Abstract
This study is a substantive-methodological synergy in which exploratory structural equation modeling is applied to investigate the factor structure of multidimensional self-concept instruments. On the basis of a sample of German students (N = 1958) who completed the Self-Description Questionnaire I and the Self-Perception Profile for Children, the results supported the superiority of exploratory structural equation modeling compared with confirmatory factor analyses for both instruments. Exploratory structural equation modeling resulted in lower factor correlations and substantively meaningful cross-loadings. The authors also proposed and contrasted 3 mechanisms for testing grade-related differences in the differentiation of self-concept facets and found no evidence of increased differentiation between Grades 3 to 6.
Notes
Although the CFI associated with regular ML estimation is monotonic with model complexity (i.e., cannot increase when constraints are added), we rely on the maximum likelihood estimator for which corrections factors are taken into account in the adjustments of the chi-square tests and resulting CFIs. When these correction factors changes across nested models, the CFI can be nonmonotonic, which should simply be ignored and interpreted as supporting equivalent levels of fit.