Robust oblique Target-rotation for small samples
Andr\'e Beauducel, Norbert Hilger

TL;DR
This paper introduces a mean oblique Target-rotation method that reduces sampling error effects in small samples, improving factor analysis accuracy compared to traditional methods.
Contribution
It proposes a novel block-wise mean cross-loading approach for oblique Target-rotation, enhancing robustness in small sample scenarios.
Findings
Mean Target-rotation reduces bias in factor inter-correlations.
Improves similarity of factors in small subsamples.
Outperforms conventional Target-rotation in simulations.
Abstract
Introduction: Oblique Target-rotation in the context of exploratory factor analysis is a relevant method for the investigation of the oblique independent clusters model. It was argued that minimizing single cross-loadings by means of target rotation may lead to large effects of sampling error on the target rotated factor solutions. Method: In order to minimize effects of sampling error on results of Target-rotation we propose to compute the mean cross-loadings for each block of salient loadings of the independent clusters model and to perform target rotation for the block-wise mean cross-loadings. The resulting transformation-matrix is than applied to the complete unrotated loading matrix in order to produce mean Target-rotated factors. Results: A simulation study based on correlated independent factor models revealed that mean oblique Target-rotation resulted in smaller negative bias…
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Taxonomy
TopicsAdvanced Statistical Modeling Techniques · Sensory Analysis and Statistical Methods · Advanced Statistical Methods and Models
