What variable is manipulated by the experimenter?

Any factor that can take on different values in an experiment is a scientific variable.

For example, in an experiment investigating the effectiveness of a new training program, the variables might be:

• Final test scores
• Student age
• Time spent on the training program
• Time to complete final test
• Student gender
• Student ratings of the training program 

Depending on how the researcher operationalizes all the variables in an experiment, the above could be either dependent or independent variables.

It’s the research design that decides which variables are manipulated and which are measured as a result of that manipulation.

What is the Independent Variable?

The independent variable is "independent" because its variation does not depend on the variation of another variable in the experiment/research project. The independent variable is controlled or changed only by the researcher. This factor is often the research question/hypothesis behind the outcome of the experiment.

In the above example, the researcher may have wanted to see if participating in the training program raised students' scores on a final test.

Mini-quiz 1

Can you identify the independent variable in this experiment?

1. Score on the test
2. Time spent on the training program
3. Participation on the training program

What do you think is correct? The answer is at the bottom of the article.

How Many Independent Variables Do You Test?

There are often not more than one or two independent variables tested in an experiment, otherwise it is difficult to determine the influence of each upon the final results. There may be several dependent variables, because manipulating the independent variable can influence many different things.

For example, an experiment to test the effects of a certain fertilizer on plant growth could measure height, number of fruits and the average weight of the fruit produced. All of these are valid analyzable factors arising from the manipulation of one independent variable, the amount of fertilizer.

Potential Complexities of the Independent Variable

The term independent variable is often a source of confusion; many people assume that the name means that the variable is independent of any manipulation. The name arises because the variable is isolated from any other factor, allowing experimental manipulation to establish analyzable results.

A useful acronym is DRY-MIX. This helps you remember which axis to plot your data should you need to draw a graph:

• D - Dependent
• R - Responding
• Y - Y-axis

• M - Manipulated
• I - Independent
• X - X-axis

Some research papers appear to give results manipulating more than one experimental variable, but this is usually a false impression.

Each manipulated variable is likely to be an experiment in itself, one area where the words 'experiment' and 'research' differ. It is simply more convenient for the researcher to bundle them into one paper, and discuss the overall results.

The researcher above might also study the effects of temperature, or the amount of water on growth, but these must be performed as discrete experiments, with only the conclusion and discussion amalgamated at the end.

Examples of the Independent Variable

Jane Elliott's Anti-Racism Experiment

Third grade teacher Jane Elliott’s famous experiment involved dividing her class into two groups: blue-eyed and brown-eyed children. She gave the blue-eyed children extra privileges and emphasized how superior they were to the brown-eyed, who were now a “minority group.”

As a result, the brown-eyed children saw a drop in confidence, academic performance and an increase in bullying. However, when she later labelled the blue-eyed group as the inferior, these effects were reversed.

Here, the independent variable was group status, i.e. whether the children where in the privileged group or not. This had various observable effects on the children. Importantly, the eye color of the children was not the independent variable here. Eye color was an arbitrary choice made by the teacher to draw parallels to racism and prejudice.

Mini-quiz 2

Can you identify a possible dependent variable in this experiment? 

1. Level of bullying
2. Academic performance 
3. Confidence level
4. All of the above

What do you think is correct? The answer is at the bottom of the article.

Bandura Bobo Doll Experiment

In the Bandura Bobo Doll experiment, whether the children were exposed to an aggressive adult, or to a passive adult, was the independent variable.

This experiment is a prime example of how the concept of experimental variables can become a little complex. Bandura also studied the differences between boys and girls, with gender as an independent variable. Surely, this is breaking the rules of only having one manipulated variable!

In fact, this is a prime example of performing multiple experiments at the same time. If you study the structure of the research design, you will see that the Bobo Doll Experiment should have been called the Bobo Doll Experiments.

It was actually four experiments, each with their own hypothesis and variables, running concurrently. It would have been expensive, and possibly unethical, to test the children four times and, if the same children were used each time, their behavior may have changed with repetition.

Careful design allowed Bandura to test different hypotheses as part of the same research.

Mini-quiz 3

Can you identify the separate independent variables in this experiment? Pick two.

1. Presence or absence of Bobo doll
2. Gender of the role models
3. Aggressiveness of the role models
4. Number of children

The answer is at the bottom of the article.

Mini-quiz Answers:

Mini-quiz 1

Can you identify the independent variable in this experiment?

Option 3. Participation on the training program.

The researcher could manipulate the variable of whether students participated on the program or not, then measure the results, for example their score on a final test.

Mini-quiz 2

Can you identify a possible dependent variable in this experiment?

Option 4. All of the above.

The experiment measured the children's overall behavior. But this could have been broken into separate dependent variables, for example academic performance, level of bullying, or confidence levels. 

Mini-quiz 3

Can you identify the separate independent variables in this experiment? Pick two.

Option 2 and 3. The gender of the role models and the aggressiveness of the role models.

Bandura was interested to see if a child would imitate their role model, but he also wanted to see if a child was more likely to imitate them if they were of the same gender.

Learning Objectives

1. Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
2. Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
3. Recognize examples of confounding variables and explain how they affect the internal validity of a study.

As we saw earlier in the book, an experiment is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. In other words, whether changes in an independent variable cause a change in a dependent variable. Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions. For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. For a new researcher, it is easy to confuse these terms by believing there are three independent variables in this situation: one, two, or five students involved in the discussion, but there is actually only one independent variable (number of witnesses) with three different levels or conditions (one, two or five students). The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables. Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words manipulation and control have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate the independent variable by systematically changing its levels and control other variables by holding them constant.

Manipulation of the Independent Variable

Again, to manipulate an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. As discussed earlier in this chapter, the different levels of the independent variable are referred to as conditions, and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore has not conducted an experiment. This distinction is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating potential alternative explanations for the results.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this type of methodology in detail later in the book.

Independent variables can be manipulated to create two conditions and experiments involving a single independent variable with two conditions is often referred to as a single factor two-level design. However, sometimes greater insights can be gained by adding more conditions to an experiment. When an experiment has one independent variable that is manipulated to produce more than two conditions it is referred to as a single factor multi level design. So rather than comparing a condition in which there was one witness to a condition in which there were five witnesses (which would represent a single-factor two-level design), Darley and Latané’s used a single factor multi-level design, by manipulating the independent variable to produce three conditions (a one witness, a two witnesses, and a five witnesses condition).

Control of Extraneous Variables

As we have seen previously in the chapter, an extraneous variable is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their gender. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to control extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of Table 5.1 show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of Table 5.1. Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in Table 5.1, which makes the effect of the independent variable easier to detect (although real data never look quite that good).

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger heterosexual women would apply to older homosexual men. In many situations, the advantages of a diverse sample (increased external validity) outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable is an extraneous variable that differs on average across levels of the independent variable (i.e., it is an extraneous variable that varies systematically with the independent variable). For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs in each condition so that the average IQ is roughly equal across the conditions, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants in one condition to have substantially lower IQs on average and participants in another condition to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse, and this effect is exactly why confounding variables are undesirable. Because they differ systematically across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable. Figure 5.1 shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 5.1 Hypothetical Results From a Study on the Effect of Mood on Memory. Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• An extraneous variable is any variable other than the independent and dependent variables. A confound is an extraneous variable that varies systematically with the independent variable.

Exercises

1. Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.
2. Practice: For each of the following topics, decide whether that topic could be studied using an experimental research design and explain why or why not.
   1. Effect of parietal lobe damage on people’s ability to do basic arithmetic.
   2. Effect of being clinically depressed on the number of close friendships people have.
   3. Effect of group training on the social skills of teenagers with Asperger’s syndrome.
   4. Effect of paying people to take an IQ test on their performance on that test.