Designing field experiments which are subject to representation bias

Rob Deardon, Steven Gilmour, Neil Butler, Kath Phelps and Roy Kennedy

Journal of Applied Statistics, 2006, vol. 33, issue 7, 663-678

Abstract: The term 'representation bias' is used to describe the disparities that exist between treatment effects estimated from field experiments, and those effects that would be seen if treatments were used in the field. In this paper we are specifically concerned with representation bias caused by disease inoculum travelling between plots, or out of the experimental area altogether. The scope for such bias is maximized in the case of airborne spread diseases. This paper extends the work of Deardon et al. (2004), using simulation methods to explore the relationship between design and representation bias. In doing so, we illustrate the importance of plot size and spacing, as well as treatment-to-plot allocation. We examine a novel class of designs, incomplete column designs, to develop an understanding of the mechanisms behind representation bias. We also introduce general methods of designing field trials, which can be used to limit representation bias by carefully controlling treatment to block allocation in both incomplete column and incomplete randomized block designs. Finally, we show how the commonly used practice of sampling from the centres of plots, rather than entire plots, can also help to control representation bias.

Keywords: Experimental design; inter-plot interference; plant pathology; plant disease dispersal simulation (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760600708681 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:33:y:2006:i:7:p:663-678

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760600708681

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().