Addressing among-group variation in covariate effects using multilevel models

Brian R. Gray, Roger J. Haro, James T. Rogala

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Multilevel models are used to model processes associated with hierarchical data structures. Despite infrequent use in the biological and environmental sciences, the use of these models with hierarchically-structured data conveys multiple advantages. These include the assessment of whether covariate effects differ among groups or clusters, and separate estimation of covariate effects by hierarchical level (thereby addressing atomistic and aggregation fallacy concerns). We illustrate these advantages using larval mayfly count data derived from annual surveys on the Mississippi River and a continuous covariate (water depth).

Original languageEnglish (US)
Pages (from-to)573-591
Number of pages19
JournalEnvironmental and Ecological Statistics
Volume17
Issue number4
DOIs
StatePublished - 2010
Externally publishedYes

Keywords

  • Hierarchical models
  • Hurdle count models
  • Mayflies
  • Mixed models
  • Multilevel models
  • Negative binomial

ASJC Scopus subject areas

  • Statistics and Probability
  • General Environmental Science
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Addressing among-group variation in covariate effects using multilevel models'. Together they form a unique fingerprint.

Cite this