Multilevel modelling (MLM) is the most frequently used approach for evaluating interventions with clustered data. MLM, however, has some limitations that are associated with numerous obstacles to model estimation and valid inferences. Longitudinal multiple-group (LMG) modelling is a longstanding approach for testing intervention effects using cluster-sampled data that has been superseded by the rise of MLM approaches, but the LMG approach can have advantages when research questions do not pertain to predicting variability at the higher levels. In this paper, we first review the advantages and limitations of MLM and LMG approaches. Second, steps in the estimation of an LMG model are presented, with some recent upgrades and changes in the modelling strategy that have particular utility for evaluating interventions. We discuss the advantages of the LMG approach as a guided confirmatory model-testing framework and how the approach places a premium on avoiding Type II errors, particularly when complex interactions are potentially at play.