AHL 4.12 (HL)—Data collection and validity

Data collection is the planned process of defining variables, selecting observations and recording measurements. Validity asks whether the method measures the intended construct; reliability asks whether it is consistent enough to repeat.

Sampling frame, wording, response options and recording conditions can introduce bias or random error. A questionnaire may be efficient, but leading questions and non-response can change who is represented and what is reported.

To estimate sleep in a school, define sleep time, choose a sampling method, pilot the question and record missing answers. A precise question about the previous night is more valid than an unbounded question about ‘usual’ sleep.

A reliable measurement can be consistently wrong, and a valid idea can be measured unreliably. Name the source of error and its direction before judging the data.

Question design should be unbiased, structured with consistent choices, precise and limited to relevant variables. For a $\chi^2$ table, justify category boundaries, keep expected frequencies above 5 and reduce degrees of freedom when parameters are estimated from data. Reliability checks include test–retest and parallel forms; validity checks include content and criterion-related validity.