SOME CONTRIBUTIONS TO DATA-FITTING FACTOR ANALYSIS WITH EMPIRICAL COMPARISONS TO COVARIANCE-FITTING FACTOR ANALYSIS

Abstract

A data-fitting factor analysis (FA) procedure was recently presented, which is very different from the prevailing covariance-fitting FA. In the former procedure, common and unique factor scores are modeled as fixed unknown parameters, and an unweighted least squares (ULS) function, which is not scale invariant, is minimized for fitting the model to a data matrix. The main purpose of this paper is to settle four remaining problems with data-fitting FA. First, we present a weighted least squares (WLS) procedure which can be scale invariant, and include the above ULS procedure as a special case according to the choice of weights. Second, we prove that the WLS loss function can be minimized, even if raw data are unknown and only their sample covariance matrix is available, despite being a data-fitting approach. Third, we propose an estimator of factor scores that cannot be uniquely determined. Fourth, we empirically compare this data-fitting FA procedure with covariance-fitting FA with respect to recovery of parameter matrices.