Why Multicollinearity Matters: A Reexamination of Relations Between Self-Efficacy, Self-Concept, and Achievement.

Abstract

Multicollinearity is a well-known general problem, but it also seriously threatens valid interpretations in structural equation models. Illustrating this problem, J. Pietsch, R. Walker, and E. Chapman (2003) found paths leading to achievement were apparently much larger for self-efficacy (.55) than self-concept (-.05), suggesting - erroneously, as the authors' reanalysis shows - that self-efficacy was a better predictor of achievement. However, because standard errors for these two paths were so huge (.25) thanks to the extremely high correlation between self-concept and self-efficacy (r = .93), interpretations were problematic. In a model comparison approach to this multicollinearity problem, constraining these two paths to be equal provided a better, more parsimonious fit to the data and also substantially reduced the standard errors (from .25 to .03).

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 183 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.