MMI: Multimodel inference or models with management implications?

John Fieberg, Douglas H. Johnson

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

We consider a variety of regression modeling strategies for analyzing observational data associated with typical wildlife studies, including all subsets and stepwise regression, a single full model, and Akaike's Information Criterion (AIC)-based multimodel inference. Although there are advantages and disadvantages to each approach, we suggest that there is no unique best way to analyze data. Further, we argue that, although multimodel inference can be useful in natural resource management, the importance of considering causality and accurately estimating effect sizes is greater than simply considering a variety of models. Determining causation is far more valuable than simply indicating how the response variable and explanatory variables covaried within a data set, especially when the data set did not arise from a controlled experiment. Understanding the causal mechanism will provide much better predictions beyond the range of data observed.

Original languageEnglish (US)
Pages (from-to)708-718
Number of pages11
JournalJournal of Wildlife Management
Volume79
Issue number5
DOIs
StatePublished - Jul 1 2015

Bibliographical note

Publisher Copyright:
© 2015 The Wildlife Society.

Keywords

  • causation
  • effect size
  • mechanism
  • model selection
  • multicollinearity
  • multimodel inference
  • observational data
  • overfitting
  • prediction
  • statistics

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