Correcting On-Site Sampling Bias: A New Method with Application to Recreation Demand Analysis

Collecting data via on-site surveys is convenient and can be cost-effective. However, the on-site sampling scheme over-samples frequent site visitors and omits nonvisitors, which can result in biased and inconsistent estimation of population parameters. A common empirical approach to addressing the sampling issues is to make adjustments directly to the assumed population distribution. We propose an alternative empirical strategy that utilizes the sample distribution and treats endogenous stratification and truncation separately. Monte Carlo simulation shows this proposed empirical strategy has merit. A case study of recreation demand for coastal beaches using on-site survey data is presented.