I have a repeated measures dataset with which I'm testing if individuals are consistent in their boldness scores (continuous variable) over time (trials). Towards this, I generated linear mixed effects model with boldness scores as the dependent variable, trials as fixed effects and individual IDs as random effects. However, when I checked the residuals of the fitted model, the assumption of normality is not met. I have attached the qqplot of the residuals.
I then transformed the boldness scores by boxcox method and again ran the model. The residuals seem to appear more normal now when compared to the residuals from the model with the non- transformed variable. However, even after transformation, shapiro tests show that the residuals are not normally distributed.
The skeweness of the residuals has also reduced from +1.111 to -0.24 after transformation.
Can I proceed with the model ignoring the assumption of normality of residuals? (see blogpost here which suggests linear mixed models are robust even if this assumption is not met).