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Structural Equation Modeling Approaches for Analyzing Partially Nested Data.

Sonya K Sterba1, Kristopher J Preacher1, Rex Forehand2

  • 1a Department of Psychology and Human Development, Vanderbilt University.

Multivariate Behavioral Research
|January 8, 2016
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Summary
This summary is machine-generated.

New structural equation modeling (SEM) methods offer flexible analysis for partially nested data, common in treatment and educational studies. These approaches provide advanced modeling capabilities beyond traditional multilevel modeling (MLM-PN).

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Area of Science:

  • Statistics and Methodology
  • Clinical Research Design
  • Educational Research

Background:

  • Partially nested designs, where clustering occurs in some but not all study arms, are prevalent in clinical and educational research.
  • Existing multilevel modeling (MLM-PN) frameworks offer solutions but have limitations in handling complex modeling features.

Purpose of the Study:

  • To introduce two novel structural equation modeling (SEM) approaches for analyzing partially nested data: multivariate single-level SEM (SSEM-PN) and multiple-arm multilevel SEM (MSEM-PN).
  • To demonstrate the equivalence of SSEM-PN and MSEM-PN to existing MLM-PN methods.
  • To highlight the extended flexibility of SSEM-PN and MSEM-PN for complex modeling features not easily handled by MLM-PN.

Main Methods:

  • Development and application of two SEM-based approaches: multivariate single-level SEM (SSEM-PN) and multiple-arm multilevel SEM (MSEM-PN).
  • Comparison of results from SSEM-PN and MSEM-PN with established multilevel modeling for partially nested data (MLM-PN).
  • Demonstration of advanced modeling capabilities including cluster-level outcomes, latent cluster means, and traditional factor inclusion.

Main Results:

  • SSEM-PN and MSEM-PN yield results comparable to existing MLM-PN methods for partially nested designs.
  • SEM approaches enable flexible specification of complex structural models, including cluster-level outcomes and latent cluster means.
  • These methods facilitate the inclusion of traditional factors and provide absolute model fit assessment, extending beyond MLM-PN capabilities.

Conclusions:

  • Structural equation modeling (SEM) provides powerful and flexible alternatives for analyzing partially nested data in clinical and educational research.
  • SSEM-PN and MSEM-PN offer enhanced capabilities for complex modeling, surpassing limitations of traditional multilevel modeling (MLM-PN).
  • The presented empirical example illustrates the practical application of these advanced SEM techniques in treatment-outcome studies.