This article presents a multilevel longitudinal nested logit model for analyzing correct response and error types in multilevel longitudinal intervention data collected under a pretest-posttest, cluster randomized trial design. The use of the model is illustrated with a real data analysis, including a model comparison study regarding model…

Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and…