Experiments dependent variable always happens second.The independent variable

Experiments are the classic way to conduct research in almost any field of study.

But do you know how true experiments really work? This lesson explains the details of experimental design, such as different types of samples, control groups and independent vs. dependent variables.

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Introduction

Scientists in almost every field of study use experiments to answer research questions. Imagine you are a psychologist, and you want to investigate whether caffeine has an effect on student behaviors and performance in the classroom. How would you go about finding out the answer to this question? The answer is that you would do an experiment. This lesson covers all of the different aspects of an experiment you would want to consider.

Independent and Dependent Variables

The first thing any experimenter needs to decide is what variables you are studying. Let’s imagine your hypothesis is that when students in school consume caffeine, their performance on tests is affected. You might hypothesize that caffeine increases test performance because it causes the students to be less sleepy and more focused, or you might hypothesize that caffeine decreases test performance because it makes the students jumpy and hyper. Either way, you have two variables involved in this study.The independent variable in an experiment is the variable that you control as the experimenter and the one that creates two or more groups in the study.

In order to study caffeine, you might give half of the students a caffeinated drink and the other half of the students simply get water. The difference between the two groups is whether they have caffeine or not. So, the independent variable is the variable that you, as the experimenter, have manipulated.The dependent variable in an experiment is the outcome variable or the one you are simply measuring. Here, you guessed that caffeine might affect test performance. So, in this example, your dependent variable is test performance.

Another way to think about independent variables and dependent variables is in terms of cause and effect. This study is testing whether caffeine (the cause) has an effect on test performance. All experiments are testing if whatever makes the groups different has an effect on some outcome variable. The independent variable is always the cause. Here, that’s the caffeine.

The dependent variable is always the effect. Here, that’s test performance. So, the independent variable always happens first, and the dependent variable always happens second.

The independent variable is the cause and the dependent is the effect.
Independent Dependent Variables Chart

Experimental vs. Control Groups

Now, let’s talk about why we need more than one group in an experiment.

Imagine you went into a classroom, gave every student caffeine and then tested them on some kind of performance measure, such as the number of times they can jump a rope. You can see how these students performed after having caffeine. But how can you know if their performance was increased or decreased compared to what they would have done without caffeine? With only one group in your study, you can’t be sure what the effects of caffeine were.So, in an experiment we always need at least two groups to compare. Let’s go back to the example of giving half of the students caffeine and half of the students water to drink.

When we’re testing for the effect of the independent variable, we want to make sure that one of the groups in our study can serve as the natural, or baseline, group. That natural or baseline group is called a control group. In our example, the control group would be all of the children who only drank water.We then compare the control group to the group of children who received caffeine. In an experiment, the group that receives some kind of change to their natural environment is called the experimental group.

In our example, the experimental group would be all of the children who drank caffeine.

When testing the effects of drugs, control groups are necessary.
representative sample.

Choosing randomly from the population gives you a better chance at a representative sample.
stratified random sample. Here you identify important variables in advance, divide up the population based on this variable, then randomly choose an equal number of people from each group for the sample. For instance, you might think that caffeine affects boys differently from girls, and you want to compare. So, you would want to make sure you randomly choose an equal number of boys and girls. When you identify variables like this in advance that you want to make sure are covered by your sample, you used a stratified random sample.

It’s partially random but also partially structured.No matter what kind of sample you use, remember that the main concern is that it is representative of the larger population of interest.

Lesson Summary

In summary, in an experiment, the variable you control that creates groups within your sample is called the independent variable. Experimenters hope that differences in the independent variable cause changes in the outcome variable, which is called the dependent variable.

The baseline group that doesn’t receive any change from their normal environment is called a control group, whereas the group that receives some change is called the experimental group. You want to make sure that you decide which participant goes into which group using random assignment so that the groups are as identical as possible. There are many different ways you can choose a sample from the larger population. In order to make sure your sample is representative of the population in terms of important variables, the best way to choose people is by using a random sample.

These issues can be complicated, but if you do them correctly, experiments can be a very useful way to answer questions from psychology. If you consider all of the issues from this lesson, you can create a true experiment.

Lesson Objectives

After watching this lesson, you should be able to:

  • Define random assignment, population and sample, and explain their importance to experimental design
  • Identify and describe the different kinds of samples
  • Explain the difference between the independent and dependent variable, as well as between the control and experimental group
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