线性相关的最大统计显著性系统的搜索及其在工作心理学中的应用。

IF 1.2 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Katarzyna Stapor, Grzegorz Kończak, Damian Grabowski, Marta Żywiołek-Szeja, Agata Chudzicka-Czupała
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引用次数: 0

摘要

本文解决了检测输入考虑关系的统计显著子集的问题。从样本中计算出的Pearson线性相关系数用于确定关系的强度。同时检验许多关系的显著性涉及到多重假设检验问题。在这种情况下,如果没有适当的错误控制,犯第一类错误的概率实际上要比假设的重要程度高得多。本文提出了一种替代方法:一种新的逐步过程(MCorrSeqPerm),允许找到最大统计显著的线性相关系统,使误差保持在假设的水平。所建议的程序依赖于一系列排列测试。在检验工作压力和工作满意度的问题中,它在关系分析中的应用与Holm的经典方法在检测显著相关性的数量方面进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MCorrSeqPerm: Searching for the Maximum Statistically Significant System of Linear Correlations and its Application in Work Psychology.

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.

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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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