ANALYZING CORRELATED COUNT DATA FROM FIELD TRIALS

Field experiments are often affected by both spatial and temporal (i.e . repeated measures) correlation. In order to obtain an analysis that is scientifically valid it is important to recognise the underlying er ror structure and analyse the data accordingly. We will discuss the an alysis of count data which is spatially and temporally correlated, and illustrate the difference between an independent error structure mode l and a marginal Quasi-Likelihood model which attempts to account for the correlation present in the data. We shall then show the possible i mpact of inefficient analysis techniques on the subsequent economic de cisions.