The growing availability of intensive longitudinal data has increased psychological researchers' interest in ideographic-statistical methods that, for example, reveal the contemporaneous or lagged associations between different variables for a specific individual. However, when researchers assess several individuals, the results of such models are difficult to generalize across individuals. Researchers recently suggested an algorithm called GIMME, which allows for the identification of coefficients that exist across all individuals (group-level coefficients) or are specific to one or a subgroup of individuals (individual-level coefficients). In three simulation studies we investigated GIMME's performance in recovering group-level and individual-level coefficients. For the former, we found that GIMME performed well when the magnitude of the parameters was moderate to high and when the number of measurements was sufficiently large. However, GIMME had problems detecting individual-level coefficients or coefficients that occurred for a subset of individuals from the whole sample.