Modeling the observation-to-variable ratio necessary for determining the number of factors by the standard error scree procedure using logistic regression

Logistic regression was used for modeling the observation-to-variable (n/v) ratio required for the standard error scree (SEscree) procedure to correctly identify the number of factors in simulated data. The correlation matrices were generated to possess known characteristics: number of factors (f), number of variables (v), sample size (n), magnitude of pattern coefficients (p), and degree of interfactor correlations (r). The results indicated that under all conditions, the nlv ratio required for the SEscree procedure to correctly identify the true number of factors with high probability exceeded the minimum of 5:1 recommended in some of the related literature. This study demonstrated the ability of the logistic regression to simplify summarizing and reporting findings from simulation studies that involve a large number of conditions.