Psychology Research Methods Flashcards

What is the key feature that distinguishes an experiment from a correlational study?

Answer: Manipulation of an independent variable to observe its effect on a dependent variable

The defining feature of an experiment is the deliberate manipulation of an independent variable (IV) while measuring its effect on a dependent variable (DV), with control of extraneous variables. Correlational studies only measure the relationship between co-variables without manipulation.

A researcher finds a correlation coefficient of r = -0.85. What does this indicate?

Answer: A strong negative correlation

The correlation coefficient ranges from -1 to +1. A value of -0.85 indicates a strong negative correlation — as one variable increases, the other tends to decrease. The closer to -1, the stronger the negative relationship.

What is a demand characteristic in psychological research?

Answer: A cue in the study that leads participants to guess the aim and alter their behaviour accordingly

Demand characteristics are cues in the experimental situation that convey the hypothesis to participants, potentially leading them to change their behaviour (e.g., through the 'please-u' effect or screw-you effect). This threatens internal validity.

Which experimental design involves each participant experiencing all conditions of the independent variable?

Answer: Repeated measures design

In a repeated measures design, the same participants take part in every condition. This controls for individual differences but introduces order effects (fatigue, practice), which can be managed through counterbalancing.

What is the purpose of random allocation in an independent groups design?

Answer: To reduce the effect of participant variables (confounding variables) across conditions

Random allocation assigns participants to conditions by chance (e.g., coin flip), distributing participant variables evenly across groups. This reduces systematic bias and increases confidence that differences in the DV are caused by the IV, not pre-existing differences.

A Type I error in hypothesis testing occurs when:

Answer: The null hypothesis is rejected when it is actually true (a false positive)

A Type I error (false positive) means concluding there is a significant effect when there isn't one. The probability of a Type I error equals the significance level (alpha), typically set at 0.05 (5%). It is the 'optimistic' error.