POST-STRATIFICATION AS A BIAS REDUCTION TECHNIQUE

Opportunistic, non-random surveys often provide information for manage ment of wildlife resources, yet managers may be seriously misled due t o biases in the data. We show how post-stratification may be used to r educe bias. For a given factor of interest, a variable is identified t hat correlates well with it. Observations on the variable are ordered, and strata are defined by determining appropriate cutpoints. The vari able might be an estimator of the factor itself, or estimated from the same data as are used to estimate the factor, and evaluated for each of a number of small geographic units, (e.g., grid squares). In this c ircumstance, post-stratification is itself biased, especially with res pect to variances, which are underestimated. We avoid this by smoothin g the individual unit estimates so that the strata tend to comprise bl ocks of adjacent units rather than many disconnected units. Where seve ral possible variables for defining strata are available, principal co mponents analysis and projection pursuit may be used to combine inform ation from the variables. Often, the estimator of a factor of interest can be separated into components, for which different stratifications may be appropriate. Post-stratification can be applied to obtain an e stimate of each component for a random point in the area occupied by t he resource, and bootstrapping may be used to yield a robust variance of the composite estimate that does not require the assumption that th e component estimates are uncorrelated. Our techniques can be applied to reduce bias in estimates of abundance (or any other factor of inter est) in a wide range of situations where available resources or field conditions preclude a random sampling design.