Research methodology

A method that manipulates an IV and measures its effect on a DV, controlling other variables.

The variable the researcher changes on purpose.

The variable the researcher measures.

One that randomly allocates participants to conditions.

One where the IV is a pre-existing feature (e.g. age), so no random allocation is possible.

It spreads individual differences evenly, so DV changes are more likely caused by the IV.

Control and random allocation isolate the IV as the likely cause of DV changes.

Best method for showing cause and effect, with high control and replicability.

Control can make them artificial, and participants may show demand characteristics.

Causality — they test whether the IV causes a change in the DV.

A method that studies behaviour by watching and systematically recording what people do.

Naturalistic = real setting (high ecological validity); controlled = set-up situation (more control).

Covert = people don't know they're watched; overt = they know.

Participant = the researcher joins the group; non-participant = watches from outside.

A clear definition of what behaviours to count, making observation systematic.

People changing their behaviour because they know they are being watched.

Captures real behaviour directly, often with high ecological validity.

It shows what people do, not why, and can suffer observer bias.

Use a clear coding scheme and a second observer (inter-rater reliability).

Measurement — it turns behaviour into recordable, countable data.

An in-depth investigation of a single person, group or event, usually using several methods.

For rare, complex or unrepeatable cases too unusual for other methods.

By combining several methods — interviews, observation, tests — often over time.

Rich, detailed, realistic data on complex cases; high ecological validity.

Findings may not generalise and they can't establish cause and effect.

A striking case can reshape a theory, even though it can't be generalised.

Case study = deep on one case; survey/experiment = shallow on many.

No — that requires a controlled experiment.

Researcher subjectivity, and distortion when relying on memory of the past.

Measurement — many kinds of data build one rich picture.

A method that measures the relationship between two variables without manipulating them.

Both variables rise together (e.g. more study, higher grades).

As one variable rises, the other falls (e.g. more screen time, less sleep).

A third variable could drive both, or the causal arrow could run the other way.

An unmeasured factor that drives both correlated variables (e.g. heat behind ice cream and drowning).

They can study variables that can't be manipulated (e.g. stress, trauma).

They can't show cause and effect; open to third-variable and reverse-causation problems.

A correlation spots a pattern; an experiment can then test whether it's causal.

Ice-cream sales and drowning rise together, both driven by hot weather.

Causality — but cautiously: a correlation is a link, not a cause.

Gathering data by asking people about their own thoughts, feelings or behaviour.

A written set of questions given to many people; efficient and often quantitative.

An interview with fixed questions asked the same way each time — easy to compare.

A free conversation guided by topics — rich data, but hard to compare.

Answering to look good rather than truthfully.

A question that pushes the respondent towards a particular answer.

It reaches thoughts and feelings that cannot be observed.

Answers may be dishonest (social desirability) or inaccurate.

Use anonymous questionnaires and neutral wording; build trust in interviews.

Measurement — it turns private experience into comparable data.

The group actually studied, chosen to represent a larger population.

The whole group the researcher wants their findings to apply to.

Everyone in the population has an equal chance of being chosen — most representative.

Using whoever is available and willing — quick but often unrepresentative.

People choose to take part, e.g. by answering an advert.

Choosing sub-groups in the same proportions as the population.

Participants recruit others — useful for hard-to-reach groups.

A biased sample limits generalisability — findings may only apply to that group.

Random/stratified = more representative; opportunity/self-selected = more practical.

Bias — an unrepresentative sample biases the findings.