To Örebro University

This study examined the performance of different model fit indices in multivariate multi-rater twin research. A Monte Carlo simulation design was used to generate six competing multi-trait multi-rater genetic models. Three commonly used model fit indices, including Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), and Root Mean Square Error of Approximation (RMSEA), were compared in terms of power and type I error in selecting the best fitting model under different true factor model conditions. Results showed that no model fit index is perfect in measuring model fit. BIC generally performs best when differentiating simple one-, two-, and three-factor models. However, AIC has the lowest error rate in distinguishing hierarchical factor models against simple confirmatory factor models. Thus both AIC and BIC have to be considered in practical research. The utility of the Monte Carlo simulation was demonstrated through the analysis of Reactive Proactive Aggression Questionnaire (RPQ)(Raine, 1997) and Child Behavior Checklist (CBCL) data in a sample collected from the USC Twin Study of Risk Factors for Antisocial Behavior (Baker et al., 2006). All analyses were conducted in the Mplus software. (Baker, L. A., Barton, M., Lozano, D. I., Raine, A. & Fowler, J. H., 2006, The Southern California Twin Register at the University of Southern California: II. Twin Research and Human Genetics, 9, 933–40; Raine A, Dodge K, Loeber R, Gatzke-Kopp L, Lynam D, Reynolds C, et al., 2006, The Reactive–Proactive Aggression Questionnaire: Differential correlates of reactive and proactive aggression in adolescent boys. Aggressive Behavior, 32, 159–171.)