Advances in Longitudinal Methods

>> Recognizing growth over time is the basis for assessing whether change, development, or learning has taken place. This recognition of growth, however, is challenged by the practical realities of the environments in which we gather data, of the instability in the attributes being measured, and of the vagaries of the subjects themselves. Thus, a common and on-going challenge across research domains is to make meaningful inferences regarding the traits underlying observed but fallible response profiles of longitudinal data.

>> Numerous statistical methods have been proposed to analyze such repeated measures data, including linear mixed effects models, multilevel models, hierarchical non/linear models, and latent growth curve modeling. While these basic models have been extended in several ways in recent years, methodological progress must be sustained in order to keep up with the myriad of complex data analytic conditions often found in practice.

>> To that end, the objective of this conference is to bring together quantitative methodologists to present new, state-of-the-art developments in longitudinal methods, and to point to the future of this critical branch of data analysis. This conference should appeal in scope and accessibility to applied researchers, academic researchers, and graduate students throughout the many disciplines of the social and behavioral sciences.