Opinion surveys often employ multiple items to measure the respondent's underlying value, belief, or attitude. To analyze such types of data, researchers have often followed a two-step approach by first constructing a composite measure and then using it in subsequent analysis. This paper presents a class of hierarchical item response models that help integrate measurement and analysis. In this approach, individual responses to multiple items stem from a latent preference, of which both the mean and variance may depend on observed covariates. Compared with the two-step approach, the hierarchical approach reduces bias, increases efficiency, and facilitates direct comparison across surveys covering different sets of items. Moreover, it enables us to investigate not only how preferences differ among groups, vary across regions, and evolve over time, but also levels, patterns, and trends of attitude polarization and ideological constraint. An open-source R package, hIRT, is available for fitting the proposed models. two anonymous reviewers for helpful comments on previous versions of this work.Opinion surveys often employ a battery of items to measure the respondent's underlying value, belief, or attitude toward a subject. In the American National Election Studies (ANES), for example, racial resentment (toward blacks) is tapped by attitudes toward four different statements: (1) Generations of slavery and discrimination have created conditions that make it difficult for blacks to work their way out of the lower class; (2) Irish, Italians, Jewish and many other minorities overcame prejudice and worked their way up. Blacks should do the same without any special favors; (3) It's really a matter of some people not trying hard enough; if blacks would only try harder they could be just as well off as whites;(4) Over the past few years blacks have gotten less than they deserve. For each of these items, the respondent can choose among a number of ordered responses, such as agree strongly, agree somewhat, neither agree or disagree, disagree somewhat, and disagree strongly.To analyze such types of data, researchers have often followed a two-step approach-by first combining the multiple ordinal responses into a composite measure and then using this composite measure as a dependent or independent variable in subsequent analysis. In fact, the rationale of using multiple items to measure a single underlying concept is that, by appropriately pooling multiple responses, a more precise indicator can be obtained of the underlying value, belief, or attitude. A number of dimension reduction techniques can be used for this purpose. First, one could use a simple additive scale, that is, to treat the ordinal responses as integers and take their arithmetic sum (or mean) as a composite measure of the underlying construct (e.g, DiMaggio, Evans and Bryson 1996). The problem with this approach is twofold. First, for each item, it treats