Elicit Data, Not Prior: On Using Expert Opinion in Ecological Studies

Subhash R. Lele

in The Nature of Scientific Evidence

Published by University of Chicago Press

Published in print October 2004 | ISBN: 9780226789552
Published online February 2013 | e-ISBN: 9780226789583 | DOI:
Elicit Data, Not Prior: On Using Expert Opinion in Ecological Studies

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Ecological studies are often hampered by insufficient data on the quantity of interest. Limited data usually lead to a relatively flat likelihood surface that is not very informative. One solution is to augment the available data by incorporating other possible sources of information. A wealth of information in the form of “soft” data, such as expert opinion about whether pollutant concentration exceeds a certain threshold, may be available. This chapter proposes a mechanism to incorporate such soft information and expert opinion in the process of inference. A commonly used approach for incorporating expert opinion in statistical inference is via the Bayesian paradigm. This chapter discusses various difficulties associated with the Bayesian approach. It introduces the idea of eliciting data instead of priors and examines its practicality. It then describes a general hierarchical model setup for combining elicited data and the observed data. It illustrates the effectiveness of this method for presence-absence data using simulations. The chapter demonstrates that incorporating expert opinion via elicited data substantially improves the estimation, prediction, and design aspects of statistical inference for spatial data.

Keywords: ecological studies; expert opinion; statistical inference; spatial data; soft data; Bayesian approach; priors; hierarchical model; presence-absence data; elicited data

Chapter.  10879 words.  Illustrated.

Subjects: Animal Pathology and Diseases

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