group size , statistical power , interrater agreement
Abstract:
This article aims to contribute to a controversy over whether excluding some
small or incomplete groups from a sample improves statistical power in group
research designs (designs that relate group-level characteristics to group-level
outcome measures). In a series of simulation studies, we examined the tradeoff
between lower reliability and smaller sample size that occurs when very
small groups, or incomplete groups are excluded. Distinguishing reflective aggregation
models (where scores for different group members are interchangeable)
and formative aggregation models (where scores for different group
members are not interchangeable), we analyzed the impact that the number of
groups, the number of individuals within groups, intraclass correlation (ICC[1])
values, and interrater agreement have on statistical power. The results provided
evidence that excluding groups is mostly ill-advised and may fail to improve
the conclusions that researchers draw from their results. Common practice
and the assumptions that researchers make when excluding groups from their
samples are discussed.
Dieser Datensatz wurde nicht während einer Tätigkeit an der Universität Mannheim veröffentlicht, dies ist eine Externe Publikation.
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