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Experimental Research: What it is + Types of designs

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

What is Experimental Research?

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

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

You can conduct experimental research in the following situations:

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

Experimental Research Design Types

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

There are three primary types of experimental design:

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

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

0 1. Pre-Experimental Design

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

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

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

0 2. True Experimental Design

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

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

This experimental research method commonly occurs in the physical sciences.

0 3. Quasi-Experimental Design

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

Importance of Experimental Design

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

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

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

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

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

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

Advantages of Experimental Research

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

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

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

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

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

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

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

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

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

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

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

Randomized Block Design

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

Factorial Design

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

Repeated Measures Design

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

Crossover Design

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

Split-plot Design

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

Nested Design

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

Laboratory Experiment

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

Field Experiment

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

Experimental Design Methods

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

Randomization

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

Control Group

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

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

Counterbalancing

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

Replication

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

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

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

Data Collection Method

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

Direct Observation

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

Self-report Measures

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

Behavioral Measures

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

Physiological Measures

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

Archival Data

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

Computerized Measures

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

Video Recording

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

Data Analysis Method

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

Descriptive Statistics

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

Inferential Statistics

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

Analysis of Variance (ANOVA)

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

Regression Analysis

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

Factor Analysis

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

Structural Equation Modeling (SEM)

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

Cluster Analysis

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

Time Series Analysis

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

Multilevel Modeling

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

Applications of Experimental Design

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

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

Examples of Experimental Design

Here are some examples of experimental design in different fields:

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

When to use Experimental Research Design

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

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

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

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

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

Purpose of Experimental Design

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

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

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

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

Advantages of Experimental Design

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

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

Limitations of Experimental Design

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

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Experimental Research Designs: Types, Examples & Methods

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

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

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

What is Experimental Research?

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

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

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

What are The Types of Experimental Research Design?

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

Pre-experimental Research Design

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

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

One-shot Case Study Research Design

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

One-group Pretest-posttest Research Design:

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

Static-group Comparison:

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

Quasi-experimental Research Design

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

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

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

True Experimental Research Design

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

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

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

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

Examples of Experimental Research

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

Administering Exams After The End of Semester

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

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

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

Employee Skill Evaluation

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

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

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

Evaluation of Teaching Method

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

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

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

What are the Characteristics of Experimental Research?

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

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

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

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

Multivariable

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

Why Use Experimental Research Design?

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

Some uses of experimental research design are highlighted below.

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

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

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

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

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

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

What are the Disadvantages of Experimental Research?

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

What are the Data Collection Methods in Experimental Research?

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

1. Observational Study

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

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

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

2. Simulations

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

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

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

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

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

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

Differences between Experimental and Non-Experimental Research

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

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

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

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

Conclusion

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

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

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

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Experimental Method In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and Loftus and Palmer’s car crash study .

Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
Limitation : They may be more expensive and time-consuming than lab experiments.
Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

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

Experimenter effects

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

Demand characteristics

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

Independent variable (IV)

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

Dependent variable (DV)

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

Extraneous variables (EV)

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

Confounding variables

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

Random Allocation

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

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

Order effects

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

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

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

Research Methodology

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

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Neag School of Education

Educational Research Basics by Del Siegle

Experimental research.

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

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

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

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

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

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

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

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

Experimental Research Design — 6 mistakes you should never make!

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

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

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

Table of Contents

What Is Experimental Research Design?

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

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

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

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

Importance of Experimental Research Design

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

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

Types of Experimental Research Designs

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

1. Pre-experimental Research Design

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

Pre-experimental research is of three types —

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

2. True Experimental Research Design

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

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

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

3. Quasi-experimental Research Design

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

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

Advantages of Experimental Research

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

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

6 Mistakes to Avoid While Designing Your Research

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

1. Invalid Theoretical Framework

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

2. Inadequate Literature Study

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

3. Insufficient or Incorrect Statistical Analysis

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

4. Undefined Research Problem

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

5. Research Limitations

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

6. Ethical Implications

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

Experimental Research Design Example

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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10 Experimental research

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

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

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

Basic concepts

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

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

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

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

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

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

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

Not conducting a pretest can help avoid this threat.

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

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

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

Two-group experimental designs

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

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

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

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

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

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

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

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

Factorial designs

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

$2 \times 2$

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

Hybrid experimental designs

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

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

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

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

Quasi-experimental designs

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

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

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

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

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

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

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

Perils of experimental research

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

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

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

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

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Experimental Research

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

This article is a part of the guide:

Pretest-Posttest
Third Variable
Research Bias
Independent Variable
Between Subjects

Browse Full Outline

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

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

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

Experimental Research is often used where:

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

(Reference: en.wikipedia.org)

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

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

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

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

Aims of Experimental Research

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

Identifying the Research Problem

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

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

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

Constructing the Experiment

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

Sampling Groups to Study

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

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

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

Here are some common sampling techniques :

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

Creating the Design

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

Typical Designs and Features in Experimental Design

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

Pilot Study

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

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

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

Conducting the Experiment

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

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

Analysis and Conclusions

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

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

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

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

Examples of Experiments

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

Here are some examples of scientific experiments:

Social Psychology

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

Oskar Blakstad (Jul 10, 2008). Experimental Research. Retrieved May 18, 2024 from Explorable.com: https://explorable.com/experimental-research

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Experimental Research: Definition, Types, Design, Examples

Appinio Research · 14.05.2024 · 31min read

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

What is Experimental Research?

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

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

Importance of Experimental Research

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

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

Understanding Experimental Design

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

Experimental Design Components

Experimental design comprises several essential elements:

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

Types of Experimental Designs

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

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

Factors Influencing Experimental Design Choices

Several factors influence the selection of an appropriate experimental design:

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

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

Experimental Research Elements

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

Independent and Dependent Variables

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

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

Control Groups and Experimental Groups

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

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

Randomization and Random Sampling

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

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

Replication and Reliability

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

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

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

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

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

How to Conduct Experimental Research?

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

Pre-Experimental Phase

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

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

Experimental Phase

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

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

Post-Experimental Phase

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

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

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

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Experimental Research Examples

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

Market Research

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

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

Software as a Service (SaaS)

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

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

Business Management

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

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

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

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

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

Experimental Research Challenges

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

Sample Size and Statistical Power

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

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

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

Confounding Variables and Bias

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

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

Researcher Effects and Experimenter Bias

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

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

External Validity and Generalizability

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

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

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

Advanced Topics in Experimental Research

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

Quasi-Experimental Designs

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

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

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

Factorial Designs

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

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

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

Longitudinal and Cross-Sectional Studies

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

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

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

Meta-Analysis and Systematic Reviews

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

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

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

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

Experimental Research Ethical Considerations

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

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

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

Conclusion for Experimental Research

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

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Types of Experiment: Overview

Last updated 6 Sept 2022

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Different types of methods are used in research, which loosely fall into 1 of 2 categories.

Experimental (Laboratory, Field & Natural) & N on experimental ( correlations, observations, interviews, questionnaires and case studies).

All the three types of experiments have characteristics in common. They all have:

an independent variable (I.V.) which is manipulated or a naturally occurring variable
a dependent variable (D.V.) which is measured
there will be at least two conditions in which participants produce data.

Note – natural and quasi experiments are often used synonymously but are not strictly the same, as with quasi experiments participants cannot be randomly assigned, so rather than there being a condition there is a condition.

Laboratory Experiments

These are conducted under controlled conditions, in which the researcher deliberately changes something (I.V.) to see the effect of this on something else (D.V.).

Control – lab experiments have a high degree of control over the environment & other extraneous variables which means that the researcher can accurately assess the effects of the I.V, so it has higher internal validity.

Replicable – due to the researcher’s high levels of control, research procedures can be repeated so that the reliability of results can be checked.

Limitations

Lacks ecological validity – due to the involvement of the researcher in manipulating and controlling variables, findings cannot be easily generalised to other (real life) settings, resulting in poor external validity.

Field Experiments

These are carried out in a natural setting, in which the researcher manipulates something (I.V.) to see the effect of this on something else (D.V.).

Validity – field experiments have some degree of control but also are conducted in a natural environment, so can be seen to have reasonable internal and external validity.

Less control than lab experiments and therefore extraneous variables are more likely to distort findings and so internal validity is likely to be lower.

Natural / Quasi Experiments

These are typically carried out in a natural setting, in which the researcher measures the effect of something which is to see the effect of this on something else (D.V.). Note that in this case there is no deliberate manipulation of a variable; this already naturally changing, which means the research is merely measuring the effect of something that is already happening.

High ecological validity – due to the lack of involvement of the researcher; variables are naturally occurring so findings can be easily generalised to other (real life) settings, resulting in high external validity.

Lack of control – natural experiments have no control over the environment & other extraneous variables which means that the researcher cannot always accurately assess the effects of the I.V, so it has low internal validity.

Not replicable – due to the researcher’s lack of control, research procedures cannot be repeated so that the reliability of results cannot be checked.

Laboratory Experiment
Field experiment
Quasi Experiment
Natural Experiment
Field experiments

Field experiments, laboratory experiments, natural experiments, control of extraneous variables, similarities and differences between classical and operant conditioning, learning approaches - social learning theory, differences between behaviourism and social learning theory, research methods in the social learning theory, our subjects.

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v.45(1); Jan-Feb 2010

Study/Experimental/Research Design: Much More Than Statistics

Kenneth l. knight.

Brigham Young University, Provo, UT

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

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

Description:

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

Advantages:

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

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

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

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

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

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

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

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

Table. Elements of a “Methods” Section

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

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

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

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

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

STATISTICAL DESIGN VERSUS STATISTICAL ANALYSIS

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

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

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

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

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

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

MULTIVARIATE RESEARCH AND THE NEED FOR STUDY DESIGNS

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

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

CONCLUSIONS

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

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

Acknowledgments

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

How it works

A Complete Guide to Experimental Research

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

A Quick Guide to Experimental Research

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

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

Independent variable
Dependent variable
Controlled variable

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

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

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

Types of Experimental Research

Laboratory Experiment

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

Example: Milgram’s experiment on obedience.

Field Experiment

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

Natural Experiments

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

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

Quasi-Experiments

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

Example: Comparing the academic performance of the two schools.

Does your Research Methodology Have the Following?

Great Research/Sources
Perfect Language
Accurate Sources

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

How to Conduct Experimental Research?

Step 1. identify and define the problem.

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

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

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

Step 2. Determine the Number of Levels of Variables

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

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

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

Step 3. Formulate the Hypothesis

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

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Step 4. Selection and Assignment of the Subjects

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

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

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

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

What is Replicability?

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

Step 5. Select a Research Design

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

Step 6. Meet Ethical and Legal Requirements

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

Tips for Meeting the Ethical Considerations

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

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

Step 7. Collect and Analyse Data.

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

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

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

Frequently Asked Questions

What is the first step in conducting an experimental research.

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

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Types of Research – Explained with Examples

By DiscoverPhDs
October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

Multistage Sampling explained with Multistage Sample

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An academic transcript gives a breakdown of each module you studied for your degree and the mark that you were awarded.

The scope and delimitations of a thesis, dissertation or paper define the topic and boundaries of a research problem – learn how to form them.

Freya’s in the final year of her PhD at the University of Leeds. Her project is about improving the precision of observations between collocated ground-based weather radar and airborne platforms.

Henry is in the first year of his PhD in the Cronin Group at the University of Glasgow. His research is based on the automation, optimisation, discovery and design of ontologies for robotic chemistry.

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How to Get Started on Your First Psychology Experiment

Acquiring even a little expertise in advance makes science research easier..

Updated May 16, 2024 | Reviewed by Ray Parker

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Students often struggle at the beginning of research projects—knowing how to begin.
Research projects can sometimes be inspired by everyday life or personal concerns.
Becoming something of an "expert" on a topic in advance makes designing a study go more smoothly.

One of the most rewarding and frustrating parts of my long career as a psychology professor at a small liberal arts college has been guiding students through the senior capstone research experience required near the end of their college years. Each psychology major must conduct an independent experiment in which they collect data to test a hypothesis, analyze the data, write a research paper, and present their results at a college poster session or at a professional conference.

The rewarding part of the process is clear: The students' pride at seeing their poster on display and maybe even getting their name on an article in a professional journal allows us professors to get a glimpse of students being happy and excited—for a change. I also derive great satisfaction from watching a student discover that he or she has an aptitude for research and perhaps start shifting their career plans accordingly.

The frustrating part comes at the beginning of the research process when students are attempting to find a topic to work on. There is a lot of floundering around as students get stuck by doing something that seems to make sense: They begin by trying to “think up a study.”

The problem is that even if the student's research interest is driven by some very personal topic that is deeply relevant to their own life, they simply do not yet know enough to know where to begin. They do not know what has already been done by others, nor do they know how researchers typically attack that topic.

Students also tend to think in terms of mission statements (I want to cure eating disorders) rather than in terms of research questions (Why are people of some ages or genders more susceptible to eating disorders than others?).

Needless to say, attempting to solve a serious, long-standing societal problem in a few weeks while conducting one’s first psychology experiment can be a showstopper.

Even a Little Bit of Expertise Can Go a Long Way

My usual approach to helping students get past this floundering stage is to tell them to try to avoid thinking up a study altogether. Instead, I tell them to conceive of their mission as becoming an “expert” on some topic that they find interesting. They begin by reading journal articles, writing summaries of these articles, and talking to me about them. As the student learns more about the topic, our conversations become more sophisticated and interesting. Researchable questions begin to emerge, and soon, the student is ready to start writing a literature review that will sharpen the focus of their research question.

In short, even a little bit of expertise on a subject makes it infinitely easier to craft an experiment on that topic because the research done by others provides a framework into which the student can fit his or her own work.

This was a lesson I learned early in my career when I was working on my own undergraduate capstone experience. Faced with the necessity of coming up with a research topic and lacking any urgent personal issues that I was trying to resolve, I fell back on what little psychological expertise I had already accumulated.

In a previous psychology course, I had written a literature review on why some information fails to move from short-term memory into long-term memory. The journal articles that I had read for this paper relied primarily on laboratory studies with mice, and the debate that was going on between researchers who had produced different results in their labs revolved around subtle differences in the way that mice were released into the experimental apparatus in the studies.

Because I already had done some homework on this, I had a ready-made research question available: What if the experimental task was set up so that the researcher had no influence on how the mouse entered the apparatus at all? I was able to design a simple animal memory experiment that fit very nicely into the psychological literature that was already out there, and this prevented a lot of angst.

Please note that my undergraduate research project was guided by the “expertise” that I had already acquired rather than by a burning desire to solve some sort of personal or social problem. I guarantee that I had not been walking around as an undergraduate student worrying about why mice forget things, but I was nonetheless able to complete a fun and interesting study.

My first experiment may not have changed the world, but it successfully launched my research career, and I fondly remember it as I work with my students 50 years later.

Frank McAndrew, Ph.D., is the Cornelia H. Dudley Professor of Psychology at Knox College.

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ORIGINAL RESEARCH article

Evaluating the impact of commercial radio occultation data using the observing system simulation experiment tool for ionospheric electron density specification provisionally accepted.

1 Orion Space Solutions LLC, United States
2 National Oceanic and Atmospheric Administration (NOAA), United States

The final, formatted version of the article will be published soon.

Decision makers c1 c2 must often choose how many sensors to deploy, of what types, and in what locations to meet a given operational or scientific outcome. An Observing System Simulation Experiment (OSSE) is a numerical experiment which can provide critical decision support to these complex and expensive choices. An OSSE uses a 'truth model' or 'nature run' to simulate what an observation system would measure, and then passes these measurements to an assimilation model. Then, the output of the assimilation model is compared to the truth model to assess improvement and the impact of the observation system. Orion Space Solutions has developed the OSSE Tool (OSSET) to perform OSSEs for ionospheric electron density quickly and accurately.In this study, we use OSSET to predict the impact of adding commercial radio occultation Total Electron Content (TEC) data to an assimilation model. c3 We compare the OSSE's predictions to the real performance at a group of validation ionosondes and find good agreement. c4 We also demonstrate the global assessments that are possible with OSSET using the improvement in critical frequency specification as an example. From this, we find that commercial radio occultation data can improve the critical frequency specification by nearly 20% at high latitudes c5 which are not covered by COSMIC-2. The commercial satellites are in sun-syncronous orbits with constant local times, and this improvement is concentrated at these local times.

Keywords: Ionospheric forecasting, OSSE, Radio occultation, Ionosonde, Total electron content, Validation, data assimilation

Received: 24 Feb 2024; Accepted: 15 May 2024.

Copyright: © 2024 Hughes, Collett, Crowley, Reynolds and Azeem. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Joseph Hughes, Orion Space Solutions LLC, Louisville, United States

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Open access
Published: 14 May 2024

Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response

Liangyu Zhang 1 , 2 na1 ,
Xun Zhang 1 , 2 na1 ,
Maohao Guan 1 , 2 ,
Jianshen Zeng 1 , 2 ,
Fengqiang Yu 1 , 2 &
Fancai Lai 2

Respiratory Research volume 25 , Article number: 206 ( 2024 ) Cite this article

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Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures.

This study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD.

Unsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients.

Conclusions

ICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

Introduction

Internationally, lung cancer continues to maintain its untoward status as the primary contributor to cancer-related deaths [ 1 ], with lung adenocarcinoma (LUAD) representing the predominant histological subtype [ 2 , 3 ]. Despite considerable progress in therapeutic approaches for LUAD, the discouraging 5-year overall survival rate remains stagnant at below 20% [ 4 ].

Iron, as an indispensable trace element, plays a crucial role in human physiology. A deficiency or excess of iron can significantly impact various biological processes [ 5 ]. Notably, cancer cells exhibit an augmented reliance on iron for proliferation, rendering them more vulnerable to iron depletion compared to normal cells. Conversely, elevated iron levels can lead to cytotoxicity via membrane lipid peroxidation, a process referred to as ferroptosis [ 6 , 7 ]. This iron-dependent form of programmed cell death has been identified as a promising strategy for cancer treatment [ 8 ]. While some investigations have hinted at the possible involvement of ferroptosis and iron metabolism in the pathogenesis and suppression of lung cancer, the precise molecular mechanisms underlying these associations remain obscure. Further elucidation of these regulatory factors may provide valuable insights into the development of novel therapeutic strategies for this devastating disease. Copper, an essential micronutrient, exercises a pivotal role in numerous biological processes, including biocompound synthesis, mitochondrial respiration, and antioxidant defense. Disruption of copper homeostasis can lead to oxidative stress and cytotoxicity [ 9 ]. Recently, mounting evidence implicates copper in the progression of cancer, particularly in the realms of metastasis, angiogenesis, and proliferation [ 10 ]. As a critical cofactor of mitochondrial cytochrome C, copper serves as a vital intermediary in energy metabolism. Consequently, cancer tissues exhibit elevated copper levels relative to healthy tissues, underscoring its integral role in sustaining malignant cellular activity [ 11 ]. The versatile element sulfur (S), present in two proteinogenic amino acids – L-cysteine (Cys) and L-methionine (Met) – also comprises a wide array of other biologically significant organic and inorganic small molecules, contributing to the multifaceted nature of this essential nutrient. Sulfur residues participate in the constitution of complex disulfide bond architectures within and intercalated among proteins, thereby influencing crucial biological processes like protein conformation, stability, and catalytic competence [ 12 ]. Sulfur-bearing molecules play a multifaceted role in various physiologic processes, including enzyme catalysis, energy transduction, and redox homeostasis. Disruptions in these activities contribute to a wide range of diseases, notably cancer [ 13 ]. Recently, Liu et al.'s groundbreaking study revealed a new form of programmed cell death, dubbed disulfidptosis. Characterized by the buildup of intracellular disulfides in glucose-deprived cells with heightened expression of SLC7A11, disulfidptosis differs from both ferroptosis and ferroptosis in its mechanism of execution [ 14 ]. Previous studies were predominantly explored genes involved in iron and copper metabolism, while neglecting the potential involvement of genes related to sulfur metabolism. In order to initiate an inquiry into the hitherto unexplored realm of sulfur metabolism in LUAD, and further explore the role of genes related to iron and copper metabolism, we collected genes related to iron, copper and sulfur metabolism, as well as ferroptosis, cuproptosis and disulfidptosis, and conducted extensive research.

This research identified two distinct subtypes of LUAD based on patients' metabolic profiles and developed an Iron, Copper, and Sulfur-Metabolism Index (ICSMI) to predict survival and immune response. Higher ICSMI levels correlated with worse prognosis and reduced immunotherapy effectiveness, suggesting ICSMI's potential as a diagnostic and prognostic tool.

Methods & materials

Source data.

The inclusion criteria for LUAD patients' data are as follows: (a) diagnosed with histologically confirmed lung adenocarcinoma, excluding other types of lung cancer such as lung squamous cell carcinoma, and so forth, (b) underwent surgical procedures, (c) possessed available overall survival (OS) data, and (d) technical replications were removed if deemed necessary. The datasets TCGA-LUAD, GSE72094, GSE68465, and GSE31210 fulfilled these criteria. For other cancer patients' data, the inclusion criteria are as follows: (a) underlying surgical procedures, (b) probable available overall survival (OS) data, and (c) technical replicates were removed if necessary. Data on LUAD patients' clinical information, transcriptomic data, as well as CNV and SNV data were downloaded from the TCGA database ( https://portal.gdc.cancer.gov/ ) [ 15 ]. The TCGA-Pancancer dataset contains data on more than 10,000 patients with 33 different cancers, also obtained from the TCGA website. The SNV data was processed by the R package Maftools, and the CNV data was analyzed using GISTIC2.0 [ 16 ]. Nine GEO datasets for lung cancer patients were obtained from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ )[ 17 ], namely GSE68465, GSE72094, GSE31210, GSE37745, GSE41271, GSE3141, GSE30219, GSE42127, and GSE81089. GSE31210, GSE72094, and GSE68465 are cohorts exclusively comprise of LUAD patients; while GSE30219, GSE37745, GSE41271, GSE42127, GSE3141, and GSE81089 are cohorts consisted of patients with variety of NSCLC type. Besides, GSE91061, a dataset includes information for cancer patients receiving immunotherapy, and GSE34228, which contains LUAD cell lines’ sensitivity to gefitinib, were also downloaded from GEO. Additionally, we gathered transcriptomic and clinical data from cancer patients who underwent anti-PD-L1 treatment within the IMvigor210 cohort. This information was sourced from the following reference: http://research-pub.gene.com/IMvigor210CoreBiologies [ 18 ]. The single-cell RNA-sequencing dataset GSE127465 was acquired from the TISCH database [ 19 ] and processed in accordance with previously outlined procedures [ 20 ]. The genes associated with iron and copper metabolism were compiled from previously published research [ 21 , 22 , 23 , 24 ]. From the MsigDB database [ 25 ], we obtained genes associated with sulfur matabolism from GO_SULFUR_COMPOUND_METABOLIC_PROCESS, GO_SULFUR_COMPOUND_BIOSYNTHETIC_PROCESS, and KEGG_SULFUR_METABOLISM genesets. Considering that iron is involved in ferroptosis, copper is involved in cuproptosis, and sulfur is involved in disulfidptosis, we also included genes associated with these three cell death modes for research [ 14 ]. As a result, we identified 839 Iron, Copper, and Sulfur Metabolism Related Genes (ICSMRGs, Table S1). LUAD patients' TIDE scores, which predict ICB response, were calculated on the TIDE website ( http://tide.dfci.harvard.edu ) [ 26 ].

Consensus clustering

Following the application of a powerful clustering method using the ConsensusClusterPlus package [ 27 ], we effectively identified two subgroups within the LUAD patient population based on the genetic characteristics of 24 prognostic ICSMIGs. LUAD patients in these two subgroups displayed significant differences in clinical and prognostic attributes across four separate cohorts.

Construction of the iron, copper and sulfur-metabolism index

Leveraging ten machine learning algorithms (GBM, RSF, SuperPC, Survival-SVM, Lasso, stepwise Cox, Ridge, Enet, CoxBoost, and plsRcox), we developed an integrative Iron, Copper, and Sulfur-Metabolism Index (ICSMI) via the CoxBoost + GBM combination. After conducting a thorough evaluation of 114 varied permutations, we opted for this selection, which mirrored our previous approach [ 20 ]. The detailed introduce of each algorithm and the specific implementations of various combinations were illustrated in Supplementary Methods. To validate the predictive efficacy of ICSMI, we calculated the area under the receiver operating characteristic curve (AUC) utilizing the timeROC package. Moreover, we performed Cox regression analysis using the survival package in R to affirm the independent prognostic significance of ICSMI. Additionally, we retrospectively compiled 102 signatures established by prior researchers and contrasted ICSMI's hazard ratio (HR) value and C-index with these markers.

Batch effect mitigation and integration: creating unified meta cohorts

We employed the "combat" function from the sva package to mitigate batch effects present in the TCGA, GSE72094, GSE68465, and GSE31210 datasets, integrating them into a unified dataset termed Meta. Principal component analysis (PCA) highlighted notable batch effects across the four datasets before applying batch effect removal (Supplementary Fig. 1C), which were successfully alleviated post-integration (Supplementary Fig. 1D).

Functional enrichment analysis

To uncover the biological pathways linked with ICSMRGs and ICSMI, we conducted enrichment analyses including GO, KEGG, and GSEA using the R package ClusterProfiler [ 28 ]. Moreover, we utilized the GSVA package [ 29 ] to perform GSVA analysis, further uncovering the potential mechanisms involved.

Quantifying patients' immune infiltration level

Seven different algorithms were used to assess LUAD patients’ immune cell infiltration in the TCGA dataset. These algorithms included quantTIseq, TIMER, EPIC, MCP-counter, ESTIMATE, and xCell were implemented using the R package 'IOBR' [ 30 ]. Besides, ssGSEA was performed by GSVA package. Additionally, the correlation of immune-related molecules’ expression and ICSMI were analyzed.

The scissor algorithm

To identify the particular cell populations responsible for the noticed variances in ICSMI status, we utilized the Scissor algorithm available in the 'Scissor' package [ 31 ]. By harnessing both bulk data and phenotypic information, this methodology facilitates the automated selection of cell subpopulations from single-cell datasets that predominantly contribute to divergent phenotypes. In our study, we compared high-ICSMI patients and low-ICSMI patients within the TCGA cohort, treating these groups as distinct phenotypes. Utilizing transcriptomics data of the high- and low-ICSMI phenotypes across all patients, we applied the 'Scissor' function to associate each cell in the GSE127465 dataset with its corresponding phenotype. By designating Scissor + cells as those most relevant to the high-ICSMI phenotype and Scissor- cells as those most pertinent to the low-ICSMI phenotype, we identified differentially expressed genes (DEGs) between these cell populations using Seurat's 'FindAllMarkers' function. Specifically, genes displaying a fold change exceeding |log2 (fold change)|> 0.25 with an adjusted p -value (Padj) below 0.05 were considered significant DEGs.

Analysis of cell–cell communication in the TME

Using the 'CellChat' package [ 32 ], we explored intercellular interaction within the TME, identifying various ligand–receptor pairs that facilitate cross-talk between different cell types.

Finding potential drugs targeting ICSMI

By combining the data from the GDSC1 database ( https://www.cancerrxgene.org/ ) [ 33 ] and the 'oncoPredict' package [ 34 ], we evaluated the susceptibility of LUAD samples to diverse therapeutics, as reflected by their IC50 values. The IC50 value represents the concentration at which a drug achieves 50% inhibition of biological processes, typically measured in in vitro experiments. In cancer research, it is commonly used to assess the degree of inhibition a drug exerts on tumor cells. A lower IC50 value indicates greater sensitivity, meaning the drug achieves a significant inhibitory effect at a lower concentration. This enabled us to identify potential targets for personalized medicine strategies.

Cell culture and transfection

The BEAS-2B normal bronchial epithelial cell line and three LUAD cell lines (A549, PC9, H1975) were obtained from the Cell Bank of the Chinese Academy of Sciences. These cells were cultured at 37 °C with 5% CO2 in DMEM medium (Bioscience, China) supplemented with 10% FBS (Gibco, USA). Small interfering RNA (siRNA), specifically si-GCDH and its corresponding negative control, si-NC, were procured from Hanheng Biology (Shanghai, China). Utilizing Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA), transfection of siRNA into cells was conducted according to the manufacturer's instructions.

Total RNA extraction was performed using an RNA extraction kit (Vazyme, China) following the manufacturer's instructions. The extracted RNA was reverse transcribed into cDNA using the All-in-One First-Strand Synthesis MasterMix kit (iScience, China). Subsequently, triplicate aliquots of each cDNA sample were prepared using the Taq SYBR® Green qPCR Premix (iScience, China). In this study, the internal reference gene utilized was β-Actin, and the primers for the five ICSMRGs and β-Actin are listed in Table S5.

Western blotting

Total protein extraction from cells was achieved using RIPA lysis buffer (Meilun Biotechnology, China). Protein concentration was determined using a bicinchoninic acid protein assay kit (#23,227, Thermo Fisher Scientific, Waltham, USA). Denatured proteins were separated by 10% SDS-PAGE and transferred onto nitrocellulose membranes (Millipore in Bedford, USA). Following a 2-h blocking step with 5% skimmed dry milk, the membranes were incubated overnight at 4 °C with primary antibodies, namely anti-GCDH (1:1000, Immunoway), and anti-β-Actin (1:1000, Immunoway), followed by incubation with horseradish peroxidase labeled secondary antibodies (ab7090, 1:5000; Abcam). β-Actin served as a normalization control for the expression of target proteins.

Wound healing assays

A549 and PC9 LUAD cells, post-transfection, were plated at a density of 10 5 cells per well in 6-well plates. After 24 h of incubation, when cells reached approximately 80% confluence, a 10-μl pipette tip was employed to create uniform scratches on the cell monolayers. Subsequently, detached cells were gently washed away using PBS, and the bottom of the dish was marked for reference. The wound area of each sample was documented at both 0-h and 24-h time points, with quantitative analysis performed using ImageJ software.

Transwell assays

Invasion and migration assays were conducted using Transwell chambers (Scipu002872; Corning Inc., Corning, USA). For the cell invasion assay, the Transwell chamber inserts were precoated with 10 μg Matrigel before the experiment. In the upper chamber, 10,000 cells with 200 μl FBS-free DMEM medium were seeded, while 600 μl of culture medium containing 10% FBS was added to the lower chamber. Following a 24-h incubation period at 37 °C, the cells remaining attached to the membrane were fixed with polyformaldehyde and subsequently stained with hematoxylin. Finally, the cells in the lower chamber were photographed under a high-powered microscope.

Statistic analysis

All statistical analyses were conducted using R (version 4.1.1). Group disparities were assessed using either the Wilcoxon test or t-test, while correlations were examined through Pearson or Spearman correlation coefficients. The log-rank test was utilized for overall survival comparisons. To assess the prognostic impact of ICSMI and clinicopathological factors, multivariate Cox regression analysis was performed. Comparison of multiple signatures' C-Index was carried out using the CompareC package. For P values, 'Ns' denotes P ≥ 0.05, '' signifies P < 0.05, '' indicates P < 0.01, and '' represents P < 0.001.

Identification of 24 hub ICSMRGs

After acquiring 839 Iron, Copper, and Sulfur-Metabolic Related Genes (ICSMRGs), we conducted enrichment analysis on them, and divulged that these genes participate in biological processes pertinent to iron, copper, and sulfur metabolism, including 'response to copper ion', 'ferroptosis', 'iron ion homeostasis', 'copper ion homeostasis', 'sulfur compound metabolic process', and 'sulfur amino acid metabolic process'(Fig. 1 A). This means that we have successfully identified a series of genes highly correlated with iron, copper, and sulfur metabolism. To identify genes with reliable prognostic value, we employed univariate Cox regression analysis in the TCGA, GSE72094, and GSE68465 cohorts based on their largest sample size, and setting the threshold at 0.05. Subsequently, we intersected the prognostic genes from the three cohorts with 839 ICSMRGs, resulting in 24 ICSMRGs with consistent prognostic significance (Fig. 1 B, Table S2). Then we conducted a comparative analysis of the differential expression patterns of these 24 genes between normal and tumor tissues in TCGA, revealing that the majority of them exhibited significant differential expression (Fig. 1 C). In TCGA cohort, 22 ICSMRGs were found to harbor varying levels of mutational activity, with the majority comprising missense mutations; the overall mutation frequency was recorded at 15.08% (Fig. 1 D). Notably, KIF14 displayed the highest mutation frequency among these ICSMRGs. Concomitantly, we observed diverse degrees of DNA copy number variation in these ICSMRGs, with the majority exhibiting variable CNVs; KIF14 also displayed highset CNV amplifications (Fig. 1 F). Correlation analyses disclosed intricate relationship amidst the 24 ICSMRGs, encompassing both positive and adverse associations (Fig. 1 E).

Identification of 24 key ICSMRGs. A GO and KEGG analyses demonstrated that these 839 genes we collected were primarily involved in processes related to iron, copper, and sulfur metabolism. B 24 ICSMRGs exhibited consensus prognostic value across three cohorts. C Differential expression analysis revealed most ICSMRGs exhibiting altered expression patterns between normal and LUAD tissues. D , F These ICSMRGs harbored both SNVs ( D ) and CNVs ( F ), indicating their potential role in driving tumorigenesis. E Correlation heatmap illustrated the interconnected relationships among the 24 genes, highlighting their complex regulatory interactions

Consensus clustering classifying LUAD patients into two Clusters

Utilizing unsupervised clustering on the 24 ICSMRGs, we aimed to uncover previously unidentified subtypes associated with iron, copper, and sulfur metabolism in LUAD. The selection of the optimal number of clusters (k = 2) revealed a notable divergence among groups, indicating a clear classification of LUAD patients into two distinct groups (Fig. 2 A, B). Across four cohorts, the differential expression of these 24 ICSMRGs in two Clusters maintained homogeneity (Fig. 2 C). Besides, we found that patients assigned to Cluster 1 exhibited significantly better prognoses compared to those in Cluster 2 (Fig. 2 D), with individuals in Cluster 2 demonstrating more advanced clinical characteristics (Fig. 2 E). Thus, our findings reveal two distinct molecular subtypes associated with iron, copper, and sulfur metabolism, potentially unveiling underlying biological heterogeneity in LUAD.

Identification of Two Distinct ICSM Clusters through Consensus Clustering. A LUAD patients were grouped into two molecular clusters (k = 2) based on 24 ICSMRGs. B The empirical cumulative distribution function plot depicts the consensus distribution for each k value. C A heatmap illustrates the expression profiles of the 24 ICSMRGs across the two clusters. D Survival analysis reveals significant differences in prognosis between the two clusters. E An alluvial diagram showcases the relationship between cluster affiliation, survival status, and clinical stage in LUAD patients

The iron, copper and sulfur-metabolic index (ICSMI) was constructed

The construction of the Iron, Copper, and Sulfur-Metabolic Index (ICSMI) was initiated by utilizing a machine learning-driven approach based on the 24 prognostic ICSMRGs. Using the TCGA dataset as training set, we developed 114 prediction models and assessed their performance on three independent validation sets (GSE68465, GSE72094, and GSE31210). While certain models, such as 'RSF,' 'Stepcox [forward] + RSF,' and 'Lasso + RSF,' exhibited high C-Index values in the TCGA dataset, their performance diminished in the validation sets, indicating overfitting. To ensure consistent predictive power across all datasets, we selected the 'CoxBoost + GBM' composition, which yielded a model with an average C-Index of 0.7 across all four datasets (C-Index: TCGA-0.740; GSE72094-0.689, GSE31210-0.726; GSE68465-0.644; Fig. 3 A). The CoxBoost algorithm selected 10 ICSMRGs (Supplementary Fig. 1A), and the GBM algorithm evaluated their relative influence within the model (Supplementary Fig. 1B, Table S3), resulting in a GBM model comprising these 10 ICSMRGs (Fig. 3 A). Kaplan–Meier analysis demonstrated a significant impact of all 10 ICSMRGs on the prognosis of LUAD patients (Supplementary Fig. 1E). Using the expression of these 10 ICSMRGs weighted by their relative influence, the model computed a riskscore for each individual, termed as ICSMI.

Integrated machine-learning for developing ICSMI. A A pragmatic evaluation of 114 distinct models was carried out through C-Index assessment across four independent cohorts. B - F Comparative analyses of prognostic variations, PCA, and time-ROC analysis were conducted between high- and low-ICSMI groups in TCGA ( B ), GSE68465 ( C ), GSE31210 ( D ), GSE72094 ( E ), and Meta ( F ) cohorts

The median ICSMI was utilized to stratify patients into two distinct groups. Patients in the high-ICSMI group exhibited significantly poorer prognoses compared to those in the low-ICSMI group, not only within the TCGA training set (Fig. 3 B) but also in three external validation cohorts, namely GSE68465 (Fig. 3 C), GSE72094 (Fig. 3 D), GSE31210 (Fig. 3 E), and Meta (Fig. 3 F). Additionally, PCA analysis revealed noticeable differences between individuals with high or low ICSMI across all datasets, and time-ROC curves illustrate the commendable predictive capabilities of ICSMI for predicting patients' prognosis, with high AUC values (Fig. 3 B-F).

A significant correlation is evident between ICSMI and clinical features of LUAD patients

Heatmaps depict the transcriptional profiles of the 10 ICSMRGs comprising ICSMI across four distinct datasets: TCGA, GSE72094, GSE31210, and GSE68465 (Fig. 4 A-D). In the TCGA cohort, ICSMI in LUAD patients increases with the progression of T (Fig. 4 E), N (Fig. 4 F), and clinical stage (Fig. 4 G). Similarly, in the GSE72094 (Fig. 4 H) and GSE31210 (F i g. 4 I) cohorts, ICSMI increases with clinical stage progression. In the GSE68465 cohort, ICSMI elevates with advanced T (Fig. 4 J) and N (Fig. 4 K) stage, with a significant association observed with LUAD histology (Fig. 4 L). Particularly, in poorly differentiated LUAD tissues, ICSMI is highest, followed by moderately differentiated tissues, and lowest in highly differentiated tissues. Furthermore, our investigation unveils a negative correlation between ICSMI and patients' Relapse-Free Survival (RFS) in the GSE31210 cohort (Fig. 4 M), accompanied by a parallel decrease in patients' Progress-Free Survival (PFS) within the TCGA cohort (Fig. 4 N). These findings underscore the potential utility of ICSMI as a prognostic biomarker in LUAD. Lastly, across all four cohorts, patients assigned to Cluster 2 exhibit markedly higher ICSMI values compared to those allocated to Cluster 1 (Fig. 4 O), indicating a notable association between Iron, Copper, and Sulfur-Metabolism (ICSM) related molecular subtypes and ICSMI.

A robust correlation is evident between ICSMI and clinical attributes among LUAD patients. A - D Heatmaps illustrate expression profiles of 10 ICSMRGs across four datasets. E – G Individuals with high ICSMI in the TCGA Cohort exhibit increased prevalence of advanced T ( E ), N ( F ), and clinical stage ( G ). (H-I) ICSMI in LUAD patients escalates with clinical stage advancement in GSE72094 ( H ) and GSE31210 (I) cohorts. J - L In the GSE68465 cohort, patients' ICSMI elevates with T ( J ) and N ( K ) stage progression, alongside poorer differentiation ( L ). M In the GSE31210 cohort, patients' RFS declines with increasing ICSMI. N In the TCGA cohort, patients' PFS diminishes with rising ICSMI. O Significantly, all four cohorts demonstrate higher levels of ICSMI among patients assigned to Cluster 2

Comparison the predictive efficacy of ICSMI with existing characteristics

To assess the predictive efficacy of ICSMI compared to traditional clinical variables in LUAD patients, we conducted an analysis of C-index and AUC values for each factor (Fig. 5 A-D). Notably, ICSMI exhibited superior predictive performance compared to most clinical markers, indicating its enhanced efficiency. Additionally, we evaluated the prognostic potential of ICSMI against established LUAD models by integrating data from 102 prior studies incorporating various biologically relevant features like apoptosis, EMT, ferroptosis, cuproptosis, necroptosis, and ICD (Table S6). Impressively, ICSMI consistently displayed the highest C-index (Fig. 5 E) and HR value (Fig. 5 F) across multiple cohorts, surpassing the majority of existing models. These findings collectively highlight ICSMI as a more effective prognostic model for LUAD.

Assessing the predictive capability of ICSMI. A - D Contrasting the C-Index and AUC value of ICSMI with clinical factors in the TCGA ( A ), GSE72094 ( B ), GSE31210 ( C ), and GSE68465 ( D ) cohorts. E Comparing the C-Index of ICSMI with 102 previously published signatures. F Comparing the HR-Value of ICSMI with 102 previously published signatures

Nomogram’s development and validation

To validate the independent predictive value of ICSMI, we conducted univariate and multivariate Cox regression analyses. After excluding the influence of clinical variables, our analysis unequivocally established ICSMI as a significant predictor of LUAD patient prognosis, confirming its status as an independent prognosticator not only within the TCGA cohort (Fig. 5 A, B), but also within the GSE68465, GSE72094, and GSE31210 cohorts (Tables 1 , 2 and 3 ). Integrating ICSMI with clinical markers such as age, gender, and clinical stage, we developed a nomogram for forecasting LUAD patient prognosis (Fig. 6 C). Our model achieved a C-index value of 0.768, with calibration plots confirming its accuracy in estimating 1-, 3-, and 5-year survival probabilities (Fig. 6 D). Additionally, employing decision curve analysis (DCA), our nomogram model demonstrated superiority over alternative predictors (Fig. 6 E). Notably, significant survival differences were observed between high- and low-nomogram score groups (Fig. 6 F). Furthermore, AUC values across four cohorts revealed the remarkable precision of our nomogram in predicting 1-, 3-, and 5-year survival prospects for LUAD patients (Fig. 6 G).

Developing a nomogram. A - B Uni- ( A ) and multi- ( B ) vadiate cox regression affirm ICSMI as an independent prognostic determinant. C , D Creation of the nomogram ( C ) and its calibration curve ( D ) showcase its predictive accuracy. E Decision curve analysis (DCA) curves indicate the superior prognostic performance of the nomogram for LUAD patients. F Patients with elevated nomogram scores exhibit poorer prognoses. G ROC curves across four cohorts underscore the remarkable predictive prowess of the nomogram

ICSMI has significantly relationships with TME

Next, we performed investigations into the underlying mechanism behind the remarkable predictive capability of ICSMI, particularly its relationships with the Tumor Microenvironment (TME). In the TCGA cohort, differential analysis highlighted genes with differing expression levels between groups with high and low ICSMI levels (Table S4). The top 50 genes, showing the most significant expression differences, were visually depicted (Fig. 7 A). Additionally, we delved into the impact of the two most up-regulated genes in the high-ICSMI group (SLC2A1, ANLN) and the two most up-regulated genes in the low-ICSMI group (SFTA3, ACSS1) on LUAD patient prognosis. Elevated expression of ANLN and SLC2A1 was associated with poorer prognosis, while increased expression of SFTA3 and ACSS1 indicated better prognosis (Fig. 7 B, C). This suggests that ICSMI serves as a risk factor for LUAD, with high expression correlating with adverse outcomes. GSEA analysis unveiled that genes positively correlated with ICSMI were predominantly involved in malignant features, while genes negatively correlated with ICSMI were associated with benign features (Fig. 7 D, E). GSVA analysis further supported these findings, with gene sets linked to malignant features showing higher activity in the high-ICSMI group, while those related to benign phenotypes exhibited greater activity in the low-ICSMI group (Fig. 7 F).

Uncover the potential involvement of ICSMI in the TME. A To identify the most highly correlated genes with ICSMI, a heatmap of the top 50 genes was generated. B , C Elevated expression of SLC2A1 and ANLN adversely affected the prognosis for LUAD patients, while elevated expression of SFTA3 and ACSS1 improved the prognosis for LUAD patients. D , E GSEA analysis unveiled the functional enrichment of genes positively ( D ) or negatively ( E ) correlated with ICSMI. F GSVA analysis disclosed the gene sets with heightened activity in high- and low- ICSMI groups. G An inverse correlation was observed between ICSMI and the infiltration of most immune cells. H ICSMI was found to be negatively correlated with the expression of TME modulators. I Patients in the low-ICSMI group exhibited lower TIDE scores. J , K Responders to immunotherapy were found to have lower ICSMI levels, and patients receiving immunotherapy with lower ICSMI tended to have better overall survival outcomes in the GSE91061 ( J ) and IMvigor210 ( K ) cohorts

Analysis across seven algorithms revealed a negative correlation between ICSMI and the majority of immune cells, while it correlated positively with epithelial cells and CAFs (Fig. 7 G). Moreover, ICSMI displayed a negative correlation with several immune checkpoint molecules (Fig. 7 H). Notably, individuals in the low-ICSMI group exhibited more pronounced 'immuno-hot' features, suggesting potential responsiveness to immunotherapeutic interventions. This was supported by lower TIDE scores in the low-ICSMI group (Fig. 7 I), indicating improved response to immunotherapy. Furthermore, analysis of GSE91061 and IMvigor210 datasets showed that responders to ICB therapy had lower ICSMI values compared to non-responders, and patients in the low-ICSMI group undergoing immunotherapy demonstrated significantly better clinical outcomes (Fig. 7 J).

Exploring ICSMI at single-cell level

In the Bulk-dataset, ICSMI was an independent risk factor for LUAD patients. We were also very interested in the effects of ICSMI at the cellular level, so we analyzed the single-cell RNA-sequencing dataset. In the GSE127465 dataset, we identified 12 distinct cell populations (Fig. 8 A) and computed the ICSMI for each cell. Remarkably, malignant cells exhibited the highest ICSMI values (Fig. 8 B), with the high-ICSMI group showing a higher proportion of malignant cells (Fig. 8 C). To pinpoint the cellular sources underlying the clinical manifestation associated with high-ICSMI, we utilized the "scissor" package to correlate bulk RNA-sequencing data with single-cell RNA-sequencing data. This algorithm autonomously selected cells exhibiting extraordinary concordance with the targeted phenotype. We designated high-ICSMI and low-ICSMI patient states as primary phenotypes, facilitating the identification of a comprehensive collection of 1566 high-ICSMI cells (Scissor +) and 2151 low-ICSMI cells (Scissor-, Fig. 8 D). Notably, Scissor + cells exhibited significantly higher ICSMI values compared to Scissor- cells (Fig. 8 E), with Scissor + cells displaying the highest ICSMI among all cell types, while Scissor- cells had the lowest (Fig. 8 F). These findings indicate our success in identifying cells in the single-cell dataset that represent different ICSMI states.The AUCell algorithm[ 35 ] was used to calculate enrichment scores for multiple gene sets, and we compare them between Scissor + and Scissor-. Scissor + scored significantly higher than Scissor- for four malignant phenotype including 'Lung Cancer Poor Survival’, ‘Melanoma Metastasis UP’, ‘Cell Cycle’, and 'Epithelial Mesenchymal Transition UP' (Fig. 8 G); while Scissor- scored significantly higher that Scissor + for four benign phenotypes including ‘Lung Cancer Good Survival’, ‘Melanoma Metastasis DN’, ‘Differentiating T Lymphocyte’, and 'Epithelial Mesenchymal Transition DN' (Fig. 8 H). This result is consistent with the GSEA analysis performed in the Bulk-data set.

Utilizing the Scissor algorithm to segregate high- and low- ICSMI characteristics within the single-cell dataset. A Mapping the distribution of 12 cell populations within the tumor microenvironment (TME) using the UMAP plot. B Violin plot illustrating the spectrum of ICSMI levels across diverse cell types. C The high-ICSMI group showed an elevated proportion of malignant cells. D Identified 1566 high-ICSMI cells (Scissor +) and 2151 low-ICSMI cells (Scissor-). E Notably, Scissor + cells exhibited substantially higher ICSMI levels compared to Scissor-. F Among all cell types, Scissor ± displayed the most varied ICSMI levels. G , H Scissor + demonstrated heightened scores indicative of a malignant phenotype ( G ), while Scissor- scored higher for a benign phenotype ( H ). I Comprehensive depiction of cellular communication networks. J Scissor- showed robust signaling transmission capabilities within the TME. K Comparison of Scissor ± cells' proficiency in signal reception and transmission within the TME. L Scissor- exhibited specific molecule expression tailored to pair with ligands from other cells. M These molecules exhibited elevated expression levels in low-ICSMI patients. N The four molecules specifically expressed by Scissor- are identified as protective factors for LUAD

Following this, we delved into the intercellular communication dynamics. The communication network among all cells is depicted in Fig. 8 I. Interestingly, compared to Scissor + cells, Scissor- cells exhibited higher effectiveness in transmitting signals to other cells (Fig. 8 J). Furthermore, when comparing the ability of Scissor + and Scissor- to both receive and transmit signals, Scissor- demonstrated greater activity in communicating with other cells within the TME (Fig. 8 K). We observed that Scissor- specifically expressed various receptors/ligands to interact with ligands/receptors from other cells, a capability not shared by Scissor + . Notably, Scissor- expressed TNFSF13, HLA-DRB5, CD4, and PECAM1 specifically (Fig. 8 L) to exchange signals with cells such as CD4Tconv, DC, Monocytes, and Fibroblasts. Analysis of bulk TCGA data revealed that the expression of these four molecules, specifically expressed by Scissor-, was significantly higher in the low-ICSMI group (Fig. 8 M), and all of them are protective factors for LUAD (Fig. 8 N).

In summary, the characteristics of Scissor ± in the single-cell dataset align with those of ICSMI-high/low in the bulk dataset, thus corroborating our conclusions from different perspectives.

Comparing the different SNV and CNV event between two ICSMI groups

We also conducted multi-omics analyses to compare the genetic landscape between the high-ICSMI and low-ICSMI groups. Initially focusing on the top 20 genes with the highest mutation frequency, we visually depicted the disparities between these groups (Fig. 9 A, B). The followed examination showed that the top 15 genes with the most notable differences in mutation frequency between the high- and low-ICSMI groups exhibited higher frequencies within the former (Fig. 9 C). Additionally, we identified co-mutation relationships among these genes (Fig. 9 D). A detailed investigation was conducted on the most significant mutation differences between the two groups, particularly in the genes COL22A1 and TP53. We investigated the prognostic implications of mutations in these genes and discovered that such mutations were linked to adverse outcomes for patients with LUAD (Fig. 9 E). Moreover, ICSMI exhibited a strong positive correlation with various forms of gene mutations (Fig. 9 F), and significantly correlated with aneuploidy score (Fig. 9 G) and SNV neoantigen (Fig. 9 H).

The genetic landscape displayed significant differences between the two ICSMI groups. A , B Comparison of somatic mutation frequencies in high- ( A ) and low- ( B ) ICSMI patient cohorts. ( C , D ) Identification of the top 15 differentially mutated genes between the two groups ( C ), accompanied by significant co-occurrences among them ( D ). E Mutations in COL22A1 and TP53 are associated with poorer prognosis. F , H Positive correlation of ICSMI with two forms of gene mutation ( F ), aneuploidy score ( G ), and SNV neoantigen (H). (I-J) Analysis of the top 20 copy number variations (CNVs) in the high- ( I ) and low- ( J ) ICSMI groups. K , L Visualization of patients' G-scores using chromosomal plots in the high- ( K ) and low- ( L ) ICSMI groups

Furthermore, our analysis revealed considerable divergence in CNV events between the two ICSMI groups (Fig. 9 I, J). Patients in the high-ICSMI group displayed a higher frequency and more complex array of CNV events, whereas those in the low-ICSMI group exhibited fewer and less elaborate CNV events. ChromPlots further demonstrated that patients in the high ICSMI group had higher G-scores compared to those in the low ICSMI group (Fig. 9 K, L), suggesting a propensity for malignant features among high-ICSMI patients with LUAD.

Exploration of latent agents targeting ICSMI

To uncover potential therapeutic avenues against ICSMI, we examined the connection between ICSMI and commonly used drugs for treating LUAD. Among the twelve medications analyzed, their IC50 values were notably lower in the high-ICSMI cohort compared to the low-ICSMI subset (Fig. 10 A). Additionally, correlation analysis indicated a negative correlation between ICSMI and the IC50 values of these drugs (Fig. 10 B), suggesting that these medications may be more effective in patients with higher ICSMI levels. Particularly noteworthy was the observation that LUAD cell lines sensitive to gefitinib exhibited significantly higher ICSMI levels compared to gefitinib-resistant cell lines (Fig. 10 C), providing further support for our findings.

Chemotherapy and targeted therapy might exhibit heightened efficacy in high-ICSMI patients. A Variations in IC50 values of 12 frequently prescribed drugs between high- and low- ICSMI cohorts are evident. B Correlation between ICSMI levels and drugs' IC50 values is observed. C Differences in ICSMI are apparent between cell lines sensitive versus resistant to gefitinib

Exploring the prognostic value of ICSMI in other cancers besides LUAD

Given the impressive performance of ICSMI in predicting the prognosis of LUAD patients, we are highly interested in exploring its value in predicting the prognosis of other types of cancer. First, we investigated the prognostic value of ICSMI in patients with other types of NSCLC. We selected six GEO datasets containing information on various types of NSCLC patients and calculated ICSMI for each patient. The results indicate that, across the six independent datasets, patients with high ICSMI have a worse prognosis compared to those with low ICSMI, and ROC curves showed that ICSMI also had good predictive power (Fig. 11 A). Next, we obtained the TCGA-Pancancer dataset, which contains information about over 11,000 patients with 33 different types of cancer. PCA analysis shows that patients between high and low ICSMI groups exhibit distinct features (Fig. 11 B). Notably, patients in the high ICSMI group have significantly lower OS than those in the low ICSMI group (Fig. 11 C). Furthermore, as the clinical stage advances, ICSMI displays a gradually increasing trend (Fig. 11 D). We then analyzed the prognostic value of ICSMI in each cancer individually. The HR value of ICSMI was found to be greater than 1 in most cancers, indicating that ICSMI is a risk factor for most cancer patients (Fig. 11 E), specifically for those patients with ACC, CESC, HNSC, KICH, KIRC, KIRP, LGG, LIHC, MESO, PAAD, UCEC, SARC (Fig. 11 F).

ICSMI’s value in pan-cancer cohort. A 6 independent cohorts affirmed that NSCLC patients with higher ICSMI had poorer prognosis. B The PCA plot uncovering distinct characteristics of different ICSMI group patients. C Patients with higher ICSMI had poorer OS. D ICSMI increased with Stage progressed. E ICSMI is a risk factor for most cancer patients. F High ICSMI leads to poorer prognosis in 12 types of cancer patients

Validation of hub ICSMRGs' expression by qRT-PCR

To improve the credibility of our study, we opted to verify the expression of hub ICSMRGs. Our analysis revealed that within the GBM model comprising 10 ICSMRGs, five exhibited a relative influence exceeding 10 in the formation of ICSMI. Therefore, we defining these 5 ICSMRGs, namely GCDH, ST3GAL4, LDHA, FKBP4, and PEBP1 as hub ICSMRGs. In comparison with BEAS-2B, the expression of GCDH, LDHA, and FKBP4 shows an increasing trend in LUAD cell lines, while the expression of PEBP1 exhibits a decreasing trend. However, the expression of ST3GAL4 does not display significant differences between normal lung epithelial cells and LUAD cells. In summary, the expression trends of these five key ICSMRGs are generally consistent with the results analyzed in the TCGA dataset, laying the foundation for future functional experiments targeting these genes (Fig. 12 ).

Validation of five hub ICSMRGs’ expression at cell lines. A - E The expression of GCDH ( A ), ST3GAL4 ( B ), LDHA ( C ), FKBP4 ( D ), and PEBP1 ( E ) in normal lung epithelial cell lines and three LUAD cell lines

Knockdown of GCDH promoted LUAD cells’ migration and invasion

Among the 10 ICSMRGs utilized in constructing ICSMI, GCDH exerts the most significant influence (Supplementary Fig. 1B). Furthermore, it's noteworthy that no study has yet explored the impact of GCDH on LUAD. Therefore, we decided to further explore the role of GCDH in LUAD. In our bioinformatics analysis, GCDH is a protective factor, which indicates that patients with higher expression of GCDH have a better prognosis (Fig. 13 A). Next, we performed in vitro experiments to explore the potential phenotypes associated with GCDH. The expression of GCDH was significantly reduced by siRNA’s knockdown in both A549 (Fig. 13 B) and PC9 (Fig. 13 C) cells. Wound healing assay showed that knockdown of GCDH improved the migration abilities of both A549 (Fig. 13 D) and PC9 (Fig. 13 E) cells. In addition, the transwell assay also demonstrated that knocking out GCDH can promote the migration and invasion of A549 (Fig. 13 F) and PC9 (Fig. 13 G) cells. The results of the wound healing experiment (Fig. 13 H) and the transwell experiment (Fig. 13 I, J) both demonstrate statistical significance, indicating that knocking down GCDH significantly promotes the migration and invasion ability of A549 and PC9 cells. Epithelial-Mesenchymal Transition (EMT) is also a malignant phenotype closely associated with migration and invasion. Tumor cells undergoing EMT exhibit higher malignancy and are more prone to metastasis. Therefore, we investigated the relationship between GCDH and EMT. We downloaded the GSE114761 dataset from the GEO database, which includes EMT data from lung adenocarcinoma cell lines. We found that the expression of GCDH was significantly lower in cells undergoing EMT compared to those not undergoing EMT, and the proportion of cells undergoing EMT in the low-GCDH group is significantly higher than that in the high-GCDH group (Fig. 13 K). Thus, low expression of GCDH may also promote EMT in LUAD cells.

Knockdown of GCDH promotes LUAD cells' malignant phenotype. A High expression of GCDH confers better prognosis in the TCGA cohort. B , C The expression of GCDH was significantly reduced by siRNA’s knockdown in both A549 ( B ) and PC9 ( C ) cells. D , E Wound healing experiment performed in A549 ( D ) and PC9 ( E ) cells. F , G Transwell experiment performed in A549 ( F ) and PC9 ( G ) cells. H The result of the wound healing experiment was statistically significant. I , J Transwell experiment showed that knockdown of GCDH promotes LUAD cells’ migration ( I ) and invasion ( J ) ability. K Lung adenocarcinoma cells undergoing EMT had lower GCDH expression

Therefore, knocking out GCDH can promote the malignant phenotype of LUAD cells, and LUAD cells undergoing EMT exhibit lower GCDH expression, suggesting a protective role of GCDH in LUAD. This finding aligns with our bioinformatics analysis, indicating that patients with high GCDH expression have better prognoses. It further validates the conclusion of our study and the reliability of ICSMI.

The labyrinthine biology and varied clinical manifestations of lung cancer present significant hurdles for medical professionals. Yet, recent progress in high-throughput sequencing have paved the way for the discovery of new prognostic markers. These advancements empower healthcare providers to predict patient outcomes more precisely and tailor therapeutic approaches accordingly. Disruptions in the regulation of iron, copper, and sulfur metabolism can predispose individuals to various diseases. Precedent research has elucidated the function of genes implicated in iron and copper metabolism on LUAD TME, unequivocally establishing their impact upon treatment efficacy [ 22 , 24 , 36 ]. Conversely, the investigation of sulfur metabolism's involvement in LUAD pathogenesis remains relatively unexplored, and iron, copper, and sulfur-metabolism genes have not been combined together to create prognostic signatures. With an eye toward unveiling the heretofore mysterious realm of sulfur metabolism in LUAD and deepening our understanding of iron and copper metabolism within this context, we comprehensively compiled iron, copper, and sulfur-metabolism related genes for studying.

This study marks the inaugural comprehensive analysis of iron, copper, and sulfur metabolism in the context of LUAD. We delineate two distinct subtypes of LUAD characterized by aberrations in iron, copper, and sulfur metabolism (ICSM), and introduce an ICSM-based predictive signature, termed ICSMI, through integrated machine learning. Across multiple independent cohorts, ICSMI demonstrates significant prognostic value, surpassing the predictive power of 102 previously published LUAD prognostic models. A nomogram incorporating clinical features and ICSMI achieves commendable performance. Single-cell analyses reveal that ICSMI is most elevated in malignant cells, while cells identified as ICSMI-high phenotype via the Scissor algorithm exhibit prominent malignant attributes. Furthermore, a significant correlation is uncovered between ICSMI and TME regulators and therapeutic responsiveness, with prognostic significance extending to other cancer types. ICSMI comprises 10 ICSM-related genes (ICSMRGs), including GCDH, ST3GAL4, LDHA, FKBP4, PEBP1, DDIT4, KIF14, RRM2, SERPINB5, and ST3GAL6. Among these, ST3GAL4, LDHA, FKBP4, DDIT4, KIF14, RRM2, and SERPINB5 emerge as risk factors for LUAD, while GCDH, PEBP1, and ST3GAL6 are identified as protective factors. Previous studies have shown that LDHA can interact with APOL3 to regulate TME and ferroptosis [ 37 , 38 ], meaning it’s an important TME and ferroptosis regulator. In addition, studies have shown that histone demethylated LDHA promotes lung metastasis of osteosarcoma [ 39 ], which further elucidated its risky role. In studies by Zong et al. and Meng et al., FKBP4 has been demonstrated to facilitate LUAD progression through distinct pathways, including NF-κB and mTOR [ 40 , 41 ]. RRM2 was identified as a factor influencing the advancement of lung cancer and impacting the infiltration of immune cells within tumors. Inhibition of RRM2 effectively induced polarization towards M1 macrophages while suppressing M2 macrophage polarization. Additionally, treatment with the ferroptosis inhibitor ferrostatin-1 efficiently restored the balance of macrophage polarization disrupted by RRM2 inhibition [ 42 ]. Furthermore, the significant correlation between two ICSMRGs, namely KIF14 and PEBP1, and GPX4—a lipid peroxidase known for its ability to trigger ferroptosis—suggests a clear association between these genes and the ferroptotic process [ 43 , 44 ]. The effects of some ICSMRGs on iron, copper, and sulfur metabolism have not been determined, but some studies suggest that they may be involved in the development of LUAD, such as SERPINB5 stimulates proliferation, metastasis, and EMT in LUAD, while elevated DDIT4 expression correlates with an unfavorable prognosis in LUAD [ 45 , 46 ]. However, studies on the role of GCDH, ST3GAL4, and ST3GAL6 in LUAD and their effects on iron, copper, and sulfur metabolism are lacking. Therefore, our future research aims to explore the function of these genes in depth.

Immunotherapy presents additional chances to prolong life for LUAD patients with malignancies, thus providing a glimmer of hope for individuals grappling with this challenging illness [ 47 ]. Through the analysis of the interaction between ICSMI and the tumor microenvironment (TME), we uncovered an inverse relationship between ICSMI and the majority of immunocytes and immunomodulators. Enrichment analysis further underscored a prevalence of immunologically significant functions within low-ICSMI cohorts. Consequently, individuals exhibiting decreased ICSMI levels demonstrate "immune hot" traits characterized by intensified immunocyte infiltration. It's worth noting that prior studies have hinted at the beneficial association between heightened infiltration of most immune cells in the TME and improved prognosis for LUAD, hinting their potential role in tumor growth suppression [ 48 ]. Therefore, this observation may partly elucidate why patients with high ICSMI levels tend to have poorer prognoses. Notably, individuals with low ICSMI levels exhibited significantly lower TIDE scores, suggesting that the TIDE algorithm predicts augmented sensitivity to immunotherapy in this group. This supposition was substantiated in the GSE91061 and IMvigor210 immunotherapy cohorts, underscoring the pivotal role of ICSMI as a predictive biomarker for immunotherapeutic efficacy. Moreover, leveraging single-cell datasets, we delved into the cellular expression of ICSMI. Our findings revealed that malignant cells displayed the highest ICSMI. Subsequently, using bulk datasets, we segregated high- and low-ICSMI samples into distinct phenotypes and utilized the Scissor algorithm to project these phenotypes onto single-cell data, identifying cells closely associated with each ICSMI status. The resulting Scissor- and Scissor + phenotypes corresponded to low and high ICSMI statuses, respectively. Scissor + exhibited more aggressive features, such as reduced interaction with the TME and a significant positive correlation with poor prognosis in lung cancer patients. In contrast, Scissor- displayed enhanced interaction with the TME, bolstering immune-related functional activity and correlating with improved prognosis in lung cancer patients. Furthermore, Scissor- cells exhibited specific expression of receptors/ligands for signaling with immune cells in the TME, including CD40, TNFSF13, HLA-DRB5, and PECAM1. Notably, Scissor- exhibited higher expression levels of these molecules compared to Scissor + , and patients from the low-ICSMI group showed even higher expression in bulk data, reinforcing the concordance between Scissor ± and ICSMI-High/Low statuses and validating our conclusions.

The Oncopredict package was also used to forecast patient responsiveness to drug therapy. The findings revealed that the IC50 values of both chemotherapeutic and targeted drugs commonly employed in treating NSCLC were lower in the High-ICSMI group. This suggests that patients with lower ICSMI levels may derive greater benefits from treatment with these drugs. Additionally, in the GSE34228 dataset, we corroborated the oncopredict prediction: gefitinib-sensitive LUAD cell lines exhibited elevated ICSMI levels. Hence, immunotherapy may be a viable option for patients with low ICSMI, whereas chemotherapy and targeted therapy may be more suitable for those with high ICSMI. Moreover, in our pan-cancer analysis, we discovered that ICSMI serves as a risk factor for most cancer patients, positively associated with malignant phenotypes while inversely correlated with immune activity. This indicates that the prognostic utility of ICSMI may extend beyond LUAD patients, potentially encompassing individuals with other cancer types.

In fact, this study’s idea was inspired to some extent by Zou et al.'s research [ 49 ], which systematically incorporated genes associated with 12 different programmed cell death, and constructed a 12-gene CDI for predicting the prognosis of breast cancer patients. However, there is currently a lack of systematic studies on genes related to iron, copper, and sulfur-metabolism in cancer. In order to create a prognostic signature composed of ICSM-related genes for the first time to predict the prognosis of LUAD patients, and to provide inspiration for future research on the role of ICSM-related genes in other cancers, we designed and completed this study. After constructing the ICSMI using machine-learning methods, we found that it has higher C-Index and AUC values compared to existing clinical features such as age, gender, smoking, and clinical stage (Fig. 13 A-D), and it has shown higher C-Index and HR values in almost all four independent cohorts compared to 102 previously published prognostic models. This indicates the significant value of ICSMI in predicting the prognosis of LUAD patients. Furthermore, there have been no prior studies investigating GCDH in LUAD. Therefore, our study is the first to reveal the potential role of GCDH in LUAD progression, highlighting it as a potential protective factor. Through in vitro experiments, we found that knockdown GCDH enhances the migration and invasion capabilities of two LUAD cell lines (A549 and PC9). However, the underlying mechanism behind why downregulating GCDH enhances the migration and invasion capabilities of LUAD cells remains unclear. Epithelial-Mesenchymal Transition (EMT) is a biological process during which epithelial cells undergo molecular changes that enable them to acquire mesenchymal-like characteristics. This transition involves the loss of epithelial features such as cell–cell adhesion and apical-basal polarity, accompanied by the acquisition of mesenchymal traits including increased motility, invasiveness, and resistance to apoptosis [ 50 , 51 ]. EMT is a critical event in embryonic development, tissue remodeling, wound healing, and cancer progression, including lung adenocarcinoma. Therefore, we hypothesized that GCDH might promote the migration and invasion of LUAD cells through the potential mechanism of EMT. To preliminarily validate our hypothesis, we downloaded the dataset GSE114761 containing EMT information of LUAD cells and found that the expression of GCDH was significantly lower in cells undergoing EMT compared to those not undergoing EMT, and the proportion of cells undergoing EMT in the low-GCDH group is significantly higher than that in the high-GCDH groups, which suggesting that low expression of GCDH might promote EMT of LUAD cells. This is consistent with the results of our in vitro experiments (low expression of GCDH promotes migration and invasion of LUAD cells). Therefore, we speculate that EMT could be a potential mechanism by which GCDH promotes invasion and migration of LUAD cells. Although the study exhibited ICSMI's impressive potential in predicting prognosis and assessing treatment response in LUAD patients, and uncover a new protective factor—GCDH of LUAD for the first time, there exist several limitations. Firstly, the majority data employed in this research were sourced from publicly available databases. Furthermore, we did not validate the predictive efficacy of ICSMI in our own clinical cohort. The practical application of ICSMI in clinical settings requires confirmation through large-sample clinical studies. Besides, we have yet to comprehensively explore the function of specific ICSMI-associated ICSMRGs in LUAD. This study primarily relies on in vitro experiments to investigate the role of GCDH in LUAD progression. While these experiments provide valuable insights into cellular mechanisms, they fail to fully capture the complexity of tumor behavior in vivo. Therefore, the lack of in vivo validation represents a significant limitation of this study. In vivo models would allow for a more comprehensive understanding of the impact of GCDH on tumor growth, metastasis, and treatment response. Without such validation, the clinical relevance of GCDH knockout in vitro remains limit. In addition, regarding GCDH, we only investigated its correlation with migration and invasion, speculating on the potential mechanism of EMT. However, we did not validate other phenotypes possibly associated with GCDH, such as proliferation, apoptosis, etc. This is also a limitation of our study. Furthermore, we only validated the most influential ICSMRGs — GCDH’s role in the progression of LUAD. Similar validation for other ICSMRGs would further enhance the robustness of the model. Finally, a more detailed analysis of the molecular mechanisms underlying the phenotypic effects of gene expression changes would further elucidate the significance of this study. While this study has made preliminary inferences about the potential role of GCDH in influencing invasion and migration of LUAD cells through EMT, deeper mechanisms (such as which signaling pathways are affected, upstream transcription factors involved, and potential epigenetic mechanisms like methylation, phosphorylation, etc.) have not been thoroughly explored, representing further limitations of this study.

To address these limitations, future research will focus on continued follow-up and the establishment of our own cohorts to validate the performance of ICSMI in larger samples. Furthermore, we will conduct deeper investigations into GCDH, exploring its relationship with phenotypes such as proliferation and apoptosis, and committed to perform in vivo experiments to establish animal models for a more in-depth study of GCDH mechanisms in LUAD. Finally, we will employ similar methods to explore the roles of other ICSMRGs that have not been validated yet in LUAD, aiming for a more comprehensive validation of ICSMI' reliability.

In essence, our present research introduces a novel Iron, Copper, and Sulfur-Metabolism Index (ICSMI) with exceptional accuracy in forecasting the clinical trajectories of Lung Adenocarcinoma (LUAD) patients. This pioneering index not only offers profound insights into the pivotal involvement of iron, copper, and sulfur metabolisms in cancer advancement but also initiates fresh avenues for probing gene expression patterns, employing single-cell RNA-sequencing methodologies, and harnessing machine learning algorithms to refine model enhancement.

Availability of data and materials

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.

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Liangyu Zhang and Xun Zhang contributed equally to this work.

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Department of Thoracic Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China

Liangyu Zhang, Xun Zhang, Maohao Guan, Jianshen Zeng & Fengqiang Yu

Department of Thoracic Surgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China

Liangyu Zhang, Xun Zhang, Maohao Guan, Jianshen Zeng, Fengqiang Yu & Fancai Lai

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FC Lai, FQ Yu conceived, designed and supervised the study. LY Zhang and X Zhang collected, analyzed the data and wrote the manuscript. MH Guan and JS Zeng conducted data analysis and revised the manuscript. All authors read and approved the final manuscript and consent for publication.

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Zhang, L., Zhang, X., Guan, M. et al. Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response. Respir Res 25 , 206 (2024). https://doi.org/10.1186/s12931-024-02839-6

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Chapter 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 (generalizability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments , conducted in field settings such as in a real organization, and 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.

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 favorably 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 receives 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 assures that each unit in the population has a positive chance of being selected into the sample. Random assignment is however 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 (generalizability) 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.

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 on a measure during a posttest to regress toward the mean of that measure 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 was 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

The simplest true experimental designs are two group designs involving one treatment group and one control group, and are ideally suited for testing the effects of a single independent variable that can be manipulated as a treatment. The two basic two-group designs are the pretest-posttest control group design and the posttest-only control group design, while variations may include covariance designs. These designs are often depicted using a standardized design notation, where R represents random assignment of subjects to groups, X represents the treatment administered to the treatment group, and O represents pretest or posttest observations of the dependent variable (with different subscripts to distinguish between pretest and posttest observations of treatment and control groups).

Figure 10.1. Pretest-posttest control group design

The effect E of the experimental treatment in the pretest posttest design is measured as the difference in the posttest and pretest scores between the treatment and control groups:

E = (O 2 – O 1 ) – (O 4 – O 3 )

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.

Figure 10.2. Posttest only control group design.

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

E = (O 1 – O 2 )

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

Covariance designs . Sometimes, measures of dependent variables may be influenced by extraneous variables called covariates . Covariates are those variables that are not of central interest to an experimental study, but should nevertheless be controlled in an experimental design in order to eliminate their potential effect on the dependent variable and therefore allow for a more accurate detection of the effects of the independent variables of interest. The experimental designs discussed earlier did not control for such covariates. A covariance design (also called a concomitant variable design) is a special type of pretest posttest control group design where the pretest measure is essentially a measurement of the covariates of interest rather than that of the dependent variables. The design notation is shown in Figure 10.3, where C represents the covariates:

Figure 10.3. Covariance design

Figure 10.4. 2 x 2 factorial design

Factorial designs can also be depicted using a design notation, such as that shown on the right panel of Figure 10.4. R represents random assignment of subjects to treatment groups, X represents the treatment groups themselves (the subscripts of X represents the level of each factor), and O represent observations of the dependent variable. Notice that the 2 x 2 factorial design will have four treatment groups, corresponding to the four combinations of the two levels of each factor. Correspondingly, the 2 x 3 design will have six treatment groups, and the 2 x 2 x 2 design will have eight treatment groups. As a rule of thumb, each cell in a factorial design should have a minimum sample size of 20 (this estimate is derived from Cohen’s power calculations based on medium effect sizes). So a 2 x 2 x 2 factorial design requires a minimum total sample size of 160 subjects, with at least 20 subjects in each cell. As you can see, the cost of data collection can increase substantially with more levels or factors in your factorial design. Sometimes, due to resource constraints, some cells in such factorial designs may not receive any treatment at all, which are called incomplete factorial designs . Such incomplete designs hurt our ability to draw inferences about the incomplete factors.

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 3 hours/week of instructional time than for 1.5 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 randomized bocks design, Solomon four-group design, and switched replications design.

Randomized 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 treatment group (receiving the same treatment) or 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.

Figure 10.5. Randomized blocks design.

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.

Figure 10.6. Solomon four-group design

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

Figure 10.7. Switched replication design.

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

Many true experimental designs can be converted to quasi-experimental designs by omitting random assignment. For instance, the quasi-equivalent version of pretest-posttest control group design is called nonequivalent groups design (NEGD), as shown in Figure 10.8, with random assignment R replaced by non-equivalent (non-random) assignment N . Likewise, the quasi -experimental version of switched replication design is called non-equivalent switched replication design (see Figure 10.9).

Figure 10.8. NEGD design.

Figure 10.9. Non-equivalent switched replication design.

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 treatment or control group based on a cutoff 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 standardized 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. The design notation can be represented as follows, where C represents the cutoff score:

Figure 10.10. RD design.

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

Figure 10.11. Proxy pretest design.

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, 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 are not available from the same subjects.

Figure 10.12. Separate pretest-posttest samples design.

Nonequivalent dependent variable (NEDV) design . This is a single-group pre-post quasi-experimental design with two outcome measures, where one measure is theoretically expected to be influenced by the treatment and the other measure is not. For instance, if you are designing a new calculus curriculum for high school students, this curriculum is likely to influence students’ posttest calculus scores but not algebra scores. However, the posttest algebra scores may still vary due to extraneous factors such as history or maturation. Hence, the pre-post algebra scores can be used as a control measure, while that of pre-post calculus can be treated as the treatment measure. The design notation, shown in Figure 10.13, indicates the single group by a single N , followed by pretest O 1 and posttest O 2 for calculus and algebra for the same group of students. This design is weak in internal validity, but its advantage lies in not having to use a separate control group.

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.

Figure 10.13. 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, many experimental research use 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 artifact 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 doubt, using tasks that are simpler and familiar for the respondent sample than tasks that are complex or unfamiliar.

Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Effect of nano-TiO 2 size and utilization ratio on the performance of photocatalytic concretes; self-cleaning, fresh, and hardened state properties

Research Article
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Published: 18 May 2024

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Hatice Gizem Şahin 1 ,
Müge Temel 1 ,
Gözde Koçak 2 ,
Ali Mardani ORCID: orcid.org/0000-0003-0326-5015 1 &
Ali Kara 2

In this study, photocatalysis technology was used to reduce water pollution. Decolorization of Reactive Black 5 using nano-TiO 2 (NT) as a photocatalyst was investigated by adsorption and degradation experiments. Effects of NT particle size and utilization ratio on the time-dependent flow performance, compressive-flexural strength, and Bohme abrasion resistance of cementitious systems were investigated. In addition to the NT-free control mixture, a total of six photocatalytic self-cleaning mortar mixtures (PSCM) were prepared using NT in two different particle sizes (28 and 38 nm) and three different ratios (0.5%, 1%, and 1.5%). The PSCM sample containing 38 nm NT exhibited superior performance in terms of photocatalytic properties compared to the 28 nm state. It was observed that the flow performance of PSCM mixtures with NT substitution is adversely affected regardless of the NT type. Mixtures containing NT with a lower particle size (28 nm) had higher compressive and flexural strengths.

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Introduction

Air and water pollution caused by rapidly developing industrialization brings along important social concerns (Kalıpcılar et al. 2016 ; Mardani-Aghabaglou et al. 2019 ; Sezer et al. 2016 ; Yiğit et al. 2020 ; Mardani-Aghabaglou 2016 ; Yüksel et al. 2016 ; Şahin and Mardani 2022a ). At the forefront of these concerns is “health problems.” It is known that approximately 9% of the total annual carbon emissions come from the construction sector. Studies are being carried out on CO 2 emissions resulting from increasing environmental requirements and different suggestions have been presented to reduce CO 2 emissions. In a very comprehensive study conducted by Riekstins et al. ( 2020 ), it was stated that one of these is to ensure energy efficiency by using grinding aids during clinker grinding, and another is to reduce cement usage by substituting certain amounts of mineral additives into the cement used. The use of mineral admixtures in cementitious systems is widely researched (Şahin et al. 2024a , 2024b ).

Volatile organic compounds and inorganic oxides (CO 2 , NO x , and SO x ) in the air accelerate global warming by causing secondary hazards such as acid rain in addition to health problems (Nath et al. 2016 ). The application of “photocatalysis” technology, which accelerates the natural decomposition process, was accepted as an effective solution to reduce and/or prevent the pollution in question (Yang et al. 2000 ). With the help of this technology, numerous pollutants, including hydrocarbons, chlorinated hydrocarbons, SO 2 , CO, and NO, can be transformed directly into H 2 O and CO 2 without the use of an additional carrier gas (Castro-Hoyos et al. 2022 ). In a study by Beeldens ( 2008 ), it was stated that concrete is an ideal substrate for “photocatalysis” reactions due to its large surface area. Similarly, it was stated by various researchers that the self-cleaning and pollutant removal performance is improved when photocatalytic materials are used in concrete (Liang et al. 2019 ). In this process, energy and time savings are achieved due to the use of sun rays and rain water (Obuchi et al. 1999 ; Yu and Brouwers 2009 ). Due to these positive effects, concretes with self-cleaning technology, produced by using materials with photocatalytic properties, have become a popular topic in recent years (Shen et al. 2015 ; Zailan et al. 2017 ). Most studies in this area have focused on the use of semiconductor oxides as photocatalysts, such as titanium dioxide (TiO 2 ), zinc oxide (ZnO), cadmium selenide (CdSe), and tungsten oxide (WO 3 ). Compared to other oxides, TiO2 is preferred more because of (i) low cost, (ii) non-toxicity and (iii) good thermal stability, (iv) easy accessibility, and (v) chemical-biological inertness (Yuranova et al. 2007 ; Yasmina et al. 2014 ; Lazar et al. 2012 ; Kweinor Tetteh et al. 2021 ; Zhang et al. 2016 ). Apart from its self-cleaning effect, it was reported that the use of nano-TiO 2 (NT) has some positive effects on the mechanical properties of cementitious systems (Sanchez and Sobolev 2010 ; Pacheco-Torgal and Jalali 2011 ). In a study conducted by Li et al. ( 2007 ), it was determined that the flexural-fatigue performance of the concrete mixture increased with the addition of 1% NT. In a study by Daniyal et al. ( 2019 ), it was emphasized that the use of NT improves the microstructure by causing a denser matrix formation. In another study by Senff et al. ( 2012 ), it was determined that the use of NT increased the compressive strength of concrete mixtures.

The large amount of waste water resulting from industrialization and globalization causes water pollution by being discharged into water resources without undergoing treatment processes. It was emphasized that dyestuffs come first among these pollutants (Lellis et al. 2019 ). It was declared that the mentioned dyestuffs are classified as nitro, azo, indigo, phtalein, anthraquinone, triphenyl, methyl, and nitrate dyes according to the chemical structures of the chromophore groups. Reactive Black 5 (RB5) dyestuff is in the azo dyestuffs class, which constitutes approximately 70% of the dyestuffs used in the industry (Berradi et al. 2019 ). It was emphasized that RB5 is a water-soluble synthetic dyestuff found most commonly in wastewater (Jalali Sarvestani and Doroudi 2020 ). It was understood that the chemical structure of RB5 is characterized by an azo (-N = N-) chromophore group and a sulfonic (-SO3-) functional group (Przystas et al. 2012 ; Sudha et al. 2014 ; Benkhaya et al. 2017 ; Kaplan et al. 2019 ). Dyestuffs cause deterioration of water quality, decrease in gas solubility, increase in toxicity, allergic reactions, and cancer in the skin by reducing photosynthesis in the aquatic ecosystem (Sudha et al. 2014 ; Asad et al. 2007 ; Shanehsaz et al. 2015 ; Imran et al. 2015 ; Slama et al. 2021 ). For this reason, the treatment of dyestuffs from wastewater has become extremely important.

Traditional treatment methods are used to reduce the environmental impact of dyestuffs. It was reported that these methods are (i) ozonation (Snider and Porter 1974 ), (ii) chlorination (Francy et al. 2012 ), (iii) sedimentation (Mazari and Abdessemed 2020 ), (iv) ultrafiltration (Barredo-Damas et al. 2010 ), and (v) adsorption (Ram et al. 2012 ; Georgiou et al. 2002 ).

It was emphasized that traditional treatment methods are not sufficient to remove dyestuffs, despite the various positive effects they provide. Advanced oxidation processes have come about as a result of this. It was understood that photocatalytic decolorization is an advanced oxidation process and is a more effective and sustainable method for the removal of dyestuffs compared to other methods (Natarajan et al. 2018 ). As emphasized earlier, this process converts organic pollutants into non-toxic small molecules such as CO 2 , H 2 O, and HCl using low-energy UV light and a semiconductor (Espulgas et al. 2002 ; Bizani et al. 2006 ; Cebeci and Selçuk 2020 ).

In this study, photocatalysis technology was used to reduce water pollution. It was reported that charcoal (Horgnies et al. 2012 ), methylene blue (Zhou et al. 2022 ), and Rhodamine-B (Ruot et al. 2009 ) are used in concrete structures to control this process. However, in the textile industry, RB5 was found to be removed from fabrics by photocatalysis technology (Tang and Chen 2004 ). It was emphasized before that RB5 is found in high amounts in wastewater as a dyestuff. In this study, it was planned to remove RB5 by applying the photocatalysis process. Thus, it is foreseen that the quality of wastewater will be increased by applying the photocatalysis process. However, the use of NT is also aimed to improve the mechanical properties of the produced photocatalytic self-cleaning mortar (PSCM) mixtures. Within the scope of this study, it was aimed to examine the effect of NT particle size and utilization ratio on photocatalytic concrete properties. In addition to the control mixture without NT, six series of NT substituted mixtures were prepared. For this purpose, PSCM mixtures were produced by replacing NT with particle sizes of 28 nm and 38 nm with cement at the rates of 0, 0.5, 1, and 1.5%. Time-dependent flow performance, photocatalytic property, compressive-flexural strength, and Böhme abrasion resistance of PSCM mixtures were determined. The photocatalytic property of the mixtures was examined in two stages: adsorption and decolorization.

Material and method

CEM I 42.5R type PC was used as a binder. The properties of the cement are shown in Table 1 .

Two NT with 28- and 38-nm particle size were used in order to fully comprehend the impact of NT fineness on the performance of cementitious systems. Some properties of the NT used are shown in Table 2 .

Crushed limestone aggregate with a D max of 2 mm was used in the preparation of photocatalytic self-cleaning mortar (PSCM) mixtures. The specific gravity and water absorption capacity values of the aggregate determined in accordance with TS EN 1097–6 were measured as 2.58 and 0.4%, respectively. Figure 1 shows the SEM analysis image and EDS results of the limestone aggregate. Additionally, the granulometry of the aggregate is shown in Fig. 2 .

SEM and EDS analysis of limestone aggregate

Grading curve of the aggregate used in the study

A polycarboxylate ether-based high range water reducing admixture (HRWRA) was used to achieve a target flow value of 240 ± 20 mm. Some properties of the HRWRA are shown in Table 3 .

In experimental studies, RB5 (dye content ≥ 50%) used to determine photocatalytic properties, from Sigma-Aldrich, polyvinyl alcohol (PVA) (Mw = 70.000, ≥ 85% hydrolyzed), toluene (≥ 99%), HCl (32%), benzoyl peroxide (with 25% H 2 O), NaOH (98%, pellet), and ethyl alcohol were obtained from Merck. The chemical structure of the RB5 dyestuff applied in the study is shown in Fig. 3 .

Reactive Black 5

Mixing ratios

In order to examine the effect of NT particle size and utilization ratio on the photocatalytic concrete properties, six series of NT substituted mixtures were prepared in addition to the NT-free control mixture. For this purpose, PSCM mixtures were produced by replacing NT with 28-nm and 38-nm particle sizes with 0, 0.5, 1, and 1.5% cement. Within the scope of the study, the amount of material used in the production of 1 m 3 PSCM mixtures with a flow value of 240 ± 20 mm is shown in Table 4 . The w/b ratio was kept as 0.45 in all mixtures. The naming of the mixtures was made according to the NT particle size and utilization ratio. For example, the mixture containing NT at a 1% substitution ratio with a particle size of 28 nm was named NT28_1%. The prepared samples were subjected to water curing in accordance with the standard until the test day.

Time-dependent flow performance, photocatalytic property, compressive-flexural strength, and Bohme abrasion resistance of PSCM mixtures were determined. The workflow applied within the scope of the study is shown in Fig. 4 .

Workflow carried out within the scope of the study

Flow performance

Nano-materials, which are highly reactive and increase the risk of agglomeration (Wiesner and Bottero 2017 ), can seriously affect fresh state properties such as time-dependent flow and rheological performance of cementitious systems (Nazar et al. 2020 ). Thus, it was thought that the properties that provide information about the homogeneity and flowability of cementitious systems should be determined in mixtures containing NT (Jiang et al. 2018 ; Senff et al. 2012 ). It was understood that these properties of paste mixtures are generally determined by rheology testing (Şahin and Mardani 2022b , Şahin and Mardani 2023a ). It was understood that these properties of mortar and concrete mixtures are generally determined by time-dependent flow performance. According to the literature, it was reported that the rheological properties are generally negatively affected by the addition of nano-materials to the mixture. However, some studies have also been found that state the opposite of this situation. In order to resolve these contradictions, time-dependent flow properties of mortar mixtures were investigated.

Time-dependent flow performance of PSCM mixtures was determined by measuring the flow value in accordance with ASTM C1437. In addition, the flow value was measured time interval 20 min for 60 min in order to examine the effect of NT usage on the flow performance of the mixtures.

Photocatalytic properties

Photocatalytic properties of the mixtures were investigated in two stages as adsorption and decolorization.

Adsorption experiments

It was reported by Ferkous et al. ( 2022 ) that the pH value of the solution is the most important factor affecting the adsorption and decolorization of dyestuffs on concrete samples, since the surface charge of the adsorbent changes. For the adsorption and decolorization studies of RB5 dyestuff, the optimum pH value was chosen as pH3 based on previous studies. Three different specimens, control, NT28_1.5%, and NT38_1.5%, were used for adsorption studies of concrete samples. Experimental studies were carried out under the optimum conditions of 25 °C ambient temperature and 30 mg/L solution concentration; it was carried out by taking 50 ml of dyestuff solution. The surface area of the concrete specimens in contact with the dye solution was measured as 75 cm 2 . RB5 dyestuff solutions containing concrete samples were kept in the dark for 24 h. At the end of the experiment, the remaining dyestuff concentration in the solutions was determined by UV–vis spectrophotometer (Shimadzu-2100 UV–vis, Japan). In Fig. 5 , the wavelength-absorbance graph of the RB5 dyestuff is shown. The adsorption capacity ( Q e ) (mg/g) of the remaining dyestuff concentration in each solution was determined using Eq. 1 (Özer et al. 2015 ; El-Bery et al. 2022 ).

where ${C}_{0}$ is the initial dye concentration (mg/L), ${C}_{e}$ is the dye remaining concentration in solution (mg/L), $v$ is the volume of solution (mL), and $m$ is the polymer amount (g).

Wavelength-absorbance graph of RB5

Decolorization experiments

Photocatalytic decolorization of RB5 dyestuff was investigated over time. The experiments were carried out under UV light with a wavelength of 366 nm and in a cabinet at a constant temperature of 25 °C. The remaining dyestuff concentration in the solution was determined by UV–vis spectrophotometer. The % removal amount was calculated using Eq. 2 (Elhadj et al. 2020 ).

where ${C}_{0}$ is the initial dye concentration (mg/cm 2 ), and ${C}_{e}$ is the dye remaining concentration in solution (mg/L).

The adsorption of RB5 dyestuff to PSCM samples is shown in Fig. 6 .

Adsorption of RB5 dyestuff to PSCM samples

A schematic depiction of the photocatalytic degradation mechanism of RB5 dyestuff with a concrete sample is presented in Fig. 7 . Photocatalytic reactions commence with the excitation of the TiO 2 semiconductor by a UV source. The electrons in the valence band move to the conduction band, creating positive vacancies (h +) in the valence band and electrons (e − ) in the conduction band. These electron–hole pairs initiate redox reactions by transferring to the surface of the TiO 2 photocatalyst. Thus, water molecules or hydroxyl (OH − ) ions in the valence band are oxidized by the vacancies to form active hydroxyl radicals (OH). The electrons transitioning to the conduction band also react with O 2 molecules on the photocatalyst surface to produce O 2 − . The free radicals facilitate the degradation of pollutant compounds, leading to their conversion into CO 2 and H 2 O (Koçak Mutlu et al. 2024 ; Navidpour et al. 2023 ). This mechanism generally describes not only the removal of pollution in water (dyes, lead, copper, mercury, etc.) but also the elimination of volatile organic compounds in air (benzene, formaldehyde, toluene, etc.) and the self-cleaning ability of the TiO 2 semiconductor with the assistance of UV rays (Tsang et al. 2019 ).

Schematic representation of the photocatalytic degradation mechanism of RB5 dyestuff with concrete sample

Mechanical properties

The 7- and 28-day compressive strength and Bohme abrasion resistance of the mixtures were determined on 50-mm cube samples, respectively, according to ASTM C109 and EN 1338 Standards. The 7- and 28-day flexural strength of the mixtures was determined by performing a three-point bending test on 40 × 40 × 160 mm prism specimens in accordance with TS EN 196–1 Standard.

Experimental results and discussion

RB5 adsorption capacities and photocatalytic decolorization of control, NT28_1.5%, and NT38_1.5% are shown in Figs. 8 and 9 , respectively.

Adsorption capacity

Graph of % removal over time

Regardless of the particle size, the adsorption capacity of the mixtures containing NT was found to be higher than the control mixture. Also, it is seen that the 38 nm NT-containing PSCM sample has better RB5 adsorption capacity and dye removal performance than the 28-nm case. It was stated by Gunnelius et al. ( 2014 ) that the surface area and activity of NT increase with increasing particle fineness. It is thought that the hydration rate is higher in mixtures containing NT with a particle size of 28 nm (Li et al. 2020 ). It was also stated by Nazari and Riahi ( 2010 ) and Nazari et al. ( 2010 ) that with the decrease in the particle fineness of NT, the potential of NTs that do not or only slightly bind to CH to leak to the surface is high. In this case, it is thought that the particles infiltrating the surface increase the photocatalytic effect. As expected, the percentage of dye removal increased over time.

In a study by Kalkan et al. ( 2014 ), the removal of RB5 from aqueous solutions using silica fume after its modification with laccase from Russulaceae ( Lactarius volemus ) was examined. It was shown that the adsorption experimental data agreed well with the Langmuir isotherm model, and the adsorption capacity was found to be 322.58 mg/g. As a result, it has been understood that laccase-modified silica fume can be used as an alternative low-cost adsorbent in the processing of aqueous solutions. In another study conducted by Erdal et al. ( 2010 ), the removal of textile dye RB5 color by actively growing the mycelium of the Penicillium chrysogenum MT-6 fungus isolated from cement-contaminated soil was examined. The maximum removal/uptake of dye by the fungus was measured to be 89% with a biomass production of 3.83 g/l at an initial dye concentration of 0.3 g/l in 100 h. As a result, it was determined that the fungus is a good biosystem for decolorizing the medium containing RB5.

In order to explain the decolorization kinetics of RB5 dyestuff in the presence of concrete samples, a suitable kinetic model was searched by drawing ln( C / C 0 ) − t graphs. The degradation kinetics of RB5 dyestuff were also evaluated by literature studies and explained with a first-order kinetic model (Hang et al. 2005 ).

where r is the rate and K app (min −1 ) the apparent first-order rate constant; the integration of Eq. ( 4 ) gives:

It is known that the ln( C / C 0 ) graph is linear and examined as a function of time (David and Vedhi 2017 ). A constant K app value is characteristic of the photocatalytic process and defines that the model conforms to the Langmuir–Hinshelwood model, where the reaction takes place in diluted medium (Elhadj et al. 2020 ). The graph of 1/ K app vs. C 0 is presented in Fig. 10 .

1/ K app -C graph

Modeling of degradation kinetics by the Langmuir–Hinshelwood

The Langmuir–Hinshelwood (L–H) model states that dyestuffs are adsorbed according to the Langmuir model before undergoing photocatalytic degradation (Elhadj et al. 2020 ). Adsorption of RB5 dyestuff on the surface of concrete samples affects the rate of degradation. The L–H equation, which expresses the connection between concentration and velocity, is given in Eq. 5 .

where Ks (L/mg) is the adsorption constant; Kr (mg/L.min) represents the rate of reaction.

Linear form of the equation:

1/ K aap is expressed as a function of Cr. Looking at the graph in Fig. 10 , it is seen that the curve is linear ( r = 0.9969). This shows that it is compatible with the L–H model.

Time-dependent flow performance

The flow value of the PSCM mixtures produced within the scope of the study, measured every 20 min for 60 min, and the HRWRA requirement where the target flow value is achieved are shown in Table 5 . Regardless of the utilization ratio and particle size, it was understood that the need for HRWRA increased in order to achieve the desired flow value with NT substitution into the mixture. In a similar study (Joshaghani et al. 2020 ), it was emphasized that the workability was adversely affected by the use of 5% NT, and this was due to the increased water requirement of the mixtures. Similar results were obtained by Nazari and Riahi ( 2010 ) and Nazari et al. ( 2010 ). According to Lee and Kurtis ( 2010 ), the use of NT in cementitious systems increases the hydration rate of C 3 S, causing the formation of C–S–H gel at earlier ages. It was emphasized that this situation causes an increase in the degree of hydration of cementitious systems and negatively affects the workability. Similar statements were also reported by Singh et al. ( 2015 ). In another study by Chen et al. ( 2012 ), it was found that when 5% and 10% NT are substituted into the mixture, the water requirement to achieve target flow value increases by 7 and 15%, respectively. It was emphasized that this may be due to the rapid free water consumption accelerating the bridging process of the voids, resulting in increased viscosity and earlier solidification. It was reported by Ma et al. ( 2016 ) that the addition of NT accelerates the formation and precipitation of early hydration products. It was emphasized that this situation caused the workability properties of NT-containing mixtures to be adversely affected.

It is also understood from Table 5 that the decrease in flow performance as a result of NT substitution into the mixture becomes more evident with the decrease in NT particle size. As a result of the addition of 1.5% of NT with 28- and 38-nm particle sizes to the mixture, it was understood that the need for HRWRA in PSCM mixtures increased by 29% and 14%, respectively. It is thought that the need for HRWRA is higher due to the increase in the wettable surface area (Durgun et al. 2022 ; Mardani-Aghabaglou et al. 2018 ; Özen et al. 2021 ; Şahin et al. 2022 , 2020 ; Şahin and Mardani 2023b ) and the amount of adsorbed water due to the increased surface area due to the decrease in NT particle size (Kuo et al. 2006 ). It was reported by Chen et al. ( 2012 ) that the rapid hardening potential, which is directly dependent on the NT particle size and utilization ratio, directly affects the consistency retention capacity of the mixtures.

It is understood from the table that 0.5% substituted PSCM mixtures in both particle sizes exhibited the best consistency retention performance at 60 min. In addition, it was observed that mixtures containing 0.5% NT have better consistency retention performance, regardless of particle size. It was observed that the consistency retention capacity decreased as the NT substitution ratio increased. It was reported by Wang et al. ( 2018 ) that this may be due to the increase in the hydration rate and the acceleration of the setting due to the increase in NT substitution.

Compressive strength

Compressive strength (CS) results of PSCM are shown in Fig. 11 . It was understood that the CS of the mixtures containing NT with a lower particle size value (28 nm) was higher regardless of the sample age. This condition is believed to be the result of the quick consumption of Ca(OH) 2 produced during the hydration of portland cement, which was brought on by the NT’s smaller particle size, increased surface area, and increased reactivity. However, it was stated by Nazari et al. ( 2010 ) that the use of NT improves the particle packing density of the cement and thus less large pores are formed in the cement paste.

Seven- and 28-day CS values of PSCM

It was observed that the 7-day CS increased by 1, 11, and 6%, respectively, with 0.5%, 1.0, and 1.5% NT substitution with 28-nm particle size to the control mixture. In 28-day samples, this rate was found to be 1, 19, and 11, respectively. Thus, it was determined that the optimum NT particle size and utilization ratio in terms of CS were 28 nm and 1%, respectively. It was understood that the use of NT above this ratio affects the CS of PSCM mixtures negatively. It was emphasized that this situation may be due to the decrease in the crystalline Ca(OH) 2 content required for C-S–H gel formation with the increase of NT, on the one hand, and the increase in the amount of voids in the matrix as a result of the presence of large amounts of nano-particles in the system (Sorathiya et al. 2017 ). Similarly, in a study where NT with a particle size of 15 nm was used at 0.5, 0.75, 1, 1.25, and 1.50% of the cement weight, it was emphasized that the optimum NT usage rate in terms of CS was 1% (Sorathiya et al. 2017 ). In another study by Nazari et al. ( 2010 ), it was determined that the CS value obtained when 2% of NT with a particle size of 15 nm is used is similar to the control mixture without NT. According to reports, the combination contains more NT particles than is necessary for them to react with the lime that is produced during the hydration process. This results in excessive particle leakage and negatively affects the mixture’s strength. Similar results were also reported by Dikkar et al. ( 2021 ). In another study conducted by Mohd Sani et al. ( 2022 ), it was reported that the CS increased with an increase of 1% in the use of NT, but decreased above this rate. However, in a study conducted by Sharma et al. ( 2019 ), it was observed that the maximum CS value in mixtures containing NT with a particle size of 15 nm is obtained when 1.5% NT is used.

Various studies in the literature (Feng et al. 2013 ; Khataee et al. 2013 ; Karapati et al. 2014 ; Zhu et al. 2022 ; Mousavi et al. 2021 ) have emphasized that the optimum NT usage rate in cementitious mixtures containing NT is between 1 and 2%. In a study conducted by Zhu et al. ( 2022 ), the compressive strength of cement paste produced using different proportions of NT was examined. It has been reported that the mixture with optimum compressive strength contains 2% NT. In a study conducted by Khushwaha et al. ( 2015 ), the compressive strength of mixtures with 1%, 2%, and 3% NT content was examined. It has been determined that the strength decreases with increasing dosage. According to Chen et al. ( 2012 ), the compressive strength of mixtures containing two different NTs with particle sizes of 21 nm and 350 nm was examined. It was stated that the 7- and 28-day compressive strengths of the mixture containing 21 nm NT were higher than the results of the mixture containing 350 nm NT. It has been emphasized that the increase in strength is greater as the particle size decreases. However, the study conducted by Li ( 2021 ) reported the opposite. It was emphasized that as the particle size increases, it can absorb less amount of water as its surface area decreases. It was emphasized that NTs used in smaller particle sizes will deteriorate the W/B ratio, negatively affect workability, and cause internal defects. It was stated in various studies that the dosage of NT usage is important to increase the compressive strength in cementitious systems and that it depends on many parameters such as particle size, distribution, W/B ratio, and curing temperature (Li et al. 2023 ; Moro et al. 2020 ; Francioso et al. 2019 , Pimenta Teixeira et al. 2016 ).

Unlike the mixtures containing NT with a particle size of 28 nm, the 7-day CS was decreased by 15, 12, and 2%, respectively, with 0.5%, 1.0, and 1.5% substitution of NT with 38-nm particle size in the control mixture. It was understood that this decrease was 7 and 6%, respectively, in the samples with 0.5% and 1% NT replacement for 28 days. A 3.5% increase in strength was measured for the mixture containing 1.5% NT. It is thought that this negative effect experienced with the use of NT with 38-nm particle size is due to its relatively lower surface area and reactivity compared to 28 nm NT.

Three-point flexural strength

Seven- and 28-day three-point flexural strength (FS) results of PSCM are presented in Fig. 12 . FS values increased with the addition of NT to the mixture, regardless of particle size and sample age. It was understood that there are different opinions about this behavior in the literature. Nazari and Riahi ( 2010 ) and Nazari et al. ( 2010 ) suggested that this is due to the acceleration of the hydration reaction, especially at early ages, with the addition of NT, and thus the formation of hydration products in larger volumes. However, according to Meng et al. ( 2012 ), this increase in FS was not due to the increase in the amount of hydration products, which was due to the decrease in the orientation index for the core function. On the other hand, it was stated by Nazari and Riahi ( 2010 ) and Nazari et al. ( 2010 ) that NTs provide the reduction of voids in cementitious systems by recovering the particle packing density of the cement, thus contributing to the increase in strength. It was determined that the 7-day FS were increased by 15, 16, and 20%, respectively, with 0.5%, 1.0, and 1.5% NT substitution with 28-nm particle size to the control mixture.

Results of FS of mixtures

In a similar study (Selvasofia et al. 2022 ), in which NT was used at 1, 2, 3, and 4% of cement weight, it was found that 2% and 3% NT substituted mixtures had the highest FS values. In order to explain the reason for this situation, FESEM images were examined. It was observed that in the 2% and 3% NT substituted state, a homogeneous C-S–H gel with relatively few spaces between the particles was formed. In addition, it was reported that NT used at the rate of 2% and 3% is fully bonded to the cement, resulting in a stronger bond and increased FS. However, it was understood that the highest FS values in the 28-day samples were obtained when the NT usage rate was 0.5% and it was not necessary to increase the usage rate. A similar statement was also stated by Meng et al. ( 2012 ).

In NT-substituted mixtures with 38-nm particle size, 4% and 13% reductions were measured in 7- and 28-day FS, respectively, with an increase in NT utilization rate from 0.5 to 1.5%. In a study conducted by Salman et al. ( 2016 ), the effect of using 0.25%, 0.75, 1.25, and 1.75% NT on the FS of mixtures was investigated. As a result, it was reported that the use of NT up to 0.75% causes the FS of the mixtures to increase, but the use of NT above this ratio causes the FS values to be adversely affected. It was stated that in the case of 0.75% of the cement weight NT is used, it acts as a filler to strengthen the microstructure of the system, reduces the amount and size of CH crystals, and increases the strength due to filling the voids of the C-S–H gel structure. However, it was reported that there is a decrease in nano-particle distance with an increase in the amount of NT use up to 1.75%, and the CH crystal cannot grow to the appropriate size due to the limited space, and this causes a decrease in strength.

As with the CS values, it was measured that the NT-containing mixtures with a particle size of 28 nm have a higher FS value than the mixtures containing NT with a particle size of 38 nm, regardless of the NT utilization rate. With smaller particle size, the number of surface atoms increases, depending on the increase in the surface area. It was emphasized that the highly active and unstable nature of these surface atoms causes an increase in the hydration reaction rate and higher strengths (Kuo et al. 2006 ).

In addition, it was observed that the optimum NT utilization rate was 0.5% in terms of FS, regardless of particle size. In this case, the mixture with the highest FS was NT28_0.5%.

Bohme abrasion resistance

The 7- and 28-day Bohme abrasion resistance (B’AR) results of PSCM specimens are shown in Fig. 13 . It was found that mixtures containing NT had higher B’AR at all ages, independent of NT particle size and utilization ratio. This situation was thought to be due to the formation of a denser matrix with the use of nano-materials and the reduction of the permeability of the system due to the nucleation effect (Selvasofia et al. 2022 ). A study by Chen et al. ( 2012 ) reported that as hydration continues, aggregations containing nano-particles expand and fill the void space around them over time, contributing to the reduction of porosity over time. On the other hand, Gartner et al. ( 2002 ) emphasized that the decrease in porosity as a result of nano-material addition is due to the physical clogging of capillary spaces.

Bohme abrasion resistance results

Effect of NT usage rate on the B’AR of the mixtures varies depending on the sample age, particle size, and NT usage rate. It was observed that the mass loss of 7-day PSCM decreased with the increase in the use of NT with 28-nm particle size up to 1%. However, the opposite was found with NT substitution above this ratio. It was understood that the mass loss increased continuously with the increase in the use of NT in the 28-day samples. In mixtures containing NT with a particle size of 38 nm, regardless of the sample age, it was understood that the mass loss increased as the NT usage rate increased up to 1%, but decreased above this rate.

It was found that NT substituted mixtures with 38-nm particle size have higher B’AR than mixtures containing 28 nm NT. This is thought to be due to the increase in the particle size of NT and the NTs that do not bind to CH leak onto the surface (Nazari and Riahi 2010 ; Nazari et al. 2010 ), contributing to the hardening of the surface and thus to the increase in B’AR. When the 7-day B’AR performance was examined, it was observed that the NT38_1.5% mixture had a 50% higher B’AR compared to the control mixture. This result was measured to be followed by the NT28_1.0% mixture, which had a 40% increase compared to the control. In the 28-day samples, the mixture with the best B’AR was found to be NT38_0.5% (44% increase compared to control), followed by NT28_0.5% (33% increase compared to control).

Microstructure ımages

Microstructure images of the mixtures produced within the scope of the study are shown in Fig. 14 . The micro-structure of the specimens was studied by using Carl Zeiss/Gemini 300 electron microscope. Scanning electron microscopy (SEM) analyses were attempted to identify the properties of mentioned materials. When Fig. 14 is examined, the effect of NT addition on density and porosity is clearly understood. It was observed that the cracks and pores in the control mixture, shown with the red line, decreased with the use of NT, regardless of the fineness of the NT. Similarly, in a study conducted by Feng et al. ( 2013 ), cement paste containing 0.1, 0.5, 1.0, and 1.5% of the cement mass with NT and a water/cement ratio of 0.4 was prepared and SEM images were examined. As a result, it was observed that NT substitution greatly reduced the amount of internal microcracks in the cement paste. Also, it was emphasized that a new type of needle-shaped hydration product was observed.

Microstructure images of mixtures

Conclusions

The following results are provided in line with the study’s materials and the practical experiments:

It was understood that the mixtures containing NT with a higher particle size value (38 nm) in terms of photocatalytic properties performed better.

It was determined that decolorization kinetics were compatible with Langmuir–Hinshelwood model in all mixtures regardless of NT particle size and utilization ratio.

It was understood that the flow performance of PSCM mixtures was negatively affected by NT substitution. This situation became more evident with the increase in NT utilization ratio and the decrease in particle size.

It was determined that the addition of 1.5% of NT with particle sizes of 28 and 38 nm to the mixture raised the requirement for HRWRA in PSCM mixes by 29% and 14%, respectively.

CS of the mixtures containing NT with lower particle size (28 nm) was higher. In terms of compressive strength, the optimum NT particle size and utilization ratio were found to be 28 nm and 1%, respectively.

It was found that when 0.5%, 1.0, and 1.5% of the NT replacement with 28-nm particle size was added to the control mixture, the 7-day CS increased by 1, 11, and 6%, respectively. This rate was determined to be 1, 19, and 11 in 28-day samples.

It was determined that flexural strength and Bohme abrasion resistance values increased with the addition of NT to the control mixture, regardless of particle size and sample age. In terms of flexural strength, the optimum NT utilization rate was 0.5%. NT-substituted mixtures with 38-nm particle size were measured to have higher abrasion resistance.

When the 7-day B’AR performance was examined, it was measured that the NT38_1.5% mixture had a 50% higher B’AR value than the control mixture, and the NT28_1.0% mixture showed a 40% increase compared to the control.

Data availability

The data that support the findings of this study are available on request from the corresponding author.

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Acknowledgements

The authors thank Bursa Uludağ University Scientific Research Coordination Center (BUUSRC) for their support to the project numbered FOA-2022-1135. First author thanks the TUBITAK 2211-A program for its support throughout her doctoral education. In addition, second author thanks the scholarship provided by BUUSRC with the project number FAY-2021-579.

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK).

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Ali Mardani, Hatice Gizem Şahin, Müge Temel, Gözde Koçak, and Ali Kara. The first draft of the manuscript was written by Ali Mardani, Hatice Gizem Şahin, Müge Temel, Gözde Koçak, and Ali Kara and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Şahin, H.G., Temel, M., Koçak, G. et al. Effect of nano-TiO 2 size and utilization ratio on the performance of photocatalytic concretes; self-cleaning, fresh, and hardened state properties. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33660-9

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