B0800
Title: Generalized linear factor score regression with different methods
Authors: Fan Wallentin - Uppsala University (Sweden) [presenting]
Abstract: Factor score regression has recently received growing interest as an alternative to structural equation modeling. However, many applications are left without guidance because of the literature's focus on normally distributed outcomes. We perform a simulation study to examine how a selection of factor scoring methods compare when estimating regression coefficients in generalized linear factor score regression. The current study evaluates the regression and correlation-preserving methods as well as two sum score methods in ordinary, logistic, and Poisson factor score regression. Our results show that scoring method performance can differ notably across the considered regression models. In addition, the results indicate that the choice of scoring method can substantially influence research conclusions. The regression method generally performs the best in terms of coefficient and standard error bias, accuracy, and empirical Type I error rates. Moreover, the regression and correlation-preserving methods mostly outperform the sum score methods.
