Date: 2020-03-27

Time: 15:30-16:30

Location: BURNSIDE 1205

Abstract:

Generalized structured component analysis (GSCA) was developed as a component-based approach to structural equation modeling, where constructs are represented by components or weighted composites of observed variables, rather than (common) factors. Unlike another long-lasting component-based approach – partial least squares path modeling, GSCA is a full-information method that optimizes a single criterion to estimate model parameters simultaneously, utilizing all information available in the entire system of equations. Over the decade, this approach has been refined and extended in various ways to enhance its data-analytic capability. I will briefly discuss the theoretical underpinnings of GSCA and demonstrate the use of an R package for GSCA - gesca. Moreover, I will outline some recent developments in GSCA, which include GSCA_M for estimating models with factors and integrated GSCA (IGSCA) for estimating models with both factors and components.

Speaker

Heungsun Hwang is a Professor in the Department of Psychology at McGill. His research program is generally devoted to the development and application of quantitative methods to address diverse issues in psychology and various other fields. His recent interests include the development of data integration tools for high-dimensional data collected from multiple sources; the development of a statistical methodology for investigating associations among genetic, brain, and behavioural/cognitive phenotypes.