CFAs of multidimensional constructs often fail to meet standards of good measurement (e.g., goodness-of-fit, measurement invariance, and well-differentiated factors). Exploratory structural equation modeling (ESEM) represents a compromise between exploratory factor analysis’ (EFA) flexibility, and CFA/SEM’s rigor and parsimony, but lacks parsimony (particularly in large models) and might confound constructs that need to be kept separate. In Set-ESEM, two or more a priori sets of constructs are modeled within a single model such that cross-loadings are permissible within the same set of factors (as in Full-ESEM) but are constrained to be zero for factors in different sets (as in CFA). The different sets can reflect the same set of constructs on multiple occasions, and/or different constructs measured within the same wave. Hence, Set-ESEM that represents a middle-ground between the flexibility of traditional-ESEM (hereafter referred to as Full-ESEM) and the rigor and parsimony of CFA/SEM. Thus, the purposes of this article are to provide an overview tutorial on Set-ESEM, juxtapose it with Full-ESEM, and to illustrate its application with simulated data and diverse “real” data applications with accessible, heuristic explanations of best practice.

Traditional reviews and previous meta-analyses of self-concept interventions have underestimated effect sizes by using an implicitly unidimensional perspective that emphasizes global self-concept. In contrast, this research employed a synergistic blend of meta-analysis and multidimensional construct validation to evaluate the impact of self-concept interventions for children in 145 primary studies (200 interventions). Overall, interventions were significantly effective (d = .51, 460 effect sizes). However, in support of the multidimensional perspective, interventions targeting a specific self-concept domain and subsequently measuring that domain were much more effective (d = 1.16), suggesting sole reliance on global self-concept is inappropriate for evaluating interventions designed to enhance a specific component of self-concept. Other moderators (e.g., feedback, experimental design, target population groups) also influenced effect sizes in ways useful to the design of new interventions. Methodologically...

The big-fish–little-pond effect (BFLPE) predicts that equally able students have lower academic self-concepts (ASCs) when attending schools where the average ability levels of classmates is high, and higher ASCs when attending schools where the school-average ability is low. BFLPE findings are remarkably robust, generalizing over a wide variety of different individual student and contextual level characteristics, settings, countries, long-term follow-ups, and research designs. Because of the importance of ASC in predicting future achievement, coursework selection, and educational attainment, the results have important implications for the way in which schools are organized (e.g., tracking, ability grouping, academically selective schools, and gifted education programs). In response to Dai and Rinn (Educ. Psychol. Rev., 2008 ), we summarize the theoretical model underlying the BFLPE, minimal conditions for testing the BFLPE, support for its robust generalizability, its relation to social comparison theory, and recent research extending previous implications, demonstrating that the BFLPE stands up to scrutiny.