Summary of Part B

Part B addressed the design, measurement, and data-quality decisions that make small-sample analyses credible. The chapters covered sampling strategies that maximise information with limited resources, measurement quality and scale development, reliability for short scales, short-scale development, data screening and diagnostic checks, and the identification and handling of missing data, including multiple imputation and the assessment of imputation quality.

The main point is that small-sample analysis begins well before the final model is fitted. Transparent sampling, careful measurement, early diagnostic checking, and defensible handling of missing data all determine whether the later inferential results can be trusted.

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