Improving the Use of Parallel Analysis by Accounting for Sampling Variability of the Observed Correlation Matrix
Xia, Yan; Zhou, Xinchang
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https://hdl.handle.net/2142/123583
Description
Title
Improving the Use of Parallel Analysis by Accounting for Sampling Variability of the Observed Correlation Matrix
Author(s)
Xia, Yan
Zhou, Xinchang
Issue Date
2024-07-21
Keyword(s)
parallel analysis
dimensionality assessment
factor analysis
sample size
Abstract
Parallel analysis has been considered one of the most accurate methods for determining the number of factors in factor analysis. One major advantage of parallel analysis over traditional factor retention methods (e.g., Kaiser’s rule) is that it addresses the sampling variability of eigenvalues obtained from the identity matrix, representing the correlation matrix for a zero factor model. The present study argues that we should also address the sampling variability of eigenvalues obtained from the observed data, such that the results would inform practitioners of the variability of the number of factors across random samples. Thus, this study proposes to revise the parallel analysis to provide the proportion of random samples that suggest k factors (k = 0, 1, 2, …) rather than a single suggested number. Simulation results support the use of the proposed strategy, especially for research scenarios with limited sample sizes where sample fluctuation is concerning.
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