Measuring those who have their minds set: An item-level meta-analysis of the implicit theories of intelligence scale in education

Fixed and growth mindsets represent implicit theories about the nature of one's abilities or traits. The existing body of research on academic achievement and the effectiveness of mindset interventions for student learning largely relies on the premise that fixed and growth mindsets are mutually exclusive. This premise has led to the common practice in which measures of one mindset are reversed and then assumed to represent the other mindset. Focusing on K-12 and university students (N = 27328), we tested the validity of this practice via a comprehensive item-level meta-analysis of the Implicit Theories of Intelligence Scale (ITIS). By means of meta-analytic structural equation modeling and network analysis, we examined (a) the ITIS item-item correlations and their heterogeneity across 32 primary studies; (b) the factor structure of the ITIS, including the distinction between fixed and growth mindset; and (c) moderator effects of sample, study, and measurement characteristics. We found positive item-item correlations within the sets of fixed and growth mindset items, with substantial between-study heterogeneity. The ITIS factor structure comprised two moderately correlated mindset factors (ρ = 0.63–0.65), even after reversing one mindset scale. This structure was moderated by the educational level and origin of the student sample, the assessment mode, and scale modifications. Overall, we argue that fixed and growth mindsets are not mutually exclusive but correlated constructs. We discuss the implications for the assessment of implicit theories of intelligence in education. •The ITIS structure is best represented by two correlated factors.•Fixed and growth mindset are positively correlated, ρ = .63-.65•Internal consistencies of the ITIS are high, α = .80-.82, ω = .83-.91•The factor structure is not fully invariant between school and university samples.•Educational level and scale modifications explain heterogeneity in factor models.