how to describe the experimental design

Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental design is a structured approach used to conduct scientific experiments. It enables researchers to explore cause-and-effect relationships by controlling variables and testing hypotheses. This guide explores the types of experimental designs, common methods, and best practices for planning and conducting experiments.

Experimental Design

Experimental design refers to the process of planning a study to test a hypothesis, where variables are manipulated to observe their effects on outcomes. By carefully controlling conditions, researchers can determine whether specific factors cause changes in a dependent variable.

Key Characteristics of Experimental Design :

Manipulation of Variables : The researcher intentionally changes one or more independent variables.
Control of Extraneous Factors : Other variables are kept constant to avoid interference.
Randomization : Subjects are often randomly assigned to groups to reduce bias.
Replication : Repeating the experiment or having multiple subjects helps verify results.

Purpose of Experimental Design

The primary purpose of experimental design is to establish causal relationships by controlling for extraneous factors and reducing bias. Experimental designs help:

Test Hypotheses : Determine if there is a significant effect of independent variables on dependent variables.
Control Confounding Variables : Minimize the impact of variables that could distort results.
Generate Reproducible Results : Provide a structured approach that allows other researchers to replicate findings.

Types of Experimental Designs

Experimental designs can vary based on the number of variables, the assignment of participants, and the purpose of the experiment. Here are some common types:

1. Pre-Experimental Designs

These designs are exploratory and lack random assignment, often used when strict control is not feasible. They provide initial insights but are less rigorous in establishing causality.

Example : A training program is provided, and participants’ knowledge is tested afterward, without a pretest.
Example : A group is tested on reading skills, receives instruction, and is tested again to measure improvement.

2. True Experimental Designs

True experiments involve random assignment of participants to control or experimental groups, providing high levels of control over variables.

Example : A new drug’s efficacy is tested with patients randomly assigned to receive the drug or a placebo.
Example : Two groups are observed after one group receives a treatment, and the other receives no intervention.

3. Quasi-Experimental Designs

Quasi-experiments lack random assignment but still aim to determine causality by comparing groups or time periods. They are often used when randomization isn’t possible, such as in natural or field experiments.

Example : Schools receive different curriculums, and students’ test scores are compared before and after implementation.
Example : Traffic accident rates are recorded for a city before and after a new speed limit is enforced.

4. Factorial Designs

Factorial designs test the effects of multiple independent variables simultaneously. This design is useful for studying the interactions between variables.

Example : Studying how caffeine (variable 1) and sleep deprivation (variable 2) affect memory performance.
Example : An experiment studying the impact of age, gender, and education level on technology usage.

5. Repeated Measures Design

In repeated measures designs, the same participants are exposed to different conditions or treatments. This design is valuable for studying changes within subjects over time.

Example : Measuring reaction time in participants before, during, and after caffeine consumption.
Example : Testing two medications, with each participant receiving both but in a different sequence.

Methods for Implementing Experimental Designs

Purpose : Ensures each participant has an equal chance of being assigned to any group, reducing selection bias.
Method : Use random number generators or assignment software to allocate participants randomly.
Purpose : Prevents participants or researchers from knowing which group (experimental or control) participants belong to, reducing bias.
Method : Implement single-blind (participants unaware) or double-blind (both participants and researchers unaware) procedures.
Purpose : Provides a baseline for comparison, showing what would happen without the intervention.
Method : Include a group that does not receive the treatment but otherwise undergoes the same conditions.
Purpose : Controls for order effects in repeated measures designs by varying the order of treatments.
Method : Assign different sequences to participants, ensuring that each condition appears equally across orders.
Purpose : Ensures reliability by repeating the experiment or including multiple participants within groups.
Method : Increase sample size or repeat studies with different samples or in different settings.

Steps to Conduct an Experimental Design

Clearly state what you intend to discover or prove through the experiment. A strong hypothesis guides the experiment’s design and variable selection.
Independent Variable (IV) : The factor manipulated by the researcher (e.g., amount of sleep).
Dependent Variable (DV) : The outcome measured (e.g., reaction time).
Control Variables : Factors kept constant to prevent interference with results (e.g., time of day for testing).
Choose a design type that aligns with your research question, hypothesis, and available resources. For example, an RCT for a medical study or a factorial design for complex interactions.
Randomly assign participants to experimental or control groups. Ensure control groups are similar to experimental groups in all respects except for the treatment received.
Randomize the assignment and, if possible, apply blinding to minimize potential bias.
Follow a consistent procedure for each group, collecting data systematically. Record observations and manage any unexpected events or variables that may arise.
Use appropriate statistical methods to test for significant differences between groups, such as t-tests, ANOVA, or regression analysis.
Determine whether the results support your hypothesis and analyze any trends, patterns, or unexpected findings. Discuss possible limitations and implications of your results.

Examples of Experimental Design in Research

Medicine : Testing a new drug’s effectiveness through a randomized controlled trial, where one group receives the drug and another receives a placebo.
Psychology : Studying the effect of sleep deprivation on memory using a within-subject design, where participants are tested with different sleep conditions.
Education : Comparing teaching methods in a quasi-experimental design by measuring students’ performance before and after implementing a new curriculum.
Marketing : Using a factorial design to examine the effects of advertisement type and frequency on consumer purchase behavior.
Environmental Science : Testing the impact of a pollution reduction policy through a time series design, recording pollution levels before and after implementation.

Experimental design is fundamental to conducting rigorous and reliable research, offering a systematic approach to exploring causal relationships. With various types of designs and methods, researchers can choose the most appropriate setup to answer their research questions effectively. By applying best practices, controlling variables, and selecting suitable statistical methods, experimental design supports meaningful insights across scientific, medical, and social research fields.

Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research . Houghton Mifflin Company.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference . Houghton Mifflin.
Fisher, R. A. (1935). The Design of Experiments . Oliver and Boyd.
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics . Sage Publications.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences . Routledge.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

Triangulation in Research – Types, Methods and...

Basic Research – Types, Methods and Examples

One-to-One Interview – Methods and Guide

Observational Research – Methods and Guide

Qualitative Research – Methods, Analysis Types...

Quasi-Experimental Research Design – Types...

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

Knowledge Base
Methodology
A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

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

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

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

There are five key steps in designing an experiment:

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

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

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

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

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

Start by simply listing the independent and dependent variables .

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

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

Diagram of the relationship between variables in a sleep experiment

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

Prevent plagiarism, run a free check.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Randomisation

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

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

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

Between-subjects vs within-subjects

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

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

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

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

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

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

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

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

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

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

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

To design a successful experiment, first identify:

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

When designing the experiment, first decide:

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

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

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

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

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

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bevans, R. (2022, December 05). A Quick Guide to Experimental Design | 5 Steps & Examples. Scribbr. Retrieved 11 November 2024, from https://www.scribbr.co.uk/research-methods/guide-to-experimental-design/