Estimating the number of factors in exploratory factor analysis via out-of-sample prediction errors.

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Psychological methods Pub Date : 2024-02-01 Epub Date: 2022-11-03 DOI:10.1037/met0000528
Jonas M B Haslbeck, Riet van Bork
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引用次数: 0

Abstract

Exploratory factor analysis (EFA) is one of the most popular statistical models in psychological science. A key problem in EFA is to estimate the number of factors. In this article, we present a new method for estimating the number of factors based on minimizing the out-of-sample prediction error of candidate factor models. We show in an extensive simulation study that our method slightly outperforms existing methods, including parallel analysis, Bayesian information criterion (BIC), Akaike information criterion (AIC), root mean squared error of approximation (RMSEA), and exploratory graph analysis. In addition, we show that, among the best performing methods, our method is the one that is most robust across different specifications of the true factor model. We provide an implementation of our method in the R-package fspe. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

通过样本外预测误差估算探索性因子分析中的因子数量。
探索性因子分析(EFA)是心理科学中最流行的统计模型之一。EFA 的一个关键问题是估计因子的数量。在本文中,我们提出了一种基于最小化候选因子模型的样本外预测误差来估计因子数量的新方法。我们通过大量的模拟研究表明,我们的方法略优于现有的方法,包括平行分析法、贝叶斯信息准则(BIC)、阿凯克信息准则(AIC)、近似均方根误差(RMSEA)和探索性图分析法。此外,我们还证明,在性能最好的方法中,我们的方法在不同的真实因子模型规格下都是最稳健的。我们在 R 软件包 fspe 中提供了我们方法的实现。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
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.
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