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• W1538607595 abstract "Item response theory (IRT) deals with the statistical analysis of data in which responses of each of the number of respondents to each of the number of items or trials are assigned to define mutually exclusive categories. This chapter presents the best known item response models specifying the probability of response in each category as a function of parameters attributed to items and of a latent value attributed to the respondent. The extension of these models to more than one latent variable and possible respondent-level covariates is also discussed in the chapter. A further critical assumption in IRT is that item responses, given the respondent's location in the latent space, are statistically independent. It is important in IRT because it allows straightforward calculation of the likelihood of the model parameters, given the item responses of a sample of respondents. When the number of respondents is large, it leads to efficient, likelihood-based methods for IRT analysis in a wide variety of applications, including not only the measurement of individual differences, such as in education or clinical psychology, but also the estimation of group effects or population quantiles without the explicit calculation of respondent locations or “scores.”" @default.
• W1538607595 created "2016-06-24" @default.
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• W1538607595 date "2006-01-01" @default.
• W1538607595 modified "2023-09-27" @default.
• W1538607595 title "15 Item Response Theory in a General Framework" @default.
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• W1538607595 doi "https://doi.org/10.1016/s0169-7161(06)26015-2" @default.
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