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Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

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

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

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

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

Randomized Block Design

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

Factorial Design

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

Repeated Measures Design

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

Crossover Design

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

Split-plot Design

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

Nested Design

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

Laboratory Experiment

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

Field Experiment

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

Experimental Design Methods

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

Randomization

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

Control Group

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

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

Counterbalancing

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

Replication

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

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

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

Data Collection Method

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

Direct Observation

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

Self-report Measures

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

Behavioral Measures

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

Physiological Measures

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

Archival Data

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

Computerized Measures

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

Video Recording

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

Data Analysis Method

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

Descriptive Statistics

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

Inferential Statistics

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

Analysis of Variance (ANOVA)

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling (SEM)

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

Cluster Analysis

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Experimental Design 

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

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

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

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

When to use Experimental Research Design 

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

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

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

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

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

Purpose of Experimental Design 

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

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

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

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

Advantages of Experimental Design 

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

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

Limitations of Experimental Design

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

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Experimental Research Design — 6 mistakes you should never make!

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

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

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

Table of Contents

What Is Experimental Research Design?

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

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

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

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

Importance of Experimental Research Design

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

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

Types of Experimental Research Designs

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

1. Pre-experimental Research Design

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

Pre-experimental research is of three types —

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

2. True Experimental Research Design

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

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

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

3. Quasi-experimental Research Design

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

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

Advantages of Experimental Research

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

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

6 Mistakes to Avoid While Designing Your Research

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

1. Invalid Theoretical Framework

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

2. Inadequate Literature Study

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

3. Insufficient or Incorrect Statistical Analysis

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

4. Undefined Research Problem

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

5. Research Limitations

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

6. Ethical Implications

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

Experimental Research Design Example

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

A Quick Guide to Experimental Design | 5 Steps & Examples

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

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

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

There are five key steps in designing an experiment:

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

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

Table of contents

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

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

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

Start by simply listing the independent and dependent variables .

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Randomisation

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

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

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

Between-subjects vs within-subjects

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

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

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

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

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

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

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

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

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

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

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

To design a successful experiment, first identify:

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

When designing the experiment, first decide:

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

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

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

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

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

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

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

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

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Rebecca Bevans

Experimental design: Guide, steps, examples

Last updated

27 April 2023

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

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

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

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

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• What is experimental research design?

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

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

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

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

What is a good experimental design?

A good experimental design requires: 

Significant planning to ensure control over the testing environment

Sound experimental treatments

Properly assigning subjects to treatment groups

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

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

Provide unbiased estimates of inputs and associated uncertainties

Enable the researcher to detect differences caused by independent variables

Include a plan for analysis and reporting of the results

Provide easily interpretable results with specific conclusions

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

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

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

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

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

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

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

When can a researcher conduct experimental research?

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

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

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

• The importance of experimental research design

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

Researchers can test Independent variables in controlled settings to:

Test the effectiveness of a new medication

Design better products for consumers

Answer questions about human health and behavior

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

Types of experimental research designs

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

Pre-experimental research design

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

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

The three subtypes of pre-experimental research design are:

One-shot case study research design

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

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

One-group pretest-posttest design

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

Static group comparison design

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

A posttest study compares the results among groups. 

True experimental research design

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

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

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

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

Posttest-only control group design

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

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

Pretest-posttest control group design

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

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

Solomon four-group design

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

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

Quasi-experimental research design

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

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

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

• 5 steps for designing an experiment

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

Here are five key steps in designing a successful experiment:

Step 1: Define variables and their relationship

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

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

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

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

Step 2: Develop a specific, testable hypothesis

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

What is the expected outcome of your study? 

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

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

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

Step 3: Design experimental treatments to manipulate your independent variable

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

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

Step 4: Assign subjects to groups

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

When choosing your study groups, consider: 

The size of your experiment

Whether you can select groups randomly

Your target audience for the outcome of the study

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

Step 5: Plan how to measure your dependent variable

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

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

• Advantages of experimental research

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

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

Researchers can determine cause and effect by manipulating variables.

It gives researchers a high level of control.

Researchers can test multiple variables within a single experiment.

All industries and fields of knowledge can use it. 

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

Replicating natural settings rapidly means immediate research.

Researchers can combine it with other research methods.

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

• Disadvantages (or limitations) of experimental research

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

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

Before conducting experimental research, consider these disadvantages and limitations:

Required professional qualification

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

Limited scope

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

Resource-intensive

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

Limited generalizability

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

Practical or ethical concerns

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

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

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

• Experimental research design example

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

Product design testing is an excellent example of experimental research. 

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

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

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

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

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• Types of experimental

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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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10 Experimental research

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

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

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

Basic concepts

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

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

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

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

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

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

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

Not conducting a pretest can help avoid this threat.

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

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

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

Two-group experimental designs

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

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

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

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

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

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

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

Factorial designs

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

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

Hybrid experimental designs

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

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

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

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

Quasi-experimental designs

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

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

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

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

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

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

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

Perils of experimental research

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

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

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

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

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Experimental Study Design: Research, Types of Design, Methods and Advantages

The CARE guidelines: developing clinical case reporting recommendations based on consensus

Case Study of the Biotechnology Industry in Medicine

Experimental design.

Experimental design is the process of researching in an objective and controlled manner to optimize precision and reach particular conclusions about a hypothesis statement. The goal is to determine the effect a factor or independent variable has on a dependent variable.

Experimental Research

Experimental research is a type of scientific examination in which one or more independent variables are changed and then applied to one or more dependent variables to see how they affect the latter. The effect of independent variables on dependent variables is frequently observed and recorded over time to help researchers reach a plausible conclusion about the link between these two types of variables. The experimental research approach is frequently employed in the physical and social sciences, psychology, and education. It is based on a simple logic that compares two or more groups, but it can be challenging to implement. Experimental research designs , most commonly associated with laboratory test procedures, entail gathering quantitative data and doing statistical analysis on it during the study process.

Experimental Research Design in Following:

• Time is a critical aspect in establishing a cause-and-effect link.
• Cause-and-effect behaviour that is consistent.
• You want to comprehend the significance of cause and effect.

Types of experimental design

The types of experimental study designs are into three types as Pre-experimental, quasi-experimental, and real experimental.

1. Pre-experimental study design: After incorporating cause and effect elements, a group, or many groups, is kept under observation. You’ll perform this inquiry to see if additional research is required for these specific groups.

Pre-experimental research can be divided into three categories:

• Case Study Research Design in a Single Session:

Only one dependent group or variable is investigated in this experimental study. It’s post-test research since it’s done after some treatment that’s supposed to induce change.

• One-group Pre- and post-testing was used in the research:

By administering a test to a single group before and after treatment, this research design incorporates post-test and pretest studies. The former is given at the start of treatment, while the latter is given at the end.

• Comparison of static groups:

In a static-group comparison study, two or more groups are observed, with only one of the groups receiving treatment while the other groups remain unchanged. All groups are retested after the therapy, and the observed differences between them are presumed to be due to the treatment.

2. True Experimental research design:

True experimental research is the maximum accurate type of study because it depends on biostatistical analysis to prove or reject a concept. Only a simple design, out of all the types of experimental design, can demonstrate a cause-and-effect link inside a group. In an actual experiment, three conditions must be met:

• Control Group that will not be affected by the modifications, and an Experimental Group that will be exposed to the altered variables.
• The researcher has control over that a variable.
• The distribution is random.

3. Quasi-experimental Research Design:

“Quasi” indicates “partial,” “half,” or “false.” As a result, while quasi-experimental research resembles actual experimental studies, it is not the same. Participants in quasi-experiments are not assigned at random, and as a result, they are employed in situations where randomization is problematic or impossible. This is a typical occurrence in educational research, where administrators refuse to allow students to be chosen at random for experimental samples. The time series, no corresponding control group design, and the counterbalanced design are quasi-experimental research designs.

What Is the Purpose of Experimental Research Design?

The experimental study design benefits physical disciplines, social sciences, education, and psychology. It’s utilized to make predictions and come to conclusions about a topic.

The following are some examples of how experimental research design can be used.

Medicine: Experimental research is utilized to develop effective disease treatments. Rather than directly employing patients as research subjects, researchers typically extract a bacteria sample from the patient’s body, which is then treated with the newly created antibiotic.

It can be used to improve the standard of an academic institution and science topics like Chemistry and Physics, which include teaching students how to do experimental research. This includes assessing students’ understanding of various topics, developing more effective teaching methods, and implementing additional programmes to help pupils learn.

Human behaviour:

Social scientists are the ones who employ experimental studies to investigate the most. Consider two people who were chosen at random to be the subjects of a social interaction study in which one person was placed in a room with no human interaction for a year.

About Pubrica:

Our team of researchers at Pubrica has a wide range of experience and expertise in developing various research studies based on the goals. We employ the randomized clinical trial in research of screening tests, diagnostics, preventive, and therapeutic intervention. However, there are numerous situations where using the experimental design is impractical or impossible, premature, or unethical. As a result, many quasi-experimental designs and descriptive and observational designs have been developed.

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Quantitative Research Methods

Statistical Analysis References

• Online Statistics Education: An Interactive Multimedia Course of Study This open and free introductory statistics textbook covers topics typical for a college-level non-math majors statistics course. Topics include distributions, probability, research design, estimation, hypothesis testing, power and effect size, comparison of means, regression, analysis of variance (ANOVA), transformations, chi square, and non-parametric (distribution-free) tests). It is available as a pdf, online, or as an epub. An Instructor's Manual and PowerPoint slides are also available upon request from the project leader at Rice University.
• Introductory Statistics A free and open introductory statistics textbook for non-math majors. "They have sought to present only the core concepts and use a wide-ranging set of exercises for each concept to drive comprehension. [...] a smaller and less intimidating textbook that trades some extended and unnecessary topics for a better-focused presentation of the central material." It covers descriptive statistics, probability, distributions, discrete and continuous random variables, estimation, hypothesis testing, comparison of means, correlation and regression, chi square, and F-tests.
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Optimisation by Taguchi’s method of the electrical parameters and geometry of an S32205 duplex weld

• ORIGINAL ARTICLE
• Published: 25 October 2024

Cite this article

• Sandra Chacón-Fernández   ORCID: orcid.org/0000-0002-0343-6497 1 ,
• José L. Meseguer-Valdenebro 1 &
• Antonio Portolés García 1  

Duplex alloys have excellent mechanical properties, as well as resistance to corrosion, but when they are welded, they present a segregation process of the alloying elements that must be controlled by the electrical welding parameters. In this work, an optimisation study is carried out on the electrical parameters for top welding two 3-mm thick flat profiles, where the smallest optimal thermal input is to be obtained. Welding is carried out with an ABB welding robot. The electrical parameters that can be controlled are the intensity and the forward speed; the rest of the electrical variables are regulated by the robot’s control cell. The Taguchi optimisation consists of an L4 matrix of two factors taken in pairs and the objective is to minimise the thermal input, as well as the geometry of the weld bead. In addition, experimental validation tests of the Taguchi model are carried out. The S32205 duplex alloy is a little-known alloy used in welding with the GTAW process and this is the main reason for its selection in this study. The influence of each of the welding variables on the geometry of the welded joint is evaluated as a percentage.

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Investigation: Sandra Chacón-Fernández, José L. Meseguer-Valdenebro, Antonio Portolés García; Writing original draft: Sandra Chacón-Fernández, José L. Meseguer-Valdenebro, Antonio Portolés García.

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Chacón-Fernández, S., Meseguer-Valdenebro, J.L. & García, A.P. Optimisation by Taguchi’s method of the electrical parameters and geometry of an S32205 duplex weld. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-14671-9

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• Published: 13 October 2024

Effects of monoglyceride blend on systemic and intestinal immune responses, and gut health of weaned pigs experimentally infected with a pathogenic Escherichia coli

• Sangwoo Park 1 ,
• Shuhan Sun 1 ,
• Lauren Kovanda 1 ,
• Adebayo O. Sokale 2 ,
• Adriana Barri 3 ,
• Kwangwook Kim 4 ,
• Xunde Li 5 &
• Yanhong Liu   ORCID: orcid.org/0000-0001-7727-4796 1 , 2  

Journal of Animal Science and Biotechnology volume  15 , Article number:  141 ( 2024 ) Cite this article

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Monoglycerides have emerged as a promising alternative to conventional practices due to their biological activities, including antimicrobial properties. However, few studies have assessed the efficacy of monoglyceride blend on weaned pigs and their impacts on performance, immune response, and gut health using a disease challenge model. Therefore, this study aimed to investigate the effects of dietary monoglycerides of short- and medium-chain fatty acids on the immunity and gut health of weaned pigs experimentally infected with an enterotoxigenic Escherichia coli F18.

Pigs supplemented with high-dose zinc oxide (ZNO) had greater ( P  < 0.05) growth performance than other treatments, but no difference was observed in average daily feed intake between ZNO and monoglycerides groups during the post-challenge period. Pigs in ZNO and antibiotic groups had lower ( P  < 0.05) severity of diarrhea than control, but the severity of diarrhea was not different between antibiotic and monoglycerides groups. Pigs fed with monoglycerides or ZNO had lower ( P  < 0.05) serum haptoglobin on d 2 or 5 post-inoculation than control. Pigs in ZNO had greater ( P  < 0.05) goblet cell numbers per villus, villus area and height, and villus height:crypt depth ratio (VH:CD) in duodenum on d 5 post-inoculation than pigs in other treatments. Pigs supplemented with monoglycerides, ZNO, or antibiotics had reduced ( P  < 0.05) ileal crypt depth compared with control on d 5 post-inoculation, contributing to the increase ( P  = 0.06) in VH:CD. Consistently, pigs in ZNO expressed the lowest ( P  < 0.05) TNFa , IL6 , IL10 , IL12 , IL1A , IL1B , and PTGS2 in ileal mucosa on d 5 post-inoculation, and no difference was observed in the expression of those genes between ZNO and monoglycerides. Supplementation of ZNO and antibiotic had significant impacts on metabolic pathways in the serum compared with control, particularly on carbohydrate and amino acid metabolism, while limited impacts on serum metabolites were observed in monoglycerides group when compared with control.

Conclusions

The results suggest that supplementation of monoglyceride blend may enhance disease resistance of weaned pigs by alleviating the severity of diarrhea and mitigating intestinal and systemic inflammation, although the effectiveness may not be comparable to high-dose zinc oxide.

Weaning piglets, the process of separating them from their mother, exposes them to nutritional, physiological, and environmental challenges [ 1 , 2 , 3 ]. These weaning stressors impair intestinal barrier function and induce intestinal and systemic inflammation, in addition to the typically occurring decrease in feed intake [ 4 , 5 ]. The compromised intestinal barrier increases the risk of external factors (e.g., toxins, antigens, and pathogens) entering the body, making piglets vulnerable to enteric diseases [ 6 , 7 ]. Post-weaning diarrhea, caused by the infection of enterotoxigenic Escherichia coli (ETEC) F18, is one of the common problems in young pigs [ 8 , 9 ]. This disease is characterized by watery diarrhea and deterioration of intestinal health, causing tremendous economic losses in swine production due to growth lag, morbidity, cost of medication, and mortality [ 10 , 11 , 12 , 13 ]. In-feed antibiotics or pharmacological doses of zinc oxide (2,000–3,000 mg/kg) have been widely applied to nursery diets for controlling post-weaning diarrhea and promoting animal health and growth [ 14 , 15 , 16 ]. However, along with the increased public health concern regarding antimicrobial resistance [ 17 , 18 , 19 , 20 , 21 , 22 ], the use of antibiotics for growth promoting purposes in animal production has been restricted since 2017 in the United States [ 23 ]. Furthermore, considering sustainable animal agriculture, it is noteworthy that Europe not only banned the use of pharmacological doses of zinc oxide but also limited dietary zinc oxide supplementation to 150 mg/kg [ 24 , 25 , 26 ]. Hence, alternative practices, including animal management and nutrition interventions, are needed to promote animal health and welfare, as increased morbidity and economic losses due to the constraints of conventional practices are inevitable.

Numerous nutritional interventions (e.g., exogenous enzymes, bioactive compounds derived from animals or plants, microbiome modulators) have been investigated and adopted in the swine industry to address the emergence of the post-antibiotic era [ 27 , 28 ]. One promising alternative is a group of products based on organic acids, specifically short-chain fatty acids (SCFA; less than 6 carbons) or medium-chain fatty acids (MCFA; 6–12 carbons). Research has shown that SCFA and MCFA have strong antibacterial activity [ 29 , 30 , 31 ]. In addition, they also exhibit various biological activities in pigs [ 32 , 33 , 34 ], including beneficial effects on growth performance, intestinal physiology, and immunity, making them more than just an energy source. However, the effectiveness of supplementing organic fatty acids is often hindered by limiting factors such as unpalatable flavor and losses prior to reaching the lower gastrointestinal tract [ 35 , 36 ]. In this respect, monoglycerides, composed of fatty acid esterified to glycerol, may address the limitations due to the two criteria: (1) they are relatively easy to handle; and (2) they allow active substances to be gradually released throughout the intestine [ 37 ]. Moreover, in vitro antimicrobial activity against a wide range of pathogenic bacteria was observed in glycerol esters derived from SCFA and MCFA [ 30 , 38 , 39 , 40 , 41 ]. There is growing interest in monoglycerides as antibacterial lipids in nutrition and health. Their physiological activities have been extensively studied in poultry [ 42 , 43 , 44 ], however, limited research has been reported on the efficacy of monoglycerides in weaned pigs using disease models. Therefore, the objective of this study was to investigate the influence of dietary supplementation of a monoglyceride blend on growth performance, intestinal health, and systemic immunity of weaned pigs experimentally infected with ETEC F18.

Materials and methods

Animals, housing, experimental design, and diet.

Sixty weaned pigs with 28 barrows and 32 gilts (average body weight [BW] = 6.49 ± 0.74 kg; around 21 to 24 d old) were obtained from the Swine Teaching and Research Center at the University of California, Davis, USA. The sows and piglets used in this experiment did not receive Escherichia coli vaccines, antibiotic injections, or antibiotics in creep feed. Before weaning, feces were collected from sows and all their piglets destined for this study to verify the absence of β-hemolytic Escherichia coli . The ETEC F18 receptor status was also tested by polymerase chain reaction (PCR)-restriction fragment length polymorphism [ 45 ], and piglets susceptible to ETEC F18 were selected for this study. After weaning, all pigs were randomly assigned to one of the four dietary treatments (15 replicates/treatment) in a randomized complete block design with BW within sex and litter as the block and pig as the experimental unit. Pigs were housed in individual pens (0.61 m × 1.22 m) for 28 d, including 7 d before and 21 d after the first ETEC challenge. All piglets had free access to feed and water. The light was on at 07:30 h and off at 19:30 h daily in the environmental control unit.

The four dietary treatments included: (1) a corn-soybean meal-based basal diet (control); (2) the basal diet with 0.3% monoglyceride blend (BalanGut™ LS L; BASF SE, Ludwigshafen, Germany) of butyric, caprylic, and capric acids; (3) the basal diet with 3,000 mg/kg of zinc oxide (ZNO); (4) the basal diet with 50 mg/kg of carbadox (antibiotic). A 2-phase feeding program was used with the first two weeks as phase 1 and the last two weeks as phase 2 (Table  1 ). Spray-dried plasma, antibiotics, and high levels of zinc oxide exceeding recommendation and normal practice were not included in basal diet. All diets were formulated to meet pig nutritional requirements [ 46 ] and provided as mash form throughout the experiment.

After 7 days of adaptation, all pigs were orally inoculated with 3 mL of ETEC F18 for three consecutive days from d 0 post-inoculation (PI). The ETEC F18 was originally isolated from a field disease outbreak by the University of Montreal (isolate number: ECL22131). The ETEC F18 expresses heat-labile toxin and heat-stable toxins a and b. The inoculums were prepared at 10 10 colony-forming units per 3 mL dose in phosphate buffered saline. This dose caused mild diarrhea in the current study, consistent with our previously published research [ 47 , 48 , 49 ].

Clinical observations and sample collections

The procedures of this experiment were adapted from previous research [ 47 , 50 , 51 , 52 ]. Clinical observations (fecal score and alertness score) were recorded twice daily throughout the study. The fecal score of each pig was assessed each day visually by two independent evaluators, with the score ranging from 1 to 5 (1 = normal feces, 2 = moist feces, 3 = mild diarrhea, 4 = severe diarrhea, and 5 = watery diarrhea). The frequency of diarrhea was calculated as the percentage of the pig days with fecal score of 3 or greater, as well as calculated as the percentage of the pig days with fecal score of 4 or greater. Alertness was scored from 1 to 3 (1 = normal, 2 = slightly depressed or listless, and 3 = severely depressed or recumbent). Scores for alertness did not exceed two throughout the experiment (data not shown).

Pigs were weighed on weaning day (d −7; initial BW), d 0 (before first inoculation), 5, 14, and 21 PI. Feed intake was recorded throughout the study. Average daily gain (ADG), average daily feed intake (ADFI), and feed efficiency (gain:feed ratio) were calculated for each period. Fecal samples were collected from the rectum of all pigs throughout the experiment using a cotton swab on d −7, 2, 5, 7, 10, 14, and 21 PI to test β-hemolytic coliforms and the percentage of β-hemolytic coliforms to total coliforms [ 47 , 50 , 51 , 52 ]. Blood samples were collected from the jugular vein of all pigs before ETEC challenge (d 0), and on d 2, 5, 14, and 21 PI to collect serum samples, which were stored at − 80°C until further analysis.

Twenty-four pigs (6 pigs/treatment, 3 barrows and 3 gilts) were euthanized on d 5 PI near the peak of ETEC infection, and the remaining pigs were euthanized at the end of the experiment (d 21 PI). Before euthanization, pigs were anesthetized with 1 mL mixture of 100 mg Telazol, 50 mg ketamine, and 50 mg xylazine (2:1:1) by intramuscular injection. After anesthesia, intracardiac injection with 78 mg Fatal-Plus solution (sodium pentobarbital, MWI Animal Health, Visalia, CA, USA) per 1 kg of BW was used to euthanize each pig. Intestinal mucosa samples were collected from jejunum and ileum, snap-frozen in liquid nitrogen, and then stored at −80 °C for gene expression analysis. Three 4-cm segments from the duodenum, the middle of the jejunum, and the ileum (10 cm close to the ileocecal junction) were collected and fixed in 10% neutral buffered formalin for intestinal morphology analysis.

Detection of β-hemolytic coliforms

Briefly, fecal samples were plated on Columbia Blood Agar with 5% sheep blood to identify hemolytic coliforms, which can lyse red blood cells surrounding the colony. Fecal samples were also plated on MacConkey agar to enumerate total coliforms. Hemolytic colonies from the blood agar were sub-cultured on MacConkey agar to confirm that they were lactose-fermenting bacteria and flat pink colonies. All plates were incubated at 37 °C for 24 h in an air incubator. Populations of both total coliforms and β-hemolytic coliforms on blood agar were visually scored from 0 to 8 (0 = no bacterial growth, 8 = very heavy bacterial growth). The ratio of scores of β-hemolytic coliforms to total coliforms was calculated.

Measurements of serum cytokine and acute phase proteins

Serum samples were analyzed for tumor necrosis factor-α (TNF-α; R&D Systems Inc., Minneapolis, MN, USA), C-reactive protein (CRP; R&D Systems Inc., Minneapolis, MN, USA), and haptoglobin (Aviva Systems Biology, San Diego, CA, USA) using porcine-specific enzyme-linked immunosorbent assay kits following the manufacturer’s procedures. All samples, including standards, were analyzed in duplicate. The intensity of the color was measured at 450 nm with the correction wavelength set at 530 nm using a plate reader (BioTek Instruments, Inc., Winooski, VT, USA). The intra-assay coefficients of variation for TNF-α, CRP, and haptoglobin were less than 7%. The inter-assay coefficients of variation for TNF-α, CRP, and haptoglobin were less than 10%. The concentrations of each analyte in the tested samples were calculated based on a standard curve.

Intestinal morphology

Fixed intestinal tissues were embedded in paraffin, sectioned at 5 μm, and stained with hematoxylin and eosin. The slides were photographed by an Olympus BX51 microscope at 10× magnification, and all measurements were conducted in the image processing and analysis software (Image J, NIH). Ten straight and integrated villi and their associated crypts and surrounding areas were selected to analyze villus height (VH), area, and width; crypt depth (CD) and width; and goblet cell number per villus as described in previous studies [ 52 , 53 ].

Immunohistochemistry

The immunohistochemistry procedures were based on previous research [ 47 , 54 ]. Briefly, the embedded ileal tissues were sectioned at 5 μm and placed on the microslides. The slides were incubated with 5 µg/mL porcine neutrophil-specific antibody PM1 (BMA Biomedicals, Augst, Switzerland) or 0.4 µg/mL porcine macrophage-specific antibody MAC387 (Thermo Scientific, Waltham, MA, USA). Antibody binding was visualized by using the avidin-biotin complex, and the diaminobenzidine chromogen (Vector Laboratories, Inc., CA, USA). Hematoxylin was applied as a counter stain. Slides incubated without the primary antibodies but with PBS were used as negative controls. Images were captured by an Olympus BX51 microscope at 10× magnification, and all measurements were analyzed by Image J software. Eight straight and integrated ileal villi were selected for measurement. The unit was the number of cells/mm 2 .

Intestinal barrier and innate immunity

Jejunal and ileal mucosa samples were analyzed for gene expression by quantitative real-time PCR (qRT-PCR). Briefly, approximately 100 mg of mucosa sample was homogenized using TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA). Then, total ribonucleic acid (RNA) was extracted following RNA extraction procedural guidelines provided by the reagent manufacturer. The quality and quantity of RNA were evaluated using a Thermo Scientific NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific, Inc., Waltham, MA, USA). The complementary DNA (cDNA) was produced from 1 µg of total RNA per sample using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems; Thermo Fisher Scientific, Inc., Waltham, MA, USA) in a total volume of 20 µL. The mRNA expression of Mucin 2 ( MUC2 ), Claudin-1 ( CLDN1 ), Zonula occludens-1 ( ZO-1 ), and Occludin ( OCLN ) in jejunal mucosa and the mRNA expression of Tumor necrosis factor-alpha ( TNFa ), Interleukin 6 ( IL6 ), Interleukin 7 ( IL7 ), Interleukin 10 ( IL10 ), Interleukin 12 ( IL12 ), Interleukin-1 alpha ( IL1A ), Interleukin-1 beta ( IL1B ), MUC2 , and Prostaglandin-endoperoxide synthase 2 ( PTGS2 ) in ileal mucosa were analyzed. Data normalization was accomplished using 18S ribosomal RNA as a housekeeping gene. Primers were designed based on published literature and commercially synthesized by Integrated DNA Technologies, Coralville, IA, USA. All primers were verified prior to qRT-PCR (Table S1 ). The qRT-PCR reaction conditions followed the published research [ 55 ]. The 2 −ΔΔCT method was used to analyze the relative expression of genes compared to control [ 56 ].

Untargeted metabolomics analysis

The untargeted metabolomics analysis was performed by the NIH West Coast Metabolomics Center at the University of California, Davis, using gas chromatography (Agilent 6890 gas chromatograph controlled using Leco ChromaTOF software version 2.32, Agilent, Santa Clara, CA, USA) coupled with time-of-flight mass spectrometry (GC/TOF-MS) (Leco Pegasus IV time-of-flight mass spectrometer controlled using Leco ChromaTOF software version 2.32, Leco, St. Joseph, MI, USA). Metabolite extraction was performed following procedures previously described by Fiehn et al. [ 57 ]. Briefly, frozen serum samples (approximately 30 µL) were homogenized using a Retsch ball mill (Retsch, Newtown, PA, USA) for 30 s at 25 times/s. After homogenization, a prechilled (−20 °C) extraction solution (isopropanol/acetonitrile/water at the volume ratio 3:3:2, degassed with liquid nitrogen) was added at a volume of 1 mL/20 mg of sample. Samples were then vortexed and shaken for metabolite extraction. After centrifugation at 12,800 × g for 2 min, the supernatant was collected and divided into two equal aliquots and concentrated at room temperature for 4 h in a cold-trap vacuum concentrator (Labconco Centrivap, Kansas City, MO, USA). To separate complex lipids and waxes, the residue was re-suspended in 500 µL of 50% aqueous acetonitrile and centrifuged at 12,800 × g for 2 min. The resultant supernatant was collected and concentrated in the vacuum concentrator. Dried sample extracts were derivatized and mixed with internal retention index markers (fatty acid methyl esters with the chain length of C8 to C30). The samples were injected for GC/TOF analysis, and all samples were analyzed in a single batch. Data acquisition by mass spectrometry and mass calibration using FC43 (perfluorotributylamine) before starting analysis sequences. Metabolite identifications were performed based on the two parameters: (1) Retention index window ± 2,000 U (around ± 2 s retention time deviation), and (2) Mass spectral similarity plus additional confidence criteria as detailed below. A detailed methodology for data acquisition and metabolite identification was described in a previously published article by Fiehn et al. [ 57 ].

Statistical analysis

The normality of data was verified and outliers were identified using the UNIVARIATE procedure (SAS Institute Inc., Cary, NC, USA). Outliers were identified and removed as values that deviated from the treatment mean by more than 3 times the interquartile range. All data except frequency of diarrhea and metabolomics were analyzed by ANOVA using the PROC MIXED of SAS (SAS Institute Inc., Cary, NC, USA) in a randomized complete block design with the pig as the experimental unit. The statistical model included diet as the main effect and block as random effect. Treatment means were separated by using the LSMEANS statement and the PDIFF option of PROC MIXED. The Chi-square test was used for analyzing the frequency of diarrhea. Statistical significance and tendency were considered at P  < 0.05 and 0.05 ≤  P  < 0.10, respectively.

The metabolomics data were analyzed using different modules of a web-based platform, MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca ) [ 58 ]. Data were filtered for peaks with detection rates less than 30% of missing abundances and normalized using logarithmic transformation and auto-scaling. Mass univariate analysis was performed using one-way ANOVA followed by Fisher’s least significant difference test (adjusted P  ≤ 0.05). Fold change analysis and t -tests were also conducted to determine the fold change and significance of each identified metabolite. Statistical significance was declared at a false discovery rate (FDR, Benjamini and Hochberg correction; q) < 0.2 and fold change > 2.0. Partial least squares discriminant analysis (PLS-DA) was carried out to further identify discriminative variables (metabolites) among the treatment groups. Pathway analysis and metabolite set enrichment analysis were performed on identified metabolites that had a Variable Importance in Projection (VIP) score > 1. The pathway with a P -value less than 0.05, as well as an impact value greater than 0.1, was defined as a significant impact pathway.

Growth performance, diarrhea, β-hemolytic coliforms

There were no significant differences in the initial (d −7) and d 0 BW of pigs among dietary treatments (Table  2 ). In comparison to control and antibiotic groups, supplementation of monoglycerides did not affect BW, ADG, and ADFI throughout the experiment. Pigs supplemented with ZNO had greater ( P  < 0.05) BW on d 5, 14, and 21 PI, increased ( P  < 0.05) ADG from d 0 to 5 PI, d 0 to 14 PI, and d 0 to 21 PI, and enhanced ( P  < 0.05) ADFI from d 0 to 14 PI and d 0 to 21 PI than the other treatments. However, the ADFI from d 0 to 21 PI was not different between ZNO and monoglycerides groups. Pigs supplemented with ZNO had greater ( P  < 0.01) gain:feed ratio from d 0 to 5 PI compared with the other treatments, but the difference did not persist throughout the post-challenge period. The gain:feed ratio on d 0 to 21 PI was lower ( P  < 0.05) in monoglycerides than in control and antibiotic groups, but did not differ from ZNO group.

Pigs in the ZNO group had the lowest ( P  < 0.05) fecal score from d 1 to 10 PI among dietary treatments (Fig.  1 ). The incidence of diarrhea was 32.09% in control, 30.41% in monoglycerides, 4.01% in ZNO, and 22.53% in antibiotic, while the severity of diarrhea was 19.26% in control, 16.22% in monoglycerides, 0.31% in ZNO, and 12.35% in antibiotic, respectively (Fig.  2 ). The incidence of diarrhea (fecal score ≥ 3) was lower ( P  < 0.05) in ZNO and antibiotic groups than control and monoglycerides groups. The severity of diarrhea (fecal score ≥ 4) in ZNO and antibiotic groups was also lower than that in control, but there was no difference observed in the severity of diarrhea between monoglycerides and antibiotic groups. The ZNO group had the lowest incidence and severity of diarrhea throughout the experimental period.

Daily fecal score of enterotoxigenic Escherichia coli F18-challenged weaned pigs fed diets supplemented with monoglycerides, high-dose zinc oxide (ZNO), or antibiotic. Fecal score = 1, normal feces; 2, moist feces; 3, mild diarrhea; 4, severe diarrhea; 5, watery diarrhea. * P  < 0.05, indicating fecal scores were significantly different among treatments. # P  < 0.10, indicating fecal scores tended to different among treatments. Each least squares mean represents 14–15 observations before d 5 post-inoculation (PI) and each least squares mean represents 8–9 observations after d 5 PI

Frequency of diarrhea (overall period) of enterotoxigenic Escherichia coli F18-challenged weaned pigs fed diets supplemented with monoglycerides, high-dose zinc oxide (ZNO), or antibiotic. Frequency of diarrhea was calculated as the percentage of pig days with fecal score ≥ 3 or 4 in the total of pig days. a–c Means without a common superscript are different ( P  < 0.05) in frequency of diarrhea ≥ 3. A–C Means without a common superscript are different ( P  < 0.05) in frequency of diarrhea ≥ 4

No β-hemolytic coliforms were identified in fecal samples of pigs in all groups prior to ETEC inoculation. Βeta-hemolytic coliforms were identified in all pigs’ feces on d 2 PI. Pigs in ZNO group had lower ( P  < 0.05) percentage of β-hemolytic coliforms in feces on d 5 PI than pigs in control, while no difference was observed among monoglycerides, ZNO, and antibiotic groups (Fig.  3 ). No difference was observed in the percentage of β-hemolytic coliforms in feces among all dietary treatments on d 7, 10, 14, and 21 PI.

The percentage (%) of β-hemolytic coliforms in fecal samples of enterotoxigenic Escherichia coli F18-challenged pigs fed diets supplemented with monoglycerides, high-dose zinc oxide (ZNO), or antibiotic. No β-hemolytic coliforms were observed in the fecal samples of pigs before Escherichia coli challenge. β-Hemolytic coliforms were only observed in control pigs on d 21 post-inoculation (PI). Each least squares mean represents 14–15 observations on d 2 and 5 PI and each least squares mean represents 8–9 observations on d 7, 10, 14, and 21 PI. a,b Means without a common superscript are different ( P  < 0.05)

• Systemic immunity

No difference was observed in serum TNF-α concentrations among all treatments at d 0 before ETEC inoculation, and at d 2, 5, and 21 PI (Table  3 ). Dietary supplements tended ( P  = 0.07) to impact serum TNF-α on d 14 PI, pigs fed with ZNO had the lowest TNF-α and pigs fed with control had the highest level of TNF-α among all treatments. Pigs in monoglycerides group had lower ( P  < 0.05) serum CRP than pigs in the antibiotic group on d 0 before ETEC inoculation. Supplementation of ZNO reduced ( P  < 0.10 and P  < 0.05) serum CRP on d 14 and 21 PI, tended ( P  = 0.06) to reduce serum haptoglobin on d 0, and reduced ( P  < 0.05) serum haptoglobin on d 2 and 5 PI. Pigs fed with monoglycerides also had lower ( P  < 0.05) serum haptoglobin on d 5 PI, compared with control pigs.

On d 5 PI, pigs in ZNO had more ( P  < 0.05) goblet cell numbers per villus, greater ( P  < 0.05) villus area and VH, and higher ( P  < 0.05) VH:CD in duodenum than pigs in other treatments (Table  4 ). Supplementation of monoglycerides, ZNO, or antibiotic reduced ( P  < 0.05) ileal CD compared with control. Consistently, pigs in ZNO group tended ( P  = 0.06) to have the biggest VH:CD in the ileum, followed by pigs in monoglycerides and antibiotic groups. On d 21 PI, pigs supplemented with ZNO tended ( P  = 0.07) to have more goblet cells per villus, and had largest ( P  < 0.05) villus area and highest ( P  < 0.05) VH in the duodenum, when compared with other treatments.

Supplementation of ZNO or antibiotic reduced ( P  < 0.05) neutrophil counts in ileal villi on d 5 PI compared with control (Table  5 ). However, no significant differences in neutrophil counts were observed among monoglycerides, ZNO, and antibiotic groups. Pigs supplemented with ZNO had the lowest ( P  < 0.05) number of macrophages in ileal villi among all treatments on d 5 PI. Pigs fed with antibiotic also had significantly lower ( P  < 0.05) recruitment of macrophages in ileal villi than control group, but comparable to that in pigs fed with monoglycerides.

No differences were observed in the mRNA expression of MUC2 , CLDN1 , ZO-1 , and OCLN in jejunal mucosa of weaned pigs among different treatments on d 5 and 21 PI (Fig.  4 ). On d 5 PI, pigs fed with ZNO had lower ( P  < 0.05) mRNA expression of TNFa , IL6 , IL10 , IL12 , IL1A , IL1B , and PTGS2 in ileal mucosa, compared with other treatments (Fig.  5 ). However, no difference in the expression of listed genes was observed between pigs supplemented with monoglycerides or ZNO. Pigs supplemented with monoglycerides expressed lowest ( P  < 0.05) PTGS2 in ileal mucosa compared with other treatments on d 21 PI.

Relative mRNA abundance of genes in jejunal mucosa of enterotoxigenic Escherichia coli F18-challenged weaned pigs fed diets supplemented with monoglycerides, high-dose zinc oxide, or antibiotic. Each least squares mean represents 6–9 observations. PI, Post-inoculation; MUC2 , Mucin 2; CLDN1 , Claudin-1;  ZO-1 , Zonula occludens-1; OCLN , Occludin

Relative mRNA abundance of genes in ileal mucosa of enterotoxigenic Escherichia coli F18-challenged pigs supplemented with monoglycerides, high-dose zinc oxide, or antibiotic on d 5 ( A ) and 21 PI ( B ). a,b Means without a common superscript are different ( P  < 0.05). Each least squares mean represents 6–9 observations. PI, Post-inoculation; TNFa , Tumor necrosis factor-alpha; IL6 , Interleukin 6; IL7 , Interleukin 7; IL10 , Interleukin 10; IL12 , Interleukin 12; IL1A , Interleukin-1 alpha, IL1B , Interleukin-1 beta; MUC2 , Mucin 2, and  PTGS2 , Prostaglandin-endoperoxide synthase 2

Metabolite profiles in serum

A total of 483 (165 identified and 318 unidentified) metabolites were detected in serum samples. Based on statistical threshold and VIP scores, pantothenic acid and fructose were up-regulated by ZNO, compared with the pigs in control group on d 5 PI (Table  6 ). Supplementation of monoglycerides changed the relative abundances of 14 metabolites (7 up-regulated and 7 down-regulated) compared with ZNO, and upregulated lactose and cellobiose compared with antibiotics on d 5 PI. On d 14 PI, supplementation of ZNO changed abundances of 10 metabolites (7 up-regulated and 3 down-regulated) compared with control. Supplementation of monoglycerides up-regulated 2 metabolites (hippuric acid and indole-3-propionic acid) and down-regulated 8 metabolites (including glutaric acid, serotonin, mannose, etc.) compared with pigs in the ZNO. Pigs fed with antibiotics had greater abundance of hippuric acid and indole-3-propionic acid, but had lower thymine, pantothenic acid, glycerol, and piperidone compared with the pigs in the ZNO group. Limited differential metabolites were identified when comparing control vs. monoglycerides, and control vs. antibiotic throughout the experiment (data not shown).

Based on the identified metabolites and VIP scores, a PLS-DA score with 95% confidence ranges (shaded areas) showed a clear separation between control and ZNO, between monoglycerides and ZNO, between monoglycerides and antibiotic, and between ZNO and antibiotic groups on d 5 PI (Fig.  6 A) and/or d 14 PI (Fig.  6 B). To further explore the metabolic profile differences among dietary treatments, PLS-DA was performed for the following comparisons: (1) control vs. ZNO, (2) monoglycerides vs. ZNO, (3) monoglycerides vs. antibiotic, and (4) ZNO vs. antibiotic on d 5 and 14 PI. The score plot again distinguished control from ZNO (Fig. S1 A and B), monoglycerides from ZNO (Fig. S1 C and D), monoglycerides from antibiotic (Fig. S2 A and B), and ZNO from antibiotic (Fig. S2 C and D).

Partial Least Squares Discriminant Analysis (PLS-DA) 2D score plot of the metabolites in serum showed separated clusters between the CON and ZNO, MG and ZNO, MG and AB, and ZNO and AB groups on d 5 ( A ) and/or d 14 ( B ) post-inoculation, respectively. CON = Control; MG = Monoglycerides; ZNO = High-dose zinc oxide; AB = Antibiotic. Shaded areas in different colors represent in 95% confidence interval

Pathway analysis and metabolite set enrichment analysis were performed on the identified metabolites in serum with VIP > 1 (Table  7 ). On d 5 PI, taurine and hypotaurine metabolism and phenylalanine metabolism were the most affected metabolic pathways in a comparison of control vs. monoglycerides (Fig. S3 A and B). Arginine biosynthesis, β-alanine metabolism, arginine and proline metabolism, pyruvate metabolism, citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolism, and glycolysis/gluconeogenesis were the most affected metabolic pathways when comparing control with ZNO (Fig. S4 A and B). Citrate cycle, taurine and hypotaurine metabolism, and β-alanine metabolism were the most affected metabolic pathways when monoglyceride blend was compared with ZNO (Fig. S5 A and B). Taurine and hypotaurine metabolism, nicotinate and nicotinamide metabolism, and β-alanine metabolism were the most affected metabolic pathways in a comparison of monoglycerides vs. antibiotic (Fig. S6 A and B). β-Alanine metabolism and citrate cycle were the most affected metabolic pathways when comparing ZNO with antibiotic (Fig. S7 A and B). On d 14 PI, glyoxylate and dicarboxylate metabolism and taurine and hypotaurine metabolism were the most affected metabolic pathways in a comparison of control vs. monoglycerides (Fig. S3 C and D). Alanine, aspartate and glutamate metabolism, citrate cycle, glyoxylate and dicarboxylate metabolism, and pyrimidine metabolism were the most affected metabolic pathways when comparing control with ZNO (Fig. S4C and D). Citrate cycle, glyoxylate and dicarboxylate metabolism, alanine, aspartate and glutamate metabolism, and pyrimidine metabolism were the most affected metabolic pathways when monoglyceride blend was compared with ZNO (Fig. S5 C and D), while citrate cycle was the most affected metabolic pathway in comparison of monoglycerides vs. antibiotic (Fig. S6 C and D). Alanine, aspartate and glutamate metabolism, glyoxylate and dicarboxylate metabolism, citrate cycle, D-glutamine and D-glutamate metabolism, pyrimidine metabolism, arginine biosynthesis, and β-alanine metabolism were the most affected metabolic pathways when comparing ZNO with antibiotic (Fig. S7 C and D).

The present study investigated the potential of a monoglyceride blend containing butyric, caprylic, and capric acids in mitigating the adverse effects of ETEC F18 infection on systemic and intestinal immune responses, as well as intestinal health in weaning pigs. Additionally, the study identified metabolic changes resulting from monoglycerides supplementation, shedding light on potential mechanisms underlying the observed physiological responses.

Post-weaning diarrhea, a prevalent gastrointestinal disease occurring shortly after weaning, is often attributed to the adhesion and proliferation of ETEC F18 or F4 in the small intestine. Clinical signs typically include watery diarrhea, dirty appearance, stunted growth, dehydration, and lethargy [ 51 , 59 ]. In this study, successful ETEC F18 infection was confirmed through fecal shedding of β-hemolytic coliforms and the manifestation of typical infection symptoms, including growth retardation and severe diarrhea. These observations are consistent with our previous research [ 50 , 52 ]. The observed pattern of gradual recovery after the peak of infection (d 3 to 5 PI) also aligns with our previous studies using the same ETEC F18 strain [ 47 , 52 , 60 ]. The results of fecal score and the frequency of diarrhea indicated that supplementation of high-dose zinc oxide or antibiotics significantly reduces both the incidence and severity of diarrhea in weaned pigs infected with ETEC F18. However, the impact of dietary monoglycerides on diarrhea was limited.

ETEC toxins can disrupt the regulation of intestinal ion transporters, leading to fluid and electrolyte imbalances [ 61 , 62 ]. Although the percentage of β-hemolytic coliforms in feces was similar across treatments post-infection, supplementation of high-dose zinc oxide notably reduced the β-hemolytic coliforms on d 5 PI, which may be attributed to zinc oxide’s antimicrobial properties and its ability to support intestinal barrier function and epithelial tissue regeneration [ 26 , 28 , 63 ]. Similarly, both monoglycerides and antibiotics showed comparable reductions in ETEC shedding, likely due to their antibacterial activity [ 37 , 64 ]. This reduction corresponded with a decreased incidence of diarrhea across all supplemented groups.

It is well known that ETEC infection can disrupt essential intestinal functions, such as nutrient transport, epithelial barrier integrity, and immune function [ 13 , 65 ]. All of these result in reduced digestive and absorptive capacity, and increased resource expenditure for maintaining intestinal homeostasis, ultimately leading to compromised performance in infected animals [ 51 , 66 , 67 ]. The beneficial effects of high-dose zinc oxide on intestinal morphology were significant, and supplementation with monoglycerides improved CD and VH:CD in the ileum of ETEC-infected pigs on d 5 PI, comparable to high-dose zinc oxide. However, there were limited changes in intestinal morphology on d 21 PI, likely due to the pigs’ recovery from ETEC infection. Consistent with our observations, previous studies have reported the positive effects of pharmacological doses of zinc oxide in managing post-weaning diarrhea caused by ETEC and have summarized its beneficial effects on growth performance, gastrointestinal tract health, and immunity [ 26 ]. Although the exact modes of action of carbadox are unclear, the observed changes in serum inflammatory markers and ileal morphology may be due to their ability to compete for sites important for nutrient absorption and ETEC colonization, thereby reducing resource costs and improving nutrient availability. Intestinal morphology results are also consistent with findings reported by Hung et al. [ 68 ], who observed that carbadox in the diet decreased CD and increased VH:CD in the small intestine of weaned pigs.

In addition to changes in intestinal morphology, high-dose zinc oxide and carbadox supplementation showed a mitigating effect on the recruitment of neutrophils and macrophages in the ileal villi. Supplementation with high-dose zinc oxide also reduced the relative gene expression of inflammatory cytokines ( TNFa , IL6 , IL10 , IL12 , IL1A , IL1B , and PTGS2 ) in ileal mucosa, indicating a moderating effect on the intestinal immune response. Although monoglycerides supplementation partially attenuated intestinal inflammation, its efficacy was not comparable to that of high-dose zinc oxide. The observed changes in the supplementation of monoglycerides suggest reduced intestinal epithelial cell renewal and attenuated inflammatory responses, indicating reduced energy and nutritional costs similar to conventional practices [ 68 ]. These findings also suggest that supplementing monoglycerides may overcome primary obstacles associated with the use of organic acids as feed additives, including undesirable losses in the upper intestine and unfavorable taste and aroma. The antibacterial effects of organic acids and their monoglycerides against Escherichia coli have been verified through numerous in vitro studies [ 30 , 38 , 41 , 69 ]. The biological activity of butyric acid, which constitutes a major portion of our glyceride blend (~ 60%), has been well documented, including its modulation of various cellular responses via histone deacetylase inhibition and G-protein-coupled receptor activation in various cell types [ 36 , 37 , 70 , 71 ], further supporting our findings.

Moreover, local inflammation can influence systemic immunity, and immune activation by external factors can exacerbate the performance status during the weaning period due to metabolic changes [ 72 , 73 , 74 ]. For instance, ETEC infection activates immune cells and increases the secretion of pro-inflammatory cytokines [ 47 , 52 , 75 ], leading to alterations in the absorption and utilization of nutrients or energy, including anorexia, decreased gut motility, and increased hepatic acute-phase protein synthesis [ 73 , 76 , 77 ]. Supplementation with high-dose zinc oxide was associated with a significant reduction in inflammatory biomarkers throughout the experiment, and an anti-inflammatory effect of monoglycerides was also observed during peak infection. This finding is supported by observations reported by Tian et al. [ 78 ], where inclusion of glycerol butyrate in pig diet reduced pro-inflammatory factors ( TNFa , IL6 , and  IL1B) in jejunum and ileum to ETEC infection by inhibiting the NF-κB/MAPK pathway.

Given the biological effects of high-dose zinc oxide discussed earlier and the observed changes in diarrhea, intestinal morphology, and intestinal and serum inflammatory markers, it is not surprising that the pigs fed with high-dose zinc oxide had the greatest growth performance throughout the experimental period among all treatments. On the other hand, carbadox supplementation reduced feed intake compared to high-dose zinc oxide, but feed efficiency was higher than that of monoglycerides throughout the post-challenge period. These results reflect the multifactorial nature of animal growth and suggest that high-dose zinc oxide and antibiotics are likely to exert their beneficial effects through different mechanisms [ 68 ]. In the present study, the monoglyceride blend had limited effects on the growth performance of weaned pigs infected with ETEC F18. This finding aligns with other research showing that dietary supplementation of SCFA or MCFA monoglycerides did not affect the performance of weaned pigs [ 79 , 80 , 81 , 82 ]. Recent studies in poultry also confirmed that dietary supplementation of monoglyceride blend (butyric, caprylic, and capric acids) did not affect the growth performance of early growth stage in broilers infected with necrotic enteritis [ 43 , 83 ]. In this study, supplementation of monoglyceride blend reduced gain:feed ratio of ETEC-infected pigs. However, it is noteworthy that this change was the result of increased feed intake. The observed improvement in feed intake in pigs fed with monoglycerides is further supported by the previously discussed anti-inflammatory effects of monoglycerides. Weaning stress is associated with reduced nutrient and energy intake, which may not recover even two weeks after weaning [ 84 , 85 ]. Thus, the potential impacts of the monoglyceride blend on the feed intake of newly weaned pigs need to be further investigated in a performance trial with a larger number of animals.

The physiological changes caused by external factors, such as nutritional interventions or disease, can be comprehensively evaluated through a metabolomics analysis, providing valuable insights into the underlying mechanisms [ 86 , 87 ]. In this study, pigs supplemented with high-dose zinc oxide exhibited significant alterations in serum metabolites primarily associated with carbohydrate and amino acid metabolism, compared to pigs in the control and monoglycerides groups. These changes are consistent with the mechanistic measurement results discussed earlier, and are also in line with the inferred effects suggested by other research related to nutrient and energy availability [ 68 ]. For example, the citrate cycle is a major metabolic pathway regulated to meet diverse cellular metabolic needs, including playing an important role in energy production and providing intermediates required for biosynthesis [ 88 ]. Recent studies have shown that these intermediates are also involved in cell signaling and have diverse functions, such as the regulation of chromatin modification and DNA methylation, as well as immunomodulation [ 86 , 89 ].

Interestingly, monoglycerides supplementation had limited effects on serum metabolites compared to the control; however, significant pathway alterations were observed in serum metabolites when pigs were supplemented with monoglycerides. Specifically, taurine and hypotaurine metabolism was one of the metabolic pathways significantly affected by the supplementation of monoglycerides during the peak of ETEC infection. Taurine and hypotaurine are known to play crucial roles in cellular homeostasis and antioxidant responses [ 90 , 91 ]. Similar to high-dose zinc oxide, carbadox supplementation had impacts on carbohydrate and amino acid metabolism in serum metabolites compared to control or monoglycerides. These changes include alterations in the citrate cycle and β-alanine metabolism. β-Alanine is a naturally occurring amino acid involved in the synthesis of carnosine, which exhibits beneficial biological activity, including antioxidant and anti-inflammatory properties [ 92 , 93 , 94 ]. Additionally, it has been reported that Mas-related G protein-coupled receptors, specifically responsive to β-alanine, may have beneficial effects on immune stress and homeostasis [ 95 , 96 ].

In conclusion, the findings of this study suggest that supplementation of monoglyceride blend including C4, C8, and C10 saturated fatty acids may enhance disease resistance by mitigating intestinal and systemic inflammation in weaned pigs challenged with enterotoxigenic Escherichia coli F18. Although the effects on performance and disease resistance were not comparable to that of high-dose zinc oxide, the efficacy was similar to the supplementation of carbadox. Additional research is needed to further evaluate the effects of monoglycerides supplementation on growth performance of weaned pigs under various external challenges in commercial conditions. Another area of research may be to explore combinations of monoglycerides with other acids, such as formic acid, as a potential alternative to conventional practices.

Availability of data and materials

All data generated or analyzed during this study are available from the corresponding author upon reasonable request.

Abbreviations

Average daily feed intake

Average daily gain

Body weight

Crypt depth

Complementary DNA

C-reactive protein

• Enterotoxigenic Escherichia coli

False discovery rate

Interleukin 6

Interleukin 7

Interleukin 10

Interleukin 12

Interleukin-1 alpha

Interleukin-1 beta

Medium-chain fatty acids

Polymerase chain reaction

Post-inoculation

Partial least squares discriminant analysis

Prostaglandin-endoperoxide synthase 2

Quantitative real-time PCR

Ribonucleic acid

Short-chain fatty acids

Tumor necrosis factor-alpha

Villus height

Variable importance in projection

High-dose zinc oxide

Zonula occludens-1

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Acknowledgements

We gratefully acknowledge financial support from BASF Corporation and the Jastro Award, granted by the University of California, Davis Animal Biology Graduate Group.

BASF Corporation/SE funded this research.

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Department of Animal Science, University of California, Davis, CA, 95616, USA

Sangwoo Park, Shuhan Sun, Lauren Kovanda & Yanhong Liu

BASF Corporation, Florham Park, 07932, USA

Adebayo O. Sokale & Yanhong Liu

BASF SE, Lampertheim, Germany

Adriana Barri

Department of Animal Science, Michigan State University, East Lansing, MI, 48824, USA

Kwangwook Kim

School of Veterinary Medicine, University of California, Davis, CA, 95616, USA

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Contributions

The contributions of the authors were as follows: SP conducted the animal work and most of the laboratory work and wrote most of the manuscript. SS, LK, and KK assisted in conducting the animal trial and part of the laboratory work. XL provided enterotoxigenic Escherichia coli F18 inoculum and helped to revise the manuscript. AOS and AB provided suggestions on experimental design and revised the manuscript. YL was the principal investigator. She oversaw the development of the study and the manuscript writing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yanhong Liu .

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Ethics approval and consent to participate.

The protocol for this study was reviewed and approved by the Institutional Animal Care and Use Committee at the University of California, Davis (UC Davis, IACUC# 21875). The study was conducted at the Cole facility at UC Davis.

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Not applicable.

Competing interests

Adebayo Sokale is an employee of BASF Corporation (Florham Park, NJ, USA) and Adriana Barri is an employee of BASF SE (Ludwigshafen am Rhein, Germany). No other authors have conflicts of interest to declare.

Supplementary Information

Additional file 1: table s1.

Gene-specific primer sequences and polymerase chain reaction conditions.

Additional file 2:

Fig. S1 Partial Least Squares Discriminant Analysis (PLS-DA) 2D score plot of the metabolites in serum showed separated clusters between the CON and ZNO ( A and B ), MG and ZNO ( C and D ) on d 5 ( A and C ) and d 14 ( B and D ) post-inoculation, respectively. CON, Control; MG, Monoglycerides; ZNO, High-dose zinc oxide. Shaded areas in different colors represent in 95% confidence interval.  Fig. S2 Partial Least Squares Discriminant Analysis (PLS-DA) 2D score plot of the metabolites in serum showed separated clusters between the MG and AB ( A and B ), ZNO and AB ( C and D ) on d 5 ( A and C ) and d 14 ( B and D ) post-inoculation, respectively. MG, Monoglycerides; ZNO, High-dose zinc oxide; AB, Antibiotic. Shaded areas in different colors represent in 95% confidence interval.

Additional file 3:

Fig. S3 Significantly changed pathways in serum between the control and monoglycerides groups on d 5 ( A ) and d 14 ( C ) post-inoculation, respectively. The x -axis represents the pathway impact values and the y -axis represents the −log( P ) values from the pathway enrichment analysis. Metabolite set enrichment analysis shows the metabolic pathways were enriched in control compared with monoglycerides on d 5 ( B ) and d 14 ( D ) post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1. Fig. S4 Significantly changed pathways in serum between the control and high-dose zinc oxide (ZNO) groups on d 5 ( A ) and d 14 ( C ) post-inoculation, respectively. The x -axis represents the pathway impact values and the y -axis represents the −log( P ) values from the pathway enrichment analysis. Metabolite set enrichment analysis shows the metabolic pathways were enriched in control compared with ZNO on d 5 ( B ) and d 14 ( D ) post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1. Fig. S5 Significantly changed pathways in serum between the monoglycerides and high-dose zinc oxide (ZNO) groups on d 5 ( A ) and d 14 ( C ) post-inoculation, respectively. The x -axis represents the pathway impact values and the y -axis represents the −log(P) values from the pathway enrichment analysis. Metabolite set enrichment analysis shows the metabolic pathways were enriched in monoglycerides compared with ZNO on d 5 ( B ) and d 14 ( D ) post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1. Fig. S6 Significantly changed pathways in serum between the monoglycerides and antibiotic groups on d 5 ( A ) and d 14 ( C ) post-inoculation, respectively. The x -axis represents the pathway impact values and the y -axis represents the −log( P ) values from the pathway enrichment analysis. Metabolite set enrichment analysis shows the metabolic pathways were enriched in monoglycerides compared with antibiotic on d 5 ( B ) and d 14 ( D ) post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1. Fig. S7 Significantly changed pathways in serum between the high-dose zinc oxide (ZNO) and antibiotic groups on d 5 ( A ) and d 14 ( C ) post-inoculation, respectively. The x -axis represents the pathway impact values and the y -axis represents the −log( P ) values from the pathway enrichment analysis. Metabolite set enrichment analysis shows the metabolic pathways were enriched in ZNO compared with antibiotic on d 5 ( B ) and d 14 ( D ) post-inoculation, respectively. Both pathway analysis and metabolite set enrichment analysis were performed using identified metabolites with VIP > 1.

Additional file 4:

Fig. S8 Intestinal morphology of enterotoxigenic Escherichia coli F18-challenged weaned pigs fed experimental diets on d 5 post-inoculation.

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Park, S., Sun, S., Kovanda, L. et al. Effects of monoglyceride blend on systemic and intestinal immune responses, and gut health of weaned pigs experimentally infected with a pathogenic Escherichia coli . J Animal Sci Biotechnol 15 , 141 (2024). https://doi.org/10.1186/s40104-024-01103-7

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Received : 03 June 2024

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Published : 13 October 2024

DOI : https://doi.org/10.1186/s40104-024-01103-7

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