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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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
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 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.
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?
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.
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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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Experiments and quasi-experiments.
This page includes an explanation of the types, key components, validity, ethics, and advantages and disadvantages of experimental design.
An experiment is a study in which the researcher manipulates the level of some independent variable and then measures the outcome. Experiments are powerful techniques for evaluating cause-and-effect relationships. Many researchers consider experiments the "gold standard" against which all other research designs should be judged. Experiments are conducted both in the laboratory and in real life situations.
Types of Experimental Design
There are two basic types of research design:
- True experiments
The purpose of both is to examine the cause of certain phenomena.
True experiments, in which all the important factors that might affect the phenomena of interest are completely controlled, are the preferred design. Often, however, it is not possible or practical to control all the key factors, so it becomes necessary to implement a quasi-experimental research design.
Similarities between true and quasi-experiments:
- Study participants are subjected to some type of treatment or condition
- Some outcome of interest is measured
- The researchers test whether differences in this outcome are related to the treatment
Differences between true experiments and quasi-experiments:
- In a true experiment, participants are randomly assigned to either the treatment or the control group, whereas they are not assigned randomly in a quasi-experiment
- In a quasi-experiment, the control and treatment groups differ not only in terms of the experimental treatment they receive, but also in other, often unknown or unknowable, ways. Thus, the researcher must try to statistically control for as many of these differences as possible
- Because control is lacking in quasi-experiments, there may be several "rival hypotheses" competing with the experimental manipulation as explanations for observed results
Key Components of Experimental Research Design
The manipulation of predictor variables.
In an experiment, the researcher manipulates the factor that is hypothesized to affect the outcome of interest. The factor that is being manipulated is typically referred to as the treatment or intervention. The researcher may manipulate whether research subjects receive a treatment (e.g., antidepressant medicine: yes or no) and the level of treatment (e.g., 50 mg, 75 mg, 100 mg, and 125 mg).
Suppose, for example, a group of researchers was interested in the causes of maternal employment. They might hypothesize that the provision of government-subsidized child care would promote such employment. They could then design an experiment in which some subjects would be provided the option of government-funded child care subsidies and others would not. The researchers might also manipulate the value of the child care subsidies in order to determine if higher subsidy values might result in different levels of maternal employment.
- Study participants are randomly assigned to different treatment groups
- All participants have the same chance of being in a given condition
- Participants are assigned to either the group that receives the treatment, known as the "experimental group" or "treatment group," or to the group which does not receive the treatment, referred to as the "control group"
- Random assignment neutralizes factors other than the independent and dependent variables, making it possible to directly infer cause and effect
Traditionally, experimental researchers have used convenience sampling to select study participants. However, as research methods have become more rigorous, and the problems with generalizing from a convenience sample to the larger population have become more apparent, experimental researchers are increasingly turning to random sampling. In experimental policy research studies, participants are often randomly selected from program administrative databases and randomly assigned to the control or treatment groups.
Validity of Results
The two types of validity of experiments are internal and external. It is often difficult to achieve both in social science research experiments.
- When an experiment is internally valid, we are certain that the independent variable (e.g., child care subsidies) caused the outcome of the study (e.g., maternal employment)
- When subjects are randomly assigned to treatment or control groups, we can assume that the independent variable caused the observed outcomes because the two groups should not have differed from one another at the start of the experiment
- For example, take the child care subsidy example above. Since research subjects were randomly assigned to the treatment (child care subsidies available) and control (no child care subsidies available) groups, the two groups should not have differed at the outset of the study. If, after the intervention, mothers in the treatment group were more likely to be working, we can assume that the availability of child care subsidies promoted maternal employment
One potential threat to internal validity in experiments occurs when participants either drop out of the study or refuse to participate in the study. If particular types of individuals drop out or refuse to participate more often than individuals with other characteristics, this is called differential attrition. For example, suppose an experiment was conducted to assess the effects of a new reading curriculum. If the new curriculum was so tough that many of the slowest readers dropped out of school, the school with the new curriculum would experience an increase in the average reading scores. The reason they experienced an increase in reading scores, however, is because the worst readers left the school, not because the new curriculum improved students' reading skills.
- External validity is also of particular concern in social science experiments
- It can be very difficult to generalize experimental results to groups that were not included in the study
- Studies that randomly select participants from the most diverse and representative populations are more likely to have external validity
- The use of random sampling techniques makes it easier to generalize the results of studies to other groups
For example, a research study shows that a new curriculum improved reading comprehension of third-grade children in Iowa. To assess the study's external validity, you would ask whether this new curriculum would also be effective with third graders in New York or with children in other elementary grades.
Glossary terms related to validity:
- internal validity
- external validity
- differential attrition
It is particularly important in experimental research to follow ethical guidelines. Protecting the health and safety of research subjects is imperative. In order to assure subject safety, all researchers should have their project reviewed by the Institutional Review Boards (IRBS). The National Institutes of Health supplies strict guidelines for project approval. Many of these guidelines are based on the Belmont Report (pdf).
The basic ethical principles:
- Respect for persons -- requires that research subjects are not coerced into participating in a study and requires the protection of research subjects who have diminished autonomy
- Beneficence -- requires that experiments do not harm research subjects, and that researchers minimize the risks for subjects while maximizing the benefits for them
- Justice -- requires that all forms of differential treatment among research subjects be justified
Advantages and Disadvantages of Experimental Design
The environment in which the research takes place can often be carefully controlled. Consequently, it is easier to estimate the true effect of the variable of interest on the outcome of interest.
It is often difficult to assure the external validity of the experiment, due to the frequently nonrandom selection processes and the artificial nature of the experimental context.
Neag School of Education
Educational Research Basics by Del Siegle
The major feature that distinguishes experimental research from other types of research is that the researcher manipulates the independent variable. There are a number of experimental group designs in experimental research. Some of these qualify as experimental research, others do not.
- In true experimental research , the researcher not only manipulates the independent variable, he or she also randomly assigned individuals to the various treatment categories (i.e., control and treatment).
- In quasi experimental research , the researcher does not randomly assign subjects to treatment and control groups. In other words, the treatment is not distributed among participants randomly. In some cases, a researcher may randomly assigns one whole group to treatment and one whole group to control. In this case, quasi-experimental research involves using intact groups in an experiment, rather than assigning individuals at random to research conditions. (some researchers define this latter situation differently. For our course, we will allow this definition).
- In causal comparative ( ex post facto ) research, the groups are already formed. It does not meet the standards of an experiment because the independent variable in not manipulated.
The statistics by themselves have no meaning. They only take on meaning within the design of your study. If we just examine stats, bread can be deadly . The term validity is used three ways in research…
- I n the sampling unit, we learn about external validity (generalizability).
- I n the survey unit, we learn about instrument validity .
- In this unit, we learn about internal validity and external validity . Internal validity means that the differences that we were found between groups on the dependent variable in an experiment were directly related to what the researcher did to the independent variable, and not due to some other unintended variable (confounding variable). Simply stated, the question addressed by internal validity is “Was the study done well?” Once the researcher is satisfied that the study was done well and the independent variable caused the dependent variable (internal validity), then the research examines external validity (under what conditions [ecological] and with whom [population] can these results be replicated [Will I get the same results with a different group of people or under different circumstances?]). If a study is not internally valid, then considering external validity is a moot point (If the independent did not cause the dependent, then there is no point in applying the results [generalizing the results] to other situations.). Interestingly, as one tightens a study to control for treats to internal validity, one decreases the generalizability of the study (to whom and under what conditions one can generalize the results).
There are several common threats to internal validity in experimental research. They are described in our text. I have review each below (this material is also included in the PowerPoint Presentation on Experimental Research for this unit):
- Subject Characteristics (Selection Bias/Differential Selection) — The groups may have been different from the start. If you were testing instructional strategies to improve reading and one group enjoyed reading more than the other group, they may improve more in their reading because they enjoy it, rather than the instructional strategy you used.
- Loss of Subjects ( Mortality ) — All of the high or low scoring subject may have dropped out or were missing from one of the groups. If we collected posttest data on a day when the honor society was on field trip at the treatment school, the mean for the treatment group would probably be much lower than it really should have been.
- Location — Perhaps one group was at a disadvantage because of their location. The city may have been demolishing a building next to one of the schools in our study and there are constant distractions which interferes with our treatment.
- Instrumentation Instrument Decay — The testing instruments may not be scores similarly. Perhaps the person grading the posttest is fatigued and pays less attention to the last set of papers reviewed. It may be that those papers are from one of our groups and will received different scores than the earlier group’s papers
- Data Collector Characteristics — The subjects of one group may react differently to the data collector than the other group. A male interviewing males and females about their attitudes toward a type of math instruction may not receive the same responses from females as a female interviewing females would.
- Data Collector Bias — The person collecting data my favors one group, or some characteristic some subject possess, over another. A principal who favors strict classroom management may rate students’ attention under different teaching conditions with a bias toward one of the teaching conditions.
- Testing — The act of taking a pretest or posttest may influence the results of the experiment. Suppose we were conducting a unit to increase student sensitivity to prejudice. As a pretest we have the control and treatment groups watch Shindler’s List and write a reaction essay. The pretest may have actually increased both groups’ sensitivity and we find that our treatment groups didn’t score any higher on a posttest given later than the control group did. If we hadn’t given the pretest, we might have seen differences in the groups at the end of the study.
- History — Something may happen at one site during our study that influences the results. Perhaps a classmate dies in a car accident at the control site for a study teaching children bike safety. The control group may actually demonstrate more concern about bike safety than the treatment group.
- Maturation –There may be natural changes in the subjects that can account for the changes found in a study. A critical thinking unit may appear more effective if it taught during a time when children are developing abstract reasoning.
- Hawthorne Effect — The subjects may respond differently just because they are being studied. The name comes from a classic study in which researchers were studying the effect of lighting on worker productivity. As the intensity of the factor lights increased, so did the work productivity. One researcher suggested that they reverse the treatment and lower the lights. The productivity of the workers continued to increase. It appears that being observed by the researchers was increasing productivity, not the intensity of the lights.
- John Henry Effect — One group may view that it is competition with the other group and may work harder than than they would under normal circumstances. This generally is applied to the control group “taking on” the treatment group. The terms refers to the classic story of John Henry laying railroad track.
- Resentful Demoralization of the Control Group — The control group may become discouraged because it is not receiving the special attention that is given to the treatment group. They may perform lower than usual because of this.
- Regression ( Statistical Regression) — A class that scores particularly low can be expected to score slightly higher just by chance. Likewise, a class that scores particularly high, will have a tendency to score slightly lower by chance. The change in these scores may have nothing to do with the treatment.
- Implementation –The treatment may not be implemented as intended. A study where teachers are asked to use student modeling techniques may not show positive results, not because modeling techniques don’t work, but because the teacher didn’t implement them or didn’t implement them as they were designed.
- Compensatory Equalization of Treatmen t — Someone may feel sorry for the control group because they are not receiving much attention and give them special treatment. For example, a researcher could be studying the effect of laptop computers on students’ attitudes toward math. The teacher feels sorry for the class that doesn’t have computers and sponsors a popcorn party during math class. The control group begins to develop a more positive attitude about mathematics.
- Experimental Treatment Diffusion — Sometimes the control group actually implements the treatment. If two different techniques are being tested in two different third grades in the same building, the teachers may share what they are doing. Unconsciously, the control may use of the techniques she or he learned from the treatment teacher.
When planning a study, it is important to consider the threats to interval validity as we finalize the study design. After we complete our study, we should reconsider each of the threats to internal validity as we review our data and draw conclusions.
Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com
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- What Is a Research Design | Types, Guide & Examples
What Is a Research Design | Types, Guide & Examples
Published on June 7, 2021 by Shona McCombes . Revised on May 31, 2023 by Pritha Bhandari.
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about:
- Your overall research objectives and approach
- Whether you’ll rely on primary research or secondary research
- Your sampling methods or criteria for selecting subjects
- Your data collection methods
- The procedures you’ll follow to collect data
- Your data analysis methods
A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.
Table of contents
Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.
Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.
There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.
The first choice you need to make is whether you’ll take a qualitative or quantitative approach.
Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.
Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.
It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.
Practical and ethical considerations when designing research
As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .
- How much time do you have to collect data and write up the research?
- Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
- Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
- Will you need ethical approval ?
At each stage of the research design process, make sure that your choices are practically feasible.
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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.
Types of quantitative research designs
Quantitative designs can be split into four main types.
- Experimental and quasi-experimental designs allow you to test cause-and-effect relationships
- Descriptive and correlational designs allow you to measure variables and describe relationships between them.
With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).
Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.
Types of qualitative research designs
Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.
The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.
Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.
In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.
Defining the population
A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.
For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?
The more precisely you define your population, the easier it will be to gather a representative sample.
- Sampling methods
Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.
To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.
Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.
For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.
Case selection in qualitative research
In some types of qualitative designs, sampling may not be relevant.
For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.
In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .
For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.
Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.
You can choose just one data collection method, or use several methods in the same study.
Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .
Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.
Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.
Other methods of data collection
There are many other ways you might collect data depending on your field and topic.
If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.
If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.
With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.
Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.
However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.
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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.
Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.
Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.
If you’re using observations , which events or actions will you count?
If you’re using surveys , which questions will you ask and what range of responses will be offered?
You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.
Reliability and validity
Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.
For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.
If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.
As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.
That means making decisions about things like:
- How many participants do you need for an adequate sample size?
- What inclusion and exclusion criteria will you use to identify eligible participants?
- How will you contact your sample—by mail, online, by phone, or in person?
If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?
If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?
It’s also important to create a data management plan for organizing and storing your data.
Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.
Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).
On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.
Quantitative data analysis
In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.
Using descriptive statistics , you can summarize your sample data in terms of:
- The distribution of the data (e.g., the frequency of each score on a test)
- The central tendency of the data (e.g., the mean to describe the average score)
- The variability of the data (e.g., the standard deviation to describe how spread out the scores are)
The specific calculations you can do depend on the level of measurement of your variables.
Using inferential statistics , you can:
- Make estimates about the population based on your sample data.
- Test hypotheses about a relationship between variables.
Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.
Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.
Qualitative data analysis
In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.
Two of the most common approaches to doing this are thematic analysis and discourse analysis .
There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.
If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.
- Simple random sampling
- Stratified sampling
- Cluster sampling
- Likert scales
- Null hypothesis
- Statistical power
- Probability distribution
- Effect size
- Poisson distribution
- Optimism bias
- Cognitive bias
- Implicit bias
- Hawthorne effect
- Anchoring bias
- Explicit bias
A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.
A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.
Quantitative research designs can be divided into two main categories:
- Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
- Experimental and quasi-experimental designs are used to test causal relationships .
Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.
The priorities of a research design can vary depending on the field, but you usually have to specify:
- Your research questions and/or hypotheses
- Your overall approach (e.g., qualitative or quantitative )
- The type of design you’re using (e.g., a survey , experiment , or case study )
- Your data collection methods (e.g., questionnaires , observations)
- Your data collection procedures (e.g., operationalization , timing and data management)
- Your data analysis methods (e.g., statistical tests or thematic analysis )
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
Operationalization means turning abstract conceptual ideas into measurable observations.
For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.
Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.
A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.
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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.
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.
Scientific manuscripts will be much easier to read and comprehend. A proper experimental design serves as a road map to the study methods, helping readers to understand more clearly how the data were obtained and, therefore, assisting them in properly analyzing the results.
Study, experimental, or research design is the backbone of good research. It directs the experiment by orchestrating data collection, defines the statistical analysis of the resultant data, and guides the interpretation of the results. When properly described in the written report of the experiment, it serves as a road map to readers, 1 helping them negotiate the “Methods” section, and, thus, it improves the clarity of communication between authors and readers.
A growing trend is to equate study design with only the statistical analysis of the data. The design statement typically is placed at the end of the “Methods” section as a subsection called “Experimental Design” or as part of a subsection called “Data Analysis.” This placement, however, equates experimental design and statistical analysis, minimizing the effect of experimental design on the planning and reporting of an experiment. This linkage is inappropriate, because some of the elements of the study design that should be described at the beginning of the “Methods” section are instead placed in the “Statistical Analysis” section or, worse, are absent from the manuscript entirely.
Have you ever interrupted your reading of the “Methods” to sketch out the variables in the margins of the paper as you attempt to understand how they all fit together? Or have you jumped back and forth from the early paragraphs of the “Methods” section to the “Statistics” section to try to understand which variables were collected and when? These efforts would be unnecessary if a road map at the beginning of the “Methods” section outlined how the independent variables were related, which dependent variables were measured, and when they were measured. When they were measured is especially important if the variables used in the statistical analysis were a subset of the measured variables or were computed from measured variables (such as change scores).
The purpose of this Communications article is to clarify the purpose and placement of study design elements in an experimental manuscript. Adopting these ideas may improve your science and surely will enhance the communication of that science. These ideas will make experimental manuscripts easier to read and understand and, therefore, will allow them to become part of readers' clinical decision making.
WHAT IS A STUDY (OR EXPERIMENTAL OR RESEARCH) DESIGN?
The terms study design, experimental design, and research design are often thought to be synonymous and are sometimes used interchangeably in a single paper. Avoid doing so. Use the term that is preferred by the style manual of the journal for which you are writing. Study design is the preferred term in the AMA Manual of Style , 2 so I will use it here.
A study design is the architecture of an experimental study 3 and a description of how the study was conducted, 4 including all elements of how the data were obtained. 5 The study design should be the first subsection of the “Methods” section in an experimental manuscript (see the Table ). “Statistical Design” or, preferably, “Statistical Analysis” or “Data Analysis” should be the last subsection of the “Methods” section.
Table. Elements of a “Methods” Section
The “Study Design” subsection describes how the variables and participants interacted. It begins with a general statement of how the study was conducted (eg, crossover trials, parallel, or observational study). 2 The second element, which usually begins with the second sentence, details the number of independent variables or factors, the levels of each variable, and their names. A shorthand way of doing so is with a statement such as “A 2 × 4 × 8 factorial guided data collection.” This tells us that there were 3 independent variables (factors), with 2 levels of the first factor, 4 levels of the second factor, and 8 levels of the third factor. Following is a sentence that names the levels of each factor: for example, “The independent variables were sex (male or female), training program (eg, walking, running, weight lifting, or plyometrics), and time (2, 4, 6, 8, 10, 15, 20, or 30 weeks).” Such an approach clearly outlines for readers how the various procedures fit into the overall structure and, therefore, enhances their understanding of how the data were collected. Thus, the design statement is a road map of the methods.
The dependent (or measurement or outcome) variables are then named. Details of how they were measured are not given at this point in the manuscript but are explained later in the “Instruments” and “Procedures” subsections.
Next is a paragraph detailing who the participants were and how they were selected, placed into groups, and assigned to a particular treatment order, if the experiment was a repeated-measures design. And although not a part of the design per se, a statement about obtaining written informed consent from participants and institutional review board approval is usually included in this subsection.
The nuts and bolts of the “Methods” section follow, including such things as equipment, materials, protocols, etc. These are beyond the scope of this commentary, however, and so will not be discussed.
The last part of the “Methods” section and last part of the “Study Design” section is the “Data Analysis” subsection. It begins with an explanation of any data manipulation, such as how data were combined or how new variables (eg, ratios or differences between collected variables) were calculated. Next, readers are told of the statistical measures used to analyze the data, such as a mixed 2 × 4 × 8 analysis of variance (ANOVA) with 2 between-groups factors (sex and training program) and 1 within-groups factor (time of measurement). Researchers should state and reference the statistical package and procedure(s) within the package used to compute the statistics. (Various statistical packages perform analyses slightly differently, so it is important to know the package and specific procedure used.) This detail allows readers to judge the appropriateness of the statistical measures and the conclusions drawn from the data.
STATISTICAL DESIGN VERSUS STATISTICAL ANALYSIS
Avoid using the term statistical design . Statistical methods are only part of the overall design. The term gives too much emphasis to the statistics, which are important, but only one of many tools used in interpreting data and only part of the study design:
The most important issues in biostatistics are not expressed with statistical procedures. The issues are inherently scientific, rather than purely statistical, and relate to the architectural design of the research, not the numbers with which the data are cited and interpreted. 6
Stated another way, “The justification for the analysis lies not in the data collected but in the manner in which the data were collected.” 3 “Without the solid foundation of a good design, the edifice of statistical analysis is unsafe.” 7 (pp4–5)
The intertwining of study design and statistical analysis may have been caused (unintentionally) by R.A. Fisher, “… a genius who almost single-handedly created the foundations for modern statistical science.” 8 Most research did not involve statistics until Fisher invented the concepts and procedures of ANOVA (in 1921) 9 , 10 and experimental design (in 1935). 11 His books became standard references for scientists in many disciplines. As a result, many ANOVA books were titled Experimental Design (see, for example, Edwards 12 ), and ANOVA courses taught in psychology and education departments included the words experimental design in their course titles.
Before the widespread use of computers to analyze data, designs were much simpler, and often there was little difference between study design and statistical analysis. So combining the 2 elements did not cause serious problems. This is no longer true, however, for 3 reasons: (1) Research studies are becoming more complex, with multiple independent and dependent variables. The procedures sections of these complex studies can be difficult to understand if your only reference point is the statistical analysis and design. (2) Dependent variables are frequently measured at different times. (3) How the data were collected is often not directly correlated with the statistical design.
For example, assume the goal is to determine the strength gain in novice and experienced athletes as a result of 3 strength training programs. Rate of change in strength is not a measurable variable; rather, it is calculated from strength measurements taken at various time intervals during the training. So the study design would be a 2 × 2 × 3 factorial with independent variables of time (pretest or posttest), experience (novice or advanced), and training (isokinetic, isotonic, or isometric) and a dependent variable of strength. The statistical design , however, would be a 2 × 3 factorial with independent variables of experience (novice or advanced) and training (isokinetic, isotonic, or isometric) and a dependent variable of strength gain. Note that data were collected according to a 3-factor design but were analyzed according to a 2-factor design and that the dependent variables were different. So a single design statement, usually a statistical design statement, would not communicate which data were collected or how. Readers would be left to figure out on their own how the data were collected.
MULTIVARIATE RESEARCH AND THE NEED FOR STUDY DESIGNS
With the advent of electronic data gathering and computerized data handling and analysis, research projects have increased in complexity. Many projects involve multiple dependent variables measured at different times, and, therefore, multiple design statements may be needed for both data collection and statistical analysis. Consider, for example, a study of the effects of heat and cold on neural inhibition. The variables of H max and M max are measured 3 times each: before, immediately after, and 30 minutes after a 20-minute treatment with heat or cold. Muscle temperature might be measured each minute before, during, and after the treatment. Although the minute-by-minute data are important for graphing temperature fluctuations during the procedure, only 3 temperatures (time 0, time 20, and time 50) are used for statistical analysis. A single dependent variable H max :M max ratio is computed to illustrate neural inhibition. Again, a single statistical design statement would tell little about how the data were obtained. And in this example, separate design statements would be needed for temperature measurement and H max :M max measurements.
As stated earlier, drawing conclusions from the data depends more on how the data were measured than on how they were analyzed. 3 , 6 , 7 , 13 So a single study design statement (or multiple such statements) at the beginning of the “Methods” section acts as a road map to the study and, thus, increases scientists' and readers' comprehension of how the experiment was conducted (ie, how the data were collected). Appropriate study design statements also increase the accuracy of conclusions drawn from the study.
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.
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.
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.
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.
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.
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 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).
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.
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
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:
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 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.
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.
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.
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. 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
- 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.
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.
This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.
This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.
Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.
A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.
Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.
Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.
Differences between Experimental and Non-Experimental Research
1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.
This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.
2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change
3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.
Experimental research 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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- examples of experimental research
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- types of experimental research
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Home » Experimental Design – Types, Methods, Guide
Experimental Design – Types, Methods, Guide
Table of Contents
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.
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.
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.
In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.
This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.
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 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:
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.
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.
This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.
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:
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 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 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 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 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 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 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 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 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 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 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 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 is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.
Applications of Experimental Design
Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:
- Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
- Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
- Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
- Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
- Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
- Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
- Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.
Examples of Experimental Design
Here are some examples of experimental design in different fields:
- Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
- Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
- Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
- Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
- Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.
When to use Experimental Research Design
Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.
Here are some situations where experimental research design may be appropriate:
- When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
- When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
- When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
- When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
- When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.
How to Conduct Experimental Research
Here are the steps to conduct Experimental Research:
- Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
- Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
- Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
- Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
- Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
- Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
- Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
- Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
- Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.
Purpose of Experimental Design
The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.
Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.
Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.
Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.
Advantages of Experimental Design
Experimental design offers several advantages in research. Here are some of the main advantages:
- Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
- Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
- Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
- Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
- Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
- Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.
Limitations of Experimental Design
Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:
- Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
- Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
- Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
- Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
- Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
- Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
- Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.
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The 3 Types Of Experimental Design
Experimental design refers to a research methodology that allows researchers to test a hypothesis regarding the effects of an independent variable on a dependent variable.
There are three types of experimental design: pre-experimental design, quasi-experimental design, and true experimental design.
Experimental Design in a Nutshell
A typical and simple experiment will look like the following:
- The experiment consists of two groups: treatment and control.
- Participants are randomly assigned to be in one of the groups (‘conditions’).
- The treatment group participants are administered the independent variable (e.g. given a medication).
- The control group is not given the treatment.
- The researchers then measure a dependent variable (e.g improvement in health between the groups).
If the independent variable affects the dependent variable, then there should be noticeable differences on the dependent variable between the treatment and control conditions.
The experiment is a type of research methodology that involves the manipulation of at least one independent variable and the measurement of at least one dependent variable.
If the independent variable affects the dependent variable, then the researchers can use the term “causality.”
Types of Experimental Design
1. pre-experimental design.
A researcher may use pre-experimental design if they want to test the effects of the independent variable on a single participant or a small group of participants.
The purpose is exploratory in nature , to see if the independent variable has any effect at all.
The pre-experiment is the simplest form of an experiment that does not contain a control condition.
However, because there is no control condition for comparison, the researcher cannot conclude that the independent variable causes change in the dependent variable.
- Action Research in the Classroom: Action research in education involves a teacher conducting small-scale research in their classroom designed to address problems they and their students currently face.
- Case Study Research : Case studies are small-scale, often in-depth, studies that are notusually generalizable.
- A Pilot Study: Pilot studies are small-scale studies that take place before the main experiment to test the feasibility of the project.
- Ethnography: An ethnographic research study will involve thick research of a small cohort to generate descriptive rather than predictive results.
2. Quasi-Experimental Design
The quasi-experiment is a methodology to test the effects of an independent variable on a dependent variable. However, the participants are not randomly assigned to treatment or control conditions. Instead, the participants already exist in representative sample groups or categories, such as male/female or high/low SES class.
Because the participants cannot be randomly assigned to male/female or high/low SES, there are limitations on the use of the term “causality.”
Researchers must refrain from inferring that the independent variable caused changes in the dependent variable because the participants existed in already formed categories before the study began.
- Homogenous Representative Sampling: When the research participant group is homogenous (i.e. not diverse) then the generalizability of the study is diminished.
- Non-Probability Sampling: When researchers select participants through subjective means such as non-probability sampling, they are engaging in quasi-experimental design and cannot assign causality.
See more Examples of Quasi-Experimental Design
3. True Experimental Design
A true experiment involves a design in which participants are randomly assigned to conditions, there exists at least two conditions (treatment and control) and the researcher manipulates the level of the independent variable (independent variable).
When these three criteria are met, then the observed changes in the dependent variable (dependent variable) are most likely caused by the different levels of the independent variable.
The true experiment is the only research design that allows the inference of causality .
Of course, no study is perfect, so researchers must also take into account any threats to internal validity that may exist such as confounding variables or experimenter bias.
- Heterogenous Sample Groups: True experiments often contain heterogenous groups that represent a wide population.
- Clinical Trials: Clinical trials such as those required for approval of new medications are required to be true experiments that can assign causality.
See More Examples of Experimental Design
Experimental Design vs Observational Design
Experimental design is often contrasted to observational design. Defined succinctly, an experimental design is a method in which the researcher manipulates one or more variables to determine their effects on another variable, while observational design involves the observation and analysis of a subject without influencing their behavior or conditions.
Observational design primarily involves data collection without direct involvement from the researcher. Here, the variables aren’t manipulated as they would be in an experimental design.
An example of an observational study might be research examining the correlation between exercise frequency and academic performance using data from students’ gym and classroom records.
The key difference between these two designs is the degree of control exerted in the experiment . In experimental studies, the investigator controls conditions and their manipulation, while observational studies only allow the observation of conditions as independently determined (Althubaiti, 2016).
Observational designs cannot infer causality as well as experimental designs; but they are highly effective at generating descriptive statistics.
For more, read: Observational vs Experimental Studies
Generally speaking, there are three broad categories of experiments. Each one serves a specific purpose and has associated limitations . The pre-experiment is an exploratory study to gather preliminary data on the effectiveness of a treatment and determine if a larger study is warranted.
The quasi-experiment is used when studying preexisting groups, such as people living in various cities or falling into various demographic categories. Although very informative, the results are limited by the presence of possible extraneous variables that cannot be controlled.
The true experiment is the most scientifically rigorous type of study. The researcher can manipulate the level of the independent variable and observe changes, if any, on the dependent variable. The key to the experiment is randomly assigning participants to conditions. Random assignment eliminates a lot of confounds and extraneous variables, and allows the researchers to use the term “causality.”
For More, See: Examples of Random Assignment
Baumrind, D. (1991). Parenting styles and adolescent development. In R. M. Lerner, A. C. Peterson, & J. Brooks-Gunn (Eds.), Encyclopedia of Adolescence (pp. 746–758). New York: Garland Publishing, Inc.
Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin.
Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behavior, 13 (5), 585–589.
Matthew L. Maciejewski (2020) Quasi-experimental design. Biostatistics & Epidemiology, 4 (1), 38-47. https://doi.org/10.1080/24709360.2018.1477468
Thyer, Bruce. (2012). Quasi-Experimental Research Designs . Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195387384.001.0001
Dave Cornell (PhD)
Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.
- Dave Cornell (PhD) #molongui-disabled-link 25 Dissociation Examples (Psychology)
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- Dave Cornell (PhD) #molongui-disabled-link Perception Checking: 15 Examples and Definition
- Dave Cornell (PhD) #molongui-disabled-link 10 Observational Research Examples
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This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.
- Chris Drew (PhD) #molongui-disabled-link 25 Dissociation Examples (Psychology)
- Chris Drew (PhD) #molongui-disabled-link 15 Zone of Proximal Development Examples
- Chris Drew (PhD) #molongui-disabled-link Perception Checking: 15 Examples and Definition
- Chris Drew (PhD) #molongui-disabled-link 6 Types of Societies (With 21 Examples)
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The Four Types of Research Design — Everything You Need to Know
Updated: May 11, 2023
Published: January 18, 2023
When you conduct research, you need to have a clear idea of what you want to achieve and how to accomplish it. A good research design enables you to collect accurate and reliable data to draw valid conclusions.
In this blog post, we'll outline the key features of the four common types of research design with real-life examples from UnderArmor, Carmex, and more. Then, you can easily choose the right approach for your project.
Table of Contents
What is research design?
The four types of research design, research design examples.
Research design is the process of planning and executing a study to answer specific questions. This process allows you to test hypotheses in the business or scientific fields.
Research design involves choosing the right methodology, selecting the most appropriate data collection methods, and devising a plan (or framework) for analyzing the data. In short, a good research design helps us to structure our research.
Marketers use different types of research design when conducting research .
There are four common types of research design — descriptive, correlational, experimental, and diagnostic designs. Let’s take a look at each in more detail.
Researchers use different designs to accomplish different research objectives. Here, we'll discuss how to choose the right type, the benefits of each, and use cases.
Research can also be classified as quantitative or qualitative at a higher level. Some experiments exhibit both qualitative and quantitative characteristics.
An experimental design is used when the researcher wants to examine how variables interact with each other. The researcher manipulates one variable (the independent variable) and observes the effect on another variable (the dependent variable).
In other words, the researcher wants to test a causal relationship between two or more variables.
In marketing, an example of experimental research would be comparing the effects of a television commercial versus an online advertisement conducted in a controlled environment (e.g. a lab). The objective of the research is to test which advertisement gets more attention among people of different age groups, gender, etc.
Another example is a study of the effect of music on productivity. A researcher assigns participants to one of two groups — those who listen to music while working and those who don't — and measure their productivity.
The main benefit of an experimental design is that it allows the researcher to draw causal relationships between variables.
One limitation: This research requires a great deal of control over the environment and participants, making it difficult to replicate in the real world. In addition, it’s quite costly.
Best for: Testing a cause-and-effect relationship (i.e., the effect of an independent variable on a dependent variable).
A correlational design examines the relationship between two or more variables without intervening in the process.
Correlational design allows the analyst to observe natural relationships between variables. This results in data being more reflective of real-world situations.
For example, marketers can use correlational design to examine the relationship between brand loyalty and customer satisfaction. In particular, the researcher would look for patterns or trends in the data to see if there is a relationship between these two entities.
Similarly, you can study the relationship between physical activity and mental health. The analyst here would ask participants to complete surveys about their physical activity levels and mental health status. Data would show how the two variables are related.
Best for: Understanding the extent to which two or more variables are associated with each other in the real world.
Descriptive research refers to a systematic process of observing and describing what a subject does without influencing them.
Methods include surveys, interviews, case studies, and observations. Descriptive research aims to gather an in-depth understanding of a phenomenon and answers when/what/where.
SaaS companies use descriptive design to understand how customers interact with specific features. Findings can be used to spot patterns and roadblocks.
For instance, product managers can use screen recordings by Hotjar to observe in-app user behavior. This way, the team can precisely understand what is happening at a certain stage of the user journey and act accordingly.
Brand24, a social listening tool, tripled its sign-up conversion rate from 2.56% to 7.42%, thanks to locating friction points in the sign-up form through screen recordings.
Carma Laboratories worked with research company MMR to measure customers’ reactions to the lip-care company’s packaging and product . The goal was to find the cause of low sales for a recently launched line extension in Europe.
The team moderated a live, online focus group. Participants were shown w product samples, while AI and NLP natural language processing identified key themes in customer feedback.
This helped uncover key reasons for poor performance and guided changes in packaging.
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- What is Experimental Research & How is it Significant for Your Business
What is Experimental Research & How is it Significant for Your Business
Experimental research uses a scientific method for conducting research, employing the most methodical research design. Known as the gold standard, it involves performing experiments to reach conclusions and can be conducted based on some of the findings from previous forms of research.
Logically, it would follow correlational research, which studies the relationships between variables. It can also follow causal research , a kind of experimental research in itself, as it establishes cause and effect relationships between previously studied variables.
Experimental research is typically used in psychology, physical and social sciences, along with education. However, it too can be applied to business.
This article expounds on experimental research, how it is conducted, how it differs from other forms of research, its key aspects and how survey studies can complement it.
Defining Experimental Research
Experimental research is a kind of study that rigidly follows a scientific research design. It involves testing or attempting to prove a hypothesis by way of experimentation . As such, it uses one or more independent variables, manipulating them and then using them on one or more dependent variables .
In this process, the researchers can measure the effect of the independent variable(s) on the dependent variable(s). This kind of study is performed over some time, so that researchers can form a corroborated conclusion about the two variables.
The experimental research design must be carried out in a controlled environment .
Throughout the experiment, the researcher collects data that can support or refute a hypothesis, thus, this research is also referred to as hypothesis testing or a deductive research method.
The Key Aspects of Experimental Research
There are various attributes that are formative of and unique to experimental research in addition to its main purpose. Understanding these is key to understanding this kind of research in-depth and what to expect when performing it.
The following enumerates the defining characteristics of this kind of research:
- It includes a hypothesis, a variable that will be manipulated by the researcher along with the variable that will be measured and compared .
- The data in this research must be able to be quantified.
- The observation of the subjects, however, must be executed qualitatively.
- The latter is rarer, as it is difficult to manipulate treatments and to control external occurrences in a live setting.
- It relies on making comparisons between two or more groups (the variables).
- Some variables are given an experimental stimulus called a treatment; this is the treatment group.
- The variables that do not receive a stimulus are known as the control group.
- First, researchers must consider how the variables are related and only afterward can they move on to making predictions that can be tested.
- Time is a crucial component when putting forth a cause-and-effect relationship.
- Pre-experimental research design
- True experimental research design
- Quasi-experimental research design
The Three Types of Experimental Research
Experimental research encompasses three subtypes that researchers can implement. They all fall under experimental research, differing in how the subjects are classified. They can be classified based on their conditions or groups.
Pre-experimental research design:
This entails a group or several groups to be observed after factors of cause and effect are implemented.
- Researchers implement this research design when they need to learn whether further investigation is required for these particular groups.
- One-shot Case Study Research Design
- One-group Pretest-posttest Research Design
- Static-group Comparison
Quasi-experimental Research Design
Representing half or pseudo, the moniker “quasi” is used to allude to resembling true experimental research, but not entirely.
- The participants are not randomly assigned, rather they are used when randomization is impossible or impractical.
- Quasi-experimental research is typically used in the education field.
- Examples include: the time series, no equivalent control group design, and the counterbalanced design.
True Experimental Research Design
This kind of experimental research design studies statistical analysis to confirm or debunk a hypothesis.
- It is regarded as the most accurate form of research.
- True experimental research can produce a cause-effect relationship within a group.
- A control group (unaltered) and an experimental group (to undergo changes in variables)
- Random distribution
- Variables can be manipulated
Why Your Business Needs Experimental Research
There are various benefits to conducting experimental research for businesses. Firstly, this form of research can help businesses test a new strategy before fully engaging in/ launching it.
The strategy can involve anything from content marketing strategy, to a new product launch. This is especially useful for technology companies, which conduct experimentation frequently. In fact, this kind of research is essential to an R & D (research and development) department.
This makes experimental research a much-needed effort when it comes to spurring innovation. Whether it involves a slight rebranding or an upgrade of products, experimental research guides these campaigns in a science-backed manner.
Secondly, a business must excel in meeting customer needs. Customer experience is an overwhelmingly important side of any business, as customers are willing to make on-the-stop purchases and pay more for a good CX .
As such, each product addition and change in a customer journey must be carried out wisely. Businesses ought to avoid creating unwanted services, or those that cause any aversion within customers. Instead, they should only invest in the most profitable services, products and experiences, a feat that cannot be accomplished solely on guesswork.
Experimenting allows brands to understand customer preferences and changes in their behaviors , as the experiments create stimuli and changes in independent variables.
Additionally, experimental research grants companions an understanding of their business environment. In turn, this helps them predict outcomes, or create hypotheses about outcomes to guide them in further research, if need be. For example, a business may consider testing the reactions of its competitors should it raise its costs on various offers.
Aside from discovering if this yields a profitable change, it can discover how companies in the same niche respond and if those responses drive more sales, etc.
Key Independent Variables
- Digital user experience (DX) such as new site features
- Marketing activity (SEO, SEM, social media announcements, retargeting, etc.)
- Inventory (new products or upgrades)
- Interactions with sales agents
Key Dependent variables
- VoC feedback (whether positive or negative)
- Site traffic
- In-store visits
- Time spent on a website, bounce rates, etc.
An Example of Experimental Research for Business
Market researchers can apply experimental research to a wide breadth of testing needs. Virtually anything that requires proof, confirmation, or is clouded by uncertainty can put experimentation into practice.
The following is an example of how a business can use this research:
A product manager needs to convince the higher-ups in a denim company to launch a new product line at a particular department store. The objective of this launch is to increase sales, expand the company’s floor presence and widen the offerings.
The manager has to prove that this line is needed in order for the company to pitch the idea to the department store. The product manager can then conduct experimental research to provide a strong case for their theory, that a new line can raise sales.
The product manager performs experimental research by executing a test in a few stores, in which the new line of denim is sold. These stores are varied in location to signify the target market sales before and after the launch. The test runs for a month to determine if the hypothesis (the new line resulting in increased attention and sales) can be proven.
This represents a field experiment. The product manager must heed the sales and foot traffic of the new product line, paying attention to spikes in revenue and overall sales to justify the new line.
Experimental Research Survey Examples
Survey research runs contrary to experimental research, unlike the other main forms of research such as exploratory, descriptive and correlational research. This is because the nature of surveys is observational, while experimental research, as its name signifies, relies on experimentations, that is testing out changes and studying the reactions to the changes.
Despite the contrast of survey research to experimental research, they are not completely at odds. In fact, surveys are a potent method to gain further insight into an existing experiment or understand variables before conducting an experiment in the first place.
As such, businesses can adopt a wide variety of surveys to complement their experimental research. Here are some of the key forms of surveys that work in tandem with experimentation:
- Discovers the aspects of statistical significance within variables.
- Helpful in that causal research is quantitative in essence.
- Delves into past events, occurrences and attitudes in regards to the variables.
- Shows whether the variables changed and how so.
- Can find causative elements between variables over a period of time.
- Useful for formulating hypotheses.
- Helps businesses zero in on variables that contribute to or result from certain kinds of customer experiences.
- Allows businesses to test CX in relation to the responses from this survey.
- Measures various matters critical in a business or organization; surveys employees.
- Deployed more frequently, so variables can always be continually tracked.
- Helps answer the what, why and how with open-ended questions.
- Extracts key high-level information in depth.
How Experimental Research Differs from Correlational, Exploratory, Descriptive and Causal Research
Experimental research differs from exploratory, descriptive and correlational research in self-evident ways. It is, however, often conflated with causal research. However, they too have notable differences.
Causal research involves finding the cause-and-effect relationships between variables. Thus, it too employs experimentation. However, this means that causal research is a form of experimental research, not the other way around.
Experimental research, on the other hand, is fully science and experiment-based, as it chiefly seeks to prove or disprove a hypothesis. While this largely involves studying independent and dependent variables, as it does in causal research, it is not solely based on these aspects. Instead, it can introduce a new variable without knowing the dependent variable or experiment on an entirely new idea (as in the example used in the previous selection).
Causal research looks into the comparison of variable relationships to find a cause and effect, while experimental research states an expected relationship between variables and is bent on testing a hypothesis.
As far as comparisons to correlational research go, while experimental research also studies the relationships between variables, it functions far beyond this by manipulating the variables and virtually all subjects involved in experiments .
On the contrary, correlational research does not apply any alterations or conditioning to variables. Instead, it is a purely observational research method. As such, it merely detects whether there is a correlation between only 2 variables. In contrast, experimental research studies and experiments with several at a time.
Exploratory research is vastly different from experimental research, as it forms the very foundation of a research problem and establishes a hypothesis for further research. As such, it is conducted as the very first kind of research around a new topic and does not fixate on variables.
Descriptive research , like exploratory research and unlike experimental research, is conducted early in the full research process, following exploratory research. Like exploratory research, it seeks to paint a picture of a problem or phenomenon , as it zeros in an already-established issue and delves further, in pursuit of all the details and conditions surrounding it.
Thus, unlike experimental research, it only observes; it does not manipulate variables in any capacity or setting.
The Advantages and Disadvantages of Experimental Research
Experimental research offers several benefits for researchers and businesses. However, as with all other research methods, it too carries a few disadvantages that researchers should be aware of.
- Researchers have a full level of control in an experiment.
- It can be used in a wide variety of fields and verticals.
- The results are specific and conclusive.
- The results allow researchers to apply their findings to similar phenomena or contexts.
- It can determine the validity of a hypothesis, or disprove one.
- Researchers can manipulate variables and use them in as many variations as they desire without tarnishing the validity of the research.
- It discovers the cause and effect among variables.
- Researchers can further analyze relationships through testing.
- It helps researchers understand a specific environment fully.
- The studies can be replicated so that the researchers can repeat their experiments to test other variables or confirm the results again.
- It involves a lot of resources, time and money, as such, it is not easy to conduct.
- It can form artificial environments when researchers unwittingly over-manipulate variables as a means of duplicating real-world instances.
- It is vulnerable to flaws in the methodology, along with other mistakes that can’t always be predicted.
- Flawed experiments may require researchers to start their experiments anew to avoid false calculations, measuring results from artificial scenarios or other mistakes.
- Some variables cannot be manipulated and some forms of research experiments are too impractical to conduct.
How to Conduct Experimental Research
Experimental research is often the final form of research conducted in the research process and is considered conclusive research. The following explains the general steps required to successfully complete experimental research.
- Form a specific research question.
- Gather all available literature and other resources around the subject.
- Conduct secondary research around the subject and primary research via surveys .
- Consider how they relate to your question and how they line up with the secondary research you conducted.
- After your initial studies, form a hypothesis.
- First, decide which variable(s) is dependent/ independent (if it doesn’t involve experimenting).
- Decide how far to vary the independent variable.
- In the experiment, manipulate the independent variable(s).
- Measure the dependent variable(s) while you study the independent variable(s) alongside.
- Make sure to control potential confounding variables.
- Keep the study size in mind; a larger study pool creates statistical findings.
- Assign your subjects to “treatment” groups randomly, with each to receive a different level of “treatment.”
- Use a control group, which receives no manipulation. This shows you the test subjects as they appear/behave without any experimental intervention.
- Completely randomized design: every subject gets randomly assigned to a treatment.
- Randomized block design : aka stratified random design, subjects get first grouped based on a shared characteristic, then assigned to treatments within their groups at random.
- Independent measure : subjects receive only one of the possible levels of an experimental treatment.
- Repeated measures design : every subject gets each of the experimental treatments consecutively, as their responses are measured. It also refers to measuring the effect of an emerging effect over time.
- Continue experimenting on variables as needed, take measurements and take notes.
- Based on your experiment(s), put together a logical conclusion. It is possible that it may need testing over time.
Using Experimental Research and Going Further
Although experimental research can be very complex, this research method is the most conclusive. Using a scientific approach, it can help you form tests on various business matters. While it is critical for understanding your target market’s and customers’ existing behaviors, it can also be used to experiment on a wide variety of other matters.
Before launching a new product, or an updated one, for example, you can conduct an experiment to understand the product in action. This helps you avoid any glitches or undesirable qualities that will incur problems for your customs and a bad reputation for your brand.
Experimental research is not for every business, yet if you decide to implement this form of research, consider using surveys in tandem. An online survey platform can help you establish and distribute your surveys to a wide network via organic sampling to avoid biases.
Although it isn’t a requirement, in today’s age of excelling in customer experience (CX), it is of the essence to have as much data on your target market as possible. An online survey tool makes this possible.
Do you want to distribute your survey? Pollfish offers you access to millions of targeted consumers to get survey responses from $0.95 per complete. Launch your survey today.
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5+ Types of Research Methods: Qualitative, Quantitative, Experimental & More
Welcome to our comprehensive guide on types of research methods . Whether you’re a student, academic, or simply interested in conducting research, understanding the various research methods available is essential. In this article, we will explore the three main types of research methods : qualitative, quantitative, and experimental . By the end, you’ll have a clear understanding of the strengths and applications of each method, helping you choose the most suitable approach for your research goals.
- Qualitative research methods explore the complexity and depth of a particular situation from the perspective of the informants.
- Quantitative research methods gather data in an organized, objective manner to study cause-and-effect relationships.
- Experimental research methods involve treatment and control groups to determine causation.
Table of Contents
Understanding Quantitative Research Methods
Quantitative research methods play a crucial role in scientific inquiry, allowing researchers to gather and analyze data in a systematic and objective manner. This section will explore different types of quantitative research methods , including experimental , descriptive , and correlational designs.
“Quantitative research is all about numbers and measurements. It aims to uncover cause-and-effect relationships, provide accurate accounts of specific situations, or explore the relationship between variables.”
In experimental research, researchers manipulate variables to determine cause-and-effect relationships. This involves administering treatments to an experimental group and comparing their outcomes to a control group that does not receive the treatment. By carefully controlling variables and randomizing participants, experimental designs provide strong evidence for causation.
Descriptive studies aim to provide an accurate account of a specific situation or group. These studies collect data through surveys , observations , or existing records and summarize findings using descriptive statistics. Descriptive studies help researchers understand the characteristics, behaviors, or attitudes of a population.
Correlational research explores the relationship between variables without manipulating them. It aims to determine whether a relationship exists and the strength and direction of that relationship. Correlational studies use statistical analysis to calculate correlation coefficients, such as Pearson’s r, to quantify the degree of association between variables.
Quantitative research methods offer valuable insights into the world around us. Experimental designs allow for rigorous testing of hypotheses, while descriptive studies provide a comprehensive understanding of specific populations or situations. Correlational research helps uncover associations between variables. By understanding these different quantitative research methods, researchers can choose the most appropriate approach for their research questions and contribute to the advancement of scientific knowledge.
Exploring Qualitative Research Methods
Qualitative research methods offer a unique approach to understanding the complexity, depth, and richness of a particular situation or phenomenon. These methods allow researchers to delve into experiences, social processes, and subcultures, providing valuable insights and in-depth understanding. In this section, we will explore three commonly used qualitative research methods : phenomenology , ethnography , and grounded theory .
Phenomenology is a qualitative research method that aims to understand and describe the lived experience or meaning of individuals with a particular condition or situation. It focuses on exploring the subjective perspectives of individuals and how they make sense of their experiences. Researchers using phenomenology engage in in-depth interviews or observations to gather rich, detailed data about the phenomenon under study. The goal is to uncover the essence and structure of the lived experience and provide a deeper understanding of the phenomenon from the participants’ perspective.
Ethnography is a qualitative research method that focuses on the culture of a specific group or community. It involves immersing oneself in the group’s natural environment and conducting participant observation, interviews , and document analysis to understand and describe the group’s behaviors, beliefs, and practices. Ethnographic research aims to provide an in-depth understanding of the cultural context and the social interactions within the group. It allows researchers to explore the group’s values, norms, rituals, and social dynamics, providing valuable insights into the group’s unique worldview.
Grounded theory is a qualitative research method that starts with a general problem or question and develops a theory through a systematic analysis of the data. Researchers using grounded theory collect and analyze data simultaneously, allowing the theory to emerge from the data rather than imposing preconceived ideas or theories onto the research. This method involves open-ended interviews , observations , and constant comparison of the data to identify categories, concepts, and relationships. Grounded theory aims to provide a theoretical understanding of a phenomenon based on the experiences and perspectives of the participants.
These qualitative research methods offer valuable tools for exploring and understanding complex social phenomena. Whether it’s uncovering the essence of lived experiences through phenomenology, immersing in a group’s culture with ethnography , or developing theory from data with grounded theory, these methods provide researchers with a deeper understanding of the human experience and the social world we inhabit.
Primary and Secondary Research
When conducting research, it is important to understand the distinction between primary and secondary research . These two approaches offer different advantages and can be used depending on the specific research question and available resources.
Primary research involves collecting original data specifically for the research question at hand. This can be done through various methods such as surveys , interviews, experiments , or direct observations. Primary research allows researchers to have control over the sampling process and the measurement methods used, ensuring the data collected is tailored to their specific needs. However, primary research can be time-consuming and require more resources compared to secondary research .
Secondary research involves using existing data collected by others. This can include data from previous studies, government reports, industry publications, or online databases. Secondary research offers the advantage of being faster and easier to access, as the data already exists. It also allows for the analysis of data over longer timescales and broader locations. However, researchers have less control over the quality and relevance of the data, as it was collected for different purposes.
Both primary and secondary research have their strengths and limitations, and the choice between them depends on the research objectives, resources, and accessibility to data. Often, a combination of both approaches may be used to provide a more comprehensive analysis of a research topic.
Collecting Quantitative Data
When conducting quantitative research, it is important to employ effective data collection methods to gather accurate and reliable information. Quantitative data can be collected through surveys , experiments , and observations, each serving a specific purpose in the research process.
Surveys are a commonly used method for collecting quantitative data. They involve asking closed-ended questions to a sample of participants, allowing for standardized responses that are easy to analyze. Surveys can be administered in various ways, such as online questionnaires, telephone interviews, or paper-based forms. The data collected can provide insights into participants’ opinions, attitudes, behaviors, or demographic information.
Experiments are another powerful quantitative data collection method, particularly useful for establishing cause-and-effect relationships. In an experimental design, researchers manipulate independent variables and measure the effects on dependent variables. This method allows researchers to control and isolate variables, providing a clear understanding of the relationship between them. Experimental data can be collected through controlled laboratory settings or real-world scenarios, depending on the research objectives.
Observations are a valuable data collection method for capturing behavior and events in natural settings. Researchers directly observe participants and record their actions, interactions, or other relevant behaviors. This method allows for the collection of real-time data without intervention, providing insights into participants’ natural behaviors. Observational data can be gathered through structured observations, where specific behaviors are recorded, or unstructured observations, where researchers observe the situation as it occurs.
By utilizing surveys, experiments, and observations, researchers can collect quantitative data to answer research questions and gain valuable insights into various phenomena. Each method offers unique advantages and should be chosen based on the research objectives and the nature of the data being collected.
Collecting Qualitative Data
When conducting qualitative research, researchers employ various data collection methods to gather rich and in-depth information. These methods enable researchers to explore the nuances and complexities of a particular situation, allowing for a deeper understanding of the research topic. The following are some commonly used qualitative data collection methods :
Interviews are a widely used method to collect qualitative data. Through one-on-one conversations, researchers can ask open-ended questions and delve into the perspectives, experiences, and insights of individuals. This method allows for flexibility and encourages participants to share their thoughts and feelings freely. Interviews can be conducted in person, over the phone, or through video conferencing platforms, depending on the preferences and availability of the participants.
2. Focus Groups
Focus groups involve bringing together a small group of individuals, typically 6-10, to discuss a specific topic of interest. This method encourages interaction and conversation among participants, allowing for the exploration of shared experiences, opinions, and attitudes. Researchers facilitate the group discussion, asking guiding questions to stimulate conversation and gather qualitative data. Focus groups are particularly useful when exploring social dynamics, group norms, and collective experiences.
Ethnography involves immersing oneself in a particular community or culture to observe and understand their behaviors, values, and practices. Researchers participate in the daily lives of individuals within the community, gaining a holistic view of their social interactions and cultural contexts. This method requires prolonged engagement and observation, often over an extended period, to capture the depth and complexity of the community being studied.
4. Literature Review
A literature review involves analyzing existing published works, such as academic articles, books, and reports, relevant to the research topic. Researchers examine the findings, theories, and methodologies presented in these works to gain insights and develop a comprehensive understanding of the subject. By synthesizing and critically evaluating the existing literature, researchers can identify gaps in knowledge and contribute to the existing body of research.
Analyzing Quantitative Data
When it comes to analyzing quantitative data, researchers rely on various statistical analysis methods to uncover relationships between variables. These methods allow for the interpretation and standardization of numerical data, providing valuable insights into the research question at hand. Statistical software like Excel, SPSS, or R are often used to perform calculations, hypothesis testing, and analyze survey responses.
Statistical analysis plays a critical role in quantitative research, helping researchers draw meaningful conclusions and make informed decisions based on the data. Some commonly used statistical analysis methods include:
- Descriptive Statistics: This method involves calculating measures such as mean, median, mode, and standard deviation to summarize and describe the characteristics of the data.
- Hypothesis Testing: Researchers use hypothesis testing to determine the likelihood that a particular relationship between variables is due to chance. Statistical tests such as t-tests, ANOVA, and chi-square tests are commonly employed in hypothesis testing.
- Regression Analysis: Regression analysis examines the relationship between a dependent variable and one or more independent variables. It helps researchers understand how changes in one variable are associated with changes in another.
- Correlation Analysis: This method explores the strength and direction of the relationship between two continuous variables. It is often represented by a correlation coefficient, such as Pearson’s r.
“Statistical analysis allows researchers to extract valuable insights and draw meaningful conclusions from quantitative data. By applying the appropriate statistical methods, researchers can identify patterns, relationships, and trends within the data, providing a solid foundation for evidence-based decision-making.”
The choice of statistical analysis method depends on the research objectives, the nature of the data, and the assumptions underlying the statistical tests. By employing rigorous statistical analysis techniques, researchers can ensure the validity and reliability of their findings, contributing to the advancement of knowledge in their respective fields.
Analyzing Qualitative Data
When it comes to qualitative research, analyzing the collected data is a crucial step in gaining insights and understanding the underlying themes and meanings. There are several qualitative data analysis methods that researchers can employ to make sense of the data they have gathered.
Qualitative Content Analysis
One popular method is qualitative content analysis , which involves systematically categorizing and interpreting the textual data. Researchers identify key words, phrases, or themes and analyze their frequency and context within the data. This method helps to uncover patterns, trends, and underlying meanings in the qualitative data.
Another widely used method is thematic analysis , which involves identifying and analyzing patterns or themes within the data. Researchers code the data based on recurring patterns and develop themes that capture the main ideas or concepts. Thematic analysis provides a structured way to interpret qualitative data and identify significant patterns or trends.
Discourse analysis is a method that focuses on how communication works within social contexts. It examines how language is used to construct meaning, power dynamics, and social relationships. Researchers analyze the structure, language, and social context of the data to gain insights into how individuals or groups create and convey meaning.
These qualitative data analysis methods provide researchers with valuable tools to analyze and interpret the data they have collected. By employing these methods, researchers can uncover rich insights and generate a deeper understanding of their research topic.
Mixed Methods Approach
A mixed methods approach is a research strategy that combines both qualitative and quantitative research methods to gain a more comprehensive understanding of a research topic. By integrating these two distinct approaches, researchers can benefit from the strengths of each method, allowing for a more robust analysis and a broader perspective on the research question at hand.
This mixed methods approach offers several advantages. First, it enables researchers to triangulate data by using multiple sources of evidence. This means that the weaknesses of one method can be compensated for by the strengths of the other. By collecting both qualitative and quantitative data, researchers can explore the depth and complexity of human experiences while also providing statistical support for their findings.
Additionally, this approach allows researchers to address research questions from multiple perspectives. Qualitative methods provide rich, in-depth insights into subjective experiences, beliefs, and motivations, while quantitative methods focus on objective measurements and statistical analyses. By combining these approaches, researchers can gain a more comprehensive understanding of the topic under investigation.
Benefits of a Mixed Methods Approach:
- Triangulation of data to ensure robust findings
- Addressing research questions from multiple perspectives
- Enhanced validity and reliability by combining qualitative and quantitative data
- Opportunity to explore complex phenomena and human experiences
In summary, a mixed methods approach offers a valuable research strategy that helps researchers gain a more complete understanding of their research topic. By incorporating both qualitative and quantitative methods, researchers can harness the strengths of each approach and overcome their respective limitations. This approach allows for the triangulation of data, the exploration of multiple perspectives, and the generation of comprehensive insights that can contribute to the advancement of knowledge in various fields.
The Importance of Data Collection
Data collection is an essential component of the research process, providing the necessary information to answer research questions and gain insights into various phenomena. Researchers employ different data collection methods based on their research objectives, available resources, and accessibility to data. The two main types of data collection methods are primary data collection and secondary data collection.
Primary Data Collection
Primary data collection involves gathering original data specifically for the research question at hand. This method allows researchers to have control over the sampling process and the measurement methods used. Common primary data collection methods include surveys, experiments, interviews, and observations. Surveys provide a structured way to gather information through closed-ended questions, allowing for standardized data collection. Experiments manipulate variables to establish cause-and-effect relationships and test hypotheses. Interviews provide in-depth understanding through open-ended questions, allowing for rich qualitative data. Observations involve directly observing and recording behaviors in their natural environment, providing valuable insights into human behavior and social interactions.
Secondary Data Collection
Secondary data collection involves using existing data that has been collected by others for a different purpose. This method is faster and easier to access compared to primary data collection, as the data already exists. Common sources of secondary data include government databases, academic journals, publicly available datasets, and market research reports. Secondary data collection allows researchers to analyze data over longer timescales and broader locations. It also enables the comparison of data from different studies or the combination of datasets to gain a more comprehensive understanding of a research topic.
Both primary and secondary data collection methods have their advantages and limitations. The choice between them depends on various factors, such as the research objectives, resources, time constraints, and the availability and quality of existing data. Researchers should carefully consider these factors to select the most appropriate data collection method for their research projects.
Comparing Quantitative and Qualitative Research
When it comes to research methods, there are two main approaches that researchers often choose from: quantitative and qualitative research. While both have their own unique strengths and weaknesses, understanding the key differences between them is essential for selecting the most appropriate method for a particular study.
Quantitative research focuses on gathering and analyzing numerical data to uncover patterns, trends, and cause-and-effect relationships. It emphasizes objectivity and generalizability by using structured data collection methods such as surveys, experiments, and observations. Quantitative research is ideal for testing hypotheses, predicting outcomes, and making statistical inferences based on large sample sizes.
Qualitative research , on the other hand, explores the depth, complexity, and meaning behind human experiences and behavior. It involves collecting non-numerical data through methods like interviews, focus groups, and participant observation. Qualitative research aims to gain a comprehensive understanding of a particular phenomenon by examining the context, perspectives, and subjective interpretations of the participants.
Quantitative research seeks to uncover patterns and relationships through numerical data, while qualitative research aims to understand the meaning and depth of human experiences through non-numerical data.
The table below highlights the key differences between quantitative and qualitative research:
Choosing between quantitative and qualitative research depends on several factors, including the research question, the nature of the topic under investigation, the research objectives, and the available resources. In some cases, researchers may even opt for a mixed methods approach, combining both quantitative and qualitative methods to gain a more comprehensive understanding of their research topic.
When to use Quantitative Research:
- When seeking to establish cause-and-effect relationships
- When aiming for generalizability and statistical inference
- When focusing on numerical data and patterns
When to use Qualitative Research:
- When exploring in-depth understanding of human experiences
- When examining subjective interpretations and meanings
- When working with smaller sample sizes
In conclusion , both quantitative and qualitative research methods play important roles in the field of research. By understanding their differences and knowing when to utilize each approach, researchers can design studies that best suit their research questions and contribute valuable insights to their respective fields.
Understanding the Methodology
When conducting research, it is important to understand the difference between research methodology and research methods. The term “methodology” refers to the overarching strategy and rationale of a research project, while “methods” are the specific tools and procedures used for data collection and analysis. In other words, methodology guides researchers in selecting the appropriate research methods that align with their research goals and objectives.
Research methodology provides a framework for researchers to design their studies and make informed decisions about data collection, analysis, and interpretation. It involves considering various factors such as the research question, the nature of the data, and the target population. By adopting a robust methodology, researchers can ensure the validity, reliability, and generalizability of their findings.
“Research methods,” on the other hand, are the techniques and procedures used to collect and analyze data. These may include surveys, experiments, interviews, or literature reviews. The choice of research methods depends on the research question, available resources, and the type of data required to answer the research question effectively.”
Understanding research methodology is essential for researchers to conduct meaningful studies and contribute to their respective fields of study. By selecting the most appropriate research methods based on their research objectives, researchers can gather valuable insights and generate reliable findings that contribute to the body of knowledge in their field.
Research Methodology vs. Research Methods
As we wrap up our exploration of research methods, it is clear that each approach offers its own unique benefits and limitations. Qualitative research methods allow for in-depth understanding of complex situations and provide rich insights into the experiences and meanings of individuals. On the other hand, quantitative research methods provide structured and objective data that allows us to establish cause-and-effect relationships.
Researchers must carefully consider their research questions, objectives, and available resources when selecting the most appropriate method for their study. By choosing the right method, researchers can generate valuable insights and contribute to their respective fields of study.
It is worth noting that a mixed methods approach can offer a comprehensive analysis by combining the strengths of both qualitative and quantitative research methods. This approach allows for triangulation of data and a deeper understanding of the research topic from multiple perspectives.
In conclusion , understanding the different types of research methods is crucial for conducting meaningful and impactful research. By selecting the appropriate method and employing rigorous data collection and analysis techniques, researchers can enhance their findings and make valuable contributions to their fields.
Additional Resources on Research Methods
For those seeking more in-depth knowledge and understanding of research methods, there are a plethora of additional articles available that delve into various topics. These resources can help researchers enhance their skills and broaden their understanding of statistics, methodology, research bias, data collection, and more.
Whether you’re a novice researcher looking to build a solid foundation or an experienced researcher seeking to expand your knowledge, these articles can provide valuable insights and guidance. By exploring these resources, you can gain a deeper understanding of the intricacies of different research methods and improve the quality of your own research.
Investing time in further education and familiarizing yourself with these additional resources will equip you with the tools necessary to conduct more comprehensive and rigorous research. Remember, the key to successful research lies not only in choosing the right methods but also in constantly honing your skills and staying up-to-date with the latest advancements in the field.
What are the different types of research methods?
The different types of research methods include qualitative, quantitative, and experimental methods.
What are qualitative research methods?
Qualitative research methods explore experiences, social processes, and subcultures, using approaches such as phenomenology, ethnography, and grounded theory.
What are quantitative research methods?
Quantitative research methods gather data in organized, objective ways to study cause-and-effect relationships, including experimental, quasi-experimental, descriptive, and correlational designs.
What is primary research?
Primary research involves collecting original data for a specific research question.
What is secondary research?
Secondary research uses existing data collected by others.
How do you collect quantitative data?
Quantitative data can be collected through surveys, experiments, and observations.
How do you collect qualitative data?
Qualitative data can be collected through interviews, focus groups, ethnography, and literature reviews.
How do you analyze quantitative data?
Quantitative data analysis involves using statistical analysis methods with software like Excel, SPSS, or R.
How do you analyze qualitative data?
Qualitative data analysis involves methods such as qualitative content analysis , thematic analysis, and discourse analysis.
What is a mixed methods approach?
A mixed methods approach combines qualitative and quantitative research methods to gain a fuller understanding of a research topic.
Why is data collection important?
Data collection is essential for research as it provides the information needed to answer research questions.
What is the difference between quantitative and qualitative research?
Quantitative research focuses on numerical data and generalizability, while qualitative research delves into words and meanings for in-depth understanding.
What is the difference between method and methodology?
Methodology refers to the overarching strategy and rationale of a research project, while methods are the specific tools and procedures used for data collection and analysis.
Are there additional resources on research methods available?
Yes, there are additional articles available that delve into topics such as statistics, methodology, research bias, and data collection.
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Definition, Examples and Types of Experimental Research Designs
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What is Experimental Research ?
Experimental research is a scientific methodology of understanding relationships between two or more variables. These sets consist of independent and dependent variables which are experimentally tested to deduce a correlation between such variables in terms of the nature and strength of such relation. Such assessment helps in deriving a cause and effect relationship and is even used for the purpose of hypothesis testing.
In such a mechanism , independent variables involved are adjusted to discover their impact on the dependent variables. The degree to which a change in the independent variables influences dependent variables is the basis of gauging the degree of strength. Such variations are recorded over a specific period of time to ensure that the conclusions drawn about the relationship are substantive and reliable enough to assist intelligent decision making.
Experimental research deals with quantitative data and its statistical analysis which makes the study extremely useful and accurate. It finds its usability in fields of psychology , social sciences , physical evaluation and academics and are time bound studies usually used for verification purposes.
Types of Experimental Research designs
1) Pre-experimental research design :
This is an observational research mechanism used to evaluate changes in a group or various groups of dependent variables after changing the independent variable values. This is the simplest form of experimental research used to assess the need for further inspection, if satisfactory results are not derived from the observations registered.
This can further be subdivided as :
- One-shot Case Study Research Design: A post-test study relying only on a single set of variables for observational purposes.
- One-group Pretest-posttest Research Design : This is a combination of pre and post tests that studies a single set of variables before and after the method of testing has been implemented.
- Static-group Comparison: The total groups of variables gets divided into 2 sub-groups, one subjected to the testing while the other group remains as it as . Observations at the end of the testing reveal the contrast between the tested and the non-tested panel.
2) True experimental research :
This is a statistical approach to establish a cause and effect relationship within a variable set. The quantitative approach of this study makes it highly accurate. The assignment of test units and treatments takes place in a randomized manner.
Apart from this , it uses the availability of a control group along with an independent variable that can be manipulated to obtain the required results.
3) Quasi- experimental research design :
Quasi-experimental research design is a partial representation of true experimental research such that it seeks to establish a cause and effect relationship by manipulating an independent variable, the only difference being that it does not adhere to random distribution of participants into groups.
Thus , Quasi- Experimental research design is only applied to those situations where there is no relevance or possibility for random distribution.
Some examples of Experimental Research design
Employee recruitment and screening
The recruitment of an employee to an organization requires the employee to go through a rigorous selection procedure that filters the highly suited individuals for the job from the rest of the lot. A screening process is conducted that tests the skills , qualification , experience and knowledge of the applicants before going ahead with selecting the required number of people. The selected individuals are then recruited and trained with respect to the work to be done. Following this training , these individuals are then observed for a specific frame. At the end of this time period , employee appraisals take place which reviews the performance of the employee to identify the need for any improvement or if the employee is capable of handling extra work while maintaining the same level of performance and consistency levels.
This is a simple example of one group pretest posttest research design that assists the creation of a progressive work environment that provides the room for employees to grow along with pushing the organization towards achieving objectives in an efficient manner.
Impact of online tuitions
A group of students belonging to the same class and scoring the same grades in their first term exams are selected to try out a new e-tuition app as against their existing tuition classes. This sample of students are divided into two groups : one that switches to the online educational tuition app while the other group continues to attend their existing tuition classes. This study continues till the next examination cycle , as it observes the differences in the students ability to learn , grasp concepts and their general attitude towards the process of online learning . At the end of the study , the students belonging to both the groups give their term end examinations and the differences between the performance of the students are noted to contrast the teaching methods and effectiveness of online learning vs e-learning.
Such a study is an example of static group comparison that helps in comparing , analysing and establishing one of the alternatives as a viable choice under the current scenario.
Disadvantages of Experimental Research
- The chances of error and bias being involved in experimental research are very high. The process of controlling independent variables to study changes in the dependent variables is highly prone to human error. Further , the results can even be skewed if the values are manipulated by the researcher.
- It is a highly expensive, time consuming and cumbersome process to carry out a thorough experimental research procedure.
- The observational nature of the pre-test experimental research study makes it a qualitative research mechanism that does not help in deriving substantive conclusions based on hard figures.
- It can produce artificial results . It is important to factor in all independent variables that bring out variation in the dependent variables . Failing to do this may not reflect the true picture with reference to the strength of the relationship between the variables in consideration.
- In certain situations, It is highly risky and can lead to ethical complications if treatment is not implemented carefully.
Methods of data collection
1) Surveys :
Surveys are the easiest and the most commonly used data collection mechanism. Surveys help in achieving the coverage of all relevant areas of interest by framing a questionnaire to be filled out by the targeted respondent. This can be done physically , however , the attractions offered by the online research software allow for advance designing , distribution, collection , reporting and analysis of the information gathered. This provides a viable alternative that offers enhanced research procedures to be conducted in a swift and efficient manner.
Care needs to be taken while designing the survey as well as selecting the limited number of respondents who will assist the surveying organizations in finding answers to their research questions to fuel intelligent decision making.
2) Observation :
This method of data collection involves keeping a check on the variables under study to monitor changes and observe behaviour. It takes a long period of observation to make significant conclusions. This method also largely relies on the observer’s judgement and so is highly subjective.
3) Simulation :
Simulation replicates real life processes and situations to understand variables under consideration. The reliability of such a method heavily depends upon the accuracy with which the simulation has been created. This method finds its applicability in fields such as operational research which seeks to break down the whole idea to study narrow concepts involved. Simulations are an effective choice where accessibility and implementation are not feasible.
4) Experiments :
Experiments are carried out in a controlled environment such as a lab where influencing factors can be controlled. This also circles around field experiments, numerical and AI studies. The usage of computerized software makes data handling and management an easy task.
Experiments assist a comprehensive overview of the variables under the scope of the study. They are statistically compatible and so deliver substantive results which are objective in nature.
Difference between experimental and non- experimental research
1) Experimental research focuses on understanding the nature of relationship between independent and dependent variables involved under a particular field of study. On the other hand , Non-experimental research is descriptive in nature and so , focuses on defining a process , situation or idea.
2) Experimental research provides the freedom to control external independent variables to decipher relationships, however , such a control mechanism is absent in Non- experimental research.
3) Experimental data does not make use of case studies and published works for establishing relationships while non-experimental research cannot be carried out using simulations.
4) Experimental research involves a scientific approach whereas such an approach is absent in non-experimental research due to the descriptive nature of the study.
The 3 types of experimental designs are :
- Pre- experimental research
- True experimental research
- Quasi- experimental research
The study of the impact of different educational levels , experience and additional skills on the nature of jobs , salaries and the type of work environment is a simple example that can be used to understand experimental research.
Experimental research is a methodology used to gauge the nature of relationship between the variables in consideration.
Experimental designs are written in terms of the hypothesis that a study tries to prove or the variables the research tries to study.
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Empirical Research: Definition, Methods, Types and Examples
Empirical research: Definition
Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.
Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.
This empirical evidence can be gathered using quantitative market research and qualitative market research methods.
For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.
LEARN ABOUT: Behavioral Research
You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.
In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.
LEARN ABOUT: Causal Research
Types and methodologies of empirical research
Empirical research can be conducted and analysed using qualitative or quantitative methods.
- Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
- Qualitative research: Qualitative research methods are used to gather non numerical data. It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.
Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.
LEARN ABOUT: Qualitative Research Questions and Questionnaires
Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.
- Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.
Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.
For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.
Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research
- Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.
For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.
- Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.
LEARN ABOUT: Level of Analysis
For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.
- Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.
For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.
- Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.
For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.
- Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.
For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.
LEARN ABOUT: Action Research
Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.
LEARN ABOUT: Qualitative Interview
- Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.
For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.
- Observational method: Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.
For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.
- One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.
For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.
- Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.
For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.
- Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.
For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.
Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.
We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?
Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.
Step #1: Define the purpose of the research
This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.
Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.
Step #2 : Supporting theories and relevant literature
The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem
Step #3: Creation of Hypothesis and measurement
Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.
Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.
Step #4: Methodology, research design and data collection
In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.
LEARN ABOUT: Best Data Collection Tools
Step #5: Data Analysis and result
Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.
Step #6: Conclusion
A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.
A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.
- Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
- Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
- Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
- Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
- Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample
LEARN MORE: Population vs Sample
There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.
- It is used to authenticate traditional research through various experiments and observations.
- This research methodology makes the research being conducted more competent and authentic.
- It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
- The level of control in such a research is high so the researcher can control multiple variables.
- It plays a vital role in increasing internal validity .
Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.
- Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
- Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
- There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
- Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.
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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.
For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.
LEARN ABOUT: 12 Best Tools for Researchers
With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.
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What Are Clinical Trials and Studies?
On this page:
What is clinical research?
Why participate in a clinical trial, what happens in a clinical trial or study, what happens when a clinical trial or study ends, what are the different phases of clinical trials, questions to ask before participating in clinical research, how do researchers decide who will participate, clinical research needs participants with diverse backgrounds.
By participating in clinical research, you can help scientists develop new medications and other strategies to treat and prevent disease. Many effective treatments that are used today, such as chemotherapy, cholesterol-lowering drugs, vaccines, and cognitive-behavioral therapy, would not exist without research participants. Whether you’re healthy or have a medical condition, people of all ages and backgrounds can participate in clinical trials. This article can help you learn more about clinical research, why people choose to participate, and how to get involved in a study.
Mr. Jackson's story
Mr. Jackson is 73 years old and was just diagnosed with Alzheimer’s disease . He is worried about how it will affect his daily life. Will he forget to take his medicine? Will he forget his favorite memories, like the births of his children or hiking the Appalachian Trail? When Mr. Jackson talked to his doctor about his concerns, she told him about a clinical trial that is testing a possible new Alzheimer’s treatment. But Mr. Jackson has concerns about clinical trials. He does not want to feel like a lab rat or take the chance of getting a treatment that may not work or could make him feel worse. The doctor explained that there are both risks and benefits to being part of a clinical trial, and she talked with Mr. Jackson about research studies — what they are, how they work, and why they need volunteers. This information helped Mr. Jackson feel better about clinical trials. He plans to learn more about how to participate.
Clinical research is the study of health and illness in people. There are two main types of clinical research: observational studies and clinical trials.
Observational studies monitor people in normal settings. Researchers gather information from people and compare changes over time. For example, researchers may ask a group of older adults about their exercise habits and provide monthly memory tests for a year to learn how physical activity is associated with cognitive health . Observational studies do not test a medical intervention, such as a drug or device, but may help identify new treatments or prevention strategies to test in clinical trials.
Clinical trials are research studies that test a medical, surgical, or behavioral intervention in people. These trials are the primary way that researchers determine if a new form of treatment or prevention, such as a new drug, diet, or medical device (for example, a pacemaker), is safe and effective in people. Often, a clinical trial is designed to learn if a new treatment is more effective or has less harmful side effects than existing treatments.
Other aims of clinical research include:
- Testing ways to diagnose a disease early, sometimes before there are symptoms
- Finding approaches to prevent a health problem, including in people who are healthy but at increased risk of developing a disease
- Improving quality of life for people living with a life-threatening disease or chronic health problem
- Studying the role of caregivers or support groups
Learn more about clinical research from MedlinePlus and ClinicalTrials.gov .
People volunteer for clinical trials and studies for a variety of reasons, including:
- They want to contribute to discovering health information that may help others in the future.
- Participating in research helps them feel like they are playing a more active role in their health.
- The treatments they have tried for their health problem did not work or there is no treatment for their health problem.
Whatever the motivation, when you choose to participate in a clinical trial, you become a partner in scientific discovery. Participating in research can help future generations lead healthier lives. Major medical breakthroughs could not happen without the generosity of clinical trial participants — young and old, healthy, or diagnosed with a disease.
Where can I find a clinical trial?
Looking for clinical trials related to aging and age-related health conditions? Talk to your health care provider and use online resources to:
- Search for a clinical trial
- Look for clinical trials on Alzheimer's, other dementias, and caregiving
- Find a registry for a particular diagnosis or condition
- Explore clinical trials and studies supported by NIA
After you find one or more studies that you are interested in, the next step is for you or your doctor to contact the study research staff and ask questions. You can usually find contact information in the description of the study.
Let your health care provider know if you are thinking about joining a clinical trial. Your provider may want to talk to the research team to make sure the study is safe for you and to help coordinate your care.
Joining a clinical trial is a personal decision with potential benefits and some risks. Learn what happens in a clinical trial and how participant safety is protected . Read and listen to testimonials from people who decided to participate in research.
Here’s what typically happens in a clinical trial or study:
- Research staff explain the trial or study in detail, answer your questions, and gather more information about you.
- Once you agree to participate, you sign an informed consent form indicating your understanding about what to expect as a participant and the various outcomes that could occur.
- You are screened to make sure you qualify for the trial or study.
- If accepted into the trial, you schedule a first visit, which is called the “baseline” visit. The researchers conduct cognitive and/or physical tests during this visit.
- For some trials testing an intervention, you are assigned by chance (randomly) to a treatment group or a control group . The treatment group will get the intervention being tested, and the control group will not.
- You follow the trial procedures and report any issues or concerns to researchers.
- You may visit the research site at regularly scheduled times for new cognitive, physical, or other evaluations and discussions with staff. During these visits, the research team collects data and monitors your safety and well-being.
- You continue to see your regular physician(s) for usual health care throughout the study.
How do researchers decide which interventions are safe to test in people?
Before a clinical trial is designed and launched, scientists perform laboratory tests and often conduct studies in animals to test a potential intervention’s safety and effectiveness. If these studies show favorable results, the U.S. Food and Drug Administration (FDA) approves the intervention to be tested in humans. Learn more about how the safety of clinical trial participants is protected.
Once a clinical trial or study ends, the researchers analyze the data to determine what the findings mean and to plan the next steps. As a participant, you should be provided information before the study starts about how long it will last, whether you will continue receiving the treatment after the trial ends (if applicable), and how the results of the research will be shared. If you have specific questions about what will happen when the trial or study ends, ask the research coordinator or staff.
Clinical trials of drugs and medical devices advance through several phases to test safety, determine effectiveness, and identify any side effects. The FDA typically requires Phase 1, 2, and 3 trials to be conducted to determine if the drug or device can be approved for further use. If researchers find the intervention to be safe and effective after the first three phases, the FDA approves it for clinical use and continues to monitor its effects.
Each phase has a different purpose:
- A Phase 1 trial tests an experimental drug or device on a small group of people (around 20 to 80) to judge its safety, including any side effects, and to test the amount (dosage).
- A Phase 2 trial includes more people (around 100 to 300) to help determine whether a drug is effective. This phase aims to obtain preliminary data on whether the drug or device works in people who have a certain disease or condition. These trials also continue to examine safety, including short-term side effects.
- A Phase 3 trial gathers additional information from several hundred to a few thousand people about safety and effectiveness, studying different populations and different dosages, and comparing the intervention with other drugs or treatment approaches. If the FDA agrees that the trial results support the intervention’s use for a particular health condition, it will approve the experimental drug or device.
- A Phase 4 trial takes place after the FDA approves the drug or device. The treatment’s effectiveness and safety are monitored in large, diverse populations. Sometimes, side effects may not become clear until more people have used the drug or device over a longer period of time.
Clinical trials that test a behavior change, rather than a drug or medical device, advance through similar steps, but behavioral interventions are not regulated by the FDA. Learn more about clinical trials , including the types of trials and the four phases.
Choosing to participate in research is an important personal decision. If you are considering joining a trial or study, get answers to your questions and know your options before you decide. Here are questions you might ask the research team when thinking about participating.
- What is this study trying to find out?
- What treatment or tests will I have? Will they hurt? Will you provide me with the test or lab results?
- What are the chances I will be in the experimental group or the control group?
- If the study tests a treatment, what are the possible risks, side effects, and benefits compared with my current treatment?
- How long will the clinical trial last?
- Where will the study take place? Will I need to stay in the hospital?
- Will you provide a way for me to get to the study site if I need it, such as through a ride-share service?
- Will I need a trial or study partner (for example, a family member or friend who knows me well) to come with me to the research site visits? If so, how long will he or she need to participate?
- Can I participate in any part of the trial with my regular doctor or at a clinic closer to my home?
- How will the study affect my everyday life?
- What steps are being taken to ensure my privacy?
- How will you protect my health while I participate?
- What happens if my health problem gets worse during the trial or study?
- Can I take my regular medicines while participating?
- Who will be in charge of my care while I am in the trial or study? Will I be able to see my own doctors?
- How will you keep my doctor informed about my participation?
- If I withdraw from the trial or study, will this affect my normal care?
- Will it cost me anything to be in the trial or study? If so, will I be reimbursed for expenses, such as travel, parking, lodging, or meals?
- Will my insurance pay for costs not covered by the research, or must I pay out of pocket? If I don’t have insurance, am I still eligible to participate?
- Will my trial or study partner be compensated for his or her time?
- Will you follow up on my health after the end of the trial or study?
- Will I continue receiving the treatment after the trial or study ends?
- Will you tell me the results of the research?
- Whom do I contact if I have questions after the trial or study ends?
To be eligible to participate, you may need to have certain characteristics, called inclusion criteria. For example, a clinical trial may need participants to have a certain stage of disease, version of a gene, or family history. Some trials require that participants have a study partner who can accompany them to clinic visits.
Participants with certain characteristics may not be allowed to participate in some trials. These characteristics are called exclusion criteria. They include factors such as specific health conditions or medications that could interfere with the treatment being tested.
Many volunteers must be screened to find enough people who are eligible for a trial or study. Generally, you can participate in only one clinical trial at a time, although this is not necessarily the case for observational studies. Different trials have different criteria, so being excluded from one trial does not necessarily mean you will be excluded from another.
Researchers need older adults to participate in clinical research so that scientists can learn more about how new drugs, tests, and other interventions will work for them. Many older adults have health needs that are different from those of younger people. For example, as people age, their bodies may react differently to certain drugs. Older adults may need different dosages of a drug to have the intended result. Also, some drugs may have different side effects in older people than in younger individuals. Having older adults enrolled in clinical trials and studies helps researchers get the information they need to develop the right treatments for this age group.
Researchers know that it may be challenging for some older adults to join a clinical trial or study. For example, if you have multiple health problems, can you participate in research that is looking at only one condition? If you are frail or have a disability, will you be strong enough to participate? If you no longer drive, how can you get to the research site? Talk to the research coordinator or staff about your concerns. The research team may have already thought about some of the potential obstacles and have a plan to make it easier for you to participate.
Read more about diversity in clinical trials .
You may also be interested in
- Learning more about the benefits, risks, and safety of clinical research
- Finding out about participating in Alzheimer's disease research
- Downloading or sharing an infographic with the benefits of participating in clinical research
Sign up for email updates on healthy aging
For more information about clinical trials.
Alzheimers.gov www.alzheimers.gov Explore the Alzheimers.gov website for information and resources on Alzheimer’s and related dementias from across the federal government.
Clinical Research Trials and You National Institutes of Health www.nih.gov/health-information/nih-clinical-research-trials-you
This content is provided by the NIH National Institute on Aging (NIA). NIA scientists and other experts review this content to ensure it is accurate and up to date.
Content reviewed: March 22, 2023
An official website of the National Institutes of Health
Experimental Research Design – Overview
Published 16 October, 2023
Research is a process of making observations, forming hypotheses, and testing them. It is important to design the experiment in such a way that it will provide reliable and accurate data. Experimental research design is a process used to test the effects of different treatments. It is important for researchers to have a clear idea of what they are testing and how it will be measured before doing their experiment. This blog post discusses experimental research design, including its importance and steps in the process.
What is Experimental Research Design?
Experimental Research Design is a type of research in which the investigator manipulates one or more independent variables and observes the results. Experimental research designs are concerned with the examination of the effect of the independent variable on the dependent variable, where the independent variable is manipulated through treatment or interventions, & the effect of those interventions is observed on the dependent variable. By facilitating such type of investigation you can have complete control over different types of variables .
In addition , experimental research design enables you to develop an understanding of the relationship between cause and effect. Experimental research is basically a type of research where you can hypothesis testing in research in a scientific manner.
The main objective of experimental research design is to study the relationship between different variables. One biggest advantage of experimental research is that it allows you to have the highest level of control. The level of detail in research and validity of outcome is completely dependent on variations in independent variables.
When the experimental study is performed?
You can perform such type of research mainly in three situations these are:
- In such a case when time is one of the important factors.
- Stable attitude or relationship between cause and effect.
- The importance of cause and effect completely relies on desirability.
Types of experimental research designs
The different types of experimental research design are:
It is basically an extent up to which you assign subjects to different groups or situations by the investigator. In experimental research design, you need to follow normal scientific procedures. By applying the pre-experimental design you can easily perform an investigation on a single group. You may need to employ a pre-experimental research design in order to determine the usefulness of further investigation. This type of experiment will collect data before implementing changes, and its purpose is simply to see if there are any noticeable effects on particular groups or individuals at all.
For example, an investigation which you are performing with the intention of identifying the need for implementation of social welfare policy.
It is a type of experimental research where independent variables are manipulated. This type of investigation includes choosing people without performing pre-selection procedures for testing variables. You need to align Quasi-experimental research designs with case studies so that you can easily perform statistical analysis. True experimental design can establish cause-effect relationships within groups better than other types of designs that are not true experiments.
For example, You want to perform an experiment on a pregnant mother in order to analyze the influence of drinking alcohol on embryos.
This is basically one of the correct types of experimental study design. As researcher has applied statistical analysis techniques for supporting or rejecting the hypothesis in research . Experimental research design helps in developing an understanding of the relationship between cause and effect.
For example, if it’s conducted with people who live together as roommates and they’re not randomly assigned to their rooms because they all know each other well enough already.
Process of conducting experimental research
Designing good experimental research, there are five main steps, as follows:
1. Defining your variables
A research question is your first step in designing an experiment. Once you have found your research question, translate the main variables into experimental hypotheses and control any extraneous or confounding factors that may skew the results of the experiment. You need to define the variables you want to test and make predictions about how they’re related before coming up with a hypothesis for what will happen if one variable changes when another stays constant.
2. Writing your hypothesis
Now that you have a strong conceptual understanding of the system, your hypotheses will be focused and testable. Designing a controlled experiment is essential for research, so here’s how it works. In order to conduct one, you need three things:
- an independent variable that can be manipulated and precisely measured;
- dependent variables that are carefully observed as they change in response to changes in the manipulations of the independent variable(s);
- controlling any potential confounding factors or extraneous sources of variation so there aren’t other explanations for why some people might have improved more than others during treatment.
3. Designing experimental treatments
You control the independent variable, but that can affect external validity. First, you might need to decide how widely to vary your independent variable, and second, you may need to choose how finely to vary your independent variable.
4. Assigning subjects to treatment groups
When conducting an experiment, the size of your sample is important. Larger samples help give more statistical power and provide greater confidence in experimental results. Once that’s figured out, it’s time to assign subjects randomly into treatment groups so each one gets a different level of the treatment. Lastly, there should also be a control group that doesn’t get any treatment
5. Measure dependent variable
When designing and executing your study, you need to decide how the variable outcomes will be measured. There are many types of measurements that can be used for reliable and valid measurement and minimize the error. For instance, if you wanted to study temperature changes in certain areas over time, one option would be using scientific instruments – and another could be measuring it directly by taking daily readings at different distances from an area where measurable heating or cooling has taken place.
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- 09 November 2023
What reproducibility crisis? New research protocol yields ultra-high replication rate
- David Adam 0
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Research teams achieved highly replicable results after steps such as preregistration of their study protocols. Credit: sshepard/Getty
Nowhere has the replication crisis in science struck harder over the past decade than in experimental psychology . A series of high-profile failures to reproduce findings has seen critics line up to dismiss work in the field as unreliable and riddled with methodological flaws.
In a bid to restore its reputation, experimental psychology has now brought its A game to the laboratory. A group of heavy-hitters in the field spent five years working on new research projects under the most rigorous and careful experimental conditions possible and getting each other’s labs to try to reproduce the findings.
Published today in Nature Human Behaviour 1 , the results show that the original findings could be replicated 86% of the time — significantly better than the 50% success rate reported by some systematic replication efforts .
The study, the authors say, shows that research in the field can indeed be top quality if all of the right steps are taken.
“People are worried that there’s all these problems that undermine the credibility or replicability of findings,” says Brian Nosek, a psychologist at the University of Virginia in Charlottesville and a co-author of the study. “What if we ran a study where we tried to eliminate all those problems and do it as rigorously as possible?” The idea, he says, was to use best practices to identify a benchmark of replicability.
Rather than trying to replicate existing published studies, the organizers of the work asked four prominent research groups based at US universities to devise and plan their own separate projects to address new questions in social psychology.
Each lab carried out its chosen projects using practices that are known to increase experimental rigour and the likelihood of replication. When research at the pilot stage suggested an interesting effect, the original lab ran a full-scale confirmatory study with a sample size of at least 1,500 participants. Both the pilot-phase and the full-scale studies were preregistered, which means that the authors specified and submitted a research plan in advance to a database.
Reproducibility trial: 246 biologists get different results from same data sets
Four candidate new discoveries were selected by each lab for the self-confirmatory testing phase. After this, the other three labs each ran a full-scale repeat of these four chosen studies, again with a sample size of at least 1,500. For the replication efforts, the labs relied on the preregistered research plans and other relevant experimental materials (such as instructional videos for participants) shared by the founding lab.
As well as checking the overall findings, these replication efforts also looked at the effect size, to see whether there was any evidence of the ‘decline effect’, in which the strength of a finding reduces with subsequent experiments. No such decline was observed: the effect sizes in the replication trials were the same as those measured in the original labs’ self-confirmatory experiments.
In principle, the results could apply to psychology more broadly, and across other fields of social science, Nosek says.
Nosek stresses that the research topics chosen for replication were not trivial questions with obvious answers, which would have been relatively simple to replicate. Instead, the projects assessed serious research questions in marketing, advertising, political science, communication, and judgement and decision-making. Several of the labs involved have already produced published papers about their findings.
One paper 2 , published last year in Scientific Reports , by a group led by Jonathan Schooler at the University of California, Santa Barbara, showed that people can misattribute the ‘a-ha!’ feeling they get when they solve an anagram to the truth of the statement in which the anagram is embedded.
The new replication effort is also a “political communication to demonstrate that not all of social sciences is trash”, says Malte Elson, a metascientist at the University of Bern. “But that’s a good thing. I think it’s very useful to show both to the community, but also to the general public, that social sciences are not inherently flawed.”
Nature 623 , 467-468 (2023)
Additional reporting by Anil Oza.
Protzko, J. et al. Nature Hum. Behav . https://doi.org/10.1038/s41562-023-01749-9 (2023).
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Laukkonen, R. E. et al. Sci. Rep . 12 , 2075 (2022).
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- Published: 14 November 2023
Differential immunomodulatory effects of epirubicin/cyclophosphamide and docetaxel in breast cancer patients
- Kerstin Wimmer 1 , 2 ,
- Monika Sachet 1 ,
- Cristiano Ramos 1 ,
- Sophie Frantal 2 ,
- Hanna Birnleitner 1 ,
- Christine Brostjan 3 ,
- Ruth Exner 1 ,
- Martin Filipits 2 , 4 ,
- Zsuzsanna Bago-Horvath 2 , 5 , 6 ,
- Margaretha Rudas 5 , 6 ,
- Rupert Bartsch 2 , 7 ,
- Michael Gnant 2 , 6 ,
- Christian F. Singer 2 , 6 , 8 ,
- Marija Balic 2 , 9 ,
- Daniel Egle 2 , 10 ,
- Rudolf Oehler ORCID: orcid.org/0000-0003-2891-7155 1 , 2 &
- Florian Fitzal 1 , 2
Journal of Experimental & Clinical Cancer Research volume 42 , Article number: 300 ( 2023 ) Cite this article
Epirubicin/cyclophosphamide (EC) and docetaxel (D) are commonly used in a sequential regimen in the neoadjuvant treatment of early, high-risk or locally advanced breast cancer (BC). Novel approaches to increase the response rate combine this treatment with immunotherapies such as PD-1 inhibition. However, the expected stimulatory effect on lymphocytes may depend on the chemotherapy backbone. Therefore, we separately compared the immunomodulatory effects of EC and D in the setting of a randomized clinical trial.
Tumor and blood samples of 154 patients from the ABCSG-34 trial were available (76 patients received four cycles of EC followed by four cycles of D; 78 patients get the reverse treatment sequence). Tumor-infiltrating lymphocytes, circulating lymphocytes and 14 soluble immune mediators were determined at baseline and at drug change. Furthermore, six BC cell lines were treated with E, C or D and co-cultured with immune cells.
Initial treatment with four cycles of EC reduced circulating B and T cells by 94% and 45%, respectively. In contrast, no comparable effects on lymphocytes were observed in patients treated with initial four cycles of D. Most immune mediators decreased under EC whereas D-treatment resulted in elevated levels of CXCL10, urokinase-type plasminogen activator (uPA) and its soluble receptor (suPAR). Accordingly, only the exposure of BC cell lines to D induced similar increases as compared to E. While treatment of BC cells with E was associated with cell shrinkage and apoptosis, D induced cell swelling and accumulation of cells in G2 phase.
The deleterious effect of EC on lymphocytes indicates strong immunosuppressive properties of this combination therapy. D, in contrast, has no effect on lymphocytes, but triggers the secretion of stimulatory proteins in vivo and in vitro, indicating a supportive effect on the immune system. Underlying differences in the induced cell death might be causal. These divergent immunomodulatory effects of epirubicin/cyclophosphamide and docetaxel should be considered when planning future combinations with immunotherapies in breast cancer.
Despite ongoing improvements in screening programs and increasing awareness, breast cancer (BC) remains a significant cause of death [ 1 ]. Thus, research on therapeutic options as well as further development of personalized treatment approaches is urgently needed. Neoadjuvant chemotherapy (NAC) with a sequential administration of anthracyclines and taxanes is currently one standard-of-care therapy option in patients with early, high-risk or locally advanced hormone receptor positive or triple negative BC. NAC is nowadays widely applied, but not all patients profit from this treatment. Continuous rise of interest in the immune system as a potent anti-tumor defense was observed. A strong infiltration of the tumor with immune cells correlates with a better prognosis in certain cancer types [ 2 , 3 , 4 , 5 ]. Especially cytotoxic CD8 + T cells and NK cells have been associated with beneficial anti-cancer immune response. In BC, a higher number of stromal CD8 + lymphocytes were associated with improved survival [ 4 ]. Increasing tumor-infiltrating lymphocytes (TILs) during taxane-based NAC correlated with favorable tumor response [ 6 ]. TNBC is known to provoke the most abundant immune cell infiltration in breast cancer. This underlines the utmost importance of cytotoxic immune cells in BC control. The balance between anti- and pro-inflammatory molecules in the tumor microenvironment and in the circulation and their interaction with immune cells determine the activity level of T cells. On the one hand, the tumor microenvironment can shift T cells into an anergic state whereas on the other hand, pro-inflammatory mechanisms—provoked by chemotherapy-induced immunogenic cell death or by other immunomodulatory factors – can stimulate them [ 7 ]. Immunogenic cell death activates the immune system and also promotes a tumor-specific adaptive immune response [ 8 ]. Several studies showed that chemotherapy-induced cell death was associated with increased tumor immune cell infiltration [ 9 ].
Immunotherapies such as immune checkpoint inhibitors, adoptive T-cell immunotherapy, and tumor vaccine immunotherapy became promising novel treatment approaches in TNBC [ 10 ]. Complementing traditional chemotherapy with immunotherapy may exert synergistic effects. The KEYNOTE-522 trial confirmed that the addition of pembrolizumab, a PD-1 inhibitor, to anthracycline and taxane chemotherapy can increase pathologic complete response (pCR) rates in early stage TNBC [ 11 ] while the combination of carboplatin and taxane based chemotherapy with atezolizumab in the NeoTRIP Michelangelo trial showed no advantages [ 12 ]. Similarly, the combination of chemotherapy with a PD-L1 inhibitor in the phase II GeparNuevo trial was also negative for the primary endpoint pCR, but suggested improved invasive DFS, distant DFS, and OS in PD-L1 immune cell-positive BC [ 13 , 14 ].
Activity of immunotherapy might be influenced by the chemotherapy backbone and particular attention must be paid to the complex interplay of chemotherapy and the immune system. The question arises, if chemotherapeutic agents differ in their influence on immune cells and hence, if the administered chemotherapeutic agents are equally compatible with immune-related therapies. For the here presented analysis, aiming to determine the particular effects of either epirubicin/cyclophosphamide (EC) or docetaxel (D) on immune cells without the interference of previous treatments, the ABCSG-34 study served as a basis. In this trial patients with BC of luminal or triple negative subtype were included and received either upfront EC followed by D or a reverse sequence with upfront D and then EC [ 15 ]. Although no differences in terms of pCR rates or residual cancer burden (RCB) score were observed between the two sequencing groups [ 16 ], the study design and a comprehensive pre-planned translational program render the ABCSG 34 cohort an ideal platform to study cytotoxic and immune-mediated therapy effects. The present analysis focused on the effects of EC and D on lymphocytes in the tumor microenvironment as well as in the circulation and on soluble immunomodulatory molecules. Therefore, we analyzed the initial cycles of NAC before the switch of drugs. This unique design allowed for a direct comparison of the distinct chemotherapeutic agents in the setting of a randomized clinical trial.
Patient populations, study design and sample collection
The already completed ABCSG-34 trial, conducted by the Austrian Breast Cancer and Colorectal Surgery Group (ABCSG), served as basis [ 15 ]. The trial addressed pre- and postmenopausal female patients with early primary invasive BC without HER2-overexpression. It was a prospectively, randomized, open, 2-arm, multicentre, phase-II study in 400 breast cancer patients, treated with or without a therapeutic cancer vaccine (L-BLP25, Stimuvax®) in the preoperative setting. Patients were 1:1 randomized to preoperative standard of care (SoC) treatment with or without vaccine-BLP25. The preoperative SoC consisted of chemotherapy or endocrine therapy with an aromatase inhibitor (AI). AI therapy was considered as SoC in postmenopausal women with intermediate (+ +) or high (+ + +) estrogen receptor (ER) expression, a grading of 1, 2 or X, and a Ki67 of < 14%. Women with TNBC, premenopausal women, patients with absent (-) or low ER expression ( +) and patients with G3 tumors were treated with anthracycline and taxane-based NAC as SoC. The chemotherapy, consisting of EC and D, was either administered in a conventional (CON) or in a reverse (REV) sequence. In the conventional sequence, 4 cycles of EC were followed by 4 cycles of D in 3-week intervals whereas 4 cycles of D were followed by 4 cycles of EC in 3-week intervals in the reverse scheme. For the assessment of tumor response to preoperative therapy, the residual cancer burden (RCB) – prognostic for long-term survival in BC patients after NAC – was used [ 17 ]. RCB 0 (i.e. pathological complete response) was confirmed by central pathological review. The here presented study is a post hoc biomarker analysis conducted in the subset of patients accrued to the chemotherapy cohort and randomized to SoC. We created two investigational sets, the “lymphocyte function & infiltration” and the “immunoassay” subsets (see Fig. 1 ). Patients were selected for the “lymphocyte function & infiltration” subgroup if whole blood analysis as well as lymphocyte subtyping data was available at baseline, midterm and the time point operation. In this set we analyzed the influence of NAC on white blood cells, leukocyte subtypes, the IFNγ production and stromal (s) as well as invasive (i) TILs. In the “immunoassay” subset we investigated the effect of NAC on 14 different soluble parameters in plasma samples. For this subset, patients of the “lymphocyte function & infiltration” group were selected if plasma samples at all three time points (i.e. baseline, midterm, operation) were available. Research biopsies as well as blood samples were obtained before treatment (baseline or B), at midterm (M; i.e., after either 4 cycles of EC or 4 cycles of D), and after the second 4 cycles of therapy before surgery (S). Blood was collected in two different types of tube: (i) in EDTA containing tubes (Greiner Bio-one, Kremsmünster, Austria) for white blood cell (WBC) counting, leukocyte subtyping and plasma preparation (ii) in BD CPT™ tubes (BD, Heidelberg, Germany) for lymphocyte stimulation assays.
Patient selection diagram. This study was based on the ABCSG-34 trial which included a total of 400 patients. Half of them got a vaccination, which was out of the scope of the current study, and they were therefore excluded. Additionally, we excluded non-vaccinated patients treated with aromatase inhibitors (AI). The remaining 154 patients were randomly assigned to two treatment arms with different sequences of neoadjuvant chemotherapy (NAC): the conventional sequence (CON, n = 76) with 4 cycles of EC followed by 4 cycles of D and the reverse sequence (REV, n = 78) with first D then EC. From this collective ( n = 154) we determined lymphocyte function (i.e. PHA-induced IFNγ production of circulating lymphocytes) and infiltration (i.e. TILs). Immunoassays for characterization of lymphocyte subpopulation (e.g. CD3 and CD19) and for quantification of soluble immune mediators were started later and were therefore only determined in a subgroup ( n = 81)
Quantification of iTILs and sTILs
Tumor-infiltrating lymphocytes (TILs) were quantified according to the recommendations by the International TILs Working Group published in 2014 [ 18 ] with modifications according to Denkert et al. 2018 [ 19 ]. TILs were quantified on H&E sections of core biopsies obtained at the indicated time. Intratumoral and stromal TILs (iTILs and sTILs, respectively) were measured as percentage of mononuclear immune cells within or in between tumor cell nests, respectively. Three predefined categories were used: low TILs (0–10%), intermediate TILs (11–59%), or high TILs (60–100%). Examples are shown in Supplementary Figure S1 .
White blood cell counting and leukocyte subtyping
The total WBC count was determined in an aliquot of the EDTA blood using a Sysmex hematologic analyzer (Sysmex Austria, Vienna, Austria). For leukocyte subtyping red blood cells were lysed in another aliquot of the EDTA blood, and the remaining leukocytes were stained with fluorescently labelled antibodies against CD3, CD4, CD8, CD14, CD15, and CD19 (all from Thermo Fisher Scientific, Vienna, Austria). Sample acquisition was performed on a Gallios Flow Cytometer (Beckman Coulter, Indianapolis, IN, USA) and data was analyzed using the Kaluza 2.1 software (Beckman Coulter). See Supplementary Figure S 2 for the gating strategy.
T cell stimulation
Peripheral blood mononuclear cells (PBMCs) were prepared from the BD CPT™ tubes (BD, Heidelberg, Germany) according to the instructions by the manufacturer. Then phytohemagglutinin (PHA) was added for T cell stimulation and the tubes were incubated at 37 °C for 18 h. Stimulated T cells were defined as the percentage of IFNγ producing CD8 + T cells. Therefore, PBMCs were first stained with antibodies against CD3 and CD8, then permeabilized and stained for intracellular IFNγ. All antibodies were from Thermo Fisher Scientific (Vienna, Austria). The percentage of CD3 + /CD8 + /IFNγ + cells was determined by flow cytometry using a Gallios Flow Cytometer (Beckman Coulter, Indianapolis, IN, USA) and data was analyzed using the Kaluza 2.1 software (Beckman Coulter). See Supplementary Figure S 2 for gating strategy.
Soluble immunomodulatory factors
Plasma was prepared by centrifugation of the remaining EDTA blood and stored in aliquots at -80 °C. In a pilot experiment, EDTA plasma of eight non-vaccinated patients before treatment initiation with AI was investigated. Therefore, an Olink® assay was performed which used matched pairs of oligonucleotide-labelled antibodies that were subsequently quantified by standard real-time PCR (Olink AB, Uppsala, Sweden). This enabled the simultaneous analysis of 92 different immunomodulatory parameters (for a detailed list of all factors see Supplementary Figure S 3 ). The assay was performed according to the instructions of the manufacturer. Referring to the 30 most abundant molecules determined in the Olink® pilot assay, we selected 14 immunomodulatory parameters according to their oncological relevance and their association with BC as revealed by the literature. The resulting list included urokinase (uPA) and its soluble receptor (suPAR), vascular endothelial growth factor (VEGF-A), osteoprotegerin (OPG), monocyte chemoattractant protein 1 (MCP1 or CCL2), monocyte chemoattractant protein 2 (MCP2 or CCL8), macrophage colony-stimulating factor (M-CSF or CSF1), TNF-related weak inducer of apoptosis (TWEAK or TNFSF12), TNF-related apoptosis-inducing ligand (TRAIL or TNFSF10), interferon gamma-induced protein 10 (IP-10 or CXCL10), eotaxin-1 (or CCL11), T cell immunoglobulin and mucin-domain containing-3 (TIM-3), soluble CD27 (sCD27), and programmed cell death 1 ligand 2 (PD-L2). The majority of the selected immunomodulatory factors were analyzed by ProcartaPlex™ multiplex immunoassay (Thermo Fisher Scientific, Vienna, Austria). Plates were assessed using the Luminex 200 System (Luminex Corp. Austin, TX, USA). Two factors, uPA and suPAR, were analyzed by individual ELISA assays (Thermo Fisher Scientific, Vienna, Austria). The ELISA and the multiplex assays were performed in accordance with the manufacturer's instructions.
Cancer cell line treatment and analysis
To represent the different BC subtypes, six cell lines were selected for in vitro experiments: MCF-7 and ZR-75–1 (both luminal A), BT-474 (luminal B), HCC-197 and HCC-1143 (both TNBC) and SK-BR-3 (HER2 +) [ 20 , 21 ].The cell lines were obtained from ATCC ( https://www.atcc.org ), and were tested for mycoplasma contamination regularly, within two weeks before the cell culture models were established. MCF-7 and SK-BR-3 were grown in DMEM containing 10% fetal bovine serum (FCS) whereas all other cells were cultured in RPMI medium containing 10% FCS. The duration of the cell culture assay was five days. On day 1, cells were washed with phosphate-buffered saline (PBS, 1x, pH 7.4), counted and 40.000 cells were seeded in 500µL culture medium with 10% FCS in 24-well plates. On day 2, cells were washed with PBS, and chemotherapy was added into the appropriate wells at a final concentration of 2 µM for epirubicin, 15 µM for the cyclophosphamide metabolite 4OOH-CY, and 1 µM for docetaxel (diluted with the respective medium). Per cell line, two wells remained untreated. On day 4, 48 h after incubation with chemotherapy, pictures were taken and WBCs were isolated by red cell lysis. Per well, 500 000 WBCs were added. WBCs were added either onto a BC cell culture or into the supernatant of the respective cell culture. On day 5, the supernatants were transferred from wells into pre-cooled µFACS tubes. Per well, 2 × 120 µl aliquots were stored at -80 °C. Supernatants of untreated tumor cells, treated tumor cells, treated tumor cells in co-culture with WBCs and of WBCs alone were analyzed for soluble immunomodulatory factors by multiplex bead array immunoassay or ELISA (see above). The cell cycle of tumor cells was analyzed using a Propidium Iodide Flow Cytometry Kit according to the instructions by the manufacturer (Abcam, Cambridge, UK). Caspase 3 activation was determined using specific antibodies (559,565, BD Pharmingen, Franklin Lanes, NJ, USA) as described previously [ 22 , 23 ].
All analyses were based on the intention-to-treat principle. Hence, patients were analyzed according to their randomized treatment. Patient characteristics are presented descriptively for patients with CON and REV and in total. “Lymphocyte function & infiltration” and “immunoassay” parameters at baseline, as well as changes in parameters between baseline and mid-therapy are presented and compared descriptively between patients with CON vs REV, RCB 0/I (≤ 1.36) vs. RCB II/III (> 1.36) and pCR yes vs. no by Wilcoxon tests. Scatterplots and boxplots show the change over time from baseline to surgery. A possible predictive role of “lymphocyte function & infiltration” parameters on RCB or pCR was assessed by logistic regression models. Primary analysis models included treatment arm only. Covariable models included treatment arm and the respective covariable (changes between baseline and mid-therapy) and were adjusted for the according covariable baseline value. Extended models additionally included a treatment-by-covariable interaction. Odds ratios (OR) with 95% confidence interval (CI) are provided. Measured parameters in the in vitro experiment of three BC cell lines in co-culture were presented descriptively. For each parameter, measurements were performed twice and the given value represents the mean. As the main ABCSG-34 study was planned for different objectives/endpoints, all results found in this subgroup analysis need to be considered exploratory. Analyses were carried out using the statistical analysis system (SAS) software (version 9.3 or higher) and GraphPad Prism (version 8.0.2.).
Of the 400 participating ABCSG-34 trial patients, 246 patients were excluded from the here presented analysis due to their randomization (see Fig. 1 ). The exclusion of 200 patients, who were vaccinated with LBLP-25, and 46, who were treated with AI-therapy, resulted in 154 patients that were selected for the “lymphocyte function & infiltration” subset. Of those, 81 patients were selected for further analyses in the “immunoassay” subgroup.
Predictive value of tumor-infiltrating lymphocytes under NAC
In the “lymphocyte function & infiltration” subset, 76 patients were treated with NAC in a conventional sequence (CON) whereas 78 were treated reversely (REV, see Fig. 1 ). Further patients’ characteristics of this subset are shown in Table 1 .
First, tumor tissue was analyzed for the purpose of comparing the effect of EC and D on TILs. Regarding the change of lymphocytes during the first four cycles of NAC, neither the initial four cycles of EC nor of D had effects on lymphocytes in the tumor microenvironment (see Fig. 2 A and Supplementary Table S1 ). Furthermore, the response of the tumor to NAC measured by the RCB-score or the rate of pCR didn’t differ between CON and REV NAC scheme. The CON arm included 34.2% responders (RCB score of ≤ 1.36) and the REV arm 35.9%. A pCR could be detected in 21.4% of all patients and was equally distributed between CON and REV (22.4% and 20.4%, respectively). Thus, both treatment arms resulted in similar response rates.
Effect of NAC on lymphocyte count and function. Tumor tissues and blood samples were collected at baseline (B), mid-therapy (M), and surgery (S) from patients treated with either conventional (CON) or reverse (REV) sequence of NAC. The color indicates the therapy administered immediately prior to the respective analysis: red for EC and blue for D. The pre-treatment analysis (baseline, B) is indicated in black. A Histological quantification of intratumoral TILs (iTILs) and stromal TILs (sTILs) in tumor tissue samples. The left panel shows an H&E stained tumor tissue. The invasive margin of the tumor is indicated by a dashed line. The arrows indicate examples of TILs. The panels on the right show the quantitative analysis. B Hemocytometric analysis of blood samples: The top graph shows typical examples of such analyses in blood samples taken at baseline (B) and midterm (M) of a CON patient. The lines indicate the cell volume distribution of the WBC populations (lymphocytes, L; monocytes, M; granulocytes, G). The bottom graph summarizes the lymphocyte counts of all patients. C Flow cytometric lymphocyte subtyping: quantification of CD3 + T cells, CD19 + B cells, and CD15 + granulocytes. D Lymphocyte function: PBMCs were prepared from peripheral blood and stimulated with PHA. IFNγ + /CD8 + T cells (left) and IFNγ + /CD8 − cells (right) were counted by flow cytometry. All graphs show individual patient values as well as median ± IQR. The statistical significance was calculated using a paired Student’s t-test (**… ≤ 0.01; ****… ≤ 0.001)
Independently of the treatment sequence (CON or REV), responders and non-responders differed greatly in terms of TILs. During the initial 4 cycles of NAC an increase of sTILs and iTILs was observable in patients with an RCB ≤ 1.36 ( n = 12; median = + 17.5% and + 2.0%, respectively). Patients with a higher RCB ( n = 62) showed a different trend (-1.0% p = 0.001 and -1.0% p = 0.023, respectively; see Supplementary Table S 2 ). Patients with ( n = 6) and without pCR ( n = 69) showed a similar dynamic regarding TILs (sTILs: + 20% vs. 0.0% p = 0.002; iTILs 9.5% vs. -1.0% p = 0.088). These changes revealed that the lymphocyte infiltration of the tumor microenvironment could be modulated by four cycles of NAC between baseline and midterm.
As NAC-induced changes of sTILs and iTILs differed between responders and non-responders, we evaluated whether they predicted response to therapy applying two different logistic regression models. The first model shown in Table 2 included either the treatment effect alone or combined with NAC-induced changes between baseline and midterm in sTILs and iTILs, respectively, as covariables. It showed that higher changes increased the chance for RCB 0/I and pCR. The second model investigated the interactions between treatment and sTILs and iTILs, respectively. It revealed that only changes of sTILs and iTILs in the CON arm were considerable, indicating an exclusive effect of initial EC, whereas D showed no effect.
Epirubicin-based chemotherapy suppresses lymphocytes whereas docetaxel maintains their number and function
The differential blood count showed a strong decrease of lymphocytes in patients treated with the initial four cycles of EC (from 1.57 × 10 3 ± 0.52 cells/µl to 0.86 × 10 3 ± 0.42 cells/µl; p < 0.001; see Fig. 2 B). In contrast, initial D had no effect on the lymphocyte count. The great majority of lymphocytes are T cells, followed by B cells and much fewer natural killer cells. A detailed analysis by flow cytometry showed an EC-induced decrease of CD19 + B cells which persisted during the subsequent 4 cycles of D until completion of NAC (see Fig. 2 C). A similar drop was also observed when EC was administered after D. The reduction of B cells did not differ between responders and non-responders (see Supplementary Figure S 4 and Supplementary Table S 3 ). Hence, EC seemed to have a sustainable and long-lasting suppressive effect on B cells. The decrease of CD3 + T cells when EC was administered initially was less pronounced compared to the one of B cells and was not observed when EC was given after D. In contrast, the initial 4 cycles of D in the REV arm did not reduce the number of circulating T or B cells. Remarkably, CD15 + granulocytes did not show statistically significant changes in their cell number in response to the different chemotherapy treatments. Similarly, we found no change in CD14 + monocytes or CD56 + NK-cells (data not shown). Because T cells are essential for an effective anti-tumor immune response, we investigated T cell function. Therefore, we isolated PBMCs from blood samples collected at baseline, midterm and surgery. They were then treated ex vivo with PHA and the ability of cytotoxic T cells to produce IFNγ was measured by flow cytometry. Neither EC nor D showed any effect on this T cell function (see Fig. 2 D).
Lymphocytes may be regulated by a variety of different cytokines, chemokines and other immunomodulatory factors. Hence, we investigated such molecules in our patient collective. For this purpose, 81 patients of the “lymphocyte function & infiltration” subset were further selected for the “immunoassay” subgroup. Fourteen above mentioned factors were measured in blood samples at baseline, midterm and at surgery. Five factors (uPA, CXCL10, sTNFSF10, suPAR, and CCL8) reacted differently to EC as compared to D (see Table 3 ; a detailed illustration for every single factor is shown in Supplementary Figure S 5 A + B). All of them decreased in response to four cycles of EC, which is in accordance with the suppressive effect of this treatment described above. Interestingly, uPA and its soluble receptor suPAR as well as CXCL10 increased in D-treated patients.
Taken together, these data show that the treatment of BC patients with EC reduces B cells, T cells and several soluble immunomodulatory factors in the peripheral blood. Treatment with D, in contrast, maintains lymphocyte number and function, even an increase of some soluble factors in response to D was observable, suggesting that this treatment might have greater inductive potential on immune signaling.
Docetaxel and epirubicin induce different forms of cell death
To investigate the source of the D-induced immunomodulatory factors we performed in vitro experiments. WBCs, breast cancer cell lines and a combination of both were exposed for 72 h to either epirubicin or docetaxel. PBS served as negative control. Then the above-mentioned 14 factors were measured in the supernatant. The heat map in Fig. 3 A gives an overview of the results (for more detailed results see Supplementary Table S 4 ). Docetaxel induced a secretion of many factors including CXCL10, suPAR, and uPA from cancer cell lines, while WBCs remained unresponsive. Epirubicin, in contrast, induced no increase of any of these factors. These results confirm that the immunomodulatory factors, which increased after four cycles of D in vivo, originated from dying tumor cells, rather than immune cells. To confirm that this is a general effect in breast cancer we repeated this experiment with six different breast cancer cell lines (HCC-1937, HCC-1143, SK-BR-3, BT-474, ZR-75–1, and MCF-7) investigating those three parameters, which showed the highest and most significant differential response in the in vivo setting (uPA, suPAR, and CXCL10). These experiments included also a treatment with 4-OOH-CY, which is an active metabolite of cyclophosphamide. Figure 3 B shows that epirubicin induced less secretion of uPA and suPAR compared to docetaxel, whereas 4-OOH-CY had no effect. The secretion of CXCL10 varied strongly between cell lines and there was no clear difference between epirubicin-treated and docetaxel-treated cells.
Effect of docetaxel, epirubicin, and cyclophosphamide on in vitro cultures of breast cancer cells. A Heat map showing the effect of epirubicin and docetaxel on the release of soluble immune mediators. Epirubicin, docetaxel, or medium as negative control were added to three different breast cancer cell lines (SK-BR3, MCF7, and HCC1143) which were then cultured for 48 h then white blood cells (WBC) were added and the co-culture was cultivated for additional 24 h. The concentration of 14 different immune mediators in the supernatant was determined using a bead array immunoassay. The red colored dots indicate high concentrations of the respective mediator. B Release of uPA, suPAR, and CXCL10 from HCC-1937, HCC-1143, SK-BR-3, BT-474, ZR-75–1, and MCF-7 cells in response to docetaxel, epirubicin, and 4-OOH-CY. C Phase contrast microscopic images of HCC1143 and ZR-75–1 cells after a 48 h cultivation in the presence of docetaxel, epirubicin, 4-OOH-CY, or a combination of the latter two. The white arrows indicate multinucleated giant cells. The black arrows point to apoptotic cells surrounded by apoptotic bodies. D Cell cycle analysis: Cells were treated as described in C and the intracellular DNA was stained with PI. The graph shows the distribution of the fluorescence intensities as assessed by flow cytometry. G1, G2, and S indicate the respective phase of the cell cycle. E Summary of the data shown in D. The results are expressed as mean G2/G1 ratio of the respective cell line (± SD, n = 3). F Quantification of apoptosis. Cells were treated as described in C, then the membrane was permeabilized and stained with antibodies against active caspase 3. The fluorescence was analyzed by flow cytometry. The graph indicates the mean relative distribution normalized to the untreated control (± SD, n = 3). The significance was calculated using a one-way ANOWA overall cell lines
To investigate the respective cell death pathways, we compared the cytotoxic effects of docetaxel, epirubicin and 4-OOH-CY on six breast cancer cell lines. Figure 3 C shows examples of microscopic images of two BC cell lines exposed for 48 h to these compounds. Docetaxel-treated cells increased in size forming multinucleated giant cells with accumulated intracellular vacuole. In contrast, epirubicin-treated cells showed classical apoptotic morphological features such as cell shrinkage and numerous apoptotic extracellular vesicles, which are known to be released during the process of apoptotic blebbing. 4-OOH-CY did not affect the cell viability. Next we performed cell cycle measurements (see Fig. 3 D). Docetaxel induced an accumulation of cells in G2 phase and a strong decrease of cells in G1. In contrast, epirubicin-treatment caused an increase of cells in G1 and the formation of sub-G1 cells, which reflects a DNA laddering during apoptosis. 4-OOH-CY as a single agent did not affect the cell cycle and showed no additive effect in combination with epirubicin. Figure 3 E summarizes the results of the cell cycle measurements. Docetaxel induced a significant increase of the G2/G1 ratio in all six cell lines. All other treatments had no significant effect as compared to control. To further specify the induction of apoptosis, an active caspase-3 assay was performed. Epirubicin-treated cells showed a significantly higher activation of caspase-3 as compared to control (see Fig. 3 F). Docetaxel-treated cell showed also some increase, but to a lower extend. 4OOH-CY had no effect. Taken together these results confirm that epirubicin and docetaxel differ in their induction of cell death in breast cancer cell lines, where docetaxel-treatment is associated with the release of various immunomodulatory mediators similar as observed above in vivo.
Neoadjuvant anthracyclines and taxanes still represent one standard-of-care therapy option in women with early, high-risk or locally advanced breast cancer [ 24 , 25 ]. In TNBC patients, further substantial benefits can be achieved by adding immune checkpoint inhibitors either in the neoadjuvant setting [ 11 , 13 , 26 ] or even in the locally advanced or metastatic setting [ 27 ]. Since the combination of chemotherapy with immunotherapy is more and more common, the question arises if there are agents that should be preferentially combined while other combinations may cause mutual interference, potentially impeding immune modulating characteristics of a particular agent. The study design of the ABCSG-34 trial allowed for a comparison of the conventional (EC followed by D) versus reverse (D followed by EC) NAC scheme with respect to in vivo immune effects. It enabled a profound investigation of the distinct effects of each chemotherapeutic agent on its own during the initial 4 cycles of therapy. Although Bartsch et al. overall found no differences regarding clinical and pathological response to NAC between patients undergoing conventional or reverse NAC [ 16 ], the here presented study revealed that EC and D differ fundamentally in their effects on lymphocytes and on immunomodulatory molecules in the circulation. Patients on upfront EC developed lymphopenia with CD19 + B cells decreasing to less than 10% of respective baseline levels, whereas D had no ablating effects. This effect was long-lasting since the suppression persisted even after the following four cycles of D. A similar durable inhibitory impact of EC on B cells was observed in a study by Verma et al. [ 28 ]. A major depletion of B cells was observed, dropping to a median of 5.4% of pre-chemotherapy levels. After 9 months, a partial recovery of B and CD4 + T cells was observed although their levels remained significantly lower than before chemotherapy. Other trials showed similar effects of EC and confirmed a B cell loss of over 90% after administration of EC in TNBC patients. Additionally, they reported decreased numbers of natural killer cells and CD4 + T lymphocytes to 50% while the frequency of CD8 + T cells was less affected [ 29 ]. Similarly, we observed that T cells were not as sensitive to EC as B cells. CD3 + T cells decreased after four cycles of EC by only 50%. EC administered after D had no detectable effect on CD3 + T cells. The latter probably has to do with the large variation observed in T cells after treatment with D, which may mask any weak EC effect. Taken together, EC administration entailed a strong and long-lasting suppressive effect on B cells and a more transient effect on T cells while D had only a minor impact on lymphocytes and their subtypes.
In the here presented study the stimulatory effects of D on soluble immunomodulatory biomarkers were clearly observable in vivo as well as in vitro. EC conversely led to a decrease of plasma levels of diverse soluble immunomodulatory molecules such as CXCL-10, uPA, suPAR, MCP-1, MCP-2 and Tweak in vivo and to decreasing levels in our in vitro experiments. Of note, we cannot clarify whether the effects of the initial four cycles of EC can be attributed either to E or C or to the combination of both. The alkylating agent C is a well-known immunosuppressant, commonly used to prevent graft rejection or to treat several chronic autoimmune disorders [ 30 ]. To adequately assess the cytotoxic effect of C by itself, it was investigated separately in the in vitro experiments, where we used concentrations of E, C and D, which were in the lower range of published plasma peak concentration: 2 µM epirubicin (range: 2–6 µM) [ 29 ], 15 µM active cyclophosphamide metabolite 4-OOH-CY cyclophosphamide (range: 15–36 µM) [ 30 ], and 1 µM docetaxel (range: 1–6 µM)s [ 31 ]. Although 4-OOH-CY is known to induce caspase-9 dependent apoptosis in 9L gliosarcoma cells at a similar concentration as it was used here [ 32 ], no cytotoxic effect was observable in any tested cell line when compared to E and D. This might be explained by the finding that the apoptosis-inducing effect of 4-OOH-CY varies strongly between cell types [ 33 ]. Since all of the six tested BC cell lines reacted similarly to E as well as to D we concluded that the observed effects were independent of the BC subtype and may affect other solid tumors in an analogous way. Even though docetaxel and epirubicin have been reported to ultimately lead to apoptosis as well [ 32 , 34 , 35 ], the pathway to this differs substantially. Docetaxel prevents microtubule depolymerisation [ 31 ], whereas epirubicin stabilizes the topoisomerase II DNA complex [ 36 ]. The group of Seamus Martin showed that dying cells release in the very early phase of apoptosis various chemokines which serve as “find-me” signals for immune cells for the clearance of the cell debris [ 37 ]. This involves apoptosis-related proteins such as RIPK1 and IAPs and depends on the apoptosis-inducing agent. Similarly, we found recently that apoptotic colorectal cancer cells secrete CXCL1, CXCL5, and CXCL8 in response to chemotherapeutic drugs such as oxaliplatin, 5-FU, or bortezomib [ 23 ]. The increased release of uPA, suPAR, and CXCL10 immunomodulatory factors by D-treated breast cancer cell lines observed here, might be related to a similar mechanism. From the data shown here, we cannot conclude whether and how these immunomodulatory factors affect immune cells in the tumor tissue. A breast cancer model in mice with a humanized immune system would be necessary for this purpose. However, this would go far beyond the scope of the present study.
In a recently published work Lopes et al. stated that the immune response is key to successful neoadjuvant chemotherapy [ 38 ]. In their study they observed higher levels of plasma immune mediators like VEGF-A, GM-CSF and IL-2 at baseline in TNBC patients responding to NAC compared with non-responders. They further observed that systemic inflammatory cytokines were positively correlated with levels of TILs in patients achieving pCR. The predictive relevance of lymphocytes in the tumor microenvironment, however, is well described in the literature [ 39 , 40 , 41 ]. Herrero-Vicent et al. demonstrated that TNBC patients with a so-called lymphocyte-predominant breast cancer receiving NAC with anthracyclines and taxanes had higher pCR rates (88%) than those with a non-lymphocyte-predominant BC (9%) and thus suggested that TILs could be routinely used as biomarker [ 42 ]. The predictive value of TILs irrespective of breast cancer subtype was confirmed in an analysis performed by the German Breast Cancer Group, where high levels of TILs predicted for higher pCR rates in patients with luminal, HER2 + and triple-negative disease [ 43 ]. In our study cohort, including HR + /HER2- and TNBC patients, we found that patients with high TILs at baseline as well as patients with increasing TILs during NAC had a better response to NAC. This agrees with observations in other studies. A recent systematic review and meta-analysis confirmed that higher levels of TILs after NAC correlated with significantly improved recurrence-free, metastasis-free and event-free survival in TNBC patients [ 44 ].
Limitations of our study were the retrospective nature of the study, the lack of long-term outcomes due to currently missing follow-up data as well as the division into subgroups depending on the NAC scheme, which led to a comparison of two smaller groups. With the intention to investigate the distinct effects of EC and D, ABCSG-34 patients receiving neoadjuvant AI therapy as well as all vaccinated patients were excluded. Reasons for this were the low risk profile of patients who were selected for neoadjuvant AI therapy in the ABCSG-34 trial and the unpredictable effect of the Mucin-1 cancer vaccine on circulating biomarkers and on sTILs or iTILs. Further, it has to be pointed out that the selection of immunomodulatory biomarkers was based on preliminary data from ABCSG-34 patients who received neoadjuvant AI therapy (see Supplementary Figure S 3 ) although those patients were excluded from further analysis in the here presented work. We assume that a potential bias might be neglectable since this preliminary data was used to define a large number of measurable parameters in patients with invasive but low risk BC. Regarding the predictive value of sTILs and iTILs during NAC, it must be emphasized that the small number of cases definitely influenced the power of this analysis with odds ratios only marginally higher than 1. Thus, the results regarding the predictive value of NAC-induced changes in sTILs and iTILs have to be interpreted with caution. Nevertheless, the statistical significance of these results indicates that EC might affect lymphocytes in a different way than D does.
As chemotherapy, either in neoadjuvant or adjuvant manner, is frequently faced with tumor regrowth or drug resistance, a combination with immunotherapy seems promising. There is evidence that the form of cell death induction plays a pivotal role in the level of anti-tumor immune response. In the here presented study, a strong inhibitory effect of EC on lymphocytes was observed. This may counteract the stimulatory effect of immunotherapies, and thus, upfront taxane-therapy may be the preferred approach for trials of immunotherapeutic approaches in early-stage breast cancer.
Availability of data and materials
The dataset used is not publicly available as it may contain information that would compromise patient consent. Please contact the corresponding author for more information. The published results of the dataset from the randomized, prospective ABCSG-34 trial conducted by the Austrian Breast Cancer and Colorectal Study Group (ABCSG) are online available ( https://doi.org/10.1016/j.ejca.2020.03.018 ).
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We thank Adenike Adesanya, Desiree Rainprecht, Dominik Stelzl, Edina Kepesidis, and Kathrin Stampf, for their support with the analyses. We would like to thank Emma Rennel Dickens and Mats Bergstrom (Olink AB, Uppsala, Sweden) for organizing and conducting the Olink®-based pilot analysis of plasma samples. We thank Martin Holcmann of the Institute of Cancer Research in Vienna for providing us with cell lines.
The research has been funded by the Austrian Breast & Colorectal Cancer Study Group (ABCSG).
Authors and affiliations.
Department of General Surgery, Division of Visceral Surgery and Comprehensive Cancer Center, Medical University of Vienna, Waehringer Guertel 18-20, A-1090, Vienna, Austria
Kerstin Wimmer, Monika Sachet, Cristiano Ramos, Hanna Birnleitner, Ruth Exner, Rudolf Oehler & Florian Fitzal
Austrian Breast & Colorectal Cancer Study Group (ABCSG), Vienna, Austria
Kerstin Wimmer, Sophie Frantal, Martin Filipits, Zsuzsanna Bago-Horvath, Rupert Bartsch, Michael Gnant, Christian F. Singer, Marija Balic, Daniel Egle, Rudolf Oehler & Florian Fitzal
Department of General Surgery, Division of Vascular Surgery, Medical University of Vienna, 1090, Vienna, Austria
Center for Cancer Research, Medical University of Vienna, 1090, Vienna, Austria
Department of Pathology, Medical University of Vienna, 1090, Vienna, Austria
Zsuzsanna Bago-Horvath & Margaretha Rudas
Comprehensive Cancer Center, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
Zsuzsanna Bago-Horvath, Margaretha Rudas, Michael Gnant & Christian F. Singer
Department of Medicine 1, Division of Oncology, Medical University of Vienna, 1090, Vienna, Austria
Department of Gynecology, Medical University of Vienna, 1090, Vienna, Austria
Christian F. Singer
Department of Oncology, Medical University of Graz, Graz, Austria
Department of Gynecology, Medical University Innsbruck, Innsbruck, Austria
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K.W., M.S, F.F. and R.O. designed the work, acquired data and played important role in interpreting the results. C.R., H.B. and C.B. contributed in laboratory work, trial design and interpreting the results as well as in revising the manuscript. S.F. played an important role in the interpretation of the results and revised the manuscript. Z.B.-H. and M.R. contributed in acquiring data as well as in interpreting the results and revising the manuscript. R.E., M.F., R.B., M.B., C.F.S., D.E. and M.G. acquired data and designed the work as well as revised the manuscript. All authors approved the final version and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Correspondence to Rudolf Oehler .
Ethics approval and consent to participate.
The randomized, prospective ABCSG-34 (EudraCT Number: 2011–004822-85) trial was conducted by the Austrian Breast Cancer and Colorectal Study Group (ABCSG) and was a registered clinical trial. The study has been approved by the responsible ethics committee EC Medical University of Vienna on 12Dec2011. The trial was conducted according to the guidelines of the Declaration of Helsinki and were carried out in accordance with the guidelines for Good Clinical Practice. All patients provided signed informed consent.
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Wimmer, K., Sachet, M., Ramos, C. et al. Differential immunomodulatory effects of epirubicin/cyclophosphamide and docetaxel in breast cancer patients. J Exp Clin Cancer Res 42 , 300 (2023). https://doi.org/10.1186/s13046-023-02876-x
Received : 17 May 2023
Accepted : 29 October 2023
Published : 14 November 2023
DOI : https://doi.org/10.1186/s13046-023-02876-x
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