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Bayesian Item Response Modeling - Theory and Applications (Hardcover, 2010 ed.)
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Bayesian Item Response Modeling - Theory and Applications (Hardcover, 2010 ed.)
Series: Statistics for Social and Behavioral Sciences
Expected to ship within 9 - 15 working days
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The modeling of item response data is governed by item response
theory, also referred to as modern test theory. The eld of inquiry
of item response theory has become very large and shows the
enormous progress that has been made. The mainstream literature is
focused on frequentist statistical methods for - timating model
parameters and evaluating model t. However, the Bayesian
methodology has shown great potential, particularly for making
further - provements in the statistical modeling process. The
Bayesian approach has two important features that make it
attractive for modeling item response data. First, it enables the
possibility of incorpor- ing nondata information beyond the
observed responses into the analysis. The Bayesian methodology is
also very clear about how additional information can be used.
Second, the Bayesian approach comes with powerful simulation-based
estimation methods. These methods make it possible to handle all
kinds of priors and data-generating models. One of my motives for
writing this book is to give an introduction to the Bayesian
methodology for modeling and analyzing item response data. A
Bayesian counterpart is presented to the many popular item response
theory books (e.g., Baker and Kim 2004; De Boeck and Wilson, 2004;
Hambleton and Swaminathan, 1985; van der Linden and Hambleton,
1997) that are mainly or completely focused on frequentist methods.
The usefulness of the Bayesian methodology is illustrated by
discussing and applying a range of Bayesian item response models.
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