PSClearn6 – Psychology Research Methods Practice Test

Handling missing data in quantitative analyses hinges on identifying why data are missing and what that means for the results. The key idea is to distinguish patterns of missingness: MCAR (missing completely at random), MAR (missing at random), and MNAR (missing not at random). Each pattern points to different reasonable strategies. If data are MCAR, deleting cases with missing values can be unbiased, though it still reduces power. If data are MAR, imputation or model-based approaches can yield unbiased estimates by using observed information to estimate missing values. If data are MNAR, standard methods may still be biased, and specialized models or sensitivity analyses are needed. Because we don’t know the pattern beforehand, the prudent move is to assess the missingness pattern first and then decide among deletion, imputation (such as multiple imputation, which accounts for uncertainty), or model-based approaches that use all available data. Deleting all cases with any missing data can waste information and bias results unless the data are truly MCAR. Replacing missing values with zeros injects artificial values that distort relationships and variance. Completing only with the available cases ignores information present in partial cases and can bias estimates unless the missingness mechanism is truly inconsequential.