Re-examining the reciprocal effects model of self-concept, self-efficacy, and academic achievement in a comparison of the Cross-Lagged Paneland Random-Intercept Cross-Lagged Panelframeworks.

Abstract

The cross-lagged panel(regression) model (CLPM) is the usual framework of choice to test the longitudinal reciprocal effects between self-concept and achievement. Criticisms of the CLPM are that causal paths are over-estimated as they fail to discriminate between- and within-person variation. The random-intercept cross-lagged panel model (RI-CLPM) is one alternative that extends the CLPM by partialling out between-person variance. We compare analyses from a CLPM and a RI-CLPM which examine the reciprocal relationships between self-concept, self-efficacy, and achievement and determine the extent CLPM estimates are inflated by between-person variance. Participants (n=314) were first-year undergraduate psychology students recruited as part of the STudent Engagement with Education and Learning (STEEL) project. Participants completed measures of self-efficacy and self-concept prior to completing fortnightly quiz assessments. Cross-Lagged Panel(regression) Model estimates are likely over-estimated in comparison with RI-CLPM estimates. Cross-Lagged Panel(regression) Model analyses identified a reciprocal effects relationship between self-concept and achievement, confirming established literature. In RI-CLPM analyses, these effects were attenuated and a skill development association between achievement and self-concept was supported. A reciprocal relationship between self-efficacy and achievement was supported. Better model fit was reported for the RI-CLPM analyses. Prior findings relating to the reciprocal effects of self-concept and achievement need to be reconsidered. Whilst such a relationship was supported in a CLPM analysis in this study, within an RI-CLPM framework, only achievement predicted self-concept. However, in both CLPM and RI-CLPM models a reciprocal effects model of self-efficacy and achievement was supported.