Repeated measures with nested data mixed effects model (singularity)

I hope you can help me. I performed a pre-post study with two Trainings (ViStra & LeStra), and I measured safety outcomes (e.g. Knowledge, attitudes, behavior, etc...) with questionnaires before (T1) and after (T2) the training ( Time ). Participants ( ID ) took just one of the Trainings were nested into 5 companies , and we want to know if there are differential changes in the trainings over time. Participants per company per training V-Training L-Training B 10 14 C 9 10 G 18 14 H 6 8 K 12 14 U 31 32 Therefore, I used GLMM to evaluate safety training outcomes, like this: Model_A When I run the model, the first warning appears: boundary (singular) fit: see help('isSingular') And when I want to calculate the ICC the following message appears: performance::icc(Model_A, by_group=TRUE) Can't compute random effect variances. Some variance components equal zero. Your model may suffer from singularity (see `?lme4::isSingular` and `?performance::check_singularity`). Solution: Respecify random structure! You may also decrease the `tolerance` level to enforce the calculation of random effectn variances. However, when I run the following model (take a look into the random effects): Model_B No warning appears and it does calculate the ICC. What am I doing wrong? Participants (IDs) are assumed to be nested in companies. Is it possible that it is because within each company there are few participants? Thank you. Here you have the Data in Long format (ID is twice as it was tested before an…