Approximating Confidence Intervals for Factor Loadings

Abstract

Practical theoretic means for assessing the sampling variability of loadings estimated by exploratory factor analytic procedures have not been readily available in the absence of restrictive distributional assumptions. It has been necessary for researchers to interpret these point estimates (loadings) through the use of arbitrary rules-of-thumb. Under these conditions, loading interpretations may be problematic. A method is presented for exploiting information in the empirical data, collected for a study's primary goals, to approximate confidence intervals for factor loadings, The method appears generalizable across factor methods, numbers of extracted factors, and rotation criteria.