Frontiers in Psychology (Apr 2014)

Analyzing Average and Conditional Effects with Multigroup Multilevel Structural EquationModels

  • Axel eMayer,
  • Axel eMayer,
  • Benjamin eNagengast,
  • John eFletcher,
  • Rolf eSteyer

DOI
https://doi.org/10.3389/fpsyg.2014.00304
Journal volume & issue
Vol. 5

Abstract

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Conventionally, multilevel analysis of covariance (ML-ANCOVA) has been therecommended approach for analyzing treatment effects in quasi-experimental multilevel designswith treatment application at the cluster-level. In this paper, we introduce the generalizedML-ANCOVA with linear effect functions that identifies average and conditional treatment effectsin the presence of treatment-covariate interactions. We show how the generalized ML-ANCOVAmodel can be estimated with multigroup multilevel structural equation models that offerconsiderable advances compared to traditional ML-ANCOVA. The proposed model takes intoaccount measurement error in the covariates, sampling error in contextual covariates,treatment-covariate interactions, and stochastic predictors. We illustrate the implementation ofML-ANCOVA with an example from educational effectiveness research where we estimateaverage and conditional effects of early transition to secondary schooling on readingcomprehension.

Keywords