Moritz Breit, Julian Preuß, Vsevolod Scherrer, Franzis Preckel
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引用次数: 0
摘要
变量之间的关系可以采取不同的形式,如线性、分段线性或非线性。分段回归分析(SRA)是检测变量之间关系中断的专门统计方法。它们通常用于社会科学的探索性分析。然而,许多关系可能不能最好地用断点和由此产生的分段线性关系来描述,而是用非线性来描述。在目前的模拟研究中,我们检查了sra的应用-特别是戴维斯测试-在各种形式的非线性的存在。我们发现,中度和强烈程度的非线性导致频繁识别统计上显著的断点,并且识别的断点分布广泛。结果清楚地表明SRA不能用于探索性分析。我们提出了用于探索性分析的替代统计方法,并概述了在社会科学中合法使用SRA的条件。(PsycInfo Database Record (c) 2025 APA,版权所有)。
Why the use of segmented regression analysis to explore change in relations between variables is problematic: A simulation study.
Relations between variables can take different forms like linearity, piecewise linearity, or nonlinearity. Segmented regression analyses (SRA) are specialized statistical methods that detect breaks in the relationship between variables. They are commonly used in the social sciences for exploratory analyses. However, many relations may not be best described by a breakpoint and a resulting piecewise linear relation, but rather by a nonlinearity. In the present simulation study, we examined the application of SRA-specifically the Davies test-in the presence of various forms of nonlinearity. We found that moderate and strong degrees of nonlinearity led to a frequent identification of statistically significant breakpoints and that the identified breakpoints were widely distributed. The results clearly indicate that SRA cannot be used for exploratory analyses. We propose alternative statistical methods for exploratory analyses and outline the conditions for the legitimate use of SRA in the social sciences. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
期刊介绍:
Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.