Basics of Research Process

Experimental Design: The Best Guideline, Tips, Examples

experimental design
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Experimental design is a research method used in scientific studies to investigate cause-and-effect relationships between variables. It involves the systematic manipulation of one or more independent variables to measure their effect on a dependent variable. During an experiment, all other potential influences or confounding variables should be controlled.

How you allocate participants to experimental and control groups may either work miracles or make matters worse. That’s why researchers should know how to rationally plan all stages of an experiemntal study.  Read this blog post from our research paper writing service and find out how to succesfully conduct an experiment. 

What Is Experimental Design: Definition

Experimental design is a series of measures related to proper and efficient organization of scientific procedures to test a hypothesis. Its primary purpose is to control maximum accuracy and validity with a small number of experiments and statistical observations.  

Experimental design is one of the most effective ways of obtaining a scientifically valid result. As was mentioned above, experiments design methods allow to reduce the necessary research amount. They help scientists develop a proper research plan, depending on the type of study and accuracy level. 

Experimental research plans always involve development and implementation of ways to assess hypotheses' validity.

Types of Experimental Design

There are 3 main types of experimental design:

  • Independent measures (also known as between-groups)
  • Repeated measures (otherwise called within-groups)
  • Matched pairs.

All these types differ depending on the way research participants are assigned to various conditions in a study. Let’s suppose you are examining how the number of hours students study influences their academic results. To conduct an experiment you should think whether you want to allocate different or same participants to each condition of your independent variable

Let’s have a look at each of these types in more detail.

Independent Measures Design

Independent measures design (between groups design) is a type of experimental design where different participants test each condition. In this type, each individual is allocated to a single condition. All participants should be assigned randomly. This approach ensures that all individuals have the same distribution opportunities allowing to avoid bias. 

Independent measures require dividing participants into 2 groups with different conditions of an independent variable. As you can see from the example below, one group of participants will study for 1 hour per day, while the second group will study for 4 hours per day. These are 2 different conditions that may lead to different research results.

Independent measures design example


It’s important that you are familiar with both advantages and disadvantages of independent measures to decide on the right experimental design strategy.

                                                        Independent measures

Repeated Measures Design

Repeated measures design, as its name suggests, is an experimental design where the same participant is assigned to try out all the conditions. This means that the conditions will be repeated for each group. Sometimes, this type is also called within groups. 

Repeated measures design is prone to the order effects. To eliminate this, a researcher should counterbalance, or change the sequence of conditions. This means that an experimental group will test the 1st condition and then the 2nd one, while a control group will first participate in the 2nd condition, and then in the 1st one. Even though the order effects will exist in both cases, counterbalancing helps you control this by creating equilibrium.

Counterbalancing in repeated measures example

As with any type of experiment, repeated measures also have pros and cons.

                                                           Repeated measures

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Matched Pairs Experiment

A matched pairs experiment is a design where researchers create pairs of participants matching each other based on critical variables (e.g., gender, age, IQ). Each individual of such matched pair is then assigned to a different group. This means that one member goes to an experimental group, while the other one is allocated to a control group. A researcher should assign the members to the groups using randomization. 

Let’s have a look at an example shown below.

Matched pairs design example


Matched pairs design has various advantages and drawbacks.

                                                             Matched pairs

How to Write an Experimental Design: The Best Tips

Every experimental research design begins with planning. You need to have a clear understanding of research subjects before designing your study. For example, let’s assume you want to identify the most effective way for teachers to organize lessons for successful remote learning. There are many ways to answer your main question tested in this experiment. Choosing the best one depends on the direction and main research project objectives. Therefore, in order to ensure an accurate result, you should first decide on your project objectives. Here are 5 steps that will help you do that right.

1. Define the Main Variables of Experimental Design

An important part of any experimental design is variables - any characteristic that can be measured. These are phenomena or characteristics that you can control during a study. There are many types of variables. For example:

  • Independent variables are factors that affect dependent factors. You can control them during an experiment.
  • Dependent variables are effects observed during a study. The experiment results rely upon the influence an independent variable has on a dependent one.
  • Extraneous variable is an additional condition or set of conditions that can also have an effect on dependent factors being investigated in an experiment.

There are also many other factors that may affect your study outcomes. So, before designing your research, you should first recognise a cause-and-effect relationship and other variables that influence it. 

2. Create a Hypothesis for Your Experimental Research Design

No experimental research design will be successful without a clear hypothesis that answers your main research question. A hypothesis is your assumption about something that you believe is true. If you need to establish a link between two or more factors, it is imperative that you prepare hypotheses before beginning your study.

Example hypothesis

Students who regularly attend classes get better results.

Next, you should build a controlled experiment to manipulate all variables that can potentially influence your dependent variable.

3. Define Experimental Treatments of Your Experimental Design

The main problem to be solved at this stage of experimental design is to determine the degree to which your independent variable will influence an external validity. In other words, your task is to manipulate an independent factor so your research outcomes can be applied in other contexts. 

First, you should determine the range within which an independent variable can vary. This could be, for example, temperature ranges if you are investigating climate. Secondly, you should understand how accurately you can measure variation of your independent variable. This variation is represented by levels. For example, you will choose such categories as "is", "is not", "weak", "moderate", "strong", or have continuous validity, such as duration in minutes.

4. Allocate Experimental Design Participants into Groups

In order for your experimental research design to be successful, you should decide what methods you will use to assign participants to treatment groups. This stage is critical to obtaining valid and relevant conclusions at the end of your study. It will also influence the study size. 

First, you must determine a number of members who will participate in an experiment. Then you should randomly divide people into treatment groups. Each experimental group should receive different levels of influence. Besides, you should have the control group where subjects aren’t exposed to any impact at all. 

When distributing participants, you can choose one of the methods we have discussed above. These are:

  • Independent measures
  • Repeated measures
  • Matched pairs.

5. Collect Data Based on Outcomes of Experimental Design

Eventually, you should select a proper data collection method to measure a dependent variable. As you may know, some parameters like age, weight, pressure or temperature can be easily measured. Other things may be abstract, and, thus, should be turned into characteristics that can be calculated. 

For example, you can’t measure life satisfaction, but you can assess the income level, employment rate and so on. Likewise, you can create a questionnaire asking participants to report their happiness level on the scale from 1 to 10. 

You should be as precise as possible. This will help you eliminate bias in research outcomes.

Experimental Design: Key Takeaways

Experimental design is a method widely used in science. A proper distribution of participants ensures that you obtain valid results. For this reason, you should consider the context of your study and keep in mind all possible variables that can influence your research.

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Frequently Asked Questions About Experimental Research Design

1. What are independent and dependent variables in an experimental design?

An experimental design involves an independent variable, an indicator that is primarily characterized by its effect on the dependent variable. The dependent variable, in turn, is a factor that can change under the influence of an independent variable. For example, an independent variable can be plant nutrients, and a dependent variable is the amount of harvested crop.

2. What is a confounding variable in an experimental research design?

A confounding variable in an experimental research design is a third variable in a study that has a link with both independent and dependent variable. It represents any incidental phenomenon that affects the experiment result in addition to the factors investigated in an experiment. For example, if you are studying the relationship between the salt amount and plant development, the pot size and soil type may be another factor that also affects a dependent variable. Therefore, you should account for confounders to ensure research validity.

4. What’s the difference between reliability and validity in good experimental design?

Reliability and validity are the main parameters of good experimental design. They illustrate the effectiveness of the measures, equipment or tests used. While they have quite a lot in common, two concepts differ in meaning. Reliability means the measurement's stability and constancy. Validity means accuracy of results. It is important not to forget about these factors when planning an experiment, developing research methods, and drawing conclusions.

3. What is the difference between internal and external validity in an experimental study design?

Internal validity is the guaranteed absence of other factors that may influence causal relationships during an experimental study design. External validity determines how well obtained results can be applied in other similar cases. A research validity is mainly determined by the experiment nature itself. To assess it, you should understand all variables involved in your study and how accurate your measurements are.

Article posted on:Nov 18, 2022
Article updated on:Nov 17, 2023

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