Attention deficit hyperactivity disorder (ADHD), in particular, is increasingly recognized as being heterogeneous, making it challenging to discover biomarkers and create guidelines for therapeutic therapy, according to a study. Researchers have historically tried classifying ADHD patients into meaningful subgroups by applying analytical clustering methods to various data sets. However, these studies typically employ algorithmic methods that do not make connections between behavior indicators, neurocognition, and genetic make-up and assume that group membership is error-free. Furthermore, complex latent classification models were rarely used in neurodevelopmental research because of the difficulties of working with small sample sizes. In this study proposed a method for evaluating mixture models on data sets typically encountered in neurodevelopmental research. Model fit was evaluated through both qualitative and quantitative means, and they detail both approaches. Using latent profile analysis (LPA), researchers compared 120 kids with and without ADHD, beginning with established neuropsychological indications and progressing to electroencephalogram (EEG) measurement integration. They found a reliable 5-class LPA model based on 7 neuropsychological indicators. Although they could not identify a trustworthy multimethod indicator model, they extrapolate the neuropsychological model results to identify unique patterns of resting EEG power across 5 frequency bands. Researchers’ approach, which emphasizes both theoretical and empirical evaluation of mixture models, had the potential to make these models more accessible to clinical researchers and aid in the dissection of heterogeneity in neurodevelopmental disorders.