I'm analyzing data from a repeated measures study using a mixed-effects linear model. My dependent variable is Y (an eye-tracking parameter: total fixation duration), and I'm investigating the effect of a continuous predictor X (Likert scale score), as well as a potential moderation by a categorical variable Z (3 groups).
I've specified two models:
- A model with X predicting Y
- A second model including the interaction term X * Z
After fitting the models, residual plots show a clear funnel shape, suggesting heteroscedasticity.
My question:
- Would a log transformation of the dependent variable (Y) be appropriate to address the heteroscedasticity, or could this issue be handled using bootstrapped standard errors instead?
- Are there best practices for addressing this issue in mixed-effects models with repeated measures?
I'm using lme4 in R. Any suggestions or clarifications would be greatly appreciated!
Edit
Just to add a bit more context:
Participants could view up to six artworks, but due to the field-based nature of the study, some participants viewed fewer. This has resulted in an unbalanced dataset with varying numbers of observations per participant, which may be contributing to the heteroscedasticity I observed in the residuals.
Eye-tracking data was collected binocularly, so there is no separation between left and right eye in the analysis.
I've attached two residuals vs. fitted value plots from my models. The first one is for the main effect model and the second one for the interaction.
Here is the scale-location plot for the interaction effect model:
Thank you all so much for the help!