000 04321nam a22005535i 4500
001 978-1-4419-0742-4
003 DE-He213
005 20140220084503.0
007 cr nn 008mamaa
008 100528s2010 xxu| s |||| 0|eng d
020 _a9781441907424
_9978-1-4419-0742-4
024 7 _a10.1007/978-1-4419-0742-4
_2doi
050 4 _aQA276-280
072 7 _aJHBC
_2bicssc
072 7 _aSOC027000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aFox, Jean-Paul.
_eauthor.
245 1 0 _aBayesian Item Response Modeling
_h[electronic resource] :
_bTheory and Applications /
_cby Jean-Paul Fox.
250 _aFirst.
264 1 _aNew York, NY :
_bSpringer New York,
_c2010.
300 _aXIV, 313p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStatistics for Social and Behavioral Sciences
505 0 _ato Bayesian Response Modeling -- Bayesian Hierarchical Response Modeling -- Basic Elements of Bayesian Statistics -- Estimation of Bayesian Item Response Models -- Assessment of Bayesian Item Response Models -- Multilevel Item Response Theory Models -- Random Item Effects Models -- Response Time Item Response Models -- Randomized Item Response Models.
520 _aThis book presents a thorough treatment and unified coverage of Bayesian item response modeling with applications in a variety of disciplines, including education, medicine, psychology, and sociology. Breakthroughs in computing technology have made the Bayesian approach particularly useful for many response modeling problems. Free from computational constraints, realistic and state-of-the-art latent variable response models are considered for complex assessment and survey data to solve real-world problems. The Bayesian framework described provides a unified approach for modeling and inference, dealing with (nondata) prior information and information across multiple data sources. The book discusses methods for analyzing item response data and the complex relationships commonly associated with human response behavior and features • Self-contained introduction to Bayesian item response modeling and a coverage of extending standard models to handle complex assessment data • A thorough overview of Bayesian estimation and testing methods for item response models, where MCMC methods are emphasized • Numerous examples that cover a wide range of application areas, including education, medicine, psychology, and sociology • Datasets and software (S+, R, and WinBUGS code) of the models and methods presented in the book are available on www.jean-paulfox.com Bayesian Item Response Modeling is an excellent book for research professionals, including applied statisticians, psychometricians, and social scientists who analyze item response data from a Bayesian perspective. It is a guide to the growing area of Bayesian response modeling for researchers and graduate students, and will also serve them as a good reference. Jean-Paul Fox is Associate Professor of Measurement and Data Analysis, University of Twente, The Netherlands. His main research activities are in several areas of Bayesian response modeling. Dr. Fox has published numerous articles in the areas of Bayesian item response analysis, statistical methods for analyzing multivariate categorical response data, and nonlinear mixed effects models.
650 0 _aStatistics.
650 0 _aEpidemiology.
650 0 _aEducational tests and measurements.
650 0 _aMarketing.
650 0 _aSocial sciences
_xMethodology.
650 0 _aPsychometrics.
650 1 4 _aStatistics.
650 2 4 _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
650 2 4 _aPsychometrics.
650 2 4 _aAssessment, Testing and Evaluation.
650 2 4 _aMethodology of the Social Sciences.
650 2 4 _aMarketing.
650 2 4 _aEpidemiology.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441907417
830 0 _aStatistics for Social and Behavioral Sciences
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-0742-4
912 _aZDB-2-SMA
999 _c110245
_d110245