What Is an Experiment?

You need to know the main features of the system or item you are researching. To find out more about them and their causal relationships, you can use experiments as a potent research method. It is about manipulating one or a few independent variables to learn their influence on dependent variables.

You should develop and use a set experimental design. It consists of a sequence of procedures that can systematically test your hypotheses.

Designing an Experiment

The experiment design includes five steps, such as defining variables and their relationships, creating a testable hypothesis, manipulating the independent variable, assigning between-subjects and within-subjects relations to groups, and measuring dependent variables. You will be able to make consistent conclusions on the basis of the results you will receive from these manipulations.

To make the results more valid, you will have to choose a representative sample and control any variables that may influence the results of the experiment. If such control is impossible for certain reasons, conducting experiments is also impossible, so you need to opt for another research method, for example, observation.

Now, let’s speak about the experimental design step-by-step.

1. How to Define Variables

Start with a unique research question. Find the main variables and predict their relationships. Make a list of all possible independent and dependent variables. That will help you translate the research question into a consistent and original experimental hypothesis.

You should also take into account all extraneous and confounding variables that can influence the experiment results. If such variables are observable, think about how you will control them. You can do it statistically and experimentally.

At the end of this stage, you can feature all these variables in a diagram. Use arrows to show the possible relationships between them.

2. Writing a Hypothesis

Now, you understand the overall system better and can create a testable hypothesis for your research questions. There can be two types of hypotheses - a null hypothesis and an alternate hypothesis.

Give preference to controlled experiments because you need to manipulate independent variables precisely and systematically, accurately measure dependent variables, and effectively control all confounding variables.

If you cannot make an experiment design that matches these criteria, you need to look for other types of research.

3. How to Design Experimental Treatments

Manipulating the independent variable properly can influence the external validity of the experiment. It means that it can increase the possibilities for result generalization and their application to some broader topics. You can vary the independent variables and decide how finely you will vary them. You can vary them continuously in time or assign them to categories.

4. Assigning Subjects to Treatment Groups

Keep in mind the size of your study. The statistical power will increase if you include many subjects in the group. Think about the control group. It should show you what would have happened if you had not intervened with the treatment, so do not apply any treatment to it.

It is also important to assign the subjects randomly to treatment groups. The levels of treatment can differ, for example, those who do some things regularly, those who do it occasionally, and participants who do not do that at all.

When you assign subjects to separate groups, think about the randomized design and randomized block design, as well as about the between-subjects and within-subjects designs.

5. How to Measure Dependable Variables

Think about reliable measurements with a high degree of validity. You can achieve this by collecting data on dependent variables. It will reduce the possibility of errors and bias.

There are variables that can be measured with specific instruments, like weight or temperature. Others need special operations to become measurable. The accuracy of dependent variable measurements influences statistical analysis. You should also consider the context of your experiment because every study system is unique, and it can produce either valid or invalid information under certain conditions.

Controlled Experiments

Researchers normally choose controlled experiments because they allow for controlling all variables apart from the independent one. When you control variables, you can:

• Decide on the constant level of variables or impose some restrictions.
• Measure variables for further statistical analysis.
• Balance all the variables via randomization.

Control in experiments is an essential part because it affects the internal validity by establishing the causal relationships between variables.

There are also extraneous variables that are not subject to your studies. Though, they can affect the dependent variables. That is why you need to remove them as soon as possible to make your experiment successful.

What Methods of Control Can You Use?

You can control the variables by applying standard procedures to them. For example, you should test all participants in the same environment with the same materials and techniques. The independent variable can be changed and swapped between the groups.

Extraneous variables can be monitored via sampling procedures. You can choose the sample by using exclusion and inclusion criteria depending on the experiment objectives. When you pick out the participants, you should divide them into groups. Control groups are mandatory for controlled experiments. They help you test participants with excessive treatment, fake, moderate, or no treatment. You can define what kind of treatment can result in specific outcomes.

Control Groups

You can isolate the effects of independent variables by introducing control groups into the experiment. They can facilitate the establishment of cause-and-effect relationships. The independent variable can be changed between the treatment groups and kept stable for the control group. That helps understand how much any changes in dependent variables are influenced by the independent variable.

Control groups play an important role in experimental design.

Control groups can also be used in non-experimental research, for example, in quasi-experiments and matching. In quasi-experiments, researchers do not control assignments. There are pre-existing groups that have already received some kind of treatment.

Control groups are important in experiments because they can provide the internal validity of any research. You may have noticed the change in the experimental group, but without the control group, it is difficult to judge whether the change has happened due to the treatment applied. The possibility of other variables’ influence is high. If you have the control group, you understand that the applied treatment is the only factor that causes the change.

There can be a risk of invalid control groups, though. Your control group can display differences from the experimental group in a way you did not predict. It means that there is a certain confounding variable that performs instead of the independent variable.

You can minimize the risk that comes from invalid control groups by the following:

• Check whether all the confounding variables are taken into account. Include this checking into your experimental design because it is almost impossible to control the confounders outside the experiment.
• Utilize the double-blinding technique to prevent modifying the behavior of the members of both groups.
• Assign the subject to treatment and control groups randomly to minimize the differences between them.

Random Assignment

All extraneous variables can be evenly distributed among participants from experimental and control groups with the help of random assignments. It helps avoid systematic differences between the members of these groups.

The random assignment can become a focus of the true experiment. It is the key that distinguishes these experiments from the quasi ones.

Random assignments are an efficient way to place participants from one sample into different groups with the help of randomization. Every member of the sample can have a chance to become a part of either an experimental or control group. Such experiments use a completely randomized design. That helps you ensure that both types of groups are completely comparable, and if there are some differences within the experiment, they are caused by random factors.

Random assignments help control the experiment because they boost its internal validity. Such an approach helps ensure that the treatment groups do not differ much from the control group in a systematic or biased aspect when you start the experiment. You often want to receive alternative backgrounds for your results. It is impossible without randomizing. However, random assignments do not mean that different groups are entirely equivalent. There may be extraneous variables that set the differences between these groups, and there are always some differences that emerge by chance.

Randomization is also helpful when you have a large sample. However, you need to be careful about such assignments for ethical or racial and any other reasons if you deal with human participants.

What Is the Difference Between Random Sampling and Random Assignment?

These are the two important concepts used in research. Random sampling is also called random selection or probability sampling. It is used to choose the members of the population that are useful for your study. Random assignment is the way to divide the participants of the formed sample into experimental and control groups.

Random sampling can be used in different formats of experimental design and types of research. Random assignment is only applied to the between-subjects designs. There are studies that can use both methods, and some studies can be limited to only one of them.

Random sampling boosts the external validity of the experiment and its results. It provides an unbiased approach to the experiment, so you can achieve better statistical inferences. You can be sure that the results can be applicable to the entire community or field of studies.

Random assignments are used to boost the internal validity of the experiment because they show that there are no inherent differences between the members of each group. It means that all the outcomes are completely applicable to the independent variable.

How to Use Random Assignments?

Assign a unique number to every member of the sample. Then, you can apply a computer program or manual manipulation to randomly divide participants into groups. These are the main techniques of using random assignments:

• Random number generator is a special computer program to pick out the random numbers from the list.
• Flipping a coin can help a lot when you have only two groups and need to divide participants between them.
• Lotteries are drawing numbers of groups randomly from the hat or bucket.
• Dices work well when you have three groups and you need to assign participants to one of them.

This method provides equality for all individuals because all of them have the same chances to become members of each group.

When the format of the experimental design is pretty complicated, you can use random assignments only after all the participants are divided into blocks. It is a usual characteristic of large samples.

Blinding

Another name for blinding is masking. It is about hiding the conditions of assignments either from participants or from researchers. When it is a double-blind experiment, the conditions are hidden from both. Such a technique is normally used in medical experiments and testing new drugs.

The problem that this technique is meant to solve is that often researchers may unintentionally urge participants in a way they need to support their hypotheses. That is why it is important to control biases that can influence the results of study. It is also essential that participants cannot guess the aim of the research and the purpose of the experiment.

When the subjects are randomly assigned to treatment and control groups, participants who know the purpose of the research may want to change their opinions or behavior, and that will affect the results. The researcher may also influence the experiment flow because they know what its purpose is and reveal it to participants.

Types of Blinding

There are several types of blinding - single-blinding, double-blinding, and triple-blinding. While single-blinding is common for different fields of study and types of experimental research, double- and triple-blinding are mostly used for medical research only.

Single-Blinding

When participants know what kind of group they are assigned to, they will adjust their behavior, and it will affect the results of the experiment. So, they should not know what group they belong to until the end of the experiment.

Double-Blinding

If researchers know what group their participants are assigned to, they may start controlling them in a different way. This can show the participants the assignment to a specific group and influence their behavior and reactions. So, in a double-blind experiment format, the group assignment should be hidden from both participants and researchers.

Triple-Blinding

This variant is used pretty rarely. These are not only participants and researchers who do not know the group assignment but also people who are involved in data analysis after the experiment is completed. That is because they may predict an outcome and analyze the information with respect to their predictions.

Importance of Blinding and Risks Related to It

Unblinding can occur before the end of the experiment when researchers find out who of the participants received which tasks and treatment. The results can be influenced in the same way they can be when the experiment is not blinded at all.

Such types as double- and triple-blinding may not be possible. When it is applicable to medical research, other fields of study may not have efficient tools to disguise the treatment either from participants or researchers. Even in medical research, many treatments cannot be faked or distorted.

Use some other methods to eliminate bias. They involve:

• single-blinding instead of double- or triple-blinding, used particularly in non-medical research
• focusing on objective measures that cannot be influenced either by researchers or participants
• registering all the data analysis techniques before using ones so that researchers cannot change them in the process of the experiment

Between-Subjects Design

You can use this type of experiment design in the research that is meant to test the influence of the independent variable by setting the environment with different types of treatment for different groups.

Different treatments are applied for testing the independent variable, but participants of every group know only one condition. So, you can compare the difference between groups in these various conditions.

This design is also called an independent-groups design or independent measures. Researchers can compare various measurements that are taken from different groups.

This type of design is used for either two or several groups that are different by their variables, for example, race, age, gender, etc.

Normally, participants have to be assigned to one of the groups randomly to know for sure that their key characteristics can be compared across different groups. You also have to apply masking to hide from participants which group they belong to. If they know that they belong to either a treatment or control group, they may change their answers to correspond with the assignments of their group. That can produce biased results.

You can use a between-subjects design to compare groups with completely different key characteristics. Make this characteristic an independent variable and differentiate the changing levels of this characteristic between the groups. You do not need to divide your participants into experimental and control groups because you will apply the same procedures to all of them.

A between-subjects design differs from a within-subject design because in the latter all the participants irrespective of their group undergo the same conditions. You research differences between conditions but not between participants in that case.

The difference is apparent even from the name of the design. ‘Between’ means that you have to compare conditions between the groups. ‘Within’ stands for comparing these various conditions within one group.

These types of research designs can be used in the same study when there are two or more independent variables that should be tested simultaneously. Every other level of one variable can be regarded with each level of another variable. That forms different conditions and is called a mixed factorial design. Here, one variable can be changed between subjects, and the other is different within subjects.

The between-subject design has its advantages and disadvantages. Even though it is favorable to internal validity, it needs more participants, who can produce some difficulties during the experiment.

✔️You can avoid carryover effects when participants learn how to deal with treatment and perform in a different way in the following treatments. Carryover effects can distort the internal validity.	❌More participants and resources should be involved for every condition, so you will need to spend more resources on recruiting participants and administering the sessions.
✔️ There will not be fatigue effects because participants will not feel bored or tired with continuous multiple treatments.	❌ Validity can be influenced by individual differences between participants. That can lead to alternative results because groups can react in a different way to several conditions.
✔️ The time for conducting the study is shorter because every participant has only one treatment which reduces the session length.

Within-Subjects Design

You conduct experiments to see how different treatments of independent variables influence each condition to detect the cause-and-effect outcomes.

If you want the participants to get involved in every condition, use a within-subjects design, also known as a within-group design. It is different from a between-subjects design because every participant is involved in only one condition in the latter format.

This design is also called a dependent groups design, or you can see the name of a repeated measures design. This name originates from the fact that you can compare similar measures obtained from the same participants in various conditions.

If your study is large and long, you need to use this type of design to evaluate all the changes experienced by the same participants over time. The aim of this is to measure the changes that result from different treatments, for example, from learning or changing ways of performance.

You can randomize or change the order of presenting every condition to participants across one group. That can prevent the influence of previous treatments on their awareness of what is going on further.

When you randomize the sequence, you can present different treatments in different orders, counterbalance the treatments by applying a reduced number of sequences within the same group.

You can use counterbalancing pretty conveniently because you apply every treatment equally in each position of the sequence, which is pretty predictable and easy to manage. That will balance the influence of the treatment sequence on the results.

When the study is long, you can use time as an independent variable because you cannot prevent or block its effects. So, you need to study correlations between time and dependent variables.

In the within-subjects design, every participant in the group is exposed to several conditions, while in the between-subjects design, participants are exposed to only one condition, and the results can be compared between the groups. This type of design uses experimental and control groups, while in the within-subjects design, there is no need for a control group because every participant can control the outcomes.

The combination of these two designs can be used in a mixed factorial study with two or more independent variables.

✔️ The samples are smaller, and it is easier to recruit participants, so the study is more cost-effective.	❌ Time-related threats can be observed that may influence internal validity because it is difficult to control the influence of time on the study process.
✔️ There are no effects of individual differences due to the conditions because all the individuals within the group take part in the same conditions, and the participants’ individual characteristics can be controlled.	❌ The presence of carryover effects can threaten internal validity because the earlier treatments and experiences can change the results of the ongoing treatments.
✔️ The design provides more statistical power because individual differences do not play a great role in the results.

You can reduce the carryover effects by randomization and counterbalancing.

Final Thoughts

Now, you know what experiments are and how to conduct them. You can use the information from this article to facilitate your research process and always come back to it when you need it.

You have to remember that the correct choice of the experiment format can influence the results of it. Utilize the methods and techniques described here to achieve your research goals.