  Score Predictor Factor Analysis (SPFA) was introduced as a method that allows
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 common 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 score predictor factor
analysis 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 conventional factor analysis. Moreover, the percentage of correctly
identified single-item indicators was substantially larger for SPFA than for
the common factor model. It is therefore argued that SPFA is a tool that can be
especially helpful when very short scales or single-item indicators are to be
identified.
