In recent decades, researchers have become increasingly interested in understanding people’s thoughts, emotions, and behaviors in their natural contexts. The commonality in methods for doing so—experience sampling, daily diary, and ecological momentary assessment methods—is that they all involve intensive longitudinal assessments. These intensive longitudinal methods allow researchers to examine processes in daily life in a way that is not possible using traditional methods.
Researchers can obtain repeated observations over the course of hours, days, and weeks. Intensive longitudinal data, however, present several data analytic challenges stemming from the various possible sources of interdependence in these data. The multilevel or hierarchical linear model (HLM) provides a flexible set of analytic tools that can take account of these complexities. Workshop topics will include: History and introduction to intensive longitudinal methods and designs; analyzing the time course of intensive longitudinal data; analyzing within-person processes; intensive longitudinal data from dyads; categorical intensive longitudinal outcomes; psychometrics of intensive longitudinal data; power in intensive longitudinal studies; and mediation in intensive longitudinal data. The course will include lectures, software demonstrations, and computer lab work. Various software packages will be used, including SAS, SPSS, HLM, and Mplus.
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Dr. Jean-Philippe Laurenceau, University of Delaware
Dr. Niall Bolger, Columbia University