Apply research methods including experimental design, variables, sampling, validity, reliability and the interpretation of descriptive and inferential statistics.

What this dot point is asking

This dot point asks you to design, evaluate and interpret psychological investigations. It is examined throughout the course, so master the vocabulary precisely.

Variables

The independent variable (IV) is the variable the researcher manipulates. The dependent variable (DV) is what is measured to see the effect. Operationalisation means defining variables in measurable terms (for example, defining "stress" as score on a validated anxiety questionnaire).

Experimental designs

• Independent groups: different participants in each condition. Quick and avoids order effects, but individual differences between groups can confound results.
• Repeated measures: the same participants in every condition. Controls individual differences, but introduces order effects (practice or fatigue), which counterbalancing can reduce.
• Matched participants: participants are paired on a relevant characteristic, then one of each pair is allocated to each condition, combining advantages of the other two designs.

A true experiment also uses a control group and random allocation of participants to conditions.

Sampling

The population is everyone the researcher wants to generalise to; the sample is the subset studied.

• Random sampling: every member of the population has an equal chance of selection, reducing bias.
• Stratified sampling: the population is divided into strata (for example, year groups) and sampled in proportion, improving representativeness.
• Convenience sampling: using readily available participants; fast but often biased and less representative.

Validity and reliability

Validity is whether a study measures what it claims to.

• Internal validity: the degree to which the IV (not confounds) caused the change in the DV.
• External validity: how well results generalise to other people and settings (ecological validity).

Reliability is consistency. Internal consistency checks whether items in a test measure the same thing; test-retest reliability checks stable results over time; inter-rater reliability checks agreement between observers. A measure can be reliable without being valid (consistent but measuring the wrong thing), but a valid measure must be reliable.

Descriptive statistics

Descriptive statistics summarise data.

• Measures of central tendency: mean, median and mode. The mean is sensitive to outliers; the median is more robust for skewed data.
• Measures of spread: range and standard deviation. The standard deviation shows how far scores typically deviate from the mean; a larger SD means more variability.

Inferential statistics

Inferential statistics test whether results are likely due to the IV or to chance. Researchers set a significance level, conventionally p < 0.05, meaning a result this extreme would occur by chance less than 5 percent of the time if the null hypothesis were true. If the obtained probability is below 0.05, the result is statistically significant and the null hypothesis is rejected.

In the exam, always state the IV, DV and how each is operationalised, identify likely confounds, and interpret p-values against the 0.05 threshold.