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Related Experiment Video

Updated: Jan 18, 2026

A User-friendly and Powerful R Analysis of Large-scale Datasets

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

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

Pier-Olivier Caron¹

¹Département des Sciences humaines, Lettres et Communication, Université TÉLUQ, Montréal, Canada.

Multivariate Behavioral Research

|June 8, 2025

Summary

This summary is machine-generated.

The Next Eigenvalue Sequence Test (NEST) offers a robust method for determining the number of factors in exploratory factor analysis. The R package Rnest provides accessible software for this statistically grounded technique.

Keywords:

Exploratory factor analysis R package factor retention number of factors

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Last Updated: Jan 18, 2026

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Area of Science:

Psychometrics
Statistical Methodology

Background:

Determining the correct number of factors in exploratory factor analysis (EFA) is a persistent challenge.
Numerous stopping rules exist, but none offer a definitive solution, leading to inconsistent application.
The Next Eigenvalue Sequence Test (NEST) has demonstrated theoretical grounding, robustness to cross-loadings, and high accuracy compared to traditional methods.

Purpose of the Study:

Introduce the Next Eigenvalue Sequence Test (NEST) for factor retention in EFA.
Present the R package Rnest, designed to implement the NEST.
Provide a practical workflow for using Rnest with a reproducible example.

Main Methods:

The study introduces the Next Eigenvalue Sequence Test (NEST) as a novel stopping rule for EFA.
The R package Rnest is developed to provide accessible implementation of NEST.
A reproducible data example is used to illustrate the package's functionality.

Main Results:

The Rnest package offers a practical and reliable implementation of the NEST.
NEST demonstrates robustness, a low false positive rate, and sensitivity to small factors.
The package facilitates the application of a theoretically grounded and accurate factor retention method.

Conclusions:

The Rnest package addresses the need for accessible software for the NEST.
Widespread adoption of Rnest can improve the quality of exploratory factor analyses.
This tool supports practitioners, psychometricians, and researchers in making informed decisions about factor retention.