MEASUREMENT MODELS MATTER: IMPLICIT ASSUMPTIONS AND

The availability of cross-national survey data has grown exponentially in recent years. While much attention has been paid to increasing the comparability of indicators across countries, less has been done to increase the comparability of measurement models. This article examines the implicit assumptions of four different approaches to measurement modeling—summative scales, pooled exploratory factor analysis, multiple-group confirmatory factor analysis, and locally-conditioned factor analysis, and explores whether substantive conclusions in cross-national work can vary depend-ing on the choice of measurement model. We find that results can vary by method and suggest that (i) the measurement modeling process itself be a critical part of cross-national research, and (ii) analysts be prepared to fully explain and defend measurement modeling decisions. A thorough understanding of the implicit assump-tions of measurement modeling is required to avoid drawing conclusions that are little more than arbitrary. During the past two decades there has been a stunning growth in cross-national survey research, due in no small part to the increasing availability of cross-national survey data. Surveys such as the World Values Surve