To assess item dimensionality, the following two approaches are described and compared: hierarchical generalized linear model (HGLM) and multidimensional item response theory (MIRT) model. Two generating models are used to simulate dichotomous responses to a 17-item test: the unidimensional and compensatory two-dimensional (C2D) models. For C2D data, seven items are modeled to load on the first and second factors, [theta][1] and [theta][2], with the remaining 10 items modeled unidimensionally emulating a mathematics test with seven items requiring an additional reading ability dimension. For both types of generated data, the multidimensionality of item responses is investigated using HGLM and MIRT. Comparison of HGLM and MIRT's results are possible through a transformation of items' difficulty estimates into probabilities of a correct response for a hypothetical examinee at the mean on [theta] and [theta][2]. HGLM and MIRT performed similarly. The benefits of HGLM for item dimensionality analyses are discussed.