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 correct ly identify the number of factors in simulated data. The correlation matric es 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 tha t under all conditions, the n/v ratio required for the SEscree procedure to correctly identify the true number of factors with high probability exceed ed the minimum of 5: 1 recommended in some of the related literature. This study demonstrated the ability of the logistic regression to simplify summa rizing and reporting findings from simulation studies that involve a large number of conditions.