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Exploratory factor analysis with small sample sizes: a comparison of three approaches.

¹School of Management, Kyung Hee University, 1 Hoegi-dong, Dongdaemon-gu, Seoul, 130-872, Republic of Korea. sunho.jung@khu.ac.kr

Behavioural Processes

|April 2, 2013

Summary

Regularized exploratory factor analysis (REFA) performs best for animal behavior studies with many factors and small sample sizes. Unweighted least squares is better for few factors, while generalized exploratory factor analysis shows poor performance.

Keywords:

Exploratory factor analysis Generalized exploratory factor analysis Regularized exploratory factor analysis Small sample size Unweighted least-squares

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Area of Science:

Animal behavior research
Statistical modeling
Psychometrics

Background:

Exploratory factor analysis (EFA) is crucial for identifying latent behavioral constructs in animal behavior studies.
Small sample sizes are a common challenge in this field, often leading to the use of unweighted least squares (ULS) for EFA.
Recent statistical advancements offer regularized exploratory factor analysis (REFA) and generalized exploratory factor analysis (GEFA) as potential alternatives for small sample sizes.

Purpose of the Study:

To compare the factor recovery performance of ULS, REFA, and GEFA in exploratory factor analysis under varying conditions relevant to animal behavior research.
To identify the optimal EFA approach based on sample size, degree of overdetermination, and level of communality.

Main Methods:

A simulation study was designed to systematically evaluate the performance of three EFA methods: ULS, REFA, and GEFA.
The simulation manipulated key experimental conditions including sample size, degree of overdetermination, and level of communality.
Factor recovery was the primary metric used to assess the relative performance of each approach.

Main Results:

Both sample size and degree of overdetermination significantly impacted the performance of the EFA methods.
REFA demonstrated superior factor recovery compared to ULS and GEFA when a larger number of factors were present.
ULS performed better than REFA when only a few factors were retained.
GEFA consistently showed the poorest factor recovery, especially as sample size increased.

Conclusions:

REFA is recommended as a robust alternative to ULS for EFA in animal behavior, particularly when numerous factors are anticipated.
ULS remains a viable option when the number of expected factors is small.
GEFA is not recommended as a suitable alternative for EFA due to its consistently poor performance.