Conjoint analysis (CA) is one approach to measuring preferences ("utilities") that estimates both overall preferences for goods or services as well as preferences for their specific attributes. This method, derived from economic theory, has been widely used in many fields, as it can provide a more flexible format for preference measurement than contingent valuation or QALYs. However, CA has not been as widely used in health care. An unresolved issue is whether CA surveys can be made easier for respondents, thereby increasing their reliability and validity. OBJECTIVE: We conducted an experiment to assess whether surveys designed to be easier for respondents would have greater reliability and validity than surveys developed using the standard CA approach where all levels of attributes are allowed to vary. METHODS: We surveyed 300 respondents being tested for HIV. Respondents were asked nine questions about whether they would choose Test A or B based on six attributes (location, cost, ease of collection, timeliness and accuracy, confidentiality, counseling). We also asked one repeated question and one dominated question (i.e., where respondents should choose the obvious choice). In the standard survey, levels of all six attributes were allowed to vary. In the easier version, levels of two attributes stayed constant. RESULTS: Based on preliminary data, we found a trend towards the easier survey having greater reliability and validity, as well as lower subjective difficulty ratings. Further analyses will examine the trade-offs between the decreased cognitive complexity of the easier survey with the statistical efficiency lost by forcing attributes to remain constant. CONCLUSION: CA can provide useful information on preferences for attributes of goods and services, including pharmaceuticals. By simplifying the method used to obtain preferences, it may be possible to obtain more reliable and valid responses without sacrificing statistical efficiency.