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 measurement 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 precision. 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 empirically comparing dynamic and static measures of political knowledge.

2013.  Political Analysis 21 (2): 141-171.  With Joshua Cutler.
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