Table 2.

Accuracy and Bias of the Factor Retention Criteria for Ordinal Indicators, Asymmetric Thresholds and Different Numbers of Categories.

Method	Acc	Acc 2−Cat	Bias 2−Cat	Acc 3−Cat	Bias 3−Cat	Acc 4−Cat	Bias 4−Cat	Acc 5−Cat	Bias 5−Cat	Acc 6−Cat	Bias 6−Cat
FF	0.812	0.737	0.054	0.803	0.056	0.829	0.065	0.842	0.072	0.848	0.074
PA	0.729	0.646	−0.548	0.721	−0.418	0.747	−0.377	0.761	−0.356	0.768	−0.352
CD	0.632	0.515	−1.759	0.603	−1.362	0.658	−1.125	0.686	−1.006	0.699	−0.944
EKC	0.648	0.633	−0.702	0.654	−0.939	0.652	−0.986	0.651	−1.005	0.649	−1.015
KG	0.501	0.394	1.902	0.493	1.300	0.523	1.098	0.542	0.995	0.553	0.937

Note. Acc stands for accuracy - so Acc_2−Cat stands for the accuracy in conditions based on ordinal indicators with two levels (and asymmetric thresholds). Bias describes the mean deviation of the suggested number of factors from the true number of factors in the respective conditions.

FF = Factor Forest; CD = Comparison Data; EKC = empirical Kaiser criterion; KG = Kaiser–Guttman rule.