Caron Pier-Olivier
Département des Sciences humaines, Lettres et Communication, Université TÉLUQ, Montréal, Canada.
Multivariate Behav Res. 2025 Jun 8:1-7. doi: 10.1080/00273171.2025.2512343.
To address the challenge of determining the number of factors to retain in exploratory factor analysis, a plethora of techniques, called stopping rules, has been developed, compared and widely used among researchers. Despite no definitive solution to this key issue, the recent Next Eigenvalue Sequence Test (NEST) showed interesting properties, such as being theoretically grounded in the factor analysis framework, robustness to cross loadings, a low false positive rate, sensitive to small but true factors, and better accuracy and unbiased compared to traditional stopping rules. Despite these strengths, there is no existing software readily available for researcher. These considerations have led to the development of the R package Rnest. This paper introduces NEST, presents the functionality of the Rnest package, and illustrates its workflow using a reproducible data example. By providing a practical and reliable approach to factor retention, this package aims to encourage its widespread adoption among practitioners, psychometricians, and methodological researchers conducting exploratory factor analyses.
为应对在探索性因素分析中确定保留因素数量这一挑战，研究人员已开发、比较并广泛使用了大量被称为停止规则的技术。尽管这个关键问题尚无定论，但最近的下一个特征值序列检验（NEST）展现出了有趣的特性，比如在理论上基于因素分析框架、对交叉载荷具有稳健性、误报率低、对小而真实的因素敏感，并且与传统停止规则相比具有更高的准确性和无偏性。尽管有这些优势，但目前还没有现成的软件可供研究人员使用。这些考虑促使了R包Rnest的开发。本文介绍了NEST，展示了Rnest包的功能，并使用一个可重现的数据示例说明了其工作流程。通过提供一种实用且可靠的因素保留方法，该包旨在鼓励从业者、心理测量学家以及进行探索性因素分析的方法学研究人员广泛采用它。
