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引用次数: 0
摘要
在进行探索性因素分析(EFA)时,确定因素的数量是一个决定性的,但非常困难的决定。在过去的几十年里,已经开发了许多所谓的因素保留标准,以从经验数据中推断潜在维度。虽然存在一些关于全民教育的教程和评论文章,就如何确定潜在因素的数量提出建议,但没有对现有方法进行分类的全面概述,也没有整合现有模拟研究的结果,评估不同数据条件下的各种方法。在本文中,我们希望提供这样一个概述,使(应用)研究人员能够在选择因素保留标准时做出明智的决定。总结了最近模拟研究中最重要的结果,我们提供了何时依赖哪种方法的指导,并呼吁对过于简单的启发式进行更周到的处理。(PsycInfo Database Record (c) 2025 APA,版权所有)。
How many factors to retain in exploratory factor analysis? A critical overview of factor retention methods.
Determining the number of factors is a decisive, yet very difficult decision a researcher faces when conducting an exploratory factor analysis (EFA). Over the last decades, numerous so-called factor retention criteria have been developed to infer the latent dimensionality from empirical data. While some tutorials and review articles on EFA exist which give recommendations on how to determine the number of latent factors, there is no comprehensive overview that categorizes the existing approaches and integrates the results of existing simulation studies evaluating the various methods in different data conditions. With this article, we want to provide such an overview enabling (applied) researchers to make an informed decision when choosing a factor retention criterion. Summarizing the most important results from recent simulation studies, we provide guidance when to rely on which method and call for a more thoughtful handling of overly simple heuristics. (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.