To assess the total value, including use and nonuse values, of nonmarket goods such as environmental amenities, researchers often apply survey techniques that allow them to explore public preferences for hypothetical goods or services. Until recently, the standard survey technique for this purpose has been the contingent valuation (CV) method. Recently, a similar but more complex technique, conjoint analysis, has been used in several environmental contexts. Conjoint analysis is a marketing technique that can be used to assess values for attributes of market or nonmarket goods based on survey respondents' willingness to trade-off different bundles of these attributes. In a conjoint analysis survey, respondents are presented with a set of scenarios that differ in terms of a series of attributes and are asked to rank the alternative scenarios, or choose their most preferred. The scenarios in the choice set differ by the levels of the different attributes. A major cost consideration in conducting surveys for environmental valuation is the per-unit cost of survey administration. At current costs, sample sizes are often limited to the smallest researchers feel is necessary for a particular problem. By employing optimal survey design techniques, practitioners can increase the informational content of each observation, producing the equivalent effect of a larger sample size. The goal of this research is to determine optimal attribute levels and choice sets for conjoint analysis questions that, given a fixed number of observations, will provide the most information possible about parameter estimators of interest, such as mean or median willingness to pay. This research will extend the existing literature on the optimal design of conjoint analysis surveys in two ways: it will consider attribute levels as well as choice sets as variables in the optimization problem and it will derive `optimal` designs as opposed to ''efficient.'' The methods to be used in deriving the design results are optimization algorithms, principally, a search routine that searches over all choice set combinations to derive optimal choice sets and an analytical optimization algorithm to derive optimal attribute levels.
