Assumptions related to multiple regression including multicollinearity, normality, homoscedasticity, outliers, missing were assessed.
Prior to the multiple regression analysis, multicollinearity (a potential confounder in multiple regression analyses) was examined (using correlational analyses), but no strong associations between independent variables existed.
The variance inflation factor was used to ascertain the degree of multicollinearity within the multiple regression models with values over 10 signifying problematic multicollinearity.
Collinearity diagnostics within multiple regression showed the presence of multicollinearity, particularly for teacher-reported self-esteem and self-efficacy [ 128].
The assumptions of multiple regression, that is, lack of multicollinearity among the independent variables and normality and linearity of residuals were assessed.
After an assumption check on outliers, normality and multicollinearity, simultaneous multiple regression analysis was conducted using the DKO score as the dependent variable, Gender, as the independent variable of interest, and NR score, TR, and Group as controlling variables.
The highest variance inflation factor in for any of the terms in the multiple regression model was 1.97 indicating that multicollinearity was well within acceptable limits.
We evaluated multicollinearity for all multiple regression models.
Individual terms from the multiple regression are not reported due to multicollinearity between several traits.
Multiple regression model diagnostics included residual analyses, multicollinearity analyses, and inferential analyses.
