Title:
Modeling Developmental Processes Using Accelerated Cohort-Sequential Data

dc.contributor.author Ferrer, Emilio
dc.contributor.corporatename Georgia Institute of Technology. School of Psychology en_US
dc.contributor.corporatename University of California, Davis. Dept. of Psychology en_US
dc.date.accessioned 2021-11-30T16:59:43Z
dc.date.available 2021-11-30T16:59:43Z
dc.date.issued 2021-11-10
dc.description Presented online via WebEx on November 10, 2021 at 3:00 p.m. en_US
dc.description Emilio Ferrer is a Professor in the Department of Psychology at the University of California, Davis and member of the Graduate Group in Biostatistics. His research interests include methods to analyze change and intra-individual variability, in particular latent change models and dynamical systems. His current research in this area involves techniques to model multivariate processes associated with the development of reasoning from childhood to adolescence, and models to capture dyadic interactions over time. en_US
dc.description Runtime: 58:24 minutes en_US
dc.description.abstract Studying the time-related course of psychological processes is a challenging endeavor, particularly over long developmental periods. Accelerated longitudinal designs (ALD) allow capturing such periods with a limited number of assessments in a much shorter time framework. In ALDs, participants from different age cohorts are measured repeatedly but the measures provided by each participant cover only a fraction of the study period. It is then assumed that the common trajectory can be studied by aggregating the information provided by the different converging cohorts. In this presentation, I report results from recent studies examining the performance of discrete- and continuous-time latent change score (LCS) models for recovering the trajectories of a developmental process from data obtained through different ALDs. These results support the effectiveness of LCS models to study developmental processes using data from ALDs under various conditions of sampling. When all cohorts are drawn from the same population, both types of models are able to recover the parameters defining the underlying developmental process. However, discrete-time models yield estimates with bias when time lags between observations are not constant. When cohorts are not from the same population and lack convergence, both discrete- and continuous-time models show bias in some dynamic parameters. en_US
dc.format.extent 58:24 minutes
dc.identifier.uri http://hdl.handle.net/1853/65523
dc.language.iso en_US en_US
dc.relation.ispartofseries Psychology Colloquium
dc.subject Assessment en_US
dc.subject Developmental methodology en_US
dc.title Modeling Developmental Processes Using Accelerated Cohort-Sequential Data en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename College of Sciences
local.contributor.corporatename School of Psychology
local.relation.ispartofseries School of Psychology Colloquiua
relation.isOrgUnitOfPublication 85042be6-2d68-4e07-b384-e1f908fae48a
relation.isOrgUnitOfPublication 768a3cd1-8d73-4d47-b418-0fc859ce897d
relation.isSeriesOfPublication da9098fa-29c9-4bda-a0d0-bb2f2a5f2bd0
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