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.…

Data in social and behavioral sciences typically contain measurement errors and also do not have predefined metrics. Structural equation modeling (SEM) is commonly used to analyze such data. This article discuss issues in latent-variable modeling as compared to regression analysis with composite-scores. Via logical reasoning and analytical results…

Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent variables, structural equation modeling (SEM) and manifest regression analysis (MRA). Main…

Many studies have shown that learning to read in a second language (L2) is similar, in many ways, to learning to read in a first language (L1). Nevertheless, reading development also relies upon oral language proficiency and is greatly influenced by orthographic consistency. This longitudinal study aimed to analyze the role of linguistic…

This study investigates the structure and cross-cultural (in)variance of mathematical self-beliefs in relation to mathematics achievement in two world regions: Confucian Asia (Singapore, South Korea, Hong Kong and Taiwan) and Europe (Denmark, The Netherlands, Finland, Serbia and Latvia). This is done both pan-culturally and at a multigroup-level,…