Confounding Variables in Psychology

A confounding variable is an unmeasured third variable that influences, or “confounds,” the relationship between an independent and a dependent variable by suggesting the presence of a spurious correlation.

Confounding Variables in Research

What Is a Confounding Variable?

Confounding variables are why a correlation between two variables does not prove that one causes the other. An extraneous variable becomes a confounding variable once it interferes with the dependent variable, the outcome a study measures.

Say the IV and DV look associated. If a third variable, C, actually affects both the IV and the DV, that association may be spurious rather than causal.

C is the confounding variable in this example.

To estimate the real effect of the IV on the DV, researchers must reduce the influence of this confound. A causal relationship that looks solid on the surface might not exist at all once it is taken into account.

Even when a real cause-and-effect relationship does exist, a confound can still make it look bigger or smaller than it really is.

Demand Characteristics and Investigator Effects

Two sources of confounding are common enough in psychology to name individually. Demand characteristics are cues in the experimental situation that reveal the hypothesis to participants. Orne (1962) argued that participants rarely stay passive.

Some try to confirm what they think the researcher wants, others deliberately do the opposite, and many act on evaluation apprehension, behaving as they think they should. A single-blind procedure controls for this by keeping participants unaware of their condition.

Investigator effects are the researcher’s version of the same problem. An experimenter’s own expectations can bias how they treat participants, record behaviour, or interpret ambiguous responses.

Rosenthal and Fode (1963) told students they were handling rats bred to be “maze-bright” or “maze-dull,” but the rats had actually been allocated at random. The bright rats performed better anyway.

Rosenthal and Jacobson (1968) found the same pattern in classrooms, an effect now known as the Pygmalion effect. Teachers were told certain randomly chosen pupils were about to bloom academically, and those pupils’ measured IQ rose more than their classmates’.

A double-blind procedure controls for this by keeping the experimenter unaware too.

Example of a Confounding Variable

Suppose we wanted to measure the effects of caloric intake (IV) on weight (DV). We would have to try to ensure that confounding variables did not affect the results. These variables could include the following:

• Metabolic rate: A faster metabolism burns calories more quickly.
• Age: Younger people tend to burn calories faster than older adults, which affects weight gain.
• Physical activity: People who exercise more burn more calories and may weigh less, even if they eat more.
• Height: Taller individuals need to consume more calories to gain weight.
• Sex: Men and women have different caloric needs to maintain the same weight.

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