This study aims to address a gap in the social and behavioral sciences literature concerning interaction effects between latent factors in multiple-group analysis. By comparing two approaches for estimating latent interactions within multiple-group analysis frameworks using simulation studies and empirical data, we assess their relative merits. Our simulation study results demonstrated the superiority of the Latent Moderated Structural Equations (LMS) approach over the Unconstrained Product Indicator (UPI) method in power and providing more accurate estimates of differences in latent interactions between groups. Utilizing multiple-group analysis on real-world data, we evaluated its efficacy in identifying latent interaction differences between groups. Empirical data analysis did not reveal discrepancies between LMS and UPI in terms of detecting differences in latent interactions between boys and girls, although they indicated varying sizes of differences in interaction effects. The study concludes with recommendations for examining differences in latent interactions and suggests avenues for future research. Our findings aim to deepen the understanding of intricate relationships among variables, group differences, and the moderating effects of latent variables, ultimately facilitating the development of more accurate theoretical models.