Any variable that you are not intentionally studying in your dissertation is an extraneous variable that could threaten the internal validity of your results [see the article: Internal validity]. In research that draws on a quantitative research design, especially experimental research designs (also called intervention studies), we try and control these extraneous variables so that they do not become confounding variables [see the section on Research Designs, to learn more]. When an extraneous variable changes systematically along with the variables that you are studying, this is called a confounding variable. A variable is considered to be confounding because it provides an alternative explanation for your results; that is, an alternative explanation for the relationship or differences between the variables and/or groups that you are measuring. This threatens the internal validity of your results. In this article, we (a) explain what extraneous variables are, providing some examples, (b) highlight when extraneous variables become confounding variables, as well as how extraneous and confounding variables can be a threat to internal validity; and (c) explain how to deal with extraneous variables in your research.
At the undergraduate and master's dissertation level, you will often focus on just two variables: an independent and a dependent variable; or sometimes, a second or third independent and/or dependent variable [see the article: Types of variables]. Only in a minority of cases are you likely to examine a large number of variables at once. However, just because you are only focusing on a small number of variables, this does not mean that these are the only variables that relate to the research you are performing. In this respect, an extraneous variable refers to any variables that you are not intentionally studying (or cannot study, perhaps because of reasons of cost or difficulty). Rather than there being just a few of these extraneous variables, there are likely to be hundreds or even thousands. In other words, it is impossible to avoid extraneous variables.
Before we explain the relationship between extraneous variables and confounding variables, let's look at some examples of extraneous variables:
Study #1
The relationship between background music and task performance amongst employees at a packing facility
The study aims to examine the relationship between background music and task performance amongst employees at a packing facility (e.g., Amazon, Wal-Mart, Tesco, etc.). In these packing facilities, the job of employees is to collect items ordered by customers from the warehouse, package them, stick on a label with the customer's address, and put the package on the delivery line. Each time an employee does this, they complete one task.
The purpose of the study is to find out what effect background music might have on employees? task performance; that is, how many packages (i.e., tasks) they process in a given hour. This is important to firms because if they find that background music has a positive effect on task performance; that is, if background music increased the number of packages processed in a given hour, they may want to rollout a programme of background music in all of their packing facilities.
Intentional variables
The intentional variables in this study are the variables that the researcher wants to examine. These include one independent variable and one dependent variable. See below:
Independent variable:
Background music (a nominal variable because employees are either provided with or without background music)
Dependent variable:
Task performance (a continuous variable, measured in terms of the number of tasks employees perform correctly per hour)
The independent variable, background noise, consists of a control and a treatment. The control refers to the normal conditions experienced by employees in the packing facility, which in this case, means that employees are not being provided with background music (i.e., employees without background music). The treatment is the intervention that we are making to compare the addition of background music with the normal conditions (i.e., with the control) in the packing facility. In other words, the treatment is providing the employees with background music. It is this independent variable (i.e., background music) that we are manipulating to examine its effect on the dependent variable (i.e., task performance). We use the word manipulating because we are taking the independent variable and changing it (i.e., with or without background music) for different groups (i.e., the control group and treatment group).
So in order to conduct this experiment, we take a sample of employees at the packing facility (e.g., a sample of 100 employees from the total of 400 employees that work there, which is known as the population). We then randomly assign half of these sample employees (i.e., 50 employees) to the control group and the other half (i.e., 50 employees) to the treatment group. At a given day and time, we start the experiment; so the control group continue with their normal day without any music, whilst the treatment group gets to listen to music. The experiment continues for an 8 hour shift. For each of these 8 hours, we record the number of tasks each employee performs correctly, both for the control group and the treatment group. This task performance is our dependent variable (also known as an outcome variable).
Under normal circumstances, we would then statistically analyse our results by comparing the scores on the dependent variable (i.e., the number of correctly performed tasks per hour) between the two groups (i.e., the control group and the treatment group). This should show us whether there are any differences in the number of tasks performed between the control group and treatment group. This would, in theory, tell us about the relationship between background music and task performance amongst the employees at the packing facility. It should tell us whether there is no relationship, a positive relationship or a negative relationship, as well as providing us with what are known as gain scores. In other words, we would know: (a) if background music is related to task performance; (b) if it is, whether background music increased or decreased task performance; and (c) by how much background music increased or decreased task performance.
Extraneous variables
The extraneous variables in this study are those variables that could also be measured, which may also affect the results. We distinguish between those extraneous variables that could act as independent variables and those that could influence the dependent variable. Whilst there are many such extraneous variables, we have given some examples below:
Independent variables:
▪Type of background music (e.g., chart music, dance/electronic music, easy listening, classical music, etc.)
▪Loudness of background music (e.g., low, medium, high volumes, etc.)
▪Time of day when the background music was played (e.g., morning, afternoon, night, etc.)
In other words, how might the task performance scores (i.e., the dependent variable) have differed if alternative independent variables had been manipulated (e.g., independent variables such as the type of background music being different; or changing the loudness of background music; or perhaps the time of day when the background music was played)?
Extraneous variables that could also affect the dependent variable:
▪Employee tiredness
▪Employee motivation
▪Job satisfaction
There are other extraneous variables that we are not manipulating, such as employee tiredness, employee motivation and job satisfaction. Whilst we are not manipulating these extraneous variables, they could still affect the task performance scores (i.e., the dependent variable) of the two groups (i.e., the control group and treatment group), so we have put them under the heading of extraneous variables that could also affect the dependent variable above. We are not trying to say that these are dependent variables; only that they are extraneous variables that could affect the dependent variable, which we should take into account. For example, the tiredness of employees could affect their task performance scores on the day of the experiment, as could their level of motivation or job satisfaction. These are not things that we are trying to manipulate (i.e., they are not independent variables in this study), but they could affect our results.
Other extraneous variables may relate to individual differences (e.g., existing employee task performance, employee age and gender, etc.), the environment in which the study is conducted (e.g., the climate inside the packing facility, especially if the facility is not air conditioned/heated; the weather outside, which could affect employee mood, etc.), as well as factors relating to the independent variable (e.g., type of music, loudness of music, time of day), and the dependent variable (e.g., employee tiredness - number of shifts - employee motivation, job satisfaction, etc.), as we have discussed.
Study #2:
The impact of learning format/teaching style (lectures/seminars) on exam performance
Intentional variables
Independent variable: learning format/teaching style (either lectures or seminars)
Dependent variable: exam performance (statistics exam ranging from 0-100 marks)
Extraneous variables
Independent variable: quality of lecturer vs. seminars; teacher
Dependent variable: student tiredness
We may want to examine how two different teaching styles in the classroom (i.e., the teaching style is the independent variable) affect the maths scores of students (i.e., the maths scores are the dependent variable).
This leads us to a discussion of when extraneous variables become confounding variables, where they offer an alternative explanation for changes in scores on the dependent variable, reducing the internal validity of your results.