Effect Partitioning in Cross-Sectionally Clustered Data Without Multilevel Models

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1 Citation (Scopus)

Abstract

Effect partitioning is almost exclusively performed with multilevel models (MLMs)–so much so that some have considered the two to be synonymous. MLMs are able to provide estimates with desirable statistical properties when data come from a hierarchical structure; but the random effects included in MLMs are not always integral to the analysis. As a result, other methods with relaxed assumptions are viable options in many cases. Through empirical examples and simulations, we show how generalized estimating equations (GEEs) can be used to effectively partition effects without random effects. We show that more onerous steps of MLMs such as determining the number of random effects and the structure for their covariance can be bypassed with GEEs while still obtaining identical or near-identical results. Additionally, violations of distributional assumptions adversely affect estimates with MLMs but have no effect on GEEs because no such assumptions are made. This makes GEEs a flexible alternative to MLMs with minimal assumptions that may warrant consideration. Limitations of GEEs for partitioning effects are also discussed.

Original languageEnglish (US)
JournalMultivariate Behavioral Research
DOIs
StatePublished - Jan 1 2019

Fingerprint

Multilevel Models
Clustered Data
Generalized Estimating Equations
Partitioning
Random Effects
Hierarchical Structure
Estimate
Statistical property
Partition
Equations
Alternatives
Simulation

Keywords

  • compositional effect
  • contextual effect
  • hierarchical linear modeling
  • Multilevel modeling

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

Cite this

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title = "Effect Partitioning in Cross-Sectionally Clustered Data Without Multilevel Models",
abstract = "Effect partitioning is almost exclusively performed with multilevel models (MLMs)–so much so that some have considered the two to be synonymous. MLMs are able to provide estimates with desirable statistical properties when data come from a hierarchical structure; but the random effects included in MLMs are not always integral to the analysis. As a result, other methods with relaxed assumptions are viable options in many cases. Through empirical examples and simulations, we show how generalized estimating equations (GEEs) can be used to effectively partition effects without random effects. We show that more onerous steps of MLMs such as determining the number of random effects and the structure for their covariance can be bypassed with GEEs while still obtaining identical or near-identical results. Additionally, violations of distributional assumptions adversely affect estimates with MLMs but have no effect on GEEs because no such assumptions are made. This makes GEEs a flexible alternative to MLMs with minimal assumptions that may warrant consideration. Limitations of GEEs for partitioning effects are also discussed.",
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