MCorrSeqPerm: Searching for the Maximum Statistically Significant System of Linear Correlations and its Application in Work Psychology

Abstract

The paper addresses the problem of detecting a statistically significant subset of input considered relationships. The Pearson linear correlation coefficient calculated from a sample was used to determine the strength of a relationship. Simultaneous testing of the significance of many relationships is related to the issue of multiple hypothesis testing. In such a scenario, the probability of making a type I error without proper error control is, in practice, much higher than the assumed level of significance. The paper proposes an alternative approach: a new stepwise procedure (MCorrSeqPerm) allowing for finding the maximum statistically significant system of linear correlations keeping the error at the assumed level. The proposed procedure relies on a sequence of permutation tests. Its application in the analysis of relationships in the problem of examining stress experienced at work and job satisfaction was compared with Holm’s classic method in detecting the number of significant correlations.

Keywords

multiple hypothesis testing linear correlation work stress job satisfaction five factors of personality

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