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Appears in Collections:Economics Journal Articles
Peer Review Status: Refereed
Title: Contrasting approaches to statistical regression in ecology and economics
Authors: Armsworth, Paul R
Gaston, Kevin J
Hanley, Nicholas
Ruffell, Robin
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Keywords: regression
interdisciplinary studies
Issue Date: Apr-2009
Publisher: Wiley-Blackwell / British Ecological Society
Citation: Armsworth PR, Gaston KJ, Hanley N & Ruffell R (2009) Contrasting approaches to statistical regression in ecology and economics, Journal of Applied Ecology, 46 (2), pp. 265-268.
Abstract: 1. Conservation and natural resource management challenges are as much social problems as biological ones. In recognition of this fact, ecologists and economists work increasingly closely together. We discuss one barrier to effective integration of the two disciplines: put simply, many ecologists and economists approach statistical regression differently. 2. Regression techniques provide the most commonly used approach for empirical analyses of land management decisions. Researchers from each discipline attribute differing importance to a range of possibly conflicting design criteria when formulating regression analyses. 3. Ecologists commonly attribute greater importance to spatial autocorrelation and parsimony than do economists when designing regressions. Economists often attribute greater importance than ecologists to concerns about endogeneity and conformance with a priori theoretical expectations. 4. Synthesis and applications. The differing importance attributed to different design characteristics may reflect a process of cultural drift within each discipline. Greater interdisciplinary collaboration can counteract this process by stimulating the flow of ideas and techniques across disciplinary boundaries.
Type: Journal Article
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Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.
Affiliation: University of Sheffield
University of Sheffield

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