Rnest: An R Package for the Next Eigenvalue Sufficiency Test for Factor Analysis

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.

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Publication Details

Journal
Multivariate Behavioral Research
Published
2025-06-08
DOI
https://doi.org/10.1080/00273171.2025.2512343
Citations
1
Primary Topic
Psychometric Methodologies and Testing
Type
article
Field-Weighted Citation Impact
2.11
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article

Rnest: An R Package for the Next Eigenvalue Sufficiency Test for Factor Analysis

Pier‐Olivier Caron
1 citations
Multivariate Behavioral Research
Psychometric Methodologies and Testing
2.11
article

Rnest: An R Package for the Next Eigenvalue Sufficiency Test for Factor Analysis

Pier‐Olivier Caron
article en
1 citations

Abstract

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.

Multivariate Behavioral ResearchVol. 60(5)
Université TÉLUQ (CA)
Openalex Percentile: Top 13%
Psychometric Methodologies and Testing
2.11
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Rnest: An R Package for the Next Eigenvalue Sufficiency Test for Factor Analysis — Pier‐Olivier Caron · Multivariate Behavioral Research (2025) | TGRS Research Map | TGRS