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Time | Wed, Feb 7, 2024 12:00 pm to 1:00 pm |
Location | HHD 101 conference room |
Presenter(s) |
Jonathan J. Park, a doctoral student in HDFS at Penn State. |
Description |
The idiographic framework has been invaluable for understanding the dynamics of individuals as they unfold through time. However, some concerns have been levied regarding the generalizability of person-specific results to broad classes of individuals. Addressing the challenge of bridging person- and group-level inference remains a pivotal issue in quantitative methods. A broad class of models—referred to as “idio-thetic” methods—describe the spectrum between purely idiographic models to fully constrained group-level or “chained” models. Much of the advancement in idio-thetic methods has been within the realm of discrete-time literature. Discrete-time models offer intuitive interpretations and ease of implementation; however, they are limited when compared to continuous-time models. The latter are adept at handling unequally spaced data—which frequently occur in real-world empirical applications—and possess other unique advantages. Despite this, development of novel methods in continuous-time still lags behind those in the discrete-time literature. In my final presentation as a graduate student in QuantDev, I introduce a continuous-time extension of the group iterative multiple model estimation (GIMME) procedure. This work streamlines the fitting of continuous-time models to individual processes and leverages person-specific information to identify common, group-level structures. The discussion will cover the formal algorithm, the strengths of moving into a continuous-time framework alongside OpenMx, and present preliminary simulation results. |
Event URL | https://psu.mediaspace.kaltura.com/media/QuantDev+Brownbag+Meeting+Spring+24/1_… |