LEADER 03645nam 2200661Ia 450 001 9910450729003321 005 20200520144314.0 010 $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 $a20040624d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aNew developments in categorical data analysis for the social and behavioral sciences$b[electronic resource] /$fedited by L. Andries van der Ark, Marcel A. Croon, Klaas Sijtsma 210 $aMahwah, N.J. $cLawrence Erlbaum$dc2005 215 $a1 online resource (274 p.) 225 1 $aQuantitative methodology series 300 $aDescription based upon print version of record. 311 $a0-415-65042-9 311 $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 Analysis9 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 $aCategorical data are quantified as either nominal variables--distinguishing different groups, for example, based on socio-economic status, education, and political persuasion--or ordinal variables--distinguishing levels of interest, such as the preferred politician or the preferred type of punishment for committing burglary. This new book is a collection of up-to-date studies on modern categorical data analysis methods, emphasizing their application to relevant and interesting data sets.This volume concentrates on latent class analysis and item response theory. These methods use latent 410 0$aQuantitative methodology series. 606 $aSocial sciences$xStatistical methods 606 $aStatistics 608 $aElectronic books. 615 0$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 $a9910450729003321 996 $aNew developments in categorical data analysis for the social and behavioral sciences$92227556 997 $aUNINA