Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and…

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…

Despite increasing interest in sluggish cognitive tempo (SCT) in children, the field is stymied by the lack of a standard symptom set that can be used across studies. Without a standard symptom set, it is difficult to determine if differences across studies are due to methodological or sample differences, or simply the way SCT was measured. To…

The present study explored the bidirectional and longitudinal associations between executive function (EF) and early academic skills (math and literacy) across four waves of measurement during the transition from preschool to kindergarten using two complementary analytical approaches: cross-lagged panel modeling and latent growth curve modeling…