**ABSTRACT NOT FOR CITATION WITHOUT AUTHOR PERMISSION. The title, authors, and abstract for this completion report are provided below.  For a copy of the full completion report, please contact the author via e-mail at lupi@msu.edu. Questions? Contact the GLFC via email at frp@glfc.org or via telephone at 734-662-3209.**





Frank Lupi


Department of Agricultural Food and Resource Economics, Michigan State University, East Lansing, MI, 48823


June 2008




Our project assessed the potential of gathering human dimensions data from creel surveys and correcting for the avidity bias associated with intercept surveys. As a part of the projects efforts to collect information on fisheries management agency use of creel surveys for human dimensions data collection, the project collected a more general set of information from each fisheries management agency in the U.S. The survey results help document the status of human dimensions data collection nationally. Some key findings include the fact that almost all agencies fairly regularly collect some form of human dimensions data, and that respondents that we classified as biologists tended to place less importance on the human dimensions data than did the contacts we classified as HD staff or upper management. In our investigation of avidity bias, we showed, via the theory and several simulation experiments under a variety of possible distributional assumptions and sampling sizes, that failure to correct for avidity bias can be quite problematic. The bias corrections were shown to be straightforward and feasible to collect as a part of a creel. The simulations showed a systematic improvement toward the true population parameters with the use of the avidity corrections. We also implemented the avidity corrections in a large scale field trail of a creel survey. Interestingly, we found that in our empirical example, the avidity bias was not particularly problematic. Never the less, the simulations do show that in some cases the bias can be substantial so that, without collecting site avidity information, managers would have no way to know how substantial the effect of avidity bias might be.