| 期刊全稱 | Bayesian Item Response Modeling | | 期刊簡(jiǎn)稱 | Theory and Applicati | | 影響因子2023 | Jean-Paul Fox | | 視頻video | http://file.papertrans.cn/182/181855/181855.mp4 | | 發(fā)行地址 | Introduces Bayesian item response modeling with examples in a wide array of contexts.Gives a unified treatment of extending traditional item response models to handle more complex assessment data.Comp | | 學(xué)科分類 | Statistics for Social and Behavioral Sciences | | 圖書(shū)封面 |  | | 影響因子 | 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 respo | | Pindex | Book 2010 |
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