Using n-Level Structural Equation Models for Causal Modeling in Fully Nested, Partially Nested, and Cross-Classified Randomized Controlled Trials

• In Event: Marriott Roundtable Session 41
  In Roundtable Session: Methodological Design and Illustrations

Abstract

The present study introduces n-level SEM (Mehta, 2013a) a flexible measurement and analytic framework for the estimation of treatment effects for complex data structures that frequently present in randomized controlled trials. In this tutorial, we explore how the notation of n-level SEM allows for parsimonious model specification whether data are observed or latent and in the presence of partial nested or cross-classified designs. By using the xxm package in R, the advantage of using n-level SEM framework is demonstrated through two examples for single outcome manifest variables with partial of full nesting.

Authors

• Yaacov Petscher, The Florida Center for Reading Research

• Chris Schatschneider, Florida State University