Behavior Genetics

, Volume 32, Issue 1, pp 37–49

The Mixed or Multilevel Model for Behavior Genetic Analysis

  • Guang Guo
  • Jianmin Wang

DOI: 10.1023/A:1014455812027

Cite this article as:
Guo, G. & Wang, J. Behav Genet (2002) 32: 37. doi:10.1023/A:1014455812027


We propose the mixed model or multilevel model as a general alternative approach to existing behavior genetic analysis—an alternative to correlation analysis, the DeFries-Fulker analysis, and structural equation modeling. The mixed or multilevel model handles readily families of behavioral genetic data, which include paired sibling data (e.g., pairs of MZ and DZ twins) and clustered sibling data (e.g., a family of more than two biological siblings) as special cases. Not only can a family of behavioral genetic data have more than two siblings, it can also contain multiple types of siblings (e.g., a pair of MZ twins, a pair of DZ twins, a full sibling, and a half sibling). In contrast to the traditional approaches, the mixed or multilevel model is insensitive to the order of the siblings in a sibling cluster. We apply our approach to a large, nationally representative behavior genetic sample collected recently by the Add Health Study. We demonstrate the approach through several applications using both clustered and family complex behavioral genetic data: conventional variance decomposition analysis, analysis of interactions between genetic and environmental influences, and analysis of the possible genetic basis for friendship selection. We compare results from the mixed or multilevel model, Pearson's correlation analysis, and the structural equation model.

Multilevel model hierarchical linear model the mixed model DF analysis structural equation models 

Copyright information

© Plenum Publishing Corporation 2002

Authors and Affiliations

  • Guang Guo
    • 1
  • Jianmin Wang
    • 2
  1. 1.Department of SociologyUniversity of North Carolina at Chapel Hill
  2. 2.Department of BiostatisticsUniversity of North Carolina at Chapel Hill

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