This paper discusses the importance of testing measurement invariance across groups in psychological research, particularly in cross-cultural studies. Measurement invariance is the assumption that a psychological instrument measures the same construct in different groups. The authors review the theoretical and methodological issues surrounding measurement invariance within the framework of confirmatory factor analysis (CFA). They provide a step-by-step empirical example of testing measurement invariance using LISREL, a statistical software for structural equation modeling. The example involves comparing two groups of boys on their verbal ability scores from the ETS Sequential Test of Educational Progress. The paper outlines the four main models used to test measurement invariance: configural, metric, scalar, and error variance invariance. It also discusses the concept of partial measurement invariance, where only a subset of parameters is constrained to be invariant across groups. The paper concludes by emphasizing the importance of explicitly evaluating measurement invariance to ensure meaningful comparisons between groups.This paper discusses the importance of testing measurement invariance across groups in psychological research, particularly in cross-cultural studies. Measurement invariance is the assumption that a psychological instrument measures the same construct in different groups. The authors review the theoretical and methodological issues surrounding measurement invariance within the framework of confirmatory factor analysis (CFA). They provide a step-by-step empirical example of testing measurement invariance using LISREL, a statistical software for structural equation modeling. The example involves comparing two groups of boys on their verbal ability scores from the ETS Sequential Test of Educational Progress. The paper outlines the four main models used to test measurement invariance: configural, metric, scalar, and error variance invariance. It also discusses the concept of partial measurement invariance, where only a subset of parameters is constrained to be invariant across groups. The paper concludes by emphasizing the importance of explicitly evaluating measurement invariance to ensure meaningful comparisons between groups.