Variables & Operationalisation

All 8 Terms & Definitions

Independent variable (IV)

The variable that the researcher deliberately manipulates or varies across conditions. It is the presumed cause in a cause-and-effect relationship. Good operationalisation of the IV specifies exactly what is changed (e.g. "0 mg, 100 mg, or 200 mg of caffeine in a 250 ml drink") rather than leaving it vague (e.g. "caffeine condition").

Dependent variable (DV)

The variable that is measured to assess whether the IV had an effect. It is the presumed outcome or effect. The DV "depends" on the IV. Operationalising the DV means specifying exactly how it is measured: not "reaction time" but "time in milliseconds from onset of a visual cue to button press on a USB response pad."

Operationalisation

The process of defining a variable in terms of the specific, measurable procedure used to assess or manipulate it. Converts an abstract construct into a concrete, replicable operation. Crucially, different operationalisations of the same construct (e.g. "anxiety" as GAD-7 score vs heart rate vs skin conductance) may yield incompatible findings — not because either study is wrong, but because they measured different facets of the construct.

Confounding variable

A variable that varies systematically with the IV and independently affects the DV, making it impossible to determine whether any observed change in the DV was caused by the IV or the confound. The key word is "systematically" — if it varies randomly rather than consistently with the IV, it is an extraneous variable that adds noise but does not bias the estimate of the IV's effect.

Extraneous variable

Any variable beyond the IV that could affect the DV. Unlike a confound, an extraneous variable varies randomly across conditions rather than systematically with the IV — adding noise and reducing statistical power, but not creating a false directional bias in the results. In practice, distinguishing extraneous from confounding variables requires careful analysis of whether the variable co-varies with condition assignment.

Control variable

A variable that is deliberately held constant across all conditions to prevent it from becoming a confound. For example: same testing room, same time of day, same experimenter, same task materials. Controlling variables improves internal validity — if only the IV differs between conditions, any change in the DV can be attributed to it with greater confidence.

Directional (one-tailed) hypothesis

A hypothesis that specifies both that the IV will affect the DV and the direction of that effect — for example, "sleep deprivation will decrease memory performance." Used when prior theory or evidence strongly predicts the direction. Allows a one-tailed statistical test, which is more powerful for detecting an effect in the predicted direction but cannot detect effects in the opposite direction.

Non-directional (two-tailed) hypothesis

A hypothesis that predicts an effect but does not specify its direction — for example, "sleep deprivation will affect memory performance." Used when the direction of effect is unclear or when the researcher wants to remain open to effects in either direction. Requires a two-tailed statistical test. The null hypothesis always takes the complementary position: no significant effect.