Abstract
Item responses can be masked before they are observed via a randomized response mechanism. This technique is used to protect individuals and improve their willingness to answer truthfully. Various traditional randomized response sampling techniques are discussed and extended to a multivariate setting. So-called randomized item response models will be introduced for analyzing multivariate randomized response data. This class of models can also be extended to handle explanatory information at di_erent hierarchical levels. The models discussed are particularly suitable for analyzing sensitive individual characteristics and their relationships to background variables.
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© 2010 Springer New York
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Fox, JP. (2010). Randomized Item Response Models. In: Bayesian Item Response Modeling. Statistics for Social and Behavioral Sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0742-4_9
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DOI: https://doi.org/10.1007/978-1-4419-0742-4_9
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Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-0741-7
Online ISBN: 978-1-4419-0742-4
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