What does it mean to "operationalise" a variable in a psychology experiment?

A: To choose which variable will be manipulated by the experimenter

B: To define a variable in terms of the specific, measurable procedure used to assess or manipulate it

C: To control all variables except the one being studied

D: To assign participants randomly to experimental conditions

Correct: To define a variable in terms of the specific, measurable procedure used to assess or manipulate it

Operationalisation is the process of converting an abstract construct into a concrete, measurable definition. For example, "anxiety" as a construct could be operationalised as "score on the GAD-7 questionnaire," "number of skin conductance responses per minute," or "heart rate during a 5-minute rest period." The operationalisation chosen determines exactly what is being measured, and therefore what the results actually tell us. Two studies on "anxiety" using different operationalisations may produce incompatible findings — not because one is wrong, but because they measured different things. Good operationalisation produces reliable, replicable measurement; poor operationalisation introduces ambiguity and limits the validity of the study.

In an experiment testing whether caffeine improves reaction time, which is the independent variable (IV) and which is the dependent variable (DV)?

A: IV = reaction time; DV = caffeine dose

B: IV = caffeine dose; DV = reaction time

C: Both reaction time and caffeine dose are dependent variables

D: IV = the participant's age; DV = caffeine dose

Correct: IV = caffeine dose; DV = reaction time

The independent variable (IV) is what the researcher deliberately manipulates or varies across conditions — in this case, caffeine dose (e.g. 0 mg, 100 mg, 200 mg). The dependent variable (DV) is what is measured to see whether the manipulation had an effect — here, reaction time. A simple way to remember the relationship: the DV depends on the IV. The researcher changes the IV and observes whether the DV changes as a result. Operationalising both variables precisely matters: the IV operationalisation must specify how caffeine is delivered and in what amounts; the DV operationalisation must specify exactly how reaction time is measured (e.g. time in milliseconds to press a button after a light appears).

What is a confounding variable, and why is it a problem?

A: A variable that is deliberately manipulated alongside the IV to test interaction effects

B: A variable that varies systematically with the IV and also affects the DV, making it impossible to determine the true cause of any observed effect

C: Any variable that the researcher fails to measure during the study

D: A variable that remains constant across all conditions to prevent it from affecting results

Correct: A variable that varies systematically with the IV and also affects the DV, making it impossible to determine the true cause of any observed effect

A confounding variable co-varies with the IV and independently affects the DV. This means that any change in the DV could be due to the IV, the confound, or both — and the researcher cannot tell which. For example, in a study comparing exam performance between students who slept 8 hours versus 5 hours, if the 8-hour group also drank more coffee, caffeine intake is a confound: it varies with the IV (sleep amount) and could also affect exam performance. The key word is "systematically" — if a variable varies randomly rather than consistently with the IV, it is an extraneous variable that adds noise but does not systematically bias the results. Confounds threaten the internal validity of an experiment.

What is the difference between an extraneous variable and a confounding variable?

A: They are synonyms — both refer to any variable other than the IV and DV

B: Extraneous variables vary randomly and add noise to results; confounding variables vary systematically with the IV and bias the results in a specific direction

C: Extraneous variables are measured; confounding variables are not

D: Confounding variables affect only the IV; extraneous variables affect only the DV

Correct: Extraneous variables vary randomly and add noise to results; confounding variables vary systematically with the IV and bias the results in a specific direction

Extraneous variables are any variables beyond the IV that could affect the DV. If they vary randomly (e.g. participants having slightly different room temperatures, varying slightly in how tired they are), they add random noise to data — increasing variability but not biasing the estimate of the IV's effect. These reduce statistical power but do not create false causal conclusions. Confounding variables are a specific and more serious subset: they vary systematically with the IV, meaning one condition consistently experiences more or less of the confound than another. This creates a systematic bias that can produce false positive results (the IV appears to have an effect when it does not) or mask real effects.

A researcher hypothesises: "Participants who sleep fewer than 6 hours will score lower on a memory test than participants who sleep 8 or more hours." What type of hypothesis is this?

A: Null hypothesis — it predicts no difference between groups

B: Non-directional (two-tailed) hypothesis — it predicts a difference but not its direction

C: Directional (one-tailed) hypothesis — it predicts the specific direction of the difference

D: A research question, not a hypothesis, because it lacks an operationalised DV

Correct: Directional (one-tailed) hypothesis — it predicts the specific direction of the difference

A directional (one-tailed) hypothesis specifies not only that the IV will affect the DV, but also the direction of that effect — in this case, that less sleep leads to lower (not just different) memory scores. A non-directional hypothesis would state only "participants who sleep fewer than 6 hours will score differently on a memory test than those who sleep 8+ hours," without specifying which direction. Directional hypotheses are used when theory or prior evidence strongly predicts the direction; they allow one-tailed statistical tests, which have more power to detect an effect in the predicted direction but cannot detect effects in the opposite direction. The null hypothesis would state "there will be no significant difference in memory scores between the two groups."

The null hypothesis always predicts that the independent variable will have no significant effect on the dependent variable.

Answer: True

The null hypothesis (H₀) is the default position that any observed difference between conditions is due to chance rather than to the IV's effect. In significance testing, researchers do not directly prove their experimental hypothesis — they attempt to reject the null hypothesis. If the probability of obtaining the observed results by chance (the p-value) falls below a predetermined threshold (typically p < .05), the null hypothesis is rejected in favour of the experimental hypothesis. The null hypothesis therefore always asserts no effect, no difference, or no relationship — which is why it is called "null." If a study fails to reject the null hypothesis, this does not prove the IV has no effect; it means there was insufficient evidence to conclude it does.

A researcher wants to study the effect of background music on reading comprehension. She gives all participants the same passage, tests them in the same room at the same time of day, and uses the same comprehension questions. What purpose do these consistent conditions serve?

A: They operationalise the independent variable

B: They serve as control variables — holding potential confounds constant so they cannot account for differences in the DV

C: They increase the ecological validity of the study by making it more naturalistic

D: They constitute the dependent variable

Correct: They serve as control variables — holding potential confounds constant so they cannot account for differences in the DV

Control variables are variables other than the IV that are deliberately held constant across conditions to prevent them from becoming confounds. In this example, the reading passage, testing room, time of day, and comprehension questions are all controlled — because any of these, if they varied systematically between the music and no-music conditions, could explain differences in comprehension independently of the music. Controlling variables improves internal validity: if the only thing that differs between conditions is the IV (presence or absence of background music), any observed difference in comprehension can more confidently be attributed to the music. Note that control variables differ from the control condition (the group that receives no treatment).

A study measures "aggression" by counting the number of times a child hits a Bobo doll during a 10-minute free-play period. What is the limitation of this operationalisation?

A: It is too precise — operationalisations should remain loosely defined to capture the full construct

B: It may lack construct validity — hitting an inflatable toy designed to bounce back may not reflect aggressive behaviour toward real people or in real situations

C: It makes the IV impossible to identify

D: It cannot be reliably scored because different observers will always disagree on what counts as a "hit"

Correct: It may lack construct validity — hitting an inflatable toy designed to bounce back may not reflect aggressive behaviour toward real people or in real situations

This operationalisation — used in Bandura's Bobo Doll experiments — has been criticised for construct validity: does hitting a Bobo doll (an inflatable toy that bounces back and is, in some ways, designed to be hit) actually measure the same construct as "aggression" toward other people or in naturalistic settings? The Bobo doll doesn't resist, retaliate, or show distress, so the social dynamics are fundamentally different. This is a question of whether the measurement procedure actually captures what it claims to capture. Note that reliability (option D) is actually achievable here — trained observers can reliably count hits using standardised criteria. The deeper problem is whether hitting-count validity generalises to real-world aggression.