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An evaluation of the Nest Eigenvalue Sufficiency Test (NEST) [r-libre/3023]

Caron, Pier-Olivier (Jul 2023). An evaluation of the Nest Eigenvalue Sufficiency Test (NEST). Paper presented at the 10e European Congress of Methodology de l'European Association of Methodology, Gand.

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Content : Slideshow
Item Type: Conference papers (unpublished)
Refereed: Yes
Status: Unpublished
Abstract: Determining the number of factors to retain in an exploratory factor analysis is an open methodological problem still after 75 years of research. A plethora of techniques exist to address this challenge. One of the most promising techniques is the Next Eigenvalue Sequence Test (NEST; Achim, 2017), which shows excellent performance (Achim, 2020; Brandenburg & Papenberg, 2022), but has not, however, been systematically compared to well-established competitors (see, Auerswald & Moshagen, 2019). The present study thus proposes a simulation with synthetic factor structures to compare NEST, parallel analysis, minimum average partial correlation, χ^2 sequential test, Hull method, and the empirical Kaiser criterion. The structures are based on 24 variables containing 1 to 8 factors, with loadings ranging from .40 to .80, inter-factor correlations from .00 to .30, on three sample sizes, 120, 240 and 480. In total, 360 scenarios are tested 1000 times. Performance is evaluated in terms of accuracy (correct identification of dimensionality), bias (tendency to over- or underestimate dimensionality) and variability (magnitude of prediction error). The results show that NEST outperforms these competitors. While most techniques do well in easy scenarios, NEST particularly stands out in difficult ones. Scenarios in which all methods fail are discussed. Some limitations of NEST are adressed. Finally, a new R package, named RNest, which implements NEST is promoted.
Official URL: https://eam2023.ugent.be/
Depositor: Caron, Pier-Olivier
Owner / Manager: Pier-Olivier Caron
Deposited: 02 Aug 2023 15:36
Last Modified: 02 Aug 2023 15:36

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