LEADER 03585nam 2200649Ia 450 001 9910783698803321 005 20230803215717.0 010 $a1-283-29386-2 010 $a9786613293862 010 $a1-135-70485-6 010 $a1-4106-1202-3 035 $a(CKB)1000000000244702 035 $a(EBL)227471 035 $a(OCoLC)62706410 035 $a(SSID)ssj0000210148 035 $a(PQKBManifestationID)11194355 035 $a(PQKBTitleCode)TC0000210148 035 $a(PQKBWorkID)10283719 035 $a(PQKB)10331044 035 $a(MiAaPQ)EBC227471 035 $a(Au-PeEL)EBL227471 035 $a(CaPaEBR)ebr10103918 035 $a(CaONFJC)MIL329386 035 $a(EXLCZ)991000000000244702 100 $a20040624h20052005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNew developments in categorical data analysis for the social and behavioral sciences /$fedited by L. Andries van der Ark, Marcel A. Croon, Klaas Sijtsma 210 1$aMahwah, N.J. :$cLawrence Erlbaum,$d2005. 210 4$aŠ2005 215 $a1 online resource (xii, 261 pages) $cillustrations 225 1 $aQuantitative methodology series 311 0 $a0-415-65042-9 311 0 $a0-8058-4728-6 320 $aIncludes bibliographical references and indexes. 327 $aContents; Preface; About the Authors; 1 Statistical Models for Categorical Variables; 2 Misclassification Phenomena in Categorical Data Analysis: Regression Toward the Mean and Tendency Toward the Mode; 3 Factor Analysis With Categorical Indicators: A Comparison Between Traditional and Latent Class Approaches; 4 Bayesian Computational Methods for Inequality Constrained Latent Class Analysis; 5 Analyzing Categorical Data by Marginal Models; 6 Computational Aspects of the E-M and Bayesian Estimation in Latent Variable Models; 7 Logistic Models for Single-Subject Time Series 327 $a8 The Effect of Missing Data Imputation on Mokken Scale Analysis; 9 Building IRT Models From Scratch: Graphical Models, Exchangeability, Marginal Freedom, Scale Types, and Latent Traits; 10 The Nedelsky Model for Multiple-Choice Items; 11 Application of the Polytomous Saltus Model to Stage-Like Proportional Reasoning Data; 12 Multilevel IRT Model Assessment; Author Index; Subject Index 330 $aCategorical data are quantified as either nominal variables--distinguishing different groups, for example, based on socio-economic status, education, and political persuasion--or ordinal variables--distinguishing levels of interest, such as the preferred politician or the preferred type of punishment for committing burglary. This new book is a collection of up-to-date studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting data sets. This volume concentrates on latent class analysis and item response theory. 410 0$aQuantitative methodology series. 606 $aSocial sciences$xStatistical methods 606 $aStatistics 615 0$aSocial sciences$xStatistical methods. 615 0$aStatistics. 676 $a300/.1/5195 701 $aArk$b L. Andries van der$0286024 701 $aCroon$b Marcel A$0286025 701 $aSijtsma$b K$g(Klaas),$f1955-$0286026 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910783698803321 996 $aNew developments in categorical data analysis for the social and behavioral sciences$93867465 997 $aUNINA