LEADER 04106nam 22006735 450 001 9911015684303321 005 20260211141954.0 010 $a9783031872716 024 7 $a10.1007/978-3-031-87271-6 035 $a(CKB)39542345300041 035 $a(MiAaPQ)EBC32189641 035 $a(Au-PeEL)EBL32189641 035 $a(DE-He213)978-3-031-87271-6 035 $a(OCoLC)1527724241 035 $a(EXLCZ)9939542345300041 100 $a20250702j2025 u| 0 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA Short Guide to Item Response Theory Models /$fby Gerhard Tutz 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (369 pages) 225 1 $aStatistics for Social and Behavioral Sciences,$x2199-7365 311 08$a9783031872709 327 $aPreface -- Introduction -- The Binary Rasch Model -- Extensions of the Rasch Model and Alternative Binary Models -- Ordinal Models -- Extended Ordinal Models Accounting for Response Styles -- The Thresholds Model a Common Framework for Discrete and Continuous Responses -- Classical Test Theory -- Response Models for Count Data -- Tree-Based Item Response Models -- Differential Item Functioning -- Explanatory Item Response Models -- R Packages -- Examples -- Bibliography. 330 $aThis book presents foundational concepts, essential principles, and practical applications of Item Response Theory (IRT). It provides a structured survey of diverse models that have been put forth, emphasizing both their differences and commonalities. The main focus is on modern latent trait theory models which provide measurement tools that clearly separate between person abilities and item parameters. The topics covered include the binary Rasch model, its extensions and alternative binary models, ordinal models and their extensions that account for response styles, the thresholds model, classical test theory, response models for count data, differential item functioning, and explanatory item response models. Tree-based item response models, typically not found in classical IRT textbooks, are also addressed. Applications of the models are illustrated on several data sets from differing areas, showing how models can be fitted and compared. All examples have been computed using R. Code snippets are provided, and the full R code for most of the examples is available online. The book is aimed at graduate students, applied statisticians, and researchers working in psychometrics, educators, and anyone curious about modeling strategies that enhance the precision and validity of their measurement tools. It serves as an introductory guide for beginners while also providing a resource for those seeking an overview of the plethora of available IRT models. 410 0$aStatistics for Social and Behavioral Sciences,$x2199-7365 606 $aStatistics 606 $aPsychometrics 606 $aSocial sciences$xStatistical methods 606 $aStatistics 606 $aStatistical Theory and Methods 606 $aPsychometrics 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 606 $aApplied Statistics 606 $aEstadística$2thub 606 $aPsicometria$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aPsychometrics. 615 0$aSocial sciences$xStatistical methods. 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 615 24$aPsychometrics. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 615 24$aApplied Statistics. 615 7$aEstadística 615 7$aPsicometria 676 $a519.5 700 $aTutz$b Gerhard$089112 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911015684303321 996 $aA Short Guide to Item Response Theory Models$94408714 997 $aUNINA