Quantitative Methods Forum

When:
October 20, 2014 @ 10:15 AM – 11:15 AM
2014-10-20T10:15:00-04:00
2014-10-20T11:15:00-04:00
Where:
Norm Endler Seminar Room (BSB 164)
Cost:
Free

sterbaSpeaker: Dr. Sonya Sterba, Vanderbilt University
Department of Psychological Sciences

Title: Structural Equation Modeling Approaches for Analyzing Partially Nested Data

Abstract: Study designs involving clustering in some study arms, but not all study arms, are common in clinical treatment-outcome and educational settings. For instance, in a treatment arm, persons may be nested in therapy groups, whereas a control arm may have no groups. Methodological approaches for handling such partially nested designs have previously been developed in a multilevel modeling framework (MLM-PN, e.g. Bauer, Sterba & Hallfors, 2008). Recently, two alternative structural equation modeling (SEM) approaches for analyzing partially nested data were introduced: a multivariate single-level SEM (SSEM-PN) and a multiple-arm multilevel SEM (MSEM-PN) (Sterba, Preacher, Forehand, Hardcastle, Cole & Compas, 2014). In this talk, I compare and contrast these approaches and show how SSEM-PN and MSEM-PN can produce results equivalent to existing MLM-PNs. I also describe how they can be extended to flexibly accommodate several modeling features that are difficult or impossible to incorporate in MLM-PN. Importantly, implementation of such features for partially nested designs differs from fully nested designs. An empirical example involving a partially nested depression intervention combines several of these features in an analysis of interest for treatment-outcome studies.