Parallel analysis is a popularly recommended method for assessing the dimensionality of a variable set. This study discussed some aspects needed consideration when selecting an appropriate PA procedure. These aspects include the PA methods (Horn’s PA, PA-PAFA, and PA-MRFA), correlation (Pearson correlation, and polychoric correlation), and the sampling technique (multivariate normal distribution, and permutation). Data for simulations were generated based on a linear CF model, ordered polytomous responses, with both major and minor factors. Simulation results recommended the polychoric 95% threshold PA-MRFA method as the best choice. If non-convergence, the Pearson mean threshold PA-MRFA is another good choice. Polychoric 95% threshold Horn’s PA is also good choice for empirical practice.
Q:
1. What is the “reduced correlation matrix” used in the PA-PAFA?
2. Selection of the PA procedure is related to the type of data. In this study, although polychoric PA-MRFA was recommended, when data type change, how to determine which PA procedure should be used?
1. it is known in linear factor model. The correlation matrix minus unique factor.
2. Didn't get u
2. Didn't get u