Traditionally, two approaches have been employed for structural equation modeling: covariance structure analysis and partial least squares. A third alternative, generalized structured component analysis, was introduced recently in the psychometric literature. The authors conduct a simulation study to evaluate the relative performance of these three approaches in terms of parameter recovery under different experimental conditions of sample size, data distribution, and model specification. In this study, model specification is the only meaningful condition in differentiating the performance of the three approaches in parameter recovery. Specifically, when the model is correctly specified, covariance structure analysis tends to recover parameters better than the other two approaches. Conversely, when the model is misspecified, generalized structured component analysis tends to recover parameters better. Finally, partial least squares exhibits inferior performance in parameter recovery compared with the other approaches. In particular, this tendency is salient when the model involves cross-loadings. Thus, generalized structured component analysis may be a good alternative to partial least squares for structural equation modeling and is recommended over covariance structure analysis unless correct model specification is ensured.

Path analysis with components, partial least squares, alternating least squares,

This study investigated the role of inattention and working memory in predicting academic achievement in 145 adolescents aged 13 to 18 referred for attention deficit/hyperactivity disorder (ADHD). Path analysis was used to examine whether auditory-verbal and visual-spatial working memory would mediate the relationships between classroom inattention symptoms and achievement outcomes. Results provide support for the mediational model. Behavioral inattention significantly predicted both auditory-verbal and visual-spatial working memory performance. Auditory-verbal working memory was strongly associated with adolescents' achievement in reading and mathematics, while visual-spatial working memory was only associated with achievement in mathematics. The path from inattention symptoms to reading was partially mediated by the working memory variables, but the path from inattention to mathematics was not mediated by working memory. The proposed model demonstrated a good fit to the data and explained a substantial amount of variance in the adolescents' achievement outcomes. These findings imply that working memory is a risk factor for academic failure for adolescents with attentional problems.

Generalized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice.

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