In one line: An experiment changes one thing on purpose and measures the effect — the one method that can show cause and effect.
An experiment is the main way psychologists test causal claims. You change the independent variable (IV) and measure the dependent variable (DV).
This links to the concept of causality: by controlling other variables and changing only the IV, an experiment can show that the IV caused the change in the DV — something no other method can do as confidently.
Memory hook: Change one thing, measure another, control the rest. That's an experiment.
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Key idea: A true experiment randomly allocates people to conditions; a quasi-experiment uses groups that already exist.
The building blocks
IV and DV
The IV is what you change; the DV is what you measure. Both must be operationalised (defined precisely).
Control
Keep other variables constant, and use a control group, so only the IV differs between conditions.
True experiment
Participants are randomly allocated to conditions, balancing out individual differences.
Quasi-experiment
The IV is a pre-existing feature (e.g. age, gender), so people can't be randomly allocated.
IV · DV · Control · Allocation
Take testing whether music affects concentration. IV = music vs silence; DV = task score; control = same task, room, time. Randomly allocating students makes it a true experiment. Comparing people who already study with or without music would be quasi — the concept of causality is safest in the true design.
Go further — higher-level insight: Random allocation is what makes cause-claims safe. It spreads individual differences evenly across conditions, so a difference in the DV is more likely due to the IV. Quasi-experiments can't do this, so their causal claims are weaker — a key evaluation point.
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Key idea: Experiments are strong on cause but can be artificial and raise other issues.
So experiments are the strongest method for testing causality, but a good evaluation notes they can be artificial, participants may guess the aim (demand characteristics), and some variables can't be manipulated ethically or at all — which is when quasi-experiments or other methods are needed.
Watch out: A quasi-experiment can't prove cause as strongly. Because the groups already differed (e.g. in age), a difference in the DV might be due to those pre-existing differences, not the IV.