複数の項目やテストにおける検定の多重性 : モンテカルロ・シミュレーションによる検証

概要

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This paper aims to highlight the problem of multiple significance testing with several dependent variables (i.e., items or tests). In many research. papers, researchers report the results of multiple significance testing without realizing they are committing Type I error, in which it can be erroneously concluded that there is a statistically significant difference, when in fact there is no statistical difference. In order to address this problem, a series of Monte Carlo simulation studies were carried out. Five artificial sets of dependent variables for two groups of subjects were generated in the simulation. Three types of data sets which varied in their degrees of intercorrelations (r=.00, r=.50, r=.95, respectively) were then compared. The results indicate that multiple significance testing, with several dependent variables, inflate Type I error, and thus caution should be exercised to control the experimentwise error rate. Implications for the strategies for controlling Type I error rate are then discussed.