Computerized Adaptive Testing for Public Opinion Surveys∗

Survey researchers avoid using large multi-item scales to measure latent traits due to both the financial costs and the risk of driving up non-response rates. Typically, investigators select a subset of available scale items rather than ask-ing the full battery. Reduced batteries, however, can sharply reduce measure-ment precision and introduce bias. In this article, we present computerized adaptive testing (CAT) as a method for minimizing the number of questions each respondent must answer while preserving measurement accuracy and pre-cision. CAT algorithms respond to individuals ’ previous answers to select sub-sequent questions that most efficiently reveal respondents ’ position on a latent dimension. We introduce the basic stages of a CAT algorithm and present the details for one approach to item-selection appropriate for public opinion research. We then demonstrate the advantages of CAT via simulation and em-pirically comparing dynamic and static measures of political knowledge