This paper compares several methods of performing conjoint analysis when there is a large number of attributes. National parks were described in terms of 17 attributes and 56 levels. Subjects were randomly assigned to one of four groups and each person responded to a calibration questionnaire that allowed the estimation of one of the following conjoint analysis models: full profile, ACA, individual-level hybrid, or full profile on the person's eight stated most important attributes. Validations were performed in terms of individual choices and aggregate choice shares. Reliabilities were assessed on both ratings and choices. Surprisingly even with a large number of attributes, the full profile method consistently validated best. Second was a full profile model estimated on the respondent's stated eight most important attributes. ACA and individual hybrid conjoint analysis performed similarly, but worse than these two methods on most measures. Validation differences were more strongly related to differences in attribute importances than desirabilities for levels within an attribute. It appears that these respondents were not able to accurately report self-explicated importances with a large number of attributes.