Hello everyone,
I posted a question about this about a month ago but I have received no answers. I am now re-posting a modified version of it, hoping that someone out there will help me clear up my doubts. So far in my mixed regression models I have 'centered' any 2-level predictors with the 'scale as numeric' command and interpreted the output as giving Anova-style effects and interactions. Recently I have had to run a model that has one 2-level predictor and one 3-level predictor. I know that using 'scale as numeric' on the 3-level predictor is not the right thing to do, so I have only centered the 2-level predictor and left the 3-level predictor as it is (I assume this means this predictor receives the default treatment coding in the model). But with this kind of set up I have had great convergence problems - practically the model converges only with intercept-only random effects (1|subj and 1|item). When it does not converge, I get messages such as "Model is nearly unidentifiable: large eigenvalue ratio- Rescale variables?" (which I do not understand, - how exactly do I re-scale the variables). Furthermore, this model is not really what I want as it does not give me the main effect of the 2-level predictor (averaged across the 3-levels of the 3-level predictor). After reading up a bit, I have investigated alternatives, and I have found that applying sum contrast coding to the two predictors (contr.sum(2) to the 2-level predictor and contr.sum(3) for the 3-level predictor) the full model converges with no problems; in addition I get what I want - the main effect of the 2-level predictor. But I would like to know the experts' opinion about this because I have read that when the data is not fully balanced, i.e. when we do not have the same number of cases per condition (for example when there is missing data), we should center our predictors (and so far I have addressed the imbalance by centering/scaling all predictors with 2 levels with "scale as numeric" and using the centered variables as predictors). So my question is: Does sum contrast coding address any imbalance in my data (which is not perfectly balanced because of missing data in some conditions)? If it does not, do you have any suggestions as to how I could solve this problem, bearing in mind that I want a model that gives me: the main effect of the 2-level predictor (in the ANOVA sense), whether each of the levels of the 3-level predictor differs from the grand mean (or at least whether two of the levels do, given that one is only the baseline) and any interaction between the two predictors (in the ANOVA sense)? Thanks so much for your help Maria Nella Carminati
