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Robust oblique Target-rotation for small samples.

André Beauducel¹, Norbert Hilger¹

¹Department of Psychology, University of Bonn, Bonn, Germany.

Frontiers in Psychology

|December 13, 2023

Summary

This summary is machine-generated.

Mean Target-rotation minimizes sampling error in exploratory factor analysis, particularly for small samples and complex models. This method improves the accuracy of oblique simple structure investigations compared to conventional Target-rotation techniques.

Keywords:

Target-rotation exploratory factor analysis factor inter-correlation factor-rotation independent clusters model

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Area of Science:

Psychometrics
Statistical Modeling

Background:

Oblique Target-rotation is used to investigate oblique simple structure in exploratory factor analysis.
Conventional Target-rotation can be susceptible to sampling error, especially with single cross-loadings.

Purpose of the Study:

To propose and evaluate a novel approach, mean Target-rotation, to mitigate sampling error effects in Target-rotation.
To enhance the reliability of factor solutions in exploratory factor analysis.

Main Methods:

Compute mean cross-loadings for blocks of salient loadings in independent clusters models.
Perform Target-rotation on block-wise mean cross-loadings.
Apply the resulting transformation matrix to the complete unrotated loading matrix.

Main Results:

Mean oblique Target-rotation demonstrated reduced bias in factor inter-correlations compared to conventional Target-rotation, especially with small sample sizes and large numbers of factors.
Empirical examples showed greater similarity between Target-rotated factors from small subsamples and the total sample using mean Target-rotation.

Conclusions:

Mean Target-rotation is recommended for oblique factor models emphasizing simple structure, particularly when dealing with small sample sizes.
The proposed method offers a more robust approach to factor rotation, reducing the impact of sampling error.