Dear Lowie,

I’d like to try to help you with your not-so-simple analysis problem. First of all, I think your model is incomplete (or I do not see the complete picture). In general, full mediation occurs if the association between the independent variable (X; e.g. autonomy) and the dependent variable (Y; intention to quit) is completely explained by the mediator variable (M; professional development). Partial mediation would mean that M explains only part of the relationship between X and Y, but there is still a significant direct effect between X and Y. In both cases, you also need to measure the direct effect(s) of X on Y, which in the presence of M either become statistically non-significant (full mediation) or decrease in effect (partial mediation).

Secondly, there seems to be no significant effect of M on Y, which would be a requirement in mediation analysis. However, as said above, you need to include the direct effects in the model as well and double check. If there is no significant relationship between M and Y when controlling for X, the hypothesized mediating process may not exist in the way it was originally conceptualized. Or perhaps the mediation is more complex and dependent on additional variables or interactions between them. Then you could explore alternative mediation models or consider additional/fewer variables.

The high residual variances in X and M may indicate that there are many other factors or influences that explain M but were not included in the model. This may indicate that the modeling of professional development is incomplete or that important variables that could better explain the construct are missing. You may also need to review and refine or revise the measurement scale of your X variables to improve reliability and validity. Finally, high residual variances could indicate multicollinearity of the various variables in the model. You should check for correlations between your X’s and M.

Lastly, I do not think your R² estimates are necessarily that low. Firstly, some of the issues described above may have contributed and “lowered” the explained variance in your model (e.g., complexity of the underlying relationships, missing variables in the model). And secondly, it is not uncommon to see R² values of

Dears

If the residual variance in your mediation analysis is too high, you can take the following actions to address it:

Ø Check for Model Misspecification: Ensure your mediation model is accurately stated. Ensure that all important variables are included, and that the relationships between the variables are accurately described.

Ø Additional Variables: If residual variance is substantial, it may suggest that other variables are impacting the relationships in your mediation model. Consider including more control variables to account for any confounding influences.

Ø Check for Measurement Error: Evaluate the reliability and validity of your measurements to ensure they accurately capture the constructs of interest. excessive measurement error may contribute to excessive residual variance in your analysis.

Ø Check for Multicollinearity: Excessive multicollinearity among independent variables can result in inflated residual variance. Check for multicollinearity with correlation matrices or variance inflation factor (VIF) values, and consider deleting strongly correlated variables.

Ø Evaluate Sample Size: A small sample size can result in unstable estimates and large residual variance. Consider increasing your sample size to improve the dependability of your results. Addressing these difficulties and making necessary improvements to your mediation analysis may help to lower the large residual variance and enhance the overall quality of your findings.