Interpreting the semi-partial correlation as a multiple regression-bound (not a bivariate) metric: A methods-oriented response to Papi and Teimouri’s (2024) response to Al-Hoorie et al. (2024)

Al-Hoorie, Hiver, and In’nami (2024) challenged the validity and corresponding validation processes of L2 Motivational Self System (L2MSS) research. A component of this challenge included claims of weak discriminant validity due to high correlations among L2MSS constructs. Papi and Teimouri (2024) countered by using semi-partial correlations to control for other L2MSS constructs, finding weak-to-moderate associations, which they claimed mollified potential discriminant validity concerns. In this methods-oriented response paper, we present a historical case that semi-partial correlations should be viewed within the context of multiple regression analysis, not as a standalone bivariate metric. Challenging Papi and Teimouri’s approach, we suggest that their method does not adequately address discriminant validity issues. Furthermore, when their semi-partial correlations are treated as multiple regression models, Al-Hoorie et al.’s concerns remain valid. Finally, we demonstrate that L2MSS literature does not support the assignment of outcome and predictor variables in Papi and Teimouri’s semi-partial correlations when correctly considered as multiple regression models.