Score predictor factor analysis as a tool for the identification of single-item indicators

Abstract

Score Predictor Factor Analysis (SPFA) was introduced as a method to compute factor score predictors that are – under some conditions – more highly correlated with the common factors resulting from factor analysis than the factor score predictors computed from the factor model. In the present study, we investigate SPFA as a model in its own rights. In order to provide a basis for this, the properties and the utility of SPFA factor score predictors and the possibility to identify single-item indicators in SPFA loading matrices were investigated. Regarding the factor score predictors, the main result is that the best linear predictor of the SPFA has not only perfect determinacy but is also correlation preserving. Regarding the SPFA loadings it was found in a simulation study that five or more population factors that are represented by only one variable with a rather substantial loading can more accurately be identified by means of SPFA than with factor analysis. Moreover, the percentage of correctly identified single-item indicators was substantially larger for SPFA than for the factor model. It is proposed that SPFA is a tool that can be especially helpful when short scales or single-item indicators are to be identified.