were generated by a 16-cm diameter loudspeaker (model CW161N, Ciare, Italy) mounted in a rigid plastic box and connected in parallel to a mesh pneumotachograph and mouthpiece on one side and to a low-resistance high-inertance tube on the other side. Pressure oscillations were applied at the mouth during tidal breathing, while subjects had their cheeks supported by the hands of an investigator to minimize upper airway shunting. The overall load over the tidal breathing frequency range was 0.98 cm H 2 O•L -1 •s. Airway opening pressure and flow were recorded by piezoresistive transducers (DCXL10DS and DCXL01DS Sensortechnics, Germany, respectively) and sampled at 200 Hz. A 15-L/min bias flow of air generated by an air pump (CMP08, 3A Health Care, Italy) was used to reduce dead space to about 35 ml. Pressure and flow signals were processed by a least-square algorithm [ 13 , 14 ] to calculate respiratory resistance at 5 and 19 Hz (R 5 and R 19 , respectively) and reactance at 5 Hz (X 5 ). Artifacts due to glottis closure or expiratory airflow limitation were avoided by discarding breaths showing any of the following features: i) tidal volume <0.1 L or >2.0 L, ii) difference between measured flow oscillation and ideal sine wave with the same Fourier coefficients >0.2 [ 15 ], and iii) ratio of minimum to average X>3.5 [ 11 ]. The same breaths were used to measure V T , breathing frequency (BF), inspiratory and total time of each breath (T I and T Tot , respectively), and estimate inspiratory drive (V T /T I ), inspiratory duty cycle (T I /T Tot ), and minute ventilation (V E ).

Pre-study day

Subjects attended the laboratory for spirometry and determination of the dose of methacholine (MCh) to be used for the study day. For this purpose, after baseline FOT measurements, MCh chloride dry-powder (Laboratorio Farmaceutico Lofarma, Milan, Italy) was dissolved in distilled water and administered by an ampoule-dosimeter system (MB3 MEFAR, Brescia, Italy) delivering aerosol particles with a median mass diameter of 1.53-1.61μm, while subjects breathed quietly in a sitting position. The starting dose was of 300 μg followed by doubling doses until R 5 increased by at least 100% from baseline.

Baseline FOT measurements were taken during 2 min of spontaneous tidal breathing. Then, the subjects were trained to breathe, by using visual feed-back of spirometry tracing, for 2 min with imposed combinations of FRC or V T . Thereafter, each subject inhaled a single dose of MCh equal to the last dose given on the pre-study day and R 5 was measured 2 min later during spontaneous tidal breathing to confirm the persistence of bronchoconstriction. Then, FOT measurements were taken while subjects maintained for 2 min each of the following imposed breathing patterns in randomized order (Fig. 1 ): A) spontaneous V T from spontaneous FRC, B) near double V T from spontaneous FRC, C) near triple V T from spontaneous FRC, D) spontaneous V T from FRC increased by 1 V T , E) near double V T from FRC increased by 1 V T , and F) spontaneous V T from FRC increased by 2 V T . For each V T increase the subjects were asked to adjust BF to prevent large increments of V E . Before each change of breathing pattern, R 5 was measured during spontaneous tidal breathing to check for the stability of bronchoconstriction. If R 5 was 10% or more lower than initial post-MCh value an additional half dose of MCh was given to restore bronchoconstriction. This happened occasionally in 6 subjects, with no relation to any specific breathing pattern. At the end of the study, aerosol albuterol was administered to relieve symptoms if any.

Patterns of breathing before after methacholine (MCh) with tidal volume (V T ) initiated from spontaneous or increased functional residual capacity (FRC). For each condition, respiratory impedance measures were calculated over the 3 mid-quintiles of the whole inspiratory phase (upper panel) or over the 3 mid-quintiles of iso-volume inspiratory portions (lower panel) as shown by the thick lines

Data analysis

For each breathing pattern, R 5 , R 19 , R 5-19 , and X 5 were calculated over the 3 mid-quintiles of the whole inspiratory phase (Fig. 1 , upper panel) or over the 3 mid-quintiles of iso-volume inspiratory portions (Fig. 1 , lower panel).

Differences in R 5 , R 19 , R 5-19 , X 5 , V T , BF, V T /T I , T I /T Tot , and V E between conditions were tested for statistical significance by a one-way repeated-measure analysis of variance (ANOVA) with Holm-Sidak post-hoc test for multiple-comparisons. Values of p<0.05 were considered statistically significant. Data are presented as mean ± standard deviation (SD).

Breathing patterns during the experimental conditions

The spontaneous breathing pattern after MCh (A) did not differ significantly from the spontaneous pattern before methacholine (Table 2 ). V T and BF changed with the imposed patterns (B-F) as per protocol. Even though great attention was paid to maintain V E as constant as possible among the imposed breathing patterns, it was with patterns C, E, and F that V E slightly but significantly increased than with patterns than A and B. These differences were associated with significant differences in mean inspiratory, V T /T I . Neither V E nor V T /T I were significantly different among breathing patters C, D, E, and F . There were no significant differences in T I /T TOT among all breathing patterns.

Mid-inspiration measures

In general, breathing at increased FRC, increased V T , or both attenuated the changes induced by MCh inhalation on R 5 , R 19 , R 5-19 , and X 5 (Fig. 2 and Supplemental Table 1).

Effects of increasing tidal volume from spontaneous functional residual capacity (patterns A , B , C ) ( A ), increasing functional residual capacity with spontaneous (patterns A , D , F ) ( B ), or both (patterns B , E ) ( C ) on mid-inspiration impedance measures. Effects of patterns achieving the same peak volume ( C vs. E and vs. F ) on mean-inspiratory impedance measurements ( D ). V T , tidal volume; FRC, functional residual capacity. R 5 , respiratory resistance at 5 Hz, R 19 , respiratory resistance at 19 Hz; R 5-19 , difference in respiratory resistance between 5 and 19 Hz; X 5 , respiratory reactance at 5 Hz. Columns heights indicate means and error bars standard deviations. *, p <0.005; **, p <0.01; p <0.001

Increasing V T from spontaneous FRC was associated with significant reductions of R 5 , R 19 , R 5-19 and less negative X 5 when V T was tripled ( pattern C ) but not doubled ( pattern B ) compared to spontaneous breathing ( pattern A ) V T . Yet, the attenuating effects of pattern C were significantly greater than those of pattern B .

Increasing FRC by either one ( pattern D ) or two ( pattern F ) V T with constant spontaneous V T was associated with significant reductions of R 5 and R 19 than pattern A, while R 5-19 was significantly reduced and X 5 less negative with pattern F but not pattern D .

Increasing both V T and FRC ( pattern E ) was associated with significantly lower R 5 , R 19 , R 5-19 and less negative X 5 than increasing V T alone ( pattern B ) and significantly lower R 19 than increasing FRC alone ( pattern D ).

Breathing patterns with the same peak volume, no matter whether achieved by increasing V T or FRC or both ( patterns B vs. D and C vs. E and vs. F ) showed insignificantly different effects on airway narrowing.

Notably, R 5 (cmH 2 O•L -1 •s) was reduced by 0.57±1.18 when V T was doubled ( pattern B vs pattern A), by 1.19±0.70 when FRC was increased by 1 V T ( pattern D vs pattern A ), and by 1.84±0.88 when both V T and FRC were increased ( pattern E vs pattern A ). Similarly, R 19 (cmH 2 O•L -1 •s) was reduced by 0.29±0.35 when V T was doubled ( pattern B vs pattern A) , by 0.48±0.46 when FRC was increased by 1 V T ( pattern D vs pattern A ), and by 0.91±0.42 when both V T and FRC were increased ( pattern E vs pattern A ). These results suggest simply additive effects, but the increase of FRC was more potent to mitigate airway narrowing than the increase in V T .

Iso-volume measures

In general, R 5 , R 19 , and R 5-19 were inversely related to the lung volume at which they were measured (Fig. 3 and Supplemental Table 2), while the X 5 values were inconsistently related to lung volumes.

Effects of increasing tidal volume from spontaneous (patterns A , B , C ) or increased (patterns D , E , F ) functional residual capacity on iso-volume inspiratory impedance measures. Other abbreviations as in Fig. 2 . Columns heights indicate means and error bars standard deviations. *, p <0.005; **, p <0.01

At low iso-volume, R 5 and R 19 , were significantly lower and X 5 was less negative than during spontaneous breathing ( pattern A ) when V T was tripled ( pattern C ) but not doubled ( pattern B ). Yet, the attenuating effects of pattern C on R 5 and X 5 were significantly greater than those of pattern B .

At mid iso-volume, R 5 , R 19 , and R 5-19 did not differ significantly with increments of V T ( patterns B and C) , or FRC ( pattern D ), or both ( pattern E ). However, X 5 was significantly less negative when both FRC and V T were increased ( pattern E ) than when V T ( pattern B ) or FRC ( pattern D ) were increased alone.

At high iso-volume, there were no significant differences with increments of V T ( pattern C ), or FRC ( panel F ), or both ( panel E ).

The main findings of the present study in healthy volunteers were that 1) the changes of respiratory impedance induced by inhaled MCh were significantly attenuated by increasing FRC, or V T , or both, 2) increasing FRC had more consistent effects than increasing V T , 3) the effects of increasing FRC and V T were additive, and ) volume-independent effects attributable to tidal stretching were observed only when V T was three times larger than during spontaneous breathing.

Comments on methodology

We used oscillometry because it is the only available method enabling intra-breath measurements of respiratory mechanics over specific portions of lung volume during tidal breathing, but it has two major limitations. First, oscillometry does not directly measure airway resistance but also lung tissue and chest wall resistances. Airway resistance is inversely related to V T whereas lung tissue resistance is inversely related to BF [ 2 ]. Therefore, it is possible that the effects of increasing V T on airway caliber were counteracted by the effects of decreasing BF on tissue resistance. We think this had no major effect on our results because the attenuation of R 5 , which reflects in large part tissue resistance, was not less than the attenuation of R 19 , which mainly reflect airway resistance. Second, breathing at increased lung volumes requires activation of inspiratory muscles, which increases chest wall elastance [ 16 ]. Therefore, we cannot exclude that changes in X 5 with different breathing patterns were counteracted by changes in chest wall stiffness.

Although our subjects were asked to maintain V E as constant as possible by decreasing BF when V T was increased, there was a tendency for V E to increase (Table 2 ), thus likely resulting in an increased alveolar ventilation and airway hypocapnia, mainly when achieved by increasing V T . Hypocapnia has a bronchoconstrictor effect [ 17 ], thus possibly counteracting the bronchodilator effects of imposed breathing patterns. We did not measure end-tidal CO 2 , but we believe this had no major impact on our results for two reasons. First, assuming normal anatomical plus instrumental dead space and CO 2 production, we estimated a mean difference in alveolar PCO 2 between patterns C and A to be approximately 7 mmHg, which was reported to have insignificant effects on the respiratory impedance of healthy subjects [ 18 ]. Second, the differences in V E between any imposed patters were insignificant and differences in alveolar PCO 2 presumably minimal.

Finally, for changes in V T were associated with changes in BF and the ratio T I /T TOT remained constant, the effects of tissue viscoelasticity could not be evaluated. Nevertheless, breathing patterns with low BF would have increased the time for airway smooth muscle relaxation during the inspiratory phase but also for re-shortening during the expiratory phase.

Interpretation of results

The present study was designed on the premises that both lung hyperinflation and increased breathing depth are mechanisms protecting against airway narrowing, but their relative efficacies are unknown.

That increasing lung volume is associated with a proportional increase of airway conductance, i.e., the reciprocal of airway resistance, was first reported in 1958 by Briscoe and Dubois [ 1 ] and subsequently confirmed in excised animal [ 19 ] and human [ 20 ] lungs with relaxed airways. This effect was simply attributed to a geometric change of airways being distended by the static radial traction of the surrounding lung parenchyma. Studies in contracted airway smooth muscle strips have consistently shown that sustained step-changes of length can rapidly attenuate active tension, possibly due to disassembly of the contractile apparatus, followed by a gradual recovery due to length adaptation [ 20 , 21 ]. By contrast, in whole bronchial segments a sustained inflationary increase of transmural pressure also caused an immediate reduction in tension, but this was followed by a continuous gradual decrease [ 22 ]. Airway wall stiffening was proposed to explain the difference between intact bronchi and muscle strips [ 22 , 23 ]. In our study, R 5 was stable or decreased between the different breathing patterns, but never increased, which makes the occurrence of length adaptation unlikely. Thus, it is possible that the attenuations of airway narrowing we observed after 2 min of breathing at increased FRC reflected not only geometric changes in airway caliber but also mechanisms opposing both the passive elastic recoil of the airway wall and the active contractile forces of airway smooth muscle.

The inhibitory effect of cycling stretching on airway smooth muscle active force generation has been reported consistently in both isolated muscle strips [ 5 , 6 ] and isolated bronchial segments [ 7 ]. It is well-established in animals [ 7 ] and humans [ 4 , 24 ] that the magnitude of the bronchodilator effects of tidal breathing increases with increasing frequency of breathing and with increasing tidal volume. Two independent lines of evidence suggest, further, that the attenuation of smooth muscle contractile force is attributable to changes of V E , which is the product V T x BF, independently of changes of either V T or BF taken individually [ 24 , 25 ]. Equivalently, neither the amplitude of tissue cyclic strain nor the cyclic frequency is as important as their product, namely, the amplitude of the tissue strain rate. To assess this phenomenon still further, in this report we used an experimental design in which we held the product V T x BF fixed so as to better isolate the effects of changes in V T alone. This is an important issue in our study, as we see that when V E could not be kept constant ( pattern C vs A ) the impedance values at low iso-volume were significantly attenuated presumably because of the higher mean inspiratory flow (V T /T I ) causing a faster lung stretching rate rather than the increase in V T itself.

Three theories can be invoked to explain the above findings [ 26 ], namely, that stretching of airway smooth muscle causes a plastic rearrangement of the contractile apparatus [ 6 , 27 , 28 ], or modifies the crossbridge cycling rate and latch bridges formation [ 5 ] or causes temporary detachment of attached cross bridges [ 29 ].

In an attempt to examine the relative bronchodilator effects of static hyperinflation and dynamic stretching, we measured inspiratory impedance in healthy subjects with MCh-induced bronchoconstriction breathing with different combinations of FRC and V T . As expected, increasing either V T or FRC significantly attenuated the changes induced by MCh on R 5 and R 19 , R 5-19 , suggestive of a generalized increase of airway caliber, but also decreased R 5-19 and made X 5 less negative. To the extent that an increase in R 5-19 and a decrease in X 5 reflect heterogeneous distribution of time constants within the lung periphery [ 30 ], the significant improvement of these variables with the increase in FRC and V T (Figs. 2 and 3 ) suggests that increasing lung volumes no matter how it was achieved made ventilation more homogeneous. While the effects of increasing V T on R 5 and R 19 were significant only when it was threefold the spontaneous V T , the effects of increasing FRC where already significant when it was increased by one V T , suggesting a more consistent effect of increasing static than dynamic tidal stretching.

The effects of increasing both V T and FRC were additive, i.e. , the effect of dynamic stretching was not blunted by an increased static stretch. This finding is in apparent contradiction with a study showing that in isolated bronchial segments hyperinflation blunted the effect of pressure oscillations corresponding to twice a normal V T [ 9 ] In that study, bronchi were hyperinflated at a transmural pressure of 20 cmH 2 O, where airway compliance is reduced [ 7 ] and so are the amplitude of volume oscillation and airway smooth muscle strain. Examining our data in the light of a previous study [ 31 ], (Fig. 3 ), we estimate that the largest end-tidal inspiratory volumes achieved as with patterns C, E and F would have not exceed the values associated with transpulmonary pressures in excess of 20 cm H 2 O. Since bronchial transmural pressure might differ from transpulmonary pressure in the presence of bronchoconstriction [ 32 ], we cannot exclude that stress on airway walls increased with the increase of end-inspiratory volume. Therefore, the increments of V T in our study were likely to reflect increments of airway smooth muscle strain but not stress. The latter, however, does not seem to be the major determinant of the decrease in airway smooth muscle contractility with breathing maneuvers [ 33 , 34 ].

The fact that the effects of FRC and V T were simply additive does suggest that lung hyperinflation and tidal swings operated via a similar mechanism, viz. increase of operational lung volume. This interpretation is supported by the lack of differences at iso-volumes among most breathing patterns. The only exceptions were the lower R 5 , R 19 , R 5-19 , and less negative X 5 at low lung volume after triple V T and the less negative X 5 at mid lung volume with breathing patterns with the highest end-inspiratory lung volume, i.e. , tripling V T ( pattern C ) and doubling V T from increased FRC ( pattern E ). These findings are consistent with a study in airway segments showing modest dilator effects with peak-to-peak pressure oscillations of 10 but not 5 cmH 2 O [ 7 ]. As FOT measurement were taken during the inspiratory phase, these findings possibly reflect volume-independent dynamic effects on airway smooth muscle persisting after the expiratory phase, even when BF and, in turn, expiratory time for re-narrowing was the largest ( pattern C ).

Why was hyperinflation more potent than tidal swings against airway narrowing in the present study is a matter of speculation. Increasing either FRC or V T results in increased mean operational lung volume, which is associates with an increase of airway caliber owing to the tethering force of lung parenchyma opposing the passive elastic recoil of airway walls. However, the mechanisms of static and dynamic stretching on airway smooth muscle active force may be different. One possibility is that in our study the sustained increments of operational lung volume maintained the airway smooth muscle in a condition of reduced force generation capacity by disassembling the contractile apparatus before the occurrence of length adaptation [ 20 , 21 ] or substantial reduction of tethering force due to stress relation of lung parenchyma [ 35 ]. By contrast, additional time-dependent effects of tidal stretching, e.g. , on cross-bridge cycling rate, were possibly obscured by the re-constriction during expiratory phase unless started from very high end-inspiratory volume. Another possible mechanism explaining the larger bronchodilator effects yielded by the increase in FRC rather than V T could be the larger amount of nitric oxide penetrating the airway lumen when narrowing is relieved by distending lung parenchyma [ 36 ].

The results of the present study in healthy subjects cannot be directly extrapolated to asthma because the mechanisms regulating airway smooth muscle contractility and heterogeneity of ventilation may differ in disease. Yet, it is known that FRC increases in asthma with the occurrence of expiratory flow limitation [ 37 ] and decreases after bronchodilator treatments [ 38 ]. Moreover, some beneficial effects of continuous positive airway pressure against airway responsiveness have been reported. To what extent hyperinflation can alleviate asthma symptoms remains to be elucidated, considering that above a given threshold it may cause an increase of inspiratory work of breathing [ 39 ] and limit the increase in V T [ 21 ].

In conclusion, this study provides evidence that both lung hyperinflation and increased tidal stretching yield substantial bronchodilatation in human lungs exposed to a constrictor agent, though the former seems more effective than the latter presumably because of additive effects on airway smooth muscle contractile force and non-contractile airway tissues.

Availability of data and materials

The data that support the findings of this study are available from the authors and are available upon request.

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Alessandro Gobbi

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A.G., R.P., J.J.F., J.S. and V.B. wrote the main manuscript text, A.G., R.P. and V.B. conducted statistical analyses and prepared figures and tables. A.A., R.P. and G.P. conducted experimental studies. All authors reviewed the manuscript.

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Correspondence to Raffaele Dellaca .

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The study has been approved by the S. Croce and Carle Hospital Ethics Committee, approval no. 40/13 of 19 th April 2013. The study was conducted in accordance with the Declaration of Helsinki.

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A.G. and R.D. are co-founders and serve as board members of RESTECH Srl, a company that designs, manufactures and sells devices for lung function testing based on Forced Oscillation Technique (FOT). R.D. also reports grants and other from RESTECH, personal fees from Philips Healthcare, outside the submitted work; In addition, R.D. has a patent on the detection of EFL by FOT with royalties paid to Philips Respironics and RESTECH Srl, a patent on monitoring lung volume recruitment by FOT with royalties paid to Vyaire, and a patent on early detection of exacerbations by home monitoring of FOT with royalties paid to RESTECH Srl. A.A., R.P., G.M. P., J.J.F., J.S, and VB have no conflict of interest related to the content of this manuscript.

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Gobbi, A., Antonelli, A., Dellaca, R. et al. Effects of increasing tidal volume and end-expiratory lung volume on induced bronchoconstriction in healthy humans. Respir Res 25 , 298 (2024). https://doi.org/10.1186/s12931-024-02909-9

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DOI : https://doi.org/10.1186/s12931-024-02909-9

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Performance evaluation of recursive mean filter using scilab, matlab, and mpi (message passing interface) †.

1. Introduction

2. materials and methods, 2.1. recursive mean filter, 2.2. implementations of the rmf, 2.2.1. rmf’s implementation in scilab, 2.2.2. rmf’s implementation in matlab, 2.2.3. rmf’s implementation with c and mpi, 2.3. experimental settings, 3. results and discussion, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Click here to enlarge figure

Image	Small Images		Big Images
Image	Resolution [pixels]	Size [kb]	Resolution [pixels]	Size [kb]
1	364 × 239	255	3641 × 2393	25,529
2	396 × 282	328	3961 × 2825	32,786
3	450 × 312	412	4281 × 2977	37,341
4	340 × 510	509	3200 × 4800	45,001
5	400 × 600	704	4000 × 6000	70,313

Name	CPU	Cores	Memory	OS
(PC1) Lenovo IdeaPad Gaming 3	Intel(R) Core(TM) i5-11320H CPU @ 3.20GHz 3.20 GHz, 11th GEN	8	8 GB	Windows 10 Pro, 64 bits
(PC2) Dell Inspiron N5110	Intel(R) Core(TM) i7-2670QM CPU @ 2.20GHz 2.20 GHz	8	8 GB	Windows 10 Pro, 64 bits

Name	Description	RMF in Scilab	RMF in MATLAB	RMF in C within MPI
t1	Time for filtering excluding operations read/write files	tic() toc()	tic() toc()	MPI_Wtime()
t2	Time for filtering including operations with files	tic() toc()	tic() toc()	MPI_Wtime()
t3	Total time for the execution of processes for filtering one image	-	-	time()
total	Total time for filtering one dataset of images	tic() toc()	tic() toc()	time()

		MATLAB			C with MPI (1 Process)
		3 × 3	5 × 5	7 × 7	3 × 3	5 × 5	7 × 7
PC1	Avg. t1 [s]	5.86	13.10	23.63	0.55	1.25	2.36
PC1	Avg. t2 [s]	6.12	13.36	23.89	2.44	3.14	4.26
PC2	Avg. t1 [s]	13.37	22.03	38.14	1.11	2.24	3.93
PC2	Avg. t2 [s]	14.12	22.76	38.85	6.62	7.78	9.48

	Mask Size	MATLAB	Scilab	C with MPI
	Mask Size	MATLAB	Scilab	1 Process	2 Processes	4 Processes	8 Processes
PC1	3 × 3	0.33	2235.50	1.03	1.01	1.08	1.24
	5 × 5	0.70	2286.45	1.06	1.04	1.10	1.26
	7 × 7	1.22	2391.15	1.14	1.11	1.14	1.29
PC2	3 × 3	0.68	4647.07	2.59	2.50	2.58	2.84
	5 × 5	1.26	4766.00	2.83	2.75	2.78	3.01
	7 × 7	2.08	4952.17	3.13	2.93	3.00	3.21

	Mask Size	MATLAB	C with MPI
	Mask Size	MATLAB	1 Process	2 Processes	4 Processes	8 Processes
PC1	3 × 3	30.61	13.13	11.88	11.46	11.43
	5 × 5	66.82	16.61	13.67	12.67	12.39
	7 × 7	119.44	22.22	16.54	14.50	13.64
PC2	3 × 3	70.58	35.90	33.35	32.34	32.19
	5 × 5	113.79	41.81	36.40	33.70	33.86
	7 × 7	194.23	50.44	41.17	36.36	36.23

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Share and Cite

Andreeva, H.; Bosakova-Ardenska, A. Performance Evaluation of Recursive Mean Filter Using Scilab, MATLAB, and MPI (Message Passing Interface). Eng. Proc. 2024 , 70 , 33. https://doi.org/10.3390/engproc2024070033

Andreeva H, Bosakova-Ardenska A. Performance Evaluation of Recursive Mean Filter Using Scilab, MATLAB, and MPI (Message Passing Interface). Engineering Proceedings . 2024; 70(1):33. https://doi.org/10.3390/engproc2024070033

Andreeva, Hristina, and Atanaska Bosakova-Ardenska. 2024. "Performance Evaluation of Recursive Mean Filter Using Scilab, MATLAB, and MPI (Message Passing Interface)" Engineering Proceedings 70, no. 1: 33. https://doi.org/10.3390/engproc2024070033

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Experimental Research: What it is + Types of designs

What is Experimental Research?

Experimental Research Design Types

0 1. Pre-Experimental Design

0 2. True Experimental Design

0 3. Quasi-Experimental Design

Importance of Experimental Design

Advantages of Experimental Research

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In this Article

What is Experimental Research?

Example of Experimental Research

What Are The Different Types of Experimental Research?

True Experimental Research

Quasi-Experimental Research

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What is the Importance of Experimental Research?

Establishing Causality

Controlling Variables

Providing Reliable and Valid Results

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Operational Efficiency

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Question 3: What are the characteristics of experimental research?

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