Abstract
<p>A primary objective of intensive longitudinal studies is to investigate within-person dynamics. In this context, item heterogeneity plays a critical role, aswithin-person processes may vary across items within a scale. A common exam-ple is the assessment of momentary affect using adjective lists (e.g., sad, angry,anxious, stressed), where each item captures different facets of positive or neg-ative affect, providing unique and non-interchangeable information. However,standard practices often overlook item heterogeneity by aggregating item scores or assuming a single within-person factor in dynamic structural equation mod-els. This simplification does not permit a fine-grained analysis of within-persondynamics and compromises cross-study comparability when item pools differacross studies. In this article, we reanalyze five large-scale intensive longitudinaldatasets assessing momentary affect to illustrate how item heterogeneity can beexplicitly modeled. We introduce a flexible modeling approach that accommo-dates item-specific and person-specific dynamics while improving comparabilityacross studies, based on residualized dynamic structural equation modeling withreference items. We compare this method to conventional modeling strategiesand provide practical guidance for addressing item heterogeneity in the analysisof intensive longitudinal data.</p>