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What is experimental research?

Experimental research is a scientifically-driven approach that involves quantitative analysis and revolves around two sets of variables. The first set, called independent variables, is deliberately manipulated by the researcher to examine their impact on the second set of variables, known as dependent variables. By employing the experimental method, it becomes possible to assess the influence and manner in which independent variables affect dependent variables. This knowledge can contribute to various decision-making processes in diverse domains, including:

Analyzing the effect of price changes on consumer purchasing patterns and intentions.

Evaluating whether altering the color of a product’s packaging enhances its appeal to customers.

Investigating the consequences of incorporating a pop-up advertisement on a website on users’ browsing and search behaviors.

Understanding how customers respond to different advertising campaigns and marketing messages.

These examples represent only a fraction of the consumer research areas that lend themselves well to experimental research. However, it is essential to note that not all experimental research designs hold the same significance. In the following sections, we will explore three distinct types of experimental designs and their potential applications in addressing various research questions.


Types of experimental design

  • Pre-experimental research design

The most basic form of experimental design is referred to as a pre-experimental research design, which can take on various forms. In a pre-experiment, a factor or treatment that is expected to induce change is implemented on one or more groups of research subjects, and their observations are recorded over a period of time.

Different types of pre-experimental research designs include:

One-shot case study design: In this design, a treatment is applied to a single group of case study participants. The group is then studied to determine if the treatment caused any observable changes. This is done by comparing the actual observations with the expected outcome if the treatment had not been applied. There is no control or comparison group involved.

One-group pretest-posttest design: This design involves observing a single group without a control or comparison group. However, the group is observed at two different points in time: before the intervention or treatment is administered, and after it is administered. For example, if you want to assess whether concentration levels increase in a group of students after they participate in a study skills course, you could employ this experimental design. Any changes observed in the dependent variable are assumed to be a result of the intervention or treatment.

Static-group comparison: This design compares two groups: one that has received a specific intervention or treatment, and another that has not. Any differences observed between the two groups are presumed to be due to the treatment.

These pre-experimental designs offer different ways to explore the impact of treatments or interventions, although they may have limitations compared to more rigorous experimental designs.

  • True experimental research design

A true experimental research design aims to test a hypothesis and determine whether a cause-effect relationship exists between two or more sets of variables. While there are several established methods for conducting experimental research designs, they all share four key characteristics:

Treatment Group: There is a group that undergoes a specific treatment or intervention being studied.

Control Group: There is a separate group that does not receive any treatment and serves as a baseline for comparison.

Random Assignment: Subjects are randomly assigned to either the treatment or control group to ensure unbiased distribution of participants.

Manipulation of Independent Variables: The researcher actively manipulates the independent variables, such as implementing a specific treatment or intervention, to examine its effect on the dependent variables.

This type of approach is commonly used in concept testing, where the impact of changes in packaging design, for example, is compared between a treatment group (exposed to the new packaging) and a control group (exposed to the original packaging). By utilizing a true experimental design, researchers can gather data to determine the causal relationship between variables under investigation.

  • Quasi-experimental research design

A quasi-experimental research design shares some similarities with a true experimental design but differs in terms of the non-random assignment of subjects to the control or treatment group. This type of research design is commonly employed in natural settings where the researcher lacks control over subject assignment.

In quasi-experimental research, the principles of cause and effect are still explored, but the assignment of subjects to groups is based on factors beyond the researcher’s control, such as pre-existing characteristics or naturally occurring events.

For instance, consider a scenario where a researcher wants to examine the impact of a welcome banner on shoppers’ perceptions of a grocery store. The researcher may observe shoppers on a Saturday when the banner is displayed and compare their perceptions with those of shoppers on a Tuesday when the banner is absent. In this case, the assignment of shoppers to the different days is not randomized, but rather based on the days of the week.

Quasi-experimental designs are valuable when true randomization is not feasible or ethical. While they may not establish as strong a cause-effect relationship as true experiments, they still allow researchers to gather meaningful insights in real-world settings.

Four steps to completing an experimental research design

Now that you know what kinds of experimental designs are available, let’s focus on the steps you should take to set up your design.

Step 1: establish your question and set variables

In the initial stage of your research, it is important to establish a clear research question. This question will serve as a guide to distinguish between the dependent and independent variables in your study.

Independent variables are the variables that you will intentionally manipulate or control during your research. They are the factors that you believe will have an impact on the outcome or dependent variable. On the other hand, dependent variables are the variables that you measure or observe to assess the effects or outcomes of the independent variables.

Let’s take the example of ad testing and use the research question you provided:

Research Question: What is the impact of different marketing messages on product appeal among viewers of a television advert?

In this case, the independent variable is the different marketing messages. You have control over the content, style, or presentation of the messages, and you believe that these variations will influence the outcome.

The dependent variable, in this case, is the product appeal. It is the variable that you want to measure or assess to determine how it is affected by the different marketing messages. The product appeal represents the outcome or response you expect to be influenced by the independent variable.

By clearly identifying and understanding the independent and dependent variables in your research question, you can design your study effectively and analyze the relationship between them.

Step 2: build your hypothesis

After formulating your research question and identifying the independent and dependent variables, the next step is to state your hypothesis. A hypothesis is a specific and testable statement that outlines your expected findings based on the research question and previous studies. It provides a clear prediction or expectation regarding the relationship between the variables.

Using the example of comparing the impact of two different marketing messages on product appeal, a hypothesis statement could be:

“Hypothesis: Marketing message A will result in higher product appeal among TV ad viewers compared to marketing message B.”

When stating a hypothesis, it is important to follow some best practices:

Describe the change being tested: Clearly mention the aspect you are manipulating or comparing, such as “marketing messages” in this example.

Describe the expected impact: Specify the anticipated effect or outcome of the change, such as “will result in higher product appeal” in this case.

Identify the target population: Clearly state the group or individuals you expect to be impacted, like “TV ad viewers” in this instance.

Ensure testability: Your hypothesis should be formulated in a way that allows it to be tested through appropriate experimental research design and data analysis.

By formulating a clear and testable hypothesis, you set the direction for your research and provide a basis for evaluating the results in relation to your initial expectations.

Step 3: designing experimental treatments

How to manipulate your independent variables

The next step in designing your experiment is to determine the specific treatments for your independent variable(s). This involves manipulating the independent variable(s) in a way that exposes different groups of research subjects to different levels or conditions of that variable. Alternatively, the same group of subjects can be exposed to different levels of the variable at different times.

Let’s consider an example to illustrate this. Suppose you are interested in investigating whether trying a new eco-friendly laundry detergent impacts people’s views towards sustainability. In this case, you might provide some subjects (the treatment group) with the new eco-friendly detergent to use for a specific period, while a control group continues to use their regular detergent.

It is important to note that the manipulation of the independent variable should involve an active intervention by the researcher. If the differences in the variable occur naturally (e.g., comparing the views on sustainability between households already using eco detergents and those using regular detergents), it does not constitute an experiment. In such cases, the observed differences between the groups may be influenced by a third unknown variable that could impact the cause-effect relationship. For instance, households containing a green activist might already be using eco detergent, making it difficult to determine whether using the eco detergent directly impacts views on sustainability or if the relationship is the other way around.

In some experiments, the independent variable may only be indirectly or incompletely manipulated. In such cases, it may be necessary to perform a manipulation check before testing the results. A manipulation check is a statistical test that ensures the manipulation of the independent variable worked as expected and had the desired impact.

By designing and implementing appropriate experimental treatments, you can effectively control and manipulate the independent variable(s) to investigate their effects on the dependent variable(s). This helps establish a causal relationship and provides meaningful insights into your research question.

How broadly should you test your variables?

You have provided a clear and accurate explanation of the concepts of internal validity and external validity in experimental research.

Internal validity refers to the extent to which the findings of a study accurately reflect the causal relationship between the variables being studied. It ensures that the observed effects can be attributed to the manipulated independent variable(s) and not to other confounding factors. Internal validity is crucial for establishing the credibility and reliability of the research findings. Researchers strive to minimize potential threats to internal validity, such as confounding variables or biases, to ensure the accuracy and integrity of their results.

External validity, on the other hand, examines the generalizability of research findings beyond the specific context or setting in which the study was conducted. It addresses the question of whether the observed effects would hold true in other populations, settings, or conditions. External validity is essential for determining the broader applicability and relevance of the research findings. Researchers aim to enhance external validity by employing diverse samples, considering ecological validity, and replicating studies across different contexts to validate the generalizability of their results.

By being mindful of both internal and external validity, researchers can ensure that their experimental designs are robust and their findings are both credible and applicable to real-world situations.


How finely should you test your variables?

When constructing your variables, one important consideration is the level of granularity or precision with which you should measure them. This decision depends on the specific goals and objectives of your research. Let’s take the example of measuring the appeal of a product to illustrate this point.

One approach is to use a three-point measure, such as “Appealing,” “Neither Appealing nor Unappealing,” and “Unappealing.” This broad approach provides a simple categorization of respondents’ perceptions, allowing for a quick assessment of whether the product is generally appealing or not. It can be useful when the primary focus is to determine the overall appeal without delving into the degree of appeal.

On the other hand, a finer-tuned approach could involve using a 10-point Likert scale measure, ranging from “Not at all Appealing” to “Extremely Appealing.” This provides a more nuanced and detailed assessment of respondents’ perceptions, capturing the degree of appeal on a continuum. This approach is beneficial when you want to gather more precise information about the range and intensity of appeal for the product.

Each approach has its advantages and drawbacks. The broader three-point measure offers simplicity and ease of interpretation, making it suitable for quick evaluations. However, it lacks the ability to capture subtle variations in respondents’ perceptions. In contrast, the 10-point Likert scale provides greater sensitivity and allows for a more nuanced analysis of respondents’ attitudes. However, it may require more effort from participants and can be more complex to analyze.

The choice between a broader or finer approach depends on your research objectives and the specific insights you seek to gain. If your primary interest is to determine whether a product is appealing or not, the broader approach may be sufficient. However, if you aim to explore the degree of appeal and capture more nuanced responses, the finer-tuned approach with a Likert scale can provide richer data for analysis.

Consider your research goals, the level of detail needed, and the trade-offs between simplicity and granularity when deciding how broadly or finely to measure your variables.

Step 4: categorize into treatment groups

Correct, the categorization of survey subjects into appropriate treatment groups is a crucial step in experimental research design. The method you choose for grouping subjects can have implications for the validity and reliability of your results. Here are a few considerations to keep in mind:

Random Assignment: Random assignment is often considered the gold standard for assigning subjects to treatment groups. It involves randomly assigning individuals to different groups, ensuring that each participant has an equal chance of being assigned to any group. Random assignment helps to minimize selection bias and increases the likelihood that any differences observed between groups are due to the treatment rather than pre-existing characteristics of the participants. This approach enhances the internal validity of the study.

Matching: Matching involves pairing subjects based on specific characteristics before assigning them to treatment groups. This method is commonly used when random assignment is not possible or when you want to ensure similarity between groups on certain variables that may influence the outcome. By matching subjects based on relevant characteristics, you can reduce potential confounding factors and enhance the internal validity of your study.

Quasi-Experimental Designs: In some cases, you may not have control over the assignment of subjects due to practical or ethical constraints. Quasi-experimental designs involve naturally occurring groups or conditions, and subjects are assigned to different groups based on existing characteristics or circumstances. While this approach may have limitations in terms of internal validity, it can still provide valuable insights, especially in real-world settings where strict experimental control is not feasible.

Non-Random Assignment: There are instances where subjects are assigned to treatment groups using non-random methods, such as self-selection or convenience sampling. While these approaches may have practical advantages, they can introduce biases and limit the generalizability of the findings. It is important to acknowledge and consider the potential limitations when using non-random assignment methods.

Ultimately, the choice of categorization method depends on the research question, the level of control you have over subject assignment, and the trade-offs between internal validity and external validity. Random assignment is generally preferred when feasible, as it helps to maximize internal validity and supports causal inferences. However, in situations where random assignment is not possible, alternative strategies such as matching or quasi-experimental designs can be employed to mitigate potential biases and strengthen the validity of the results.


There are two main approaches to randomization: a completely randomized design and a randomized block design.

Completely randomized design

A completely randomized design places random subjects into the treatment or control group. The reason for randomization is that the experimenter assumes that on average, potentially confounding variables will affect each condition equally; so that any observed significant differences between the treatment and control conditions can probably be attributed to the independent variable.

Randomized block design

Using the randomized block design, the researcher first looks for confounding variables, then assigns subjects to blocks based on that variable, before randomizing subjects to different groups. In our product appeal study, men and women might find a product appealing for different reasons, so a group of participants might first be assigned to gender-based blocks, and then randomly assigned to different treatment groups in order to ensure gender parity.

Subject design

There are two ways of assigning your research participants to different conditions

Between-Subjects design

Using the between-subjects research design, different people test each condition, so that each person is only exposed to a single treatment or condition.

Within-Subjects design

Using the within-subjects, or repeated-measures design, the same group of individuals tests all the conditions, and the researcher compares the results across each condition.

Control group

In true experimental research designs, the presence of a control group is a fundamental component. The control group serves as a reference point against which the effects of the treatment or intervention can be compared. It consists of individuals who either do not receive any treatment or receive a neutral or placebo treatment.

The purpose of the control group is to provide a baseline for comparison, allowing researchers to assess the causal impact of the treatment or intervention. By comparing the outcomes of the treatment group with those of the control group, researchers can determine whether the observed effects are due to the treatment itself or to other factors.

The control group helps control for confounding variables and minimize bias, increasing the internal validity of the study. It allows researchers to isolate the specific effects of the treatment by providing a counterfactual scenario where no treatment is administered.

In some cases, researchers may use a placebo control group, where individuals receive a treatment that mimics the real treatment in appearance or procedure but does not have the active component. This helps to account for any potential placebo effects and further strengthens the validity of the findings.

Including a control group is essential in true experimental research designs as it enables researchers to make valid causal inferences and draw conclusions about the effects of the treatment or intervention being studied.

Pros and cons to experimental research design

Advantages of experimental design

The experimental research design offers you a wide range of advantages:

High Level of Control: Experimental research designs offer a high level of control over the research environment, allowing researchers to manipulate the independent variables and minimize the influence of confounding variables. This control enhances the internal validity of the study, making it easier to establish cause-and-effect relationships.

Wide Applicability: Experimental research designs can be applied across various subjects and disciplines, including the natural and social sciences, business, and marketing. This versatility makes experimental research a valuable tool for investigating a wide range of research questions.

Specific Conclusions: The controlled nature of experimental research enables researchers to draw specific and precise conclusions. By carefully manipulating the independent variables and controlling extraneous factors, researchers can attribute observed changes in the dependent variable to the treatment or intervention being tested.

Replicability: Experimental research designs emphasize replicability, as the detailed documentation of the research process allows others to replicate the study and validate the findings. Replication increases the validity and reliability of the research, contributing to the advancement of knowledge.

Causal Relationships: Experimental research is particularly well-suited for establishing causal relationships between variables. By manipulating the independent variable and randomly assigning subjects to treatment and control groups, researchers can determine whether changes in the independent variable directly cause changes in the dependent variable. This distinguishes experimental research from correlational or cross-sectional designs, which cannot establish causality.

Overall, experimental research designs offer a powerful approach for investigating causal relationships, providing control, replicability, and the ability to draw specific conclusions. These advantages make experimental research a valuable methodological tool in various fields of study.

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Limitations of experimental design

Opportunity for Human Error: Like any research methodology, experimental research is prone to human error. Researchers may make mistakes during the manipulation of variables, data collection, or data analysis, which could impact the reliability and validity of the results. Careful planning, rigorous protocols, and attention to detail are necessary to minimize such errors.

Limited Generalizability: Experiments are often conducted in controlled settings that may not fully represent real-world conditions. This can limit the external validity of the findings, making it challenging to generalize the results to broader populations or real-life situations. It is important to consider the trade-off between control and generalizability when deciding on an experimental design.

Time and Resource Intensive: Experimental research designs typically require significant time, effort, and resources. Designing the study, recruiting participants, implementing treatments, and collecting and analyzing data can be a lengthy and resource-intensive process. Researchers need to carefully plan and allocate resources to ensure the feasibility and success of the study.

Ethical and Practical Concerns: Experimental research designs involving manipulation of variables raise ethical considerations. Researchers must ensure the well-being and rights of participants, including informed consent, protection from harm, and equitable treatment. Additionally, practical challenges such as participant attrition and maintaining treatment fidelity throughout the study can affect the validity and reliability of the findings.

Limited Scope of Research Questions: Experimental research designs are most suitable for addressing research questions that involve testing causal relationships between variables. They may not be the optimal choice for exploring complex phenomena or examining broader contextual factors that influence behavior or outcomes. Alternative research designs, such as observational studies or qualitative approaches, may be better suited for addressing such research questions.

It is important to carefully consider these disadvantages and evaluate whether experimental research aligns with the specific research objectives, resources, and ethical considerations of a study. Depending on the nature of the research question, alternative research designs may be more appropriate or complementary to experimental approaches.

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