The authors empirically examined whether the validity of a residualized dependent variable after covariance adjustment is comparable to that of the original variable of interest. When variance of a dependent variable is removed as a result of one or more covariates, the residual variance may not reflect the same meaning. Using the pretest-posttest…

In this study, the role of teacher-student relationships and students' social and emotional skills as potential predictors of students' emotional and behavioural difficulties was investigated by tapping into 962 primary school students' perceptions via questionnaires. While significant correlations were found linking teachers' interpersonal…

This study examined the prediction of academic self-concept (English and Mathematics) and learning strategies (deep and surface), and their direction of effect, on academic achievement (English and Mathematics) of 8,354 students from 16 secondary schools in Hong Kong. Two competing models were tested to ascertain the direction of effect: Model A…

Statistical procedures based on the general linear model (GLM) share much in common with one another, both conceptually and practically. The use of structural equation modeling path diagrams as tools for teaching the GLM as a body of connected statistical procedures is presented. A heuristic data set is used to demonstrate a variety of univariate…

Continuing a discussion of the topic of fit assessment in structural equation modeling, this article accepts the compromise proposed by H. Marsh (1998) and offers tentative heuristic models for interpreting fit indices that involve the new baseline model proposed by E. Rigdon (1998). (SLD)

A general linear model (GLM) framework is used to suggest that structure coefficients ought to be interpreted in structural equation modeling confirmatory factor analysis (CFA) studies in which factors are correlated. The computation of structure coefficients in explanatory factor analysis and CFA is explained. Two heuristic data sets are used to…

This paper describes structural equation modeling (SEM) in comparison with another overarching analysis within the general linear model (GLM) analytic family: canonical correlation analysis. The uninitiated reader can gain an understanding of SEM's basic tenets and applications. Latent constructs discovered via a measurement model are explored and…