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Conducting an Experiment

Science revolves around experiments, and learning the best way of conducting an experiment is crucial to obtaining useful and valid results.

This article is a part of the guide:

Experimental Research
Pretest-Posttest
Third Variable
Research Bias
Independent Variable

Browse Full Outline

1 Experimental Research
2.1 Independent Variable
2.2 Dependent Variable
2.3 Controlled Variables
2.4 Third Variable
3.1 Control Group
3.2 Research Bias
3.3.1 Placebo Effect
3.3.2 Double Blind Method
4.1 Randomized Controlled Trials
4.2 Pretest-Posttest
4.3 Solomon Four Group
4.4 Between Subjects
4.5 Within Subject
4.6 Repeated Measures
4.7 Counterbalanced Measures
4.8 Matched Subjects

When scientists speak of experiments, in the strictest sense of the word, they mean a true experiment , where the scientist controls all of the factors and conditions.

Real world observations, and case studies , should be referred to as observational research , rather than experiments.

For example, observing animals in the wild is not a true experiment, because it does not isolate and manipulate an independent variable .

The Basis of Conducting an Experiment

With an experiment, the researcher is trying to learn something new about the world, an explanation of 'why' something happens.

The experiment must maintain internal and external validity, or the results will be useless.

When designing an experiment , a researcher must follow all of the steps of the scientific method , from making sure that the hypothesis is valid and testable , to using controls and statistical tests.

Whilst all scientists use reasoning , operationalization and the steps of the scientific process , it is not always a conscious process.

Experience and practice mean that many scientists follow an instinctive process of conducting an experiment, the 'streamlined' scientific process . Following the basic steps will usually generate valid results, but where experiments are complex and expensive, it is always advisable to follow the rigorous scientific protocols. Conducting an experiment has a number of stages, where the parameters and structure of the experiment are made clear.

Whilst it is rarely practical to follow each step strictly, any aberrations must be justified, whether they arise because of budget, impracticality or ethics .

After deciding upon a hypothesis , and making predictions, the first stage of conducting an experiment is to specify the sample groups. These should be large enough to give a statistically viable study, but small enough to be practical.

Ideally, groups should be selected at random , from a wide selection of the sample population. This allows results to be generalized to the population as a whole.

In the physical sciences, this is fairly easy, but the biological and behavioral sciences are often limited by other factors.

For example, medical trials often cannot find random groups. Such research often relies upon volunteers, so it is difficult to apply any realistic randomization . This is not a problem, as long as the process is justified, and the results are not applied to the population as a whole.

If a psychological researcher used volunteers who were male students, aged between 18 and 24, the findings can only be generalized to that specific demographic group within society.

The sample groups should be divided, into a control group and a test group, to reduce the possibility of confounding variables .

This, again, should be random, and the assigning of subjects to groups should be blind or double blind . This will reduce the chances of experimental error , or bias, when conducting an experiment.

Ethics are often a barrier to this process, because deliberately withholding treatment, as with the Tuskegee study , is not permitted.

Again, any deviations from this process must be explained in the conclusion. There is nothing wrong with compromising upon randomness, where necessary, as long as other scientists are aware of how, and why, the researcher selected groups on that basis.

Stage Three

This stage of conducting an experiment involves determining the time scale and frequency of sampling , to fit the type of experiment.

For example, researchers studying the effectiveness of a cure for colds would take frequent samples, over a period of days. Researchers testing a cure for Parkinson's disease would use less frequent tests, over a period of months or years.

The penultimate stage of the experiment involves performing the experiment according to the methods stipulated during the design phase.

The independent variable is manipulated, generating a usable data set for the dependent variable .

The raw data from the results should be gathered, and analyzed, by statistical means. This allows the researcher to establish if there is any relationship between the variables and accept, or reject, the null hypothesis .

These steps are essential to providing excellent results. Whilst many researchers do not want to become involved in the exact processes of inductive reasoning , deductive reasoning and operationalization , they all follow the basic steps of conducting an experiment. This ensures that their results are valid .

Preparing a Coordination Schema of the Whole Research Plan

Preparing a coordination schema of the research plan may be another useful tool in undertaking research planning. While preparing a coordination schema, one may have to identify the broad variable in the form of parameters, complex variables and disaggregate those in the form of simple variables. Coordination Schema: A Methodological Tool in Research Planning by Purnima Mohapatra is a very useful tool. Arranging everything in a schema not only makes the research more organised, it also saves a lot of valuable time for the researcher.

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Martyn Shuttleworth (May 24, 2008). Conducting an Experiment. Retrieved Sep 03, 2024 from Explorable.com: https://explorable.com/conducting-an-experiment

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Education and Communications
Science Experiments

How to Conduct a Science Experiment

Last Updated: June 5, 2024 Fact Checked

This article was co-authored by Meredith Juncker, PhD . Meredith Juncker is a PhD candidate in Biochemistry and Molecular Biology at Louisiana State University Health Sciences Center. Her studies are focused on proteins and neurodegenerative diseases. There are 10 references cited in this article, which can be found at the bottom of the page. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 196,130 times.

Experimentation is the method by which scientists test natural phenomena in the hopes of gaining new knowledge.. Good experiments follow a logical design to isolate and test specific, precisely-defined variables. By learning the fundamental principles behind experimental design, you'll be able to apply these principles to your own experiments. Regardless of their scope, all good experiments operate according to the logical, deductive principles of the scientific method, from fifth-grade potato clock science fair projects to cutting-edge Higgs Boson research. [1] X Research source

Designing a Scientifically Sound Experiment

For instance, if you want to do an experiment on agricultural fertilizer, don't seek to answer the question, "Which kind of fertilizer is best for growing plants?" There are many different types of fertilizer and many different kinds of plants in the world - one experiment won't be able to draw universal conclusions about either. A much better question to design an experiment around would be "What concentration of nitrogen in fertilizer produces the largest corn crops?"
Modern scientific knowledge is very, very vast. If you intend to do serious scientific research, research your topic extensively before you even begin to plan your experiment. Have past experiments answered the question you want your experiment to study? If so, is there a way to adjust your topic so that it addresses questions left unanswered by existing research?

For instance, in our fertilizer experiment example, our scientist would grow multiple corn crops in soil supplemented with fertilizers whose nitrogen concentration differs. He would give each corn crop the exact same amount of fertilizer. He would make sure the chemical composition of his fertilizers used did not differ in some way besides its nitrogen concentration - for instance, he would not use a fertilizer with a higher concentration of magnesium for one of his corn crops. He would also grow the exact same number and species of corn crops at the same time and in the same type of soil in each replication of his experiment.

Typically, a hypothesis is expressed as a quantitative declarative sentence. A hypothesis also takes into account the ways that the experimental parameters will be measured. A good hypothesis for our fertilizer example is: "Corn crops supplemented with 1 pound of nitrogen per bushel will result in a greater yield mass than equivalent corn crops grown with differing nitrogen supplements."

Timing is incredibly important, so stick to your plan as close as possible. That way, if you see changes in your results, you can rule out different time constraints as the cause of the change.
Making a data table beforehand is a great idea - you'll be able to simply insert your data values into the table as you record them.
Know the difference between your dependent and independent variables. An independent variable is a variable that you change and a dependent variable is the one affected by the independent variable. In our example, "nitrogen content" is the independent variable, and "yield (in kg)" is the dependent variable. A basic table will have columns for both variables as they change over time.

Step 5 Conduct your experiment methodically.

Good experimental design incorporates what's known as a control. One of your experimental replications should not include the variable you're testing for at all. In our fertilizer example, we'll include one corn crop which receives a fertilizer with no nitrogen in it. This will be our control - it will be the baseline against which we'll measure the growth of our other corn crops.
Observe any and all safety measures associated with hazardous materials or processes in your experiment. [6] X Research source

It's always a good idea to represent your data visually if you can. Plot data points on a graph and express trends with a line or curve of best fit. This will help you (and anyone else who sees the graph) visualize patterns in the data. For most basic experiments, the independent variable is represented on the horizontal x axis and the dependent variable is on the vertical y axis.

Step 7 Analyse your data and come to a conclusion.

To share your results, write a comprehensive scientific paper. Knowing how to write a scientific paper is a useful skill - the results of most new research must be written and published according to a specific format, often dictated by the style guide for a relevant, peer-reviewed academic journal.

Running an Example Experiment

Step 1 Pick a topic and define your variables.

In this case, the type of aerosol fuel we use is the independent variable (the variable we change), while the range of the projectile is the dependent variable.
Things to consider for this experiment - is there a way to ensure each potato projectile has the same weight? Is there a way to administer the same amount of aerosol fuel for each firing? Both of these can potentially affect the range of the gun. Weigh each projectile beforehand and fuel each shot with the same amount of aerosol spray.

The furthest-left column will be labeled "Trial #." The cells in this column will simply contain the numbers 1-10, signifying each firing attempt.
The following four columns will be labeled with the names of the aerosol sprays we're using in our experiment. The ten cells beneath each column header will contain the range (in meters) of each firing attempt.
Below the four columns for each fuel, leave a space to write the average value of the ranges.

Like many experiments, our experiment has certain safety concerns we need to observe. The aerosol fuels we're using are flammable - we should be sure to close the potato gun's firing cap securely and to wear heavy gloves while igniting the fuel. To avoid accidental injuries from the projectiles, we should also make sure that we (and any observers) are standing to the side of the gun as it fires - not in front of it or behind it.

We can even share our results with the world in the form of a scientific paper - given the subject matter of our experiment, it may be more appropriate to present this information in the form of a tri-fold science fair display.

Community Q&A

Science is about asking big questions. Don't be afraid to choose a topic you haven't looked at before. Thanks Helpful 0 Not Helpful 0
Have fun and stay safe. Thanks Helpful 0 Not Helpful 0
In upper-level sciences, most data isn't used unless it is reproducible at least 3 times. Thanks Helpful 0 Not Helpful 0

Wear eye protection Thanks Helpful 29 Not Helpful 1
Wash your hands before and after an experiment. Thanks Helpful 29 Not Helpful 3
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If anything gets in your eyes rinse them out thoroughly with water for 15 minutes, then seek immediate medical attention. Thanks Helpful 7 Not Helpful 0
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↑ https://www.khanacademy.org/science/high-school-biology/hs-biology-foundations/hs-biology-and-the-scientific-method/a/experiments-and-observations
↑ https://www.sciencebuddies.org/science-fair-projects/project-ideas/list
↑ https://www.sciencebuddies.org/science-fair-projects/science-fair/variables
↑ https://www.livescience.com/21490-what-is-a-scientific-hypothesis-definition-of-hypothesis.html
↑ https://sciencing.com/collect-data-science-project-5988780.html
↑ https://ehsdailyadvisor.blr.com/2012/04/11-rules-for-safe-handling-of-hazardous-materials/
↑ https://www.sciencebuddies.org/science-fair-projects/science-fair/conducting-an-experiment
↑ https://www.sciencebuddies.org/science-fair-projects/science-fair/writing-a-hypothesis
↑ https://www.sciencebuddies.org/science-fair-projects/science-fair/steps-of-the-scientific-method
↑ https://www.sciencebuddies.org/science-fair-projects/science-fair/data-analysis-graphs

About This Article

If you want to conduct a science experiment, first come up with a question you want to answer, then devise a way to test that question. Make sure you have a control, or an untested component to your experiment. For example, if you want to find out which fertilizer is best for growing crops, you would have one plant for each type of fertilizer, plus one plant that doesn’t get any fertilizer. Write down each step of your experiment carefully, along with the final result. For tips on organizing your data collection, read on! Did this summary help you? Yes No

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Experiment Definition in Science – What Is a Science Experiment?

In science, an experiment is simply a test of a hypothesis in the scientific method . It is a controlled examination of cause and effect. Here is a look at what a science experiment is (and is not), the key factors in an experiment, examples, and types of experiments.

Experiment Definition in Science

By definition, an experiment is a procedure that tests a hypothesis. A hypothesis, in turn, is a prediction of cause and effect or the predicted outcome of changing one factor of a situation. Both the hypothesis and experiment are components of the scientific method. The steps of the scientific method are:

Make observations.
Ask a question or identify a problem.
State a hypothesis.
Perform an experiment that tests the hypothesis.
Based on the results of the experiment, either accept or reject the hypothesis.
Draw conclusions and report the outcome of the experiment.

Key Parts of an Experiment

The two key parts of an experiment are the independent and dependent variables. The independent variable is the one factor that you control or change in an experiment. The dependent variable is the factor that you measure that responds to the independent variable. An experiment often includes other types of variables , but at its heart, it’s all about the relationship between the independent and dependent variable.

Examples of Experiments

Fertilizer and plant size.

For example, you think a certain fertilizer helps plants grow better. You’ve watched your plants grow and they seem to do better when they have the fertilizer compared to when they don’t. But, observations are only the beginning of science. So, you state a hypothesis: Adding fertilizer increases plant size. Note, you could have stated the hypothesis in different ways. Maybe you think the fertilizer increases plant mass or fruit production, for example. However you state the hypothesis, it includes both the independent and dependent variables. In this case, the independent variable is the presence or absence of fertilizer. The dependent variable is the response to the independent variable, which is the size of the plants.

Now that you have a hypothesis, the next step is designing an experiment that tests it. Experimental design is very important because the way you conduct an experiment influences its outcome. For example, if you use too small of an amount of fertilizer you may see no effect from the treatment. Or, if you dump an entire container of fertilizer on a plant you could kill it! So, recording the steps of the experiment help you judge the outcome of the experiment and aid others who come after you and examine your work. Other factors that might influence your results might include the species of plant and duration of the treatment. Record any conditions that might affect the outcome. Ideally, you want the only difference between your two groups of plants to be whether or not they receive fertilizer. Then, measure the height of the plants and see if there is a difference between the two groups.

Salt and Cookies

You don’t need a lab for an experiment. For example, consider a baking experiment. Let’s say you like the flavor of salt in your cookies, but you’re pretty sure the batch you made using extra salt fell a bit flat. If you double the amount of salt in a recipe, will it affect their size? Here, the independent variable is the amount of salt in the recipe and the dependent variable is cookie size.

Test this hypothesis with an experiment. Bake cookies using the normal recipe (your control group ) and bake some using twice the salt (the experimental group). Make sure it’s the exact same recipe. Bake the cookies at the same temperature and for the same time. Only change the amount of salt in the recipe. Then measure the height or diameter of the cookies and decide whether to accept or reject the hypothesis.

Examples of Things That Are Not Experiments

Based on the examples of experiments, you should see what is not an experiment:

Making observations does not constitute an experiment. Initial observations often lead to an experiment, but are not a substitute for one.
Making a model is not an experiment.
Neither is making a poster.
Just trying something to see what happens is not an experiment. You need a hypothesis or prediction about the outcome.
Changing a lot of things at once isn’t an experiment. You only have one independent and one dependent variable. However, in an experiment, you might suspect the independent variable has an effect on a separate. So, you design a new experiment to test this.

Types of Experiments

There are three main types of experiments: controlled experiments, natural experiments, and field experiments,

Controlled experiment : A controlled experiment compares two groups of samples that differ only in independent variable. For example, a drug trial compares the effect of a group taking a placebo (control group) against those getting the drug (the treatment group). Experiments in a lab or home generally are controlled experiments
Natural experiment : Another name for a natural experiment is a quasi-experiment. In this type of experiment, the researcher does not directly control the independent variable, plus there may be other variables at play. Here, the goal is establishing a correlation between the independent and dependent variable. For example, in the formation of new elements a scientist hypothesizes that a certain collision between particles creates a new atom. But, other outcomes may be possible. Or, perhaps only decay products are observed that indicate the element, and not the new atom itself. Many fields of science rely on natural experiments, since controlled experiments aren’t always possible.
Field experiment : While a controlled experiments takes place in a lab or other controlled setting, a field experiment occurs in a natural setting. Some phenomena cannot be readily studied in a lab or else the setting exerts an influence that affects the results. So, a field experiment may have higher validity. However, since the setting is not controlled, it is also subject to external factors and potential contamination. For example, if you study whether a certain plumage color affects bird mate selection, a field experiment in a natural environment eliminates the stressors of an artificial environment. Yet, other factors that could be controlled in a lab may influence results. For example, nutrition and health are controlled in a lab, but not in the field.
Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments. Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
Holland, Paul W. (December 1986). “Statistics and Causal Inference”. Journal of the American Statistical Association . 81 (396): 945–960. doi: 10.2307/2289064
Stohr-Hunt, Patricia (1996). “An Analysis of Frequency of Hands-on Experience and Science Achievement”. Journal of Research in Science Teaching . 33 (1): 101–109. doi: 10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z

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1.3 - steps for planning, conducting and analyzing an experiment.

The practical steps needed for planning and conducting an experiment include: recognizing the goal of the experiment, choice of factors, choice of response, choice of the design, analysis and then drawing conclusions. This pretty much covers the steps involved in the scientific method.

Recognition and statement of the problem
Choice of factors, levels, and ranges
Selection of the response variable(s)
Choice of design
Conducting the experiment
Statistical analysis
Drawing conclusions, and making recommendations

What this course will deal with primarily is the choice of the design. This focus includes all the related issues about how we handle these factors in conducting our experiments.

Factors Section

We usually talk about "treatment" factors, which are the factors of primary interest to you. In addition to treatment factors, there are nuisance factors which are not your primary focus, but you have to deal with them. Sometimes these are called blocking factors, mainly because we will try to block on these factors to prevent them from influencing the results.

There are other ways that we can categorize factors:

Experimental vs. Classification Factors

Quantitative vs. qualitative factors, try it section .

Think about your own field of study and jot down several of the factors that are pertinent in your own research area? Into what categories do these fall?

Get statistical thinking involved early when you are preparing to design an experiment! Getting well into an experiment before you have considered these implications can be disastrous. Think and experiment sequentially. Experimentation is a process where what you know informs the design of the next experiment, and what you learn from it becomes the knowledge base to design the next.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 6: Experimental Research

Conducting Experiments

Learning Objectives

Describe several strategies for recruiting participants for an experiment.
Explain why it is important to standardize the procedure of an experiment and several ways to do this.
Explain what pilot testing is and why it is important.

The information presented so far in this chapter is enough to design a basic experiment. When it comes time to conduct that experiment, however, several additional practical issues arise. In this section, we consider some of these issues and how to deal with them. Much of this information applies to nonexperimental studies as well as experimental ones.

Recruiting Participants

Of course, at the start of any research project you should be thinking about how you will obtain your participants. Unless you have access to people with schizophrenia or incarcerated juvenile offenders, for example, then there is no point designing a study that focuses on these populations. But even if you plan to use a convenience sample, you will have to recruit participants for your study.

There are several approaches to recruiting participants. One is to use participants from a formal subject pool —an established group of people who have agreed to be contacted about participating in research studies. For example, at many colleges and universities, there is a subject pool consisting of students enrolled in introductory psychology courses who must participate in a certain number of studies to meet a course requirement. Researchers post descriptions of their studies and students sign up to participate, usually via an online system. Participants who are not in subject pools can also be recruited by posting or publishing advertisements or making personal appeals to groups that represent the population of interest. For example, a researcher interested in studying older adults could arrange to speak at a meeting of the residents at a retirement community to explain the study and ask for volunteers.

The Volunteer Subject

Even if the participants in a study receive compensation in the form of course credit, a small amount of money, or a chance at being treated for a psychological problem, they are still essentially volunteers. This is worth considering because people who volunteer to participate in psychological research have been shown to differ in predictable ways from those who do not volunteer. Specifically, there is good evidence that on average, volunteers have the following characteristics compared with nonvolunteers (Rosenthal & Rosnow, 1976) [1] :

They are more interested in the topic of the research.
They are more educated.
They have a greater need for approval.
They have higher intelligence quotients (IQs).
They are more sociable.
They are higher in social class.

This difference can be an issue of external validity if there is reason to believe that participants with these characteristics are likely to behave differently than the general population. For example, in testing different methods of persuading people, a rational argument might work better on volunteers than it does on the general population because of their generally higher educational level and IQ.

In many field experiments, the task is not recruiting participants but selecting them. For example, researchers Nicolas Guéguen and Marie-Agnès de Gail conducted a field experiment on the effect of being smiled at on helping, in which the participants were shoppers at a supermarket. A confederate walking down a stairway gazed directly at a shopper walking up the stairway and either smiled or did not smile. Shortly afterward, the shopper encountered another confederate, who dropped some computer diskettes on the ground. The dependent variable was whether or not the shopper stopped to help pick up the diskettes (Guéguen & de Gail, 2003) [2] . Notice that these participants were not “recruited,” but the researchers still had to select them from among all the shoppers taking the stairs that day. It is extremely important that this kind of selection be done according to a well-defined set of rules that is established before the data collection begins and can be explained clearly afterward. In this case, with each trip down the stairs, the confederate was instructed to gaze at the first person he encountered who appeared to be between the ages of 20 and 50. Only if the person gazed back did he or she become a participant in the study. The point of having a well-defined selection rule is to avoid bias in the selection of participants. For example, if the confederate was free to choose which shoppers he would gaze at, he might choose friendly-looking shoppers when he was set to smile and unfriendly-looking ones when he was not set to smile. As we will see shortly, such biases can be entirely unintentional.

Standardizing the Procedure

It is surprisingly easy to introduce extraneous variables during the procedure. For example, the same experimenter might give clear instructions to one participant but vague instructions to another. Or one experimenter might greet participants warmly while another barely makes eye contact with them. To the extent that such variables affect participants’ behaviour, they add noise to the data and make the effect of the independent variable more difficult to detect. If they vary across conditions, they become confounding variables and provide alternative explanations for the results. For example, if participants in a treatment group are tested by a warm and friendly experimenter and participants in a control group are tested by a cold and unfriendly one, then what appears to be an effect of the treatment might actually be an effect of experimenter demeanor. When there are multiple experimenters, the possibility for introducing extraneous variables is even greater, but is often necessary for practical reasons.

Experimenter’s Sex as an Extraneous Variable

For example, in a recent study on pain perception, participants immersed their hands in icy water for as long as they could (Ibolya, Brake, & Voss, 2004) [4] . Male participants tolerated the pain longer when the experimenter was a woman, and female participants tolerated it longer when the experimenter was a man.

Researcher Robert Rosenthal has spent much of his career showing that this kind of unintended variation in the procedure does, in fact, affect participants’ behaviour. Furthermore, one important source of such variation is the experimenter’s expectations about how participants “should” behave in the experiment. This outcome is referred to as an experimenter expectancy effect (Rosenthal, 1976) [5] . For example, if an experimenter expects participants in a treatment group to perform better on a task than participants in a control group, then he or she might unintentionally give the treatment group participants clearer instructions or more encouragement or allow them more time to complete the task. In a striking example, Rosenthal and Kermit Fode had several students in a laboratory course in psychology train rats to run through a maze. Although the rats were genetically similar, some of the students were told that they were working with “maze-bright” rats that had been bred to be good learners, and other students were told that they were working with “maze-dull” rats that had been bred to be poor learners. Sure enough, over five days of training, the “maze-bright” rats made more correct responses, made the correct response more quickly, and improved more steadily than the “maze-dull” rats (Rosenthal & Fode, 1963) [6] . Clearly it had to have been the students’ expectations about how the rats would perform that made the difference. But how? Some clues come from data gathered at the end of the study, which showed that students who expected their rats to learn quickly felt more positively about their animals and reported behaving toward them in a more friendly manner (e.g., handling them more).

The way to minimize unintended variation in the procedure is to standardize it as much as possible so that it is carried out in the same way for all participants regardless of the condition they are in. Here are several ways to do this:

Create a written protocol that specifies everything that the experimenters are to do and say from the time they greet participants to the time they dismiss them.
Create standard instructions that participants read themselves or that are read to them word for word by the experimenter.
Automate the rest of the procedure as much as possible by using software packages for this purpose or even simple computer slide shows.
Anticipate participants’ questions and either raise and answer them in the instructions or develop standard answers for them.
Train multiple experimenters on the protocol together and have them practice on each other.
Be sure that each experimenter tests participants in all conditions.

Another good practice is to arrange for the experimenters to be “blind” to the research question or to the condition that each participant is tested in. The idea is to minimize experimenter expectancy effects by minimizing the experimenters’ expectations. For example, in a drug study in which each participant receives the drug or a placebo, it is often the case that neither the participants nor the experimenter who interacts with the participants know which condition he or she has been assigned to. Because both the participants and the experimenters are blind to the condition, this technique is referred to as a double-blind study . (A single-blind study is one in which the participant, but not the experimenter, is blind to the condition.) Of course, there are many times this blinding is not possible. For example, if you are both the investigator and the only experimenter, it is not possible for you to remain blind to the research question. Also, in many studies the experimenter must know the condition because he or she must carry out the procedure in a different way in the different conditions.

A comic of two stick figures talking. Image description available.

Record Keeping

It is essential to keep good records when you conduct an experiment. As discussed earlier, it is typical for experimenters to generate a written sequence of conditions before the study begins and then to test each new participant in the next condition in the sequence. As you test them, it is a good idea to add to this list basic demographic information; the date, time, and place of testing; and the name of the experimenter who did the testing. It is also a good idea to have a place for the experimenter to write down comments about unusual occurrences (e.g., a confused or uncooperative participant) or questions that come up. This kind of information can be useful later if you decide to analy z e sex differences or effects of different experimenters, or if a question arises about a particular participant or testing session.

It can also be useful to assign an identification number to each participant as you test them. Simply numbering them consecutively beginning with 1 is usually sufficient. This number can then also be written on any response sheets or questionnaires that participants generate, making it easier to keep them together.

Pilot Testing

It is always a good idea to conduct a pilot test of your experiment. A pilot test is a small-scale study conducted to make sure that a new procedure works as planned. In a pilot test, you can recruit participants formally (e.g., from an established participant pool) or you can recruit them informally from among family, friends, classmates, and so on. The number of participants can be small, but it should be enough to give you confidence that your procedure works as planned. There are several important questions that you can answer by conducting a pilot test:

Do participants understand the instructions?
What kind of misunderstandings do participants have, what kind of mistakes do they make, and what kind of questions do they ask?
Do participants become bored or frustrated?
Is an indirect manipulation effective? (You will need to include a manipulation check.)
Can participants guess the research question or hypothesis?
How long does the procedure take?
Are computer programs or other automated procedures working properly?
Are data being recorded correctly?

Of course, to answer some of these questions you will need to observe participants carefully during the procedure and talk with them about it afterward. Participants are often hesitant to criticize a study in front of the researcher, so be sure they understand that their participation is part of a pilot test and you are genuinely interested in feedback that will help you improve the procedure. If the procedure works as planned, then you can proceed with the actual study. If there are problems to be solved, you can solve them, pilot test the new procedure, and continue with this process until you are ready to proceed.

Key Takeaways

There are several effective methods you can use to recruit research participants for your experiment, including through formal subject pools, advertisements, and personal appeals. Field experiments require well-defined participant selection procedures.
It is important to standardize experimental procedures to minimize extraneous variables, including experimenter expectancy effects.
It is important to conduct one or more small-scale pilot tests of an experiment to be sure that the procedure works as planned.
elderly adults
unemployed people
regular exercisers
math majors
Discussion: Imagine a study in which you will visually present participants with a list of 20 words, one at a time, wait for a short time, and then ask them to recall as many of the words as they can. In the stressed condition, they are told that they might also be chosen to give a short speech in front of a small audience. In the unstressed condition, they are not told that they might have to give a speech. What are several specific things that you could do to standardize the procedure?

Image Descriptions

A comic of two stick figures talking.

Person 1: Some researchers are starting to figure out the mechanism behind the placebo effect. We’ve used their work to create a new drug: A placebo effect blocker. Now we just need to run a trial. We’ll get two groups, give them both placebos, then give one the REAL placebo blocker, and the other a…. wait.

[The two people scratch their heads]

Person 2: My head hurts.

Person 1: Mine too. Here, want a sugar pill?

[Return to Image]

Media Attributions

Study by XKCD CC BY-NC (Attribution NonCommercial)
Placebo blocker by XKCD CC BY-NC (Attribution NonCommercial)
Rosenthal, R., & Rosnow, R. L. (1976). The volunteer subject . New York, NY: Wiley. ↵
Guéguen, N., & de Gail, Marie-Agnès. (2003). The effect of smiling on helping behaviour: Smiling and good Samaritan behaviour. Communication Reports, 16 , 133–140. ↵
Rosenthal, R. (1976). Experimenter effects in behavioural research (enlarged ed.). New York, NY: Wiley. ↵
Ibolya, K., Brake, A., & Voss, U. (2004). The effect of experimenter characteristics on pain reports in women and men. Pain, 112 , 142–147. ↵
Rosenthal, R., & Fode, K. (1963). The effect of experimenter bias on performance of the albino rat. Behavioural Science, 8 , 183-189. ↵

An established group of people who have agreed to be contacted about participating in research studies.

A source of variation in which the experimenter’s expectations about how participants “should” be have in the experiment.

An experiment in which both the participants and the experimenters are blind to which condition the participants have been assigned to.

A small-scale study conducted to make sure that a new procedure works as planned.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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6.3 Conducting Experiments

Learning objectives.

Describe several strategies for recruiting participants for an experiment.
Explain why it is important to standardize the procedure of an experiment and several ways to do this.
Explain what pilot testing is and why it is important.

Recruiting Participants

Of course, you should be thinking about how you will obtain your participants from the beginning of any research project. Unless you have access to people with schizophrenia or incarcerated juvenile offenders, for example, then there is no point designing a study that focuses on these populations. But even if you plan to use a convenience sample, you will have to recruit participants for your study.

The Volunteer Subject

They are more interested in the topic of the research.
They are more educated.
They have a greater need for approval.
They have higher intelligence quotients (IQs).
They are more sociable.
They are higher in social class.

This can be an issue of external validity if there is reason to believe that participants with these characteristics are likely to behave differently than the general population. For example, in testing different methods of persuading people, a rational argument might work better on volunteers than it does on the general population because of their generally higher educational level and IQ.

In many field experiments, the task is not recruiting participants but selecting them. For example, researchers Nicolas Guéguen and Marie-Agnès de Gail conducted a field experiment on the effect of being smiled at on helping, in which the participants were shoppers at a supermarket. A confederate walking down a stairway gazed directly at a shopper walking up the stairway and either smiled or did not smile. Shortly afterward, the shopper encountered another confederate, who dropped some computer diskettes on the ground. The dependent variable was whether or not the shopper stopped to help pick up the diskettes (Guéguen & de Gail, 2003). Notice that these participants were not “recruited,” but the researchers still had to select them from among all the shoppers taking the stairs that day. It is extremely important that this kind of selection be done according to a well-defined set of rules that is established before the data collection begins and can be explained clearly afterward. In this case, with each trip down the stairs, the confederate was instructed to gaze at the first person he encountered who appeared to be between the ages of 20 and 50. Only if the person gazed back did he or she become a participant in the study. The point of having a well-defined selection rule is to avoid bias in the selection of participants. For example, if the confederate was free to choose which shoppers he would gaze at, he might choose friendly-looking shoppers when he was set to smile and unfriendly-looking ones when he was not set to smile. As we will see shortly, such biases can be entirely unintentional.

Standardizing the Procedure

It is surprisingly easy to introduce extraneous variables during the procedure. For example, the same experimenter might give clear instructions to one participant but vague instructions to another. Or one experimenter might greet participants warmly while another barely makes eye contact with them. To the extent that such variables affect participants’ behavior, they add noise to the data and make the effect of the independent variable more difficult to detect. If they vary across conditions, they become confounding variables and provide alternative explanations for the results. For example, if participants in a treatment group are tested by a warm and friendly experimenter and participants in a control group are tested by a cold and unfriendly one, then what appears to be an effect of the treatment might actually be an effect of experimenter demeanor.

Experimenter’s Sex as an Extraneous Variable

It is well known that whether research participants are male or female can affect the results of a study. But what about whether the experimenter is male or female? There is plenty of evidence that this matters too. Male and female experimenters have slightly different ways of interacting with their participants, and of course participants also respond differently to male and female experimenters (Rosenthal, 1976). For example, in a recent study on pain perception, participants immersed their hands in icy water for as long as they could (Ibolya, Brake, & Voss, 2004). Male participants tolerated the pain longer when the experimenter was a woman, and female participants tolerated it longer when the experimenter was a man.

Researcher Robert Rosenthal has spent much of his career showing that this kind of unintended variation in the procedure does, in fact, affect participants’ behavior. Furthermore, one important source of such variation is the experimenter’s expectations about how participants “should” behave in the experiment. This is referred to as an experimenter expectancy effect (Rosenthal, 1976). For example, if an experimenter expects participants in a treatment group to perform better on a task than participants in a control group, then he or she might unintentionally give the treatment group participants clearer instructions or more encouragement or allow them more time to complete the task. In a striking example, Rosenthal and Kermit Fode had several students in a laboratory course in psychology train rats to run through a maze. Although the rats were genetically similar, some of the students were told that they were working with “maze-bright” rats that had been bred to be good learners, and other students were told that they were working with “maze-dull” rats that had been bred to be poor learners. Sure enough, over five days of training, the “maze-bright” rats made more correct responses, made the correct response more quickly, and improved more steadily than the “maze-dull” rats (Rosenthal & Fode, 1963). Clearly it had to have been the students’ expectations about how the rats would perform that made the difference. But how? Some clues come from data gathered at the end of the study, which showed that students who expected their rats to learn quickly felt more positively about their animals and reported behaving toward them in a more friendly manner (e.g., handling them more).

Create a written protocol that specifies everything that the experimenters are to do and say from the time they greet participants to the time they dismiss them.
Create standard instructions that participants read themselves or that are read to them word for word by the experimenter.
Automate the rest of the procedure as much as possible by using software packages for this purpose or even simple computer slide shows.
Anticipate participants’ questions and either raise and answer them in the instructions or develop standard answers for them.
Train multiple experimenters on the protocol together and have them practice on each other.
Be sure that each experimenter tests participants in all conditions.

Another good practice is to arrange for the experimenters to be “blind” to the research question or to the condition that each participant is tested in. The idea is to minimize experimenter expectancy effects by minimizing the experimenters’ expectations. For example, in a drug study in which each participant receives the drug or a placebo, it is often the case that neither the participants nor the experimenter who interacts with the participants know which condition he or she has been assigned to. Because both the participants and the experimenters are blind to the condition, this is referred to as a double-blind study. (A single-blind study is one in which the participant, but not the experimenter, is blind to the condition.) Of course, there are many times this is not possible. For example, if you are both the investigator and the only experimenter, it is not possible for you to remain blind to the research question. Also, in many studies the experimenter must know the condition because he or she must carry out the procedure in a different way in the different conditions.

Record Keeping

It is essential to keep good records when you conduct an experiment. As discussed earlier, it is typical for experimenters to generate a written sequence of conditions before the study begins and then to test each new participant in the next condition in the sequence. As you test them, it is a good idea to add to this list basic demographic information; the date, time, and place of testing; and the name of the experimenter who did the testing. It is also a good idea to have a place for the experimenter to write down comments about unusual occurrences (e.g., a confused or uncooperative participant) or questions that come up. This kind of information can be useful later if you decide to analyze sex differences or effects of different experimenters, or if a question arises about a particular participant or testing session.

Pilot Testing

Do participants understand the instructions?
What kind of misunderstandings do participants have, what kind of mistakes do they make, and what kind of questions do they ask?
Do participants become bored or frustrated?
Is an indirect manipulation effective? (You will need to include a manipulation check.)
Can participants guess the research question or hypothesis?
How long does the procedure take?
Are computer programs or other automated procedures working properly?
Are data being recorded correctly?

Of course, to answer some of these questions you will need to observe participants carefully during the procedure and talk with them about it afterward. Participants are often hesitant to criticize a study in front of the researcher, so be sure they understand that this is a pilot test and you are genuinely interested in feedback that will help you improve the procedure. If the procedure works as planned, then you can proceed with the actual study. If there are problems to be solved, you can solve them, pilot test the new procedure, and continue with this process until you are ready to proceed.

Key Takeaways

There are several effective methods you can use to recruit research participants for your experiment, including through formal subject pools, advertisements, and personal appeals. Field experiments require well-defined participant selection procedures.
It is important to standardize experimental procedures to minimize extraneous variables, including experimenter expectancy effects.
It is important to conduct one or more small-scale pilot tests of an experiment to be sure that the procedure works as planned.
Practice: List two ways that you might recruit participants from each of the following populations: (a) elderly adults, (b) unemployed people, (c) regular exercisers, and (d) math majors.
Discussion: Imagine a study in which you will visually present participants with a list of 20 words, one at a time, wait for a short time, and then ask them to recall as many of the words as they can. In the stressed condition, they are told that they might also be chosen to give a short speech in front of a small audience. In the unstressed condition, they are not told that they might have to give a speech. What are several specific things that you could do to standardize the procedure?

Guéguen, N., & de Gail, Marie-Agnès. (2003). The effect of smiling on helping behavior: Smiling and good Samaritan behavior. Communication Reports, 16 , 133–140.

Ibolya, K., Brake, A., & Voss, U. (2004). The effect of experimenter characteristics on pain reports in women and men. Pain, 112 , 142–147.

Rosenthal, R. (1976). Experimenter effects in behavioral research (enlarged ed.). New York, NY: Wiley.

Rosenthal, R., & Fode, K. (1963). The effect of experimenter bias on performance of the albino rat. Behavioral Science, 8 , 183-189.

Rosenthal, R., & Rosnow, R. L. (1976). The volunteer subject . New York, NY: Wiley.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

How to... Conduct experiments

An experiment is a deliberate attempt to manipulate a situation, in order to test a hypothesis that a particular cause creates a particular effect, in other words that varying the input will affect the output.

A procedure adopted on the chance of its succeeding, for testing a hypothesis etc., or to demonstrate a known fact. Oxford Dictionary of English

It is a structured and manipulated process, a deliberate imposition of a treatment.
It has a number of independent variables, as causes or inputs, and one dependent variable, or effect or output, with the goal being to see how changing the former affects the latter.
It needs to control other variables which might cause the observable changes in the dependent variable, so that you can isolate all possible reasons why the selected variable might behave that particular way.
It usually tests a hypothesis, derived from a particular theory.

"Basically, an experimental design requires several factors: a setting where the real world can be simulated, one or more independent variables that can be varied, and resultant effects on dependent variables which can be observed."

Jacob, F. and Ehret, M. (2006) "Self-protection vs opportunity seeking in business buying behavior: an experimental study", Journal of Business & Industrial Marketing , Vol. 21 No. 2

The experiment is a particularly useful method to explain change, to look at cause and effect, or to deduce a hypotheses from a theory. An important proviso is the ability to isolate the independent, or causal, variable from other causes of the particularly effect you are examining.

In a biological experiment, we can vary the effect of the light (the independent variable) on a plant, and so show how light affects plant growth. It is possible to grow the plant in laboratory conditions, from which other factors can be excluded.

The experiment in management research – drawbacks

Maylor and Blackmon (2005, pp. 202-3) point out how important it is, in drawing up a hypothesis, to ensure that the cause of A is B and not C or D. In order to do this, you need to isolate the causes, and examine each in turn. In a laboratory, you would set up experimental conditions for each factor, test each and assess its likely impact on the dependent variable.

In scientific experiments it is usually possible to create conditions that exclude other possible causes from the one you are examining – that is part of the function of a laboratory. Humans, however, operate as part of social organisms, which are inevitably more complex and difficult to categorise than natural organisms.

Imagine you are investigating the cause of absenteeism at work. You hypothesise that the cause is stress. Senior management, however, believe that its cause is inadequate supervision. How would you set up a measure for stress, or exclude other factors? How easy would it be to investigate stress, if management had other ideas?

The above example illustrates three difficulties in the experimental method in management: the difficulty of measuring aspects of human behaviour, of disentangling causes, and the fact that many of the environments where you are likely to undertake field research may well be subject to other influences creating conditions which may be outside your control, and unsympathetic to your need to prove a particular hypothesis.

Furthermore, humans have the attribute of consciousness, which makes observation difficult; they may behave differently if they know they are being watched, for example they may adopt behaviours which they think are expected of them. The following quote from the famous sociologist Anthony Giddens is also applicable in the business and management area:

"An experiment can...be defined as an attempt, within artificial conditions established by an investigator, to test the influence of one or more variables upon others. Experiments are widely used in the natural sciences, but the scope for experimentation in sociology is limited. We can only bring small groups of individuals into a laboratory setting and in such experiments, people know they are being studied and may behave differently from normal". (Giddens, 1989)

The experiment in management research – advantages

Despite its drawbacks, experiments have been used in management research including some famous ones.

Harvard Business School professor Elton Mayo studied the productivity of workers in the Hawthorne Plant of the Western Electric Company in Cicero, Illinois in the 1920s, with a view to determining what affected worker productivity. The researchers manipulated the conditions of the workers in various ways, and came to a number of conclusions:

Individual aptitude is a poor predictor of performance.
There was a "group life" amongst the workers which affected performance.
Each group had its own norm of a fair day's work.
The workplace is a social system.

However, they also observed that productivity tended to increase whatever the conditions, and came to the conclusion that observation had an impact on performance – which substantiates the point made by Giddens (see above).

In 1911, Frederick W. Taylor published The Principles of Scientific Management in which he looked at how the application of scientific method could aid productivity. He introduced time and motion studies, which looked at the sequence of motions used to perform a job, and expounded the idea of scientific management, which comprised:

Replacing rule-of-thumb work methods with scientific ones.
Placing emphasis on the importance of training.
Ensuring that scientific methods are followed on an ongoing basis.
Dividing work up between workers and managers, with the workers carrying out the tasks and the managers implementing scientific management in order to plan work.

It would almost be true to say, therefore, that the science of management owes its genesis partly to experimental design!

One of the key advantages of experimental design in management research is the fact that it requires "a setting where the real world can be simulated". The advantage of a simulation is that you can set up an imaginary situation with realistic elements, so you are not dependent on the constraints of the real world. Thus, if you want to investigate buying behaviour, or reaction to brands, you are not dependent on finding real buyers buying real products, or reacting to real brands. This means that you can set up the variables to reflect the hypotheses that you want to test. Used in conjunction with the questionnaire (see using questionnaires effectively ), the experiment can help yield some quite sophisticated information on attitudes and behaviour (see the examples in types of experiment ).

Experimental design can also provide excellent opportunities for observing behaviour – both the Hawthorne and the Taylor experiments used forms of observation, and yielded interesting results.

However, experiments differ from observation in that they deliberately attempt to manipulate a situation, as opposed to observing what is there, or else, as with Taylor, fitting what is observed into a framework. The Hawthorne researchers may have observed, but their presence changed the workers' environment and conditions. This may well be beyond the researcher's control and can be a cumbersome process – the Hawthorne research took five years because of the difficulties in manipulating the physical conditions.

The experimental method also differs from the survey in that it seeks to explain causes, while surveys look at relationships between variables (in the absenteeism example quoted above, a survey could be used to ask staff members what their reasons for absenteeism were, but these would merely yield related factors rather than proven causes).

In summary, the experiment remains of value in management research, although it is used differently and "pure" experiments remain relatively rare. As an undergraduate or MBA student, you should probably use an experimental design with extreme care, and certainly under the close counsel of your supervisor.

Giddens, A. (1989), Sociology , Polity Press, Cambridge, UK

Mayor, H. and Blackman, K. (2005), Researching Business and Management , Palgrave Macmillan, Basingstoke, UK

On this page, we shall look in more detail at the design considerations that create the best conditions for experiments. (We shall look at particular designs in the next section, Types of experiment.)

True cause and effect

An experiment tests a hypothesis that is deduced from a theory.

In Self-protection vs opportunity seeking in business buying behavior: an experimental study ( Journal of Business & Industrial Marketing , Vol. 21 No. 2), Frank Jacob and Michael Ehret use an experiment to test Prospect theory, according to which successful economic agents tend to be more self protective, with under performers taking a bigger risk.

However, as we saw in the previous section, successful experimentation depends on being able to isolate and exclude other factors, i.e. to prove the hypothesis that X is the cause of Y, you have to exclude A, B or C. In a scientific experiment, you would be able to set up laboratory conditions that looked at A's, B's and C's effect on Y independently; in a business, it may not be so easy to do this.

To take a very simplistic example, suppose you were to assess the effect on productivity of increasing the temperature in the office. Assuming you were to be able to establish a measure of productivity, you would need to check that there was no other cause of the (presumed) decrease in productivity – such as workload, time of day, the work taking place after a sociable lunch where alcohol was consumed, or an e-mail having just been issued about proposed redundancies!

It is important to identify the hypothesis you are testing very precisely, as well as all the possible variables, and:

create a means for establishing which of the variables is the cause
prove not merely that X has the effect of Y, but also if X is absent, then so is Y.

Experiments can be most effective if you can limit the number of variables you are looking at.

Imagine for example you had two groups of workers, one of whom had had a particular form of training and the other had not. If you compare performance measures of the two groups against attendance on the course, you can tell whether or not there is a relationship between course attendance and performance.

So far, however, all you will have done is to prove a relationship between the two variables. To indicate cause and effect it is necessary to look at the sequence in time, and to prove that the dependent variable, in this case job performance, followed the training course. You would need, therefore, to observe performance (or look at performance records) both before and after the course.

Developers of educational software will often plan "experiments" with their courseware by carrying out trials with students. In order for these trials to be effective, however, it is necessary to take "measurements" (of motivation, aptitude or whatever the claim is that the software can help with) both before and after the trial.

Experimental treatment

There is a scientific protocol for eliminating alternative causes, which involves defining variables and separating out those which it's important to keep constant, in order to minimise confusion.

Variables are thus categorised as follows:

Experimental variables – the inputs – in the above examples, it would be the office temperature, or the training.
Dependent variables – the effect on the output - worker productivity or performance.
Controlled variables – factors that you need to hold constant, such as the time of day, the aptitude of employees attending the training.
Uncontrolled variables – factors over which you do not have control, for example, in the case of the temperature experiment, the liquid lunch or the redundancy e-mail.

The control group

The control group is a vital principle in experimental design, and involves having a group which does not receive treatment, for comparison purposes.

In the above examples, it could be that groups of workers are not subjected to increased temperature/training.

A common design occurs in double-blind drugs trials, where one group is treated with the drug and the other is not; neither group knows which group has the treatment and which has the placebo.

You need however to ensure that your two groups, treatment and non treatment, are matched. In order to do this, you need to pay attention to sampling.

Jankowicz (2005, pp. 237-8) suggests two possible approaches to sampling, both of which depend on a fairly large population and on the researcher knowing quite a bit about the group:

purposive sampling, in which you deliberately select groups which have the same characteristics
random sampling – as the name suggests, this depends on random assignment to the group, with the effect that additional factors and differences are also randomly assigned.

Meneses and Palacio (2006) maintain that convenience samples are good when you are dependent on co-operation with your subjects – see Quasi experiments .

Random assignment

A principle of experimental design is that of random assignment , which means assigning people to groups on a random basis, from a common pool, in order to cancel out group differences which might otherwise occur, and ensure similarity in the groups.

A recent experiment concerned the effect of prayer on heart patients awaiting operations for arteriosclerosis (blocked arteries). As soon as subjects were recommended for the operation, they were randomly assigned to one of two groups, one of which were prayed for while the other was not.

As above, you would need to have sufficient control over the situation to be able to assign people on this basis.

Treatment groups

In the literature describing experimental design, you will often find reference to "between-subjects design" and "within-subjects design".

A between-subjects design occurs when two or more groups are compared. The groups are comparable but are subject to different treatment.
A within-subjects design occurs when one group is subject to two different treatments, as when, for example, a class does a test at two different points in time.

Measurement

You need to find an appropriate measurement for your variables. One form of measurement which is often used in management experiments is the questionnaire. Questions may be factual, e.g. position in organisation, salary band etc., or may be more sophisticated, designed to test attitude or behaviour. You will obviously need to do give careful thought to your questions, and you may well find that the literature surrounding your hypothesis provides you with some useful measures, as in the examples below. You can then tabulate the responses and compare the independent and dependent variables.

The following are some examples of questionnaires, as well as their analysis, used in experiments.

In " Self-protection vs opportunity seeking in business buying behavior: an experimental study " by Frank Jacob and Michael Ehret ( Journal of Business & Industrial Marketing , Vol. 21 No. 2), the authors provide an example of a questionnaire used to assess decision-making.

In " The effect of strategic and tactical cause-related marketing on consumers' brand loyalty " by Douwe van den Brink et al. ( Journal of Consumer Marketing , vol. 23 no. 1), the authors use a questionnaire to measure both attitudes and behaviour, using commonly accepted scales.

In " Different kinds of consumer response to the reward recycling technique: similarities at the desired routine level " ( Asia Pacific Journal of Marketing and Logistics , vol. 18 no. 1), Gonzalo Díaz Meneses and Asunción Beerli Palacio use three questionnaires over a period of time with Likert-type scales to measure ecological conscience.

Alternatively, you can use information kept by the organisation, such as sales performance figures, or a form of experimental measurement, as in the length of time taken to perform certain tasks in Taylor's time and motion study.

Whatever measurement method you choose, you will need to tabulate your data, and look for a systematic relationship between the dependent and independent variable, and having done so, subject the data to appropriate statistical tests. If you are not familiar with these, look at our articles on using statistical tests .

Minimising bias

Bias can be a threat to the validity of experiments:

The experimenter can introduce errors in the recording of the data or, simply by virtue of having certain expectations of the outcome, design for this outcome in mind. This is a case of self fulfilling prophecy rather than fraud.
The subjects can alter their behaviour to accommodate the perceived expectations of the experimenter, or the group can fail to reflect accurately the population at large.

Such possible biases need to be taken account of when you design the experiments.

Ethical considerations

There are many ethical issues to consider with experiments, and you would do well to check with your university as to whether they have any policies. For example, could the subjects come to any harm by either participating in the experiment, or would they feel disadvantaged if they did not participate (if they had training withheld for example)? Are there issues of confidentiality?

It's a good idea to get participant's informed consent before their participation, and explain the purpose of the experiment to them.

When is an experiment a true experiment?

When the following criteria are observed:

experimental treatment – possible variables are isolated
presence of a control group
random assignment
measurement before and after treatment.

For the design of an experiment to be rigorous, you need to set up a contrived setting, which is difficult in the real world. Particularly when you are dealing with large groups or complex systems, experiments can be difficult because of the large number of variables; getting control of your sample can also be a problem.

"The bottom line here is that experimental design is intrusive and difficult to carry out in most real world contexts. And, because an experiment is often an intrusion, you are to some extent setting up an artificial situation so that you can assess your causal relationship with high internal validity. If so, then you are limiting the degree to which you can generalise your results to real contexts where you haven't set up an experiment. That is, you have reduced your external validity in order to achieve greater internal validity."

Trochim, W. M. (2006), The Research Methods Knowledge Base , available at http://www.socialresearchmethods.net/kb/ [accessed 23rd April 2007]

Jankowicz, A.D. (2005), Business Research Projects , Fourth Edition, Thomson, London

Meneses, G.D. and Palacio, A.B. (2006), "Different kinds of consumer response to the reward recycling technique: similarities at the desired routine level", Asia Pacific Journal of Marketing and Logistics , Vol. 18 No. 1.

Laboratory experiments

A laboratory experiment is one that takes place in a situation isolated from what is going on around it, as in a laboratory for scientific experiments. The whole purpose of a laboratory is to create conditions where possible causal factors can be dealt with in isolation.

In management research, it is relatively unusual to set up an experiment in a laboratory: the term is used figuratively to refer to a setting outside the distractions of normal working life, probably a room chosen and set aside for that purpose.

It needs to conform to the conditions described in How to conduct experiments The location will generally be set up specifically for the experiment, and subjects are expected to behave according to a prescribed pattern, for example looking at a piece of courseware, sampling a product etc.

Examples of laboratory experiments include:

Testing reactions to a food product, for example a few years ago people were asked if they could tell the difference between butter and margarine.
Testing educational software – participants are sat in a computer lab and their use of the software observed.
The reality TV show Big Brother , where participants are isolated in a specially built house.

A laboratory experiment creates a highly contrived situation and some consider it inappropriate for investigating complex phenomena which are dependent on social interaction or organisational dynamics, such as how people relate to change. On the other hand, some have used its very contrived nature to create simulations and scenarios and invite response.

In " Self-protection vs opportunity seeking in business buying behavior: an experimental study " ( Journal of Business & Industrial Marketing , Vol. 21 No. 2), Frank Jacob and Michael Ehret describe how they use a laboratory design to create a simulated environment where industrial buying behaviour can be investigated. (In a field setting, it would presumably not have been possible to create the conditions or control the variables for such a complex subject.) Participants are classified into subgroups (or levels of variables) according to the hypothetical performance of their division (under or over achievement). The measurement tool was a questionnaire.

In " The effect of strategic and tactical cause-related marketing on consumers' brand loyalty " ( Journal of Consumer Marketing , Vol. 23 No. 1), Douwe van den Brink et al. describe an experiment conducted with 240 participants on the effect of cause-related marketing. Although the setting was actually a library, the scenario used was a simulated one ("Story boards about a non-existing company, brand and CRM campaign were used as stimulus materials") and the location chosen for its quietness allowing participants to concentrate. The measure was a questionnaire using scales, and the data was analysed with a t -test and ANOVA. The design is described as a "two-by-two between subjects design".

In " An empirical analysis of the brand personality effect " ( Journal of Product & Brand Management , Vol. 14 No. 7), Traci H. Freling and Lukas P. Forbes conduct a highly structured experiment that looks at the role of personality in brand strategy and development. The research took place in a classroom with subjects being randomly assigned to different groups each of which was given a separate vignette with product information and comments suggestive of a particular personality. All subjects were handed a booklet which gave an introduction to the project, instructions, stimulus material and the measures, and were required to write down their thoughts and fill in the questionnaire.

Field experiments

The difference between a field experiment and a laboratory experiment is that the former takes place in a natural setting as opposed to a contrived one – for example a classroom, an office, a shop, shopping mall, factory etc. The setting is realistic, which has the advantage that you are not imposing artificial conditions but the disadvantage that you will have less control.

Pragmatic considerations may make field experiments more common in the social and management sciences.

In " Differential effects of price-beating versus price-matching guarantee on retailers' price image " ( Journal of Product & Brand Management , Vol. 14 No. 6), Pierre Desmet and Emmanuelle Le Nagard describe an experiment on the effects of low price guarantees which took place in a shopping mall using face to face interviews along with stimulus materials in the form of an advertisement.

Some experimental designs

The most common form of experimental design is the pre-test post-test randomised design , which, as the name suggests, randomly assigns to groups, has a control group, and measures both before and after the experimental programme.

There are a number of different variants on this design: some of the most common are listed below.

Two-group experimental design

This is a post-test only randomised experiment, where the effect of a particular programme on two groups is examined. The group participants are randomly selected, and the main interest is in seeing the difference after the programme, hence the term post-test. The difference is measured using a T test or one-way analysis of variance (ANOVA). This is one of the best tests for measuring cause and effect, and, requiring only one test, it is relatively cheap to administer.

Factorial designs

This is a useful design when we want to examine the effect of variations within factors. For example, we might want to examine the effect of temperature and time of day on worker productivity: these would be the key variables but the different times and temperatures would be the levels. We can use this design to explore the interaction between levels and factors – for example, whether a hotter temperature has a worse effect at different times of day. The number of factors would be expressed as: n x n , with the number values indicating the number of levels – for example if we had three different times of day and four variations of temperature, we would call the design a 3 x 4 factorial design. We would analyse the design using a regression model.

Randomised block designs

Similar to stratified random sampling, this involves dividing your sample into homogenous groups, and then repeating the experiment within each group. For example, if you were conducting an experiment in an organisation you might want to divide people up according to department or function. The reason for so doing is to reduce overall variation. Again, you would analyse using a regression model.

Covariance designs

This term is used when although the design the basic pre-test post-test randomised variety, but variables are adjusted to remove extraneous effects.

Hybrid experimental designs

These are designs which combine features of the more established designs described above. For example:

The Solomon four-group design is a way of triangulating the effects of testing. Two groups receive the treatment, and two do not; only one of each group has a pretest.
The Switching replication design is a way of overcoming the ethical objection of giving a treatment to one group but not another, simply by switching groups around so that the first control group becomes the treatment group in the next phase of the experiment, and vice versa.

Quasi experiments

These lack the rigorous conditions of "true" experiments, i.e. manipulation of variables, random assignment etc. They occur when the researcher takes advantage of naturally occurring events to implement some aspect of experimental design, for example a before and after measurement. The researcher's role is reduced to that of an observer; he cannot manipulate or control the conditions of the experiment. He also faces difficulties of unobtrusive observation, defining an appropriate measure for the dependent variable, and lack of control over variables.

Examples of natural events could be a strike, a threat of redundancy, a new policy which is implemented in some departments and not in others, a training course which only some managers go on. Such events create the possibility of a before and after measurement, or a control group - both aspects of experimental design. Not all criteria –- isolation of variables, control group, random assignment, before and after measurement – are present, hence the term "quasi experiment".

The big advantage of such experiments, however, is that they take advantage of natually occurring events, and they can thus offer useful triangulation with other research methods.

In " Different kinds of consumer response to the reward recycling technique: similarities at the desired routine level " ( Asia Pacific Journal of Marketing and Logistics , Vol. 18 No. 1), Gonzalo Díaz Meneses and Asunción Beerli Palacio use a contrived situation but is based on a convenience, and hence not randomised, sample. Because of the lack of randomisation, the experiment is not a true one, but the sampling method is deliberately chosen as the authors claim that:

[convenience sampling] is recommendable when the collaboration of those surveyed requires, as in the case of this longitudinal research, intensive questionnaire completion. Furthermore, if those surveyed belong to the same social network as the surveyor, there is greater opportunity for observation and control of the individuals in the experiment

Volunteers apply the treatment to a member of their household, who has to fill in three questionnaires over a period of time, looking at whether rewards, or beliefs, affect recycling behaviour.

Some quasi-experimental designs

Non equivalent group design.

This is very similar to the pre-test post-test randomised design, but lacks randomisation. Two groups are selected for their similarity, but they are not as similar as if assignment had been purely random, hence the name.

Selection regression

The distinguishing feature of this type of design is the way it assigns to groups – people are measured prior to the programme and are assigned based on their score. The basic design is a pre-test post-test two-group design, with measures before and after the programme. The advantage is that assignment is based on need – for example, the sickest patients for a drug, the lowest scoring children for a remedial programme.

All Subjects

Conducting Experiments

The process of designing and carrying out a study to gather data and test hypotheses in order to answer specific research questions.

Related terms

Control Group : A group in an experiment that does not receive the experimental treatment, used as a baseline for comparison.

Independent Variable : The variable that is manipulated or changed by the researcher in an experiment.

Dependent Variable : The variable that is measured or observed by the researcher to determine if it changes as a result of manipulating the independent variable.

" Conducting Experiments " appears in:

Practice questions ( 1 ).

What ethical principle may be violated when conducting experiments about group conformity without considering cultural differences?

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Scientific Method Example

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Cell Biology
Weather & Climate
B.A., Biology, Emory University
A.S., Nursing, Chattahoochee Technical College

The scientific method is a series of steps that scientific investigators follow to answer specific questions about the natural world. Scientists use the scientific method to make observations, formulate hypotheses , and conduct scientific experiments .

A scientific inquiry starts with an observation. Then, the formulation of a question about what has been observed follows. Next, the scientist will proceed through the remaining steps of the scientific method to end at a conclusion.

The six steps of the scientific method are as follows:

Experiments

Learning objectives.

Describe the experimental process, including ways to control for bias
Identify and differentiate between independent and dependent variables

Causality: Conducting Experiments and Using the Data

Experimental hypothesis.

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon (Figure 1).

A photograph shows a child pointing a toy gun.

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.

We also need to precisely define, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts is recorded.

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

A photograph shows three glass bottles of pills labeled as placebos.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 2).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 3). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.

A box labeled “independent variable: type of television programming viewed” contains a photograph of a person shooting an automatic weapon. An arrow labeled “influences change in the…” leads to a second box. The second box is labeled “dependent variable: violent behavior displayed” and has a photograph of a child pointing a toy gun.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 4). If possible, we should use a random sample (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.

In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

(a) A photograph shows an aerial view of crowds on a street. (b) A photograph shows s small group of children.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design . With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

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Analyzing Findings. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/2-3-analyzing-findings . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction.

group designed to answer the research question; experimental manipulation is the only difference between the experimental and control groups, so any differences between the two are due to experimental manipulation rather than chance

description of what actions and operations will be used to measure the dependent variables and manipulate the independent variables

researcher expectations skew the results of the study

experiment in which the researcher knows which participants are in the experimental group and which are in the control group

experiment in which both the researchers and the participants are blind to group assignments

people's expectations or beliefs influencing or determining their experience in a given situation

variable that is influenced or controlled by the experimenter; in a sound experimental study, the independent variable is the only important difference between the experimental and control group

variable that the researcher measures to see how much effect the independent variable had

subjects of psychological research

using a probability-based method to select a subset of individuals for the sample from the population.

using a probability-based method to divide a sample into treatment groups.

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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

Laboratory Experiment

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.

Pros	Cons
The researcher has control over variables. Easy to establish the relationship between cause and effect. Inexpensive and convenient. Easy to replicate.	The artificial environment may impact the behaviour of the participants. Inaccurate results The short duration of the lab experiment may not be enough to get the desired results.

Field Experiment

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.

Pros	Cons
Participants are observed in the natural environment. Participants are more likely to behave naturally. Useful to study complex social issues.	It doesn’t allow control over the variables. It may raise ethical issues. Lack of internal validity

Natural Experiments

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?

Pros	Cons
The source of variation is clear. It’s carried out in a natural setting. There is no restriction on the number of participants.	The results obtained may be questionable. It does not find out the external validity. The researcher does not have control over the variables.

Quasi-Experiments

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.

Pros	Cons
Quasi-experiments are widely conducted as they are convenient and practical for a large sample size. It is suitable for real-world natural settings rather than true experimental research design. A researcher can analyse the effect of independent variables occurring in natural conditions.	It cannot the influence of independent variables on the dependent variables. Due to the absence of a control group, it becomes difficult to establish the relationship between dependent and independent variables.

Does your Research Methodology Have the Following?

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Perfect Language
Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

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.

Independent variables	Dependent variables	Confounding Variable
The number of hours youngsters spend on social media daily.	The overuse of social media among the youngsters and negative impact on their behaviour.	Measure the difference between youngsters’ behaviour with the minimum social media usage and maximum social media utilisation. You can control and minimise the number of hours of using the social media of the participants.
The overconsumption of junk food.	Adverse effects of junk food on human health like obesity, indigestion, constipation, high cholesterol, etc.	Identify the difference between people’s health with a healthy diet and people eating junk food regularly. You can divide the participants into two groups, one with a healthy diet and one with junk food.

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.

Null hypothesis
The usage of social media does not correlate with the negative behaviour of youngsters.	Over-usage of social media affects the behaviour of youngsters adversely.
There is no relationship between the consumption of junk food and the health issues of the people.	The over-consumption of junk food leads to multiple health issues.

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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.

Randomisation	Stratified sampling
Participants are randomly selected and assigned a specific number of hours to spend on social media.	Participants are divided into groups as per their age and then assigned a specific number of hours to spend on social media.
Participants are randomly selected and assigned a balanced diet.	Participants are divided into various groups based on their age, gender, and health conditions and assigned to each group’s treatment group.

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.

Type of Research Design	Definition
Two-group Post-test only	It includes a control group and an experimental group selected randomly or through matching. This experimental design is used when the sample of subjects is large. It is carried out outside the laboratory. Group’s dependent variables are compared after the experiment.
Two-group pre-test post-test only.	It includes two groups selected randomly. It involves pre-test and post-test measurements in both groups. It is conducted in a controlled environment.
Soloman 4 group design	It includes both post-test-only group and pre-test-post-test control group design with good internal and external validity.
Factorial design	Factorial design involves studying the effects of two or more factors with various possible values or levels. Example: Factorial design applied in optimisation technique.
Randomised block design	It is one of the most widely used experimental designs in forestry research. It aims to decrease the experimental error by using blocks and excluding the known sources of variation among the experimental group.
Cross over design	In this type of experimental design, the subjects receive various treatments during various periods.
Repeated measures design	The same group of participants is measured for one dependant variable at various times or for various dependant variables. Each individual receives experimental treatment consistently. It needs a minimum number of participants. It uses counterbalancing (randomising and reversing the order of subjects and treatment) and increases the treatments/measurements’ time interval.

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.

What is an Experiment?

An experiment is a type of study carried out to investigate the cause-and-effect relationship between two or more variables .

The purpose of conducting an experiment is to find evidence to decide whether that cause-and-effect relationship exists or not, but how exactly do you do this? See the following section to better understand what is involved in conducting an experiment.

Conducting an Experiment Meaning

To be able to understand the meaning of conducting an experiment, you need to familiarize yourself with the terminology used in this type of study.

Roles in an Experiment

First, let's look at the different roles involved in conducting an experiment in the table below.

Role	Definition	Example
	People involved in the planning and execution of the experiment.	A researcher or group of researchers working together in an experiment.
	These are the that are , as part of an experiment. , they are called .	These can be people, animals, plants, or objects.
	If required, some experiments might involve , who are responsible for evaluating the results of the experiment, providing an unbiased and reliable evaluation of the outcomes obtained.	A senior/more experienced group of people within a research group.

Types of Variables in an Experiment

Other important terms that you need to be aware of when conducting experiments are related to the different types of variables involved in the process, these are described in the following table.

Type of Variable	Definition	Example
	The (also known as a ) is the variable that you in an experiment. It is the variable that you can control.It is independent because its value is not dependent on, or affected by, any other variable in the experiment.	In an experiment to investigate the effect of a new energy drink on race performance. The explanatory variable would be the amount of energy drink consumed by the participants.
	The (also known as ) are the different levels of a factor, or combinations of levels, in the case when two or more factors are considered, that an experimental unit is assigned.	In the same experiment, some possible treatments could be:
	The is the variable that you in an experiment. The dependent variable is sometimes referred to as the , as it responds to changes in the independent variable.	The dependent variable in the same experiment would be the race performance of the participants (i.e. time taken to run \(100\ m\)).
	A is a variable that is related to the explanatory variable, and it also affects the response variable, making it difficult to determine which one of the two is really causing the response.	Some possible confounding variables in this experiment could be the participants' age, diet, level of exercise, etc., which are related to the amount of energy drinks that a person consumes, and also to their performance in a race.

Table 2. Types of variables in experiments, conducting an experiment.

To help you visualize the concepts more clearly, Figure 1 illustrates the main difference between the explanatory variable and the response variable in an experiment.

Now, that you have a better idea of the terminology involved in conducting an experiment, you can define the meaning of conducting an experiment as follows:

Conducting an experiment is the process where researchers manipulate an explanatory variable to define treatments, which are randomly assigned to experimental units or subjects, to then compare the responses of the different groups to the treatments received.

In simple terms, an experiment is a process of testing a hypothesis with the aim of accepting or rejecting it. Researchers often develop hypotheses when conducting scientific experiments to guide experimental design.

A hypothesis is an assumption of what will happen as a result of an experiment.

Characteristics of a Well-Designed and Conducted Experiment

A well-designed and conducted experiment should have the following characteristics:

Characteristic	Description
	You will need to have to be able to compare their response. Those groups should comprise: (that receives or receives a ) to be used as a baseline, (that receive a treatment). This is to be able to compare the response of those groups receiving the treatments with the one of the control group, thus seeing more clearly the effect of the treatments on the experimental units or subjects.
	Experimental units or subjects are .
	A well-designed and conducted experiment should be able to be .
	Controlling events or conditions surrounding an experiment is very important for a well-designed experiment. An experiment with control is set to ensure that the response is the result of the independent variables in the study and not the result of any other source of variability.Control involves making sure that all participants in an experiment are treated equally, and that they are exposed to the . The only difference between groups in an experiment should be the treatment that they are receiving.

Table 3. Experiment characteristics, conducting an experiment.

A placebo is used in well-designed experiments to eliminate the p lacebo effect , which refers to the response that individuals can have to treatment just because they are expected to have one, even when they are not given any treatment at all. The placebo is a treatment that is meant to cause no effect on the subject. Its purpose is to make the conditions the same for all groups involved in an experiment and to be able to differentiate the response from just receiving treatment from the one related to the effectiveness of the treatment itself.

Read our explanation about Experiment Methods, to learn more about the different types of methods that you can use in a well-conducted experiment.

Conducting an Experiment Steps

The steps involved in conducting an experiment are as follows:

1. State the question that the experiment intends to answer.

This can be a question or a hypothesis. As mentioned before, a hypothesis is an assumption of what will happen as a result of an experiment. The words "If" and "Then" are often used in describing or writing hypotheses. For example, "If I do not expose my plants to sunlight, then they will die". The "If" and "Then" statements suggest the independent and dependent variables.

2. Specify the response variable.

What is the dependent variable that you will measure? and how will you measure the response?

3. Specify the factor levels/treatments.

Depending on the number of factors involved in the experiment, what levels of those factors will be used as treatments?

4. Define the experimental units.

Who or what will be receiving the treatments?

5. Make sure that the characteristics of a well-designed and constructed experiment are followed (comparison, random assignment, replication, and control).

6. Draw a diagram.

This will help you visualize the different components of the experiment.

7. Data collection.

Carry out the experiment and collect the data.

8. Analysis of results.

Analyze and compare the data obtained from the response of the different groups to their corresponding treatments, so that the initial question/hypothesis can be answered. This step might be carried out by the evaluators if required.

Example of Conducting an Experiment

Now that you are familiar with the terminology and the steps for conducting a well-designed experiment, let's look at an example.

Imagine the following hypothetical scenario: A group of researchers is carrying out an experiment to investigate if a new drug, called \(Drug\ A\), can provide faster pain relief for the treatment of headaches than other available drugs in the market. Follow the steps to conduct a well-designed experiment.

To solve this, you can go through steps \(1 - 6\) from the previous section. Steps \(7\) and \(8\) can be omitted as they require carrying out the experiment.

In this example, the question is: Does \(Drug\ A\) provide faster pain relief for the treatment of headaches than other available drugs in the market?

The response variable will be the time that it takes the participants to get effective pain relief from their treatment. This variable will be measured in minutes.

You need to set up a placebo treatment for the control group to be used as a baseline. In this case, you can use a "sugar pill" that looks as similar as possible to the other drugs being used in the experiment. This is to eliminate the placebo effect so that all participants feel part of the experiment.

The explanatory variable or factor in this example will be the type of pain relief drug given to the participants.

The treatments will be as follows:

Group \(1\) will get the placebo treatment of a "sugar pill".
Group \(2\) will get a dose of a regular pain relief drug.
Group \(3\) will get a dose of \(Drug\ A\).

The experimental units, in this case, subjects or participants, will be three groups of \(10\) people to be assigned the \(3\) treatments at random. The groups of people should be comprised of people of a similar range of ages, and gender, to avoid the effect of confounding variables on the response to the treatments.

Comparison - \(3\) groups are receiving different treatments to be able to compare their response.
Random Assignment - The treatments will be assigned to the different groups at random.
Replication - \(10\) participants in each group will receive treatment.
Control - The same conditions will be provided to all \(3\) groups so that their environment doesn't affect their response to the treatment.

Figure 2 shows the diagram illustrating the different components of the pain relief experiment, including participants, groups, treatments, and response variable.

One more example.

Going back to the scenario from the introduction of this article, let's design an experiment to investigate how to make a fluffier cake. To do this, you can try adding one more egg to the original recipe.

Does adding one more egg to a cake recipe result in a fluffier cake?

Consistency of the cake: is it fluffier?

In this case, the explanatory variable or factor that you will manipulate will be the number of eggs in the recipe of cake.

Group \(1\) will be the original recipe, to be used as a baseline.
Group \(2\) will the original recipe but add an additional egg.

The experimental unit in this case will be the cake batter.

5. Make sure that characteristics of a well-designed and constructed experiment are followed (comparison, random assignment, replication, and control).

Comparison - \(2\) groups of \(3\) cakes each will be baked using \(2\) different recipes to be able to compare their results.
Random Assignment - The same ingredients will be used for all cakes and distributed among each group, ensuring that they are all checked for the quality and quantities required.
Replication - \(3\) cakes will be baked in each group.
Control - Experimental conditions will be the same for all cakes being baked including the type of oven used, cooking time, and temperature so that no other confounding variable affects the results.

Figure 3 shows the diagram illustrating the different components of the cake recipe experiment, including experimental units, groups, treatments, and response variable.

Importance of Conducting an Experiment

Some of the reasons why conducting experiments are of vital importance are as follows:

Allows finding evidence about cause-effect relationships between two or more variables.
Explains change.
Answers questions previously unanswered.
Demonstrates or proves a hypothesis to be able to accept it or reject it.
Determines the effects of something previously unconfirmed.

Experiments are a powerful method of data collection that facilitates important research projects that benefit everything around us, finding the reasons why something might be happening, better ways to do things, and making new discoveries.

Experiments are carried out in many fields, including medicine, science, technology, and engineering, among many other areas. They are part of our daily lives, and they play an important role in learning about our environment. So, let's carry on experimenting!

Conducting an Experiment - Key takeaways

An experiment is a type of study carried out to investigate the cause-and-effect relationship between two or more variables.
The different roles involved in conducting an experiment are researchers, experimental units, and evaluators.
The different types of variables involved in an experiment are explanatory variable, treatments or experimental conditions, response variable, and confounding variable.
The characteristics of a well-designed and conducted experiment are comparison, random assignment, replication, and control.

Flashcards in Conducting an Experiment 29

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Frequently Asked Questions about Conducting an Experiment

What does it mean to conduct an experiment?

What is the step-by-step process of conducting an experiment?

The steps involved in conducting an experiment are:

1. State the question that the experiment intends to answer.

What is the purpose of conducting an experiment?

The purpose of conducting an experiment is to find evidence to decide whether that cause-and-effect relationship exists or not.

What are the things to consider when conducting an experimentation?

Make sure that characteristics of a well-designed and constructed experiment are followed (comparison, random assignment, replication, and control).

What are the dos and don'ts in conducting an experiment?

State a clear question of what the experiment intends to answer.
Define the explanatory and response variables.
Specify the treatments.
Eliminate the placebo effect.
Let participants choose their own treatment.
Allow confounding variables to affect the response in an experiment.
Set up different conditions for each group.
Set up groups with different characteristics and size.
Use only one experimental group.
Have no control group with a placebo treatment or no treatment at all.

Test your knowledge with multiple choice flashcards

If the experimental units are people, they are called ________ or ________.

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How the Experimental Method Works in Psychology

sturti/Getty Images

The Experimental Process

Types of experiments, potential pitfalls of the experimental method.

The experimental method is a type of research procedure that involves manipulating variables to determine if there is a cause-and-effect relationship. The results obtained through the experimental method are useful but do not prove with 100% certainty that a singular cause always creates a specific effect. Instead, they show the probability that a cause will or will not lead to a particular effect.

At a Glance

While there are many different research techniques available, the experimental method allows researchers to look at cause-and-effect relationships. Using the experimental method, researchers randomly assign participants to a control or experimental group and manipulate levels of an independent variable. If changes in the independent variable lead to changes in the dependent variable, it indicates there is likely a causal relationship between them.

What Is the Experimental Method in Psychology?

The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis.

For example, researchers may want to learn how different visual patterns may impact our perception. Or they might wonder whether certain actions can improve memory . Experiments are conducted on many behavioral topics, including:

The scientific method forms the basis of the experimental method. This is a process used to determine the relationship between two variables—in this case, to explain human behavior .

Positivism is also important in the experimental method. It refers to factual knowledge that is obtained through observation, which is considered to be trustworthy.

When using the experimental method, researchers first identify and define key variables. Then they formulate a hypothesis, manipulate the variables, and collect data on the results. Unrelated or irrelevant variables are carefully controlled to minimize the potential impact on the experiment outcome.

History of the Experimental Method

The idea of using experiments to better understand human psychology began toward the end of the nineteenth century. Wilhelm Wundt established the first formal laboratory in 1879.

Wundt is often called the father of experimental psychology. He believed that experiments could help explain how psychology works, and used this approach to study consciousness .

Wundt coined the term "physiological psychology." This is a hybrid of physiology and psychology, or how the body affects the brain.

Other early contributors to the development and evolution of experimental psychology as we know it today include:

Gustav Fechner (1801-1887), who helped develop procedures for measuring sensations according to the size of the stimulus
Hermann von Helmholtz (1821-1894), who analyzed philosophical assumptions through research in an attempt to arrive at scientific conclusions
Franz Brentano (1838-1917), who called for a combination of first-person and third-person research methods when studying psychology
Georg Elias Müller (1850-1934), who performed an early experiment on attitude which involved the sensory discrimination of weights and revealed how anticipation can affect this discrimination

Key Terms to Know

To understand how the experimental method works, it is important to know some key terms.

Dependent Variable

The dependent variable is the effect that the experimenter is measuring. If a researcher was investigating how sleep influences test scores, for example, the test scores would be the dependent variable.

Independent Variable

The independent variable is the variable that the experimenter manipulates. In the previous example, the amount of sleep an individual gets would be the independent variable.

A hypothesis is a tentative statement or a guess about the possible relationship between two or more variables. In looking at how sleep influences test scores, the researcher might hypothesize that people who get more sleep will perform better on a math test the following day. The purpose of the experiment, then, is to either support or reject this hypothesis.

Operational definitions are necessary when performing an experiment. When we say that something is an independent or dependent variable, we must have a very clear and specific definition of the meaning and scope of that variable.

Extraneous Variables

Extraneous variables are other variables that may also affect the outcome of an experiment. Types of extraneous variables include participant variables, situational variables, demand characteristics, and experimenter effects. In some cases, researchers can take steps to control for extraneous variables.

Demand Characteristics

Demand characteristics are subtle hints that indicate what an experimenter is hoping to find in a psychology experiment. This can sometimes cause participants to alter their behavior, which can affect the results of the experiment.

Intervening Variables

Intervening variables are factors that can affect the relationship between two other variables.

Confounding Variables

Confounding variables are variables that can affect the dependent variable, but that experimenters cannot control for. Confounding variables can make it difficult to determine if the effect was due to changes in the independent variable or if the confounding variable may have played a role.

Psychologists, like other scientists, use the scientific method when conducting an experiment. The scientific method is a set of procedures and principles that guide how scientists develop research questions, collect data, and come to conclusions.

The five basic steps of the experimental process are:

Identifying a problem to study
Devising the research protocol
Conducting the experiment
Analyzing the data collected
Sharing the findings (usually in writing or via presentation)

Most psychology students are expected to use the experimental method at some point in their academic careers. Learning how to conduct an experiment is important to understanding how psychologists prove and disprove theories in this field.

There are a few different types of experiments that researchers might use when studying psychology. Each has pros and cons depending on the participants being studied, the hypothesis, and the resources available to conduct the research.

Lab Experiments

Lab experiments are common in psychology because they allow experimenters more control over the variables. These experiments can also be easier for other researchers to replicate. The drawback of this research type is that what takes place in a lab is not always what takes place in the real world.

Field Experiments

Sometimes researchers opt to conduct their experiments in the field. For example, a social psychologist interested in researching prosocial behavior might have a person pretend to faint and observe how long it takes onlookers to respond.

This type of experiment can be a great way to see behavioral responses in realistic settings. But it is more difficult for researchers to control the many variables existing in these settings that could potentially influence the experiment's results.

Quasi-Experiments

While lab experiments are known as true experiments, researchers can also utilize a quasi-experiment. Quasi-experiments are often referred to as natural experiments because the researchers do not have true control over the independent variable.

A researcher looking at personality differences and birth order, for example, is not able to manipulate the independent variable in the situation (personality traits). Participants also cannot be randomly assigned because they naturally fall into pre-existing groups based on their birth order.

So why would a researcher use a quasi-experiment? This is a good choice in situations where scientists are interested in studying phenomena in natural, real-world settings. It's also beneficial if there are limits on research funds or time.

Field experiments can be either quasi-experiments or true experiments.

Examples of the Experimental Method in Use

The experimental method can provide insight into human thoughts and behaviors, Researchers use experiments to study many aspects of psychology.

A 2019 study investigated whether splitting attention between electronic devices and classroom lectures had an effect on college students' learning abilities. It found that dividing attention between these two mediums did not affect lecture comprehension. However, it did impact long-term retention of the lecture information, which affected students' exam performance.

An experiment used participants' eye movements and electroencephalogram (EEG) data to better understand cognitive processing differences between experts and novices. It found that experts had higher power in their theta brain waves than novices, suggesting that they also had a higher cognitive load.

A study looked at whether chatting online with a computer via a chatbot changed the positive effects of emotional disclosure often received when talking with an actual human. It found that the effects were the same in both cases.

One experimental study evaluated whether exercise timing impacts information recall. It found that engaging in exercise prior to performing a memory task helped improve participants' short-term memory abilities.

Sometimes researchers use the experimental method to get a bigger-picture view of psychological behaviors and impacts. For example, one 2018 study examined several lab experiments to learn more about the impact of various environmental factors on building occupant perceptions.

A 2020 study set out to determine the role that sensation-seeking plays in political violence. This research found that sensation-seeking individuals have a higher propensity for engaging in political violence. It also found that providing access to a more peaceful, yet still exciting political group helps reduce this effect.

While the experimental method can be a valuable tool for learning more about psychology and its impacts, it also comes with a few pitfalls.

Experiments may produce artificial results, which are difficult to apply to real-world situations. Similarly, researcher bias can impact the data collected. Results may not be able to be reproduced, meaning the results have low reliability .

Since humans are unpredictable and their behavior can be subjective, it can be hard to measure responses in an experiment. In addition, political pressure may alter the results. The subjects may not be a good representation of the population, or groups used may not be comparable.

And finally, since researchers are human too, results may be degraded due to human error.

What This Means For You

Every psychological research method has its pros and cons. The experimental method can help establish cause and effect, and it's also beneficial when research funds are limited or time is of the essence.

At the same time, it's essential to be aware of this method's pitfalls, such as how biases can affect the results or the potential for low reliability. Keeping these in mind can help you review and assess research studies more accurately, giving you a better idea of whether the results can be trusted or have limitations.

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Schweizer M, Braun B, Milstone A. Research methods in healthcare epidemiology and antimicrobial stewardship — quasi-experimental designs . Infect Control Hosp Epidemiol . 2016;37(10):1135-1140. doi:10.1017/ice.2016.117

Glass A, Kang M. Dividing attention in the classroom reduces exam performance . Educ Psychol . 2019;39(3):395-408. doi:10.1080/01443410.2018.1489046

Keskin M, Ooms K, Dogru AO, De Maeyer P. Exploring the cognitive load of expert and novice map users using EEG and eye tracking . ISPRS Int J Geo-Inf . 2020;9(7):429. doi:10.3390.ijgi9070429

Ho A, Hancock J, Miner A. Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot . J Commun . 2018;68(4):712-733. doi:10.1093/joc/jqy026

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Torresin S, Pernigotto G, Cappelletti F, Gasparella A. Combined effects of environmental factors on human perception and objective performance: A review of experimental laboratory works . Indoor Air . 2018;28(4):525-538. doi:10.1111/ina.12457

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Psychological Research

Experiments, learning objectives.

Describe the experimental process, including ways to control for bias
Identify and differentiate between independent and dependent variables

Causality: Conducting Experiments and Using the Data

Experimental hypothesis.

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that the use of technology in the classroom has negative impacts on learning, then you have basically formulated a hypothesis—namely, that the use of technology in the classroom should be limited because it decreases learning. How might you have arrived at this particular hypothesis? You may have noticed that your classmates who take notes on their laptops perform at lower levels on class exams than those who take notes by hand, or those who receive a lesson via a computer program versus via an in-person teacher have different levels of performance when tested (Figure 1).

Many rows of students are in a classroom. One student has an open laptop on his desk.

Figure 1 . How might the use of technology in the classroom impact learning? (credit: modification of work by Nikolay Georgiev/Pixabay)

Designing an Experiment

In our example of how the use of technology should be limited in the classroom, we have the experimental group learn algebra using a computer program and then test their learning. We measure the learning in our control group after they are taught algebra by a teacher in a traditional classroom. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation.

We also need to precisely define, or operationalize, how we measure learning of algebra. An operational definition is a precise description of our variables, and it is important in allowing others to understand exactly how and what a researcher measures in a particular experiment. In operationalizing learning, we might choose to look at performance on a test covering the material on which the individuals were taught by the teacher or the computer program. We might also ask our participants to summarize the information that was just presented in some way. Whatever we determine, it is important that we operationalize learning in such a way that anyone who hears about our study for the first time knows exactly what we mean by learning. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered use of technology and what is considered learning in our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants spend 45 minutes learning algebra (either through a computer program or with an in-person math teacher) and then give them a test on the material covered during the 45 minutes.

Ideally, the people who score the tests are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how they interpret ambiguous responses, such as sloppy handwriting or minor computational mistakes. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study, meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

Figure 2 . Providing the control group with a placebo treatment protects against bias caused by expectancy. (credit: Elaine and Arthur Shapiro)

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how technology use in the classroom affects learning, the independent variable is the type of learning by participants in the study (Figure 2.17). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the learning exhibited by our participants.

A box labeled “independent variable: taking notes on a laptop or by hand” contains a photograph of a classroom of students with an open laptop on one student's desk. An arrow labeled “influences change in the…” leads to a second box. The second box is labeled “dependent variable: performance on measure of learning” and has a photograph of a student at a desk, taking a test.

Figure 3 . In an experiment, manipulations of the independent variable are expected to result in changes in the dependent variable. (credit: “classroom” modification of work by Nikolay Georgiev/Pixabay; credit “note taking”: modification of work by KF/Wikimedia)

Selecting and Assigning Experimental Participants

Figure 4 . Researchers may work with (a) a large population or (b) a sample group that is a subset of the larger population. (credit “crowd”: modification of work by James Cridland; credit “students”: modification of work by Laurie Sullivan)

Issues to Consider

Research Design Review

Learn more about research design and methods in the following interactive.

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Knowledge Base

Methodology

What Is a Controlled Experiment? | Definitions & Examples

What Is a Controlled Experiment? | Definitions & Examples

Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

holding variables at a constant or restricted level (e.g., keeping room temperature fixed).
measuring variables to statistically control for them in your analyses.
balancing variables across your experiment through randomization (e.g., using a random order of tasks).

Why does control matter in experiments, methods of control, problems with controlled experiments, other interesting articles, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables. Strong validity also helps you avoid research biases , particularly ones related to issues with generalizability (like sampling bias and selection bias .)

Your independent variable is the color used in advertising.
Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

Design and description of the meal,
Study environment (e.g., temperature or lighting),
Participant’s frequency of buying fast food,
Participant’s familiarity with the specific fast food brand,
Participant’s socioeconomic status.

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You can control some variables by standardizing your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., ad color) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with color blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect ), and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

To test the effect of colors in advertising, each participant is placed in one of two groups:

A control group that’s presented with red advertisements for a fast food meal.
An experimental group that’s presented with green advertisements for the same fast food meal.

Random assignment

To avoid systematic differences and selection bias between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a “true experiment”—it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers—or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs and is critical for avoiding several types of research bias .

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses , leading to observer bias . In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics . If participants behave a particular way due to awareness of being observed (called a Hawthorne effect ), your results could be invalidated.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity —the extent to which your results can be generalized to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritize control or generalizability in your experiment.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

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In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

A control group that receives a standard treatment, a fake treatment, or no treatment.
Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

A testable hypothesis
At least one independent variable that can be precisely manipulated
At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

How you will manipulate the variable(s)
How you will control for any potential confounding variables
How many subjects or samples will be included in the study
How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

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Conducting an Experiment
Experience and practice mean that many scientists follow an instinctive process of conducting an experiment, the 'streamlined' scientific process. Following the basic steps will usually generate valid results, but where experiments are complex and expensive, it is always advisable to follow the rigorous scientific protocols.
How to Conduct a Science Experiment: 13 Steps (with Pictures)
4. Conduct the experiment. We will use each aerosol spray to fire ten projectiles, using the same amount of aerosol spray to fire each projectile. After each firing, we will use a long tape measure to measure the range our projectile traveled. Record this data in the data table.
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Table of contents. 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. Other interesting articles. Frequently asked questions about experiments.
Steps of the Scientific Method
The six steps of the scientific method include: 1) asking a question about something you observe, 2) doing background research to learn what is already known about the topic, 3) constructing a hypothesis, 4) experimenting to test the hypothesis, 5) analyzing the data from the experiment and drawing conclusions, and 6) communicating the results ...
Experiment Definition in Science
Experiment Definition in Science. By definition, an experiment is a procedure that tests a hypothesis. A hypothesis, in turn, is a prediction of cause and effect or the predicted outcome of changing one factor of a situation. Both the hypothesis and experiment are components of the scientific method. The steps of the scientific method are:
1.3
The practical steps needed for planning and conducting an experiment include: recognizing the goal of the experiment, choice of factors, choice of response, choice of the design, analysis and then drawing conclusions. This pretty much covers the steps involved in the scientific method. Recognition and statement of the problem.
Conducting Experiments
Conducting Experiments. Learning Objectives. Describe several strategies for recruiting participants for an experiment. Explain why it is important to standardize the procedure of an experiment and several ways to do this. Explain what pilot testing is and why it is important. The information presented so far in this chapter is enough to design ...
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Field experiments require well-defined participant selection procedures. It is important to standardize experimental procedures to minimize extraneous variables, including experimenter expectancy effects. It is important to conduct one or more small-scale pilot tests of an experiment to be sure that the procedure works as planned.
Conduct experiments
Conduct experiments. An experiment is a deliberate attempt to manipulate a situation, in order to test a hypothesis that a particular cause creates a particular effect, in other words that varying the input will affect the output. A procedure adopted on the chance of its succeeding, for testing a hypothesis etc., or to demonstrate a known fact.
Conducting Experiments
Control Group: A group in an experiment that does not receive the experimental treatment, used as a baseline for comparison. Independent Variable: The variable that is manipulated or changed by the researcher in an experiment. Dependent Variable: The variable that is measured or observed by the researcher to determine if it changes as a result ...
Conducting an Experiment in Psychology
When conducting an experiment, it is important to follow the seven basic steps of the scientific method: Ask a testable question. Define your variables. Conduct background research. Design your experiment. Perform the experiment. Collect and analyze the data. Draw conclusions.
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Experiment has a different meaning in the scientific context than in everyday life. In everyday conversation, we often use it to describe trying something for the first time, such as experimenting with a new hairstyle or new food. However, in the scientific context, an experiment has precise requirements for design and implementation.
Scientific Method: Definition and Examples
Regina Bailey. Updated on August 16, 2024. The scientific method is a series of steps that scientific investigators follow to answer specific questions about the natural world. Scientists use the scientific method to make observations, formulate hypotheses, and conduct scientific experiments . A scientific inquiry starts with an observation.
Conducting the Experiment for Your Science Fair Project
It is very important to take very detailed notes as you conduct your experiments. In addition to your data, record your observations as you perform the experiment. Write down any problems that occur, anything you do that is different than planned, ideas that come to mind, or interesting occurrences. Be on the lookout for the unexpected.
Experiments
Causality: Conducting Experiments and Using the Data As you've learned, the only way to establish that there is a cause-and-effect relationship between two variables is to conduct a scientific experiment. Experiment has a different meaning in the scientific context than in everyday life. In everyday conversation, we often use it to describe ...
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2 meanings: the manner in which a person behaves; behaviour [...].... Click for more definitions.
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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.
Conducting an Experiment: Meaning & Steps
Conducting an Experiment Steps. The steps involved in conducting an experiment are as follows: 1. State the question that the experiment intends to answer. This can be a question or a hypothesis. As mentioned before, a hypothesis is an assumption of what will happen as a result of an experiment.
CONDUCT AN EXPERIMENT definition in American English
experiment. (ɪksperɪmənt ) (ɪksperɪment ) variable noun B1. An experiment is a scientific test which is done in order to discover what happens to something in particular conditions. [...] See full entry for 'experiment'. Collins COBUILD Advanced Learner's Dictionary.
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The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis. For example, researchers may want to learn how different visual patterns may impact our perception.
Khan Academy
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Experiments
Causality: Conducting Experiments and Using the Data As you've learned, the only way to establish that there is a cause-and-effect relationship between two variables is to conduct a scientific experiment. Experiment has a different meaning in the scientific context than in everyday life. In everyday conversation, we often use it to describe ...
What Is a Controlled Experiment?
Revised on June 22, 2023. In experiments, researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment, all variables other than the independent variable are controlled or held constant so they don't influence the dependent variable. Controlling variables can involve:

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