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

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Complex data structures in educational research require advanced methods. N-level structural equation modeling (SEM) offers a flexible framework for analyzing partially nested and cross-classified data in randomized controlled trials.

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Area of Science:

  • Psychological Research
  • Educational Psychology
  • Quantitative Methods

Background:

  • Complex data structures, including partial nesting and cross-classification, are common in educational randomized controlled trials.
  • Existing methods may not comprehensively handle both manifest and latent variables in these complex designs.

Purpose of the Study:

  • Introduce n-level structural equation modeling (SEM) as a flexible analytic framework.
  • Provide a comprehensive approach for estimating treatment effects in complex nested and cross-classified data structures.
  • Demonstrate the utility of n-level SEM for both manifest and latent variables.

Main Methods:

  • Utilized n-level structural equation modeling (SEM).
  • Employed the 'xxm' package in R for analysis.
  • Illustrated the framework with five examples covering manifest and latent variables.

Main Results:

  • N-level SEM provides a parsimonious model specification for complex data.
  • The framework effectively handles partial nesting, full nesting, and cross-classification.
  • Demonstrated successful application for both single outcome manifest variables and latent variable applications.

Conclusions:

  • N-level SEM is a versatile and powerful tool for analyzing complex data structures in educational research.
  • This framework enhances the accurate estimation of treatment effects in randomized controlled trials with intricate data hierarchies.
  • The study highlights the practical advantages of n-level SEM using real-world examples.