Working with Variables in Research, Explained

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Working with Variables in Research

Research Methods in Psychology · Chapter 3: Research Design and Methodological PlanningSection 3.4 · lesson 19 of 9410 min · free with a free account

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In this lesson

Defining, measuring, and controlling the building blocks of psychological studies.

  • Distinguish independent, dependent, extraneous and control variables
  • Write and evaluate an operational definition
  • Identify the four levels of measurement
  • Control extraneous variables by design

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Everything said in the video, section by section (1,481 words). Read along, or search it.

Research Design and Methodological Planning

Working with Variables

Every study you will ever read is built out of variables. This section is about the four jobs a variable can do, and the ways that go wrong. A variable is anything that can take different values across people, conditions, or time. That definition sounds harmless. It hides the hardest decision in research design. Because before you can measure anything, you have to decide what counts as the thing. By the end you will distinguish independent, dependent, extraneous, and control variables. You will write an operational definition, and judge whether someone else's is any good. You will identify the four levels of measurement, and what each one permits. And you will know five ways to stop an extraneous variable from wrecking a study.

Currency Of Research

What Every Study Is Made Of

A variable is any characteristic, attribute, or quantity that can take different values. Across individuals, across conditions, or across points in time. Variables are the working currency of psychological research. They are what researchers measure, what they manipulate, and what they compare. Every study is built around them, whatever its method or complexity. But two things are always in play, and they are not the same thing. There is the abstract construct you care about, and the concrete measurement standing in for it. The gap between those two decides what you may legitimately conclude. Here is what that looks like when it goes wrong. Two researchers both study aggression, and reach completely different conclusions. One measured self-reported anger. The other measured physical acts of harm.

Manipulated And Measured

Independent and Dependent Variables

The independent variable is the factor the researcher deliberately manipulates, controls, or selects. You choose it, in order to examine its effect on something else. In a true experiment, the independent variable is actively manipulated. Participants are assigned to different levels or conditions of it. But that is not the only case. In quasi-experimental and correlational research, it may be a pre-existing characteristic. Something participants bring with them, which you select rather than create. The dependent variable is the outcome you measure, to detect any effect. The name is literal. Its values are expected to depend on the independent variable. And the dependent variable has to pass two tests at once. Sensitive enough to detect the predicted effect, and specific enough to reflect the construct. Fail either one, and the study cannot answer the question you asked.

The Hidden Third

When Extraneous Becomes Confounding

Extraneous variables are any factors other than the independent variable that could influence the outcome. Most of the time they are simply background noise. The trouble starts when one of them varies systematically with the independent variable. At that point it has become a confounding variable. Its effect on the outcome can no longer be separated from the effect you care about. Notice what the distinction is actually about. It is not a property of the variable itself. It is about how that variable behaves across your conditions. Age is extraneous in most studies. It becomes a confound the moment your groups differ in age. A confound is a serious threat to internal validity. It hands a critic a rival explanation for the exact result you wanted to claim.

Held Constant

Control Variables

The defense against confounds is the control variable. A control variable is an extraneous factor deliberately held constant across conditions. Held constant, so it cannot vary with the independent variable, and cannot become a confound. The examples are unglamorous, and that is the point. Keeping testing conditions standardized for every session. Using the same experimenter throughout, rather than whoever happens to be free. Scheduling every session at the same time of day. None of that is clever. All of it is what makes a causal claim survivable. One thing to notice. Nothing is inherently a control variable. It becomes one when you decide to hold it constant, instead of letting it vary.

One Study, Five Roles

Sleep and Memory, Decomposed

Put all five roles into one study, and they stop being abstract. The question is whether sleep affects memory. The independent variable is sleep duration. Four hours versus eight hours, the night before testing. The dependent variable is the score on a word-recall memory test. Extraneous variables are everything else that could move that score. Age, prior vocabulary knowledge, caffeine consumption. Now watch one of them turn into a confound. Suppose the short-sleep group all test at seven in the morning. And the full-sleep group tests at eleven. Time of day now varies with sleep duration, so the two can no longer be told apart. Control is what stops that. Everyone tested in the same room, with the same instructions.

Idea Into Procedure

Operational Definitions

So how does an abstract construct become something you can actually record? Through an operational definition. It specifies the exact procedure used to measure or manipulate a construct. In one particular study. Not in general, and not forever. Anxiety, motivation, intelligence, wellbeing. All abstract, none directly observable. Each has to be translated into a concrete, measurable indicator. A good operational definition passes three tests. It is precise, so another researcher could follow it exactly. It is practical, so you can actually run it. And it is valid, so it captures the construct it claims to capture. Now the consequence people underestimate. Different definitions of the same construct can yield different results, which is why conceptual replication matters.

Wellbeing, Three Ways

One Construct, Three Operationalizations

Subjective wellbeing makes that concrete. It has been operationalized in at least three quite different ways. The first is a score on the Satisfaction with Life Scale. That is a five item self-report measure of how satisfied a person says they are. The second is the Positive and Negative Affect Schedule. It measures positive and negative affect, which is a different question entirely. The third is the Experience Sampling Method. Participants report their mood at random intervals, through a smartphone. Each of those captures a different facet of wellbeing. And each may produce different findings from the very same participants. So say which one you used. Without it, a reader cannot tell what your result is about.

Four Levels

How a Variable Is Measured

Now a decision that quietly constrains everything downstream. The level at which a variable is measured determines which statistics are appropriate. Stanley Stevens identified four levels of measurement. Each one provides more information than the last. Nominal is the floor. Categories with no inherent order, where numbers are labels only. Ordinal adds order. The categories line up, but the gaps between them are unequal or unknown. Interval adds equal intervals between values, but it still has no true zero point. Ratio adds that true zero, which is what makes ratios meaningful. Read it as a ladder, not a list. Every level keeps what the level below it had, and adds one more property. And each new property unlocks analyses the level below could not support.

Level Decides Statistics

What Each Level Lets You Do

So what does each level actually permit? At the nominal level, the mode, chi-square, and frequencies. Gender, diagnostic category, ethnicity. Counting is what you can do with those. At the ordinal level, the median, Spearman's r, and the Mann-Whitney U test. Likert-scale items, class rank, and severity ratings all sit here. At the interval level you can finally use the mean and the standard deviation. Along with Pearson's r, the t-test, and analysis of variance. Intelligence scores and temperature in Celsius are the classic examples. And at the ratio level, all parametric statistics are open to you. Reaction time, number of errors, weight, height. Two traps are worth naming. Nominal numbers are labels, so averaging them produces a number that means nothing. And Celsius has no true zero, so a ratio of two Celsius values is not a real ratio.

Five Strategies

Controlling Extraneous Variables

Control is not one technique. It is five, and they do different jobs. Random assignment distributes extraneous variables evenly across conditions. Matching equates conditions on key extraneous variables, participant by participant. Holding constant keeps a variable identical for everyone. Same testing time, same experimenter, same room. Counterbalancing belongs to within-subjects designs. The order of conditions is systematically varied, to control order and carryover effects. Statistical control is the fallback, for variables you cannot control by design. You measure them anyway, and enter them into the analysis as covariates. Here is why random assignment is the special one. Matching and holding constant only work on variables you thought of, and it also spreads the rest.

Putting It Together

Summary, and One Test Case

Variables are the building blocks of empirical research, and they come in four roles. Independent, dependent, extraneous, and control. Operationalize every construct precisely and validly. Choose the level of measurement deliberately, because it decides your statistics. And control whatever could otherwise explain your result. Now test yourself on one. A researcher studies the effect of classroom lighting, bright versus dim, on student attention. The bright classrooms happen to be newer and better equipped than the dim ones. Name the confound before you hear the answer. The quality of the rooms varies systematically with the lighting condition. So the two effects cannot be separated, and internal validity is gone. That is the danger in one sentence. A rival explanation you cannot rule out.

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Research Methods in Psychology carries a NCCRS credit recommendation. These universities work with NCCRS; some are UPI Study's signed partners. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.

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FAQ

Questions students ask us

About Working with Variables in Research

What is the difference between an independent variable and a dependent variable?
The independent variable is the factor a researcher manipulates, controls or selects, while the dependent variable is the outcome measured to detect its effect. In a study of sleep and memory, sleep duration of four versus eight hours is the independent variable and the score on a word-recall test is the dependent variable. In correlational or quasi-experimental research, the independent variable may be a pre-existing characteristic that is selected rather than created.
What is the difference between an extraneous variable and a confounding variable?
An extraneous variable is any factor other than the independent variable that could affect the outcome; it becomes a confounding variable when it varies systematically with the independent variable. Age is usually just extraneous, but it becomes a confound if the groups differ in age. If a short-sleep group is tested at 7 a.m. and a full-sleep group at 11 a.m., time of day is confounded with sleep, and the two effects cannot be separated.
What is a control variable in an experiment?
A control variable is an extraneous factor that the researcher deliberately holds constant across all conditions so it cannot vary with the independent variable and become a confound. Common examples are testing everyone in the same room, using the same experimenter, giving identical instructions and scheduling sessions at the same time of day. Nothing is a control variable by nature; it becomes one when the researcher chooses to keep it constant.
Why can different operational definitions of the same construct give different results?
Each operational definition captures a different facet of the construct, so the same people can score differently depending on the measure. Subjective wellbeing, for example, can be measured with the five-item Satisfaction with Life Scale, with the Positive and Negative Affect Schedule, or by experience sampling of mood through a smartphone. Researchers should report which definition they used, and conceptual replication with different definitions tests whether a finding holds.

About the Research Methods in Psychology course

How many college credits is Research Methods in Psychology?
Research Methods in Psychology (Psychology 111) carries a NCCRS credit recommendation for 3 semester credits. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.
How much does Research Methods in Psychology cost and what is included?
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