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Effect Partitioning in Cross-Sectionally Clustered Data Without Multilevel Models.

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This summary is machine-generated.

Generalized Estimating Equations (GEEs) offer a flexible alternative to Multilevel Models (MLMs) for effect partitioning. GEEs bypass complex random effects, providing similar results with fewer assumptions and greater robustness to distributional violations.

Keywords:
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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Effect partitioning is commonly performed using Multilevel Models (MLMs).
  • MLMs are suitable for hierarchical data but often include random effects that are not essential for analysis.
  • This reliance on random effects can lead to complex model specification and sensitivity to distributional assumptions.

Purpose of the Study:

  • To demonstrate the utility of Generalized Estimating Equations (GEEs) for effect partitioning as an alternative to MLMs.
  • To show that GEEs can achieve similar results to MLMs without the need for specifying random effects.
  • To highlight the advantages of GEEs, including relaxed assumptions and robustness to distributional violations.

Main Methods:

  • Empirical examples and simulation studies were used to compare GEEs and MLMs.
  • GEEs were applied to partition effects in hierarchical data structures.
  • Model complexity, assumption violations, and result equivalence were assessed.

Main Results:

  • GEEs effectively partitioned effects without requiring the specification of random effects.
  • GEEs bypassed complex MLM procedures like determining the number and covariance structure of random effects.
  • GE estimates were identical or near-identical to MLM estimates in simulations.
  • GEEs showed robustness to violations of distributional assumptions, unlike MLMs.

Conclusions:

  • GEEs provide a flexible and less assumption-laden alternative to MLMs for effect partitioning.
  • The use of GEEs can simplify the analysis of hierarchical data by avoiding complex random effect specifications.
  • GEEs warrant consideration as a viable method for effect partitioning, particularly when distributional assumptions are a concern.