Bartus, Tamás – Kisfalusi, Dorottya – Koltai, Júlia (2019) Logisztikus regressziós együtthatók összehasonlítása (The Comparison of Coefficients in Logistic Regression) In: Statisztikai Szemle (Hungarian Statistical Review) 97(3): 221-240.

2019.03.09. Publication Data Science in Social Research

Recently, increasing attention has been devoted to the problem that estimated coefficients of logistic (and other non-linear) regression models cannot be compared across groups, samples, or nested model specifications due to the possible differences in the magnitude of unobserved heterogeneity. This study reviews methods which aim to solve this problem and investigates their effectiveness through simulation. Parameter estimates of nested model specifications can be made comparable using y-standardization or by comparing the estimates of the multivariate model to the estimates of a special, quasi-univariate model. Methods which aim to make coefficients comparable across groups and samples (such as testing the proportionality of interaction effects and heterogeneous choice models), however, do not provide adequate solutions for the problem. Causes behind this failure are discussed.