About this Workshop

Studies of time-related change are key to understanding the processes and dynamics of aging, but are often subject to data and methodological limitations arising from the confounding of different dimensions of time, including age (A), period (P), and cohort (C). The goal of this workshop is to introduce recent advances in data and analytic tools for modeling temporal variations due to A, P, and C effects in longitudinal analyses, teach the basic rationale and process of APC analysis, and give attendees a working knowledge of how to use this methodological approach in their own research on aging. The workshop builds on the previous GSA workshop on Coordinated Data Analysis (CDA) (2025) to provide useful guidelines for APC analysis based on both basic and complex longitudinal designs and data structures involved in CDA, which can vastly enhance analysts’ ability to make inferences about time-related change compared to conventional studies of single datasets. The workshop provides an interactive virtual environment to facilitate active learning. It involves the participation of attendees throughout the session, engaging them during the lecture and through breakout discussions, as well as a moderated Q&A for targeted problem-solving.
 

Presented by

Yang Claire, PhD, University of Notre Dame