LEADER 04218nam 2200637 a 450 001 9910458196603321 005 20200520144314.0 010 $a1-281-14854-7 010 $a9786611148546 010 $a0-387-73186-5 024 7 $a10.1007/978-0-387-73186-5 035 $a(CKB)1000000000401391 035 $a(EBL)338520 035 $a(OCoLC)233971361 035 $a(SSID)ssj0000167901 035 $a(PQKBManifestationID)11170242 035 $a(PQKBTitleCode)TC0000167901 035 $a(PQKBWorkID)10177803 035 $a(PQKB)10654812 035 $a(DE-He213)978-0-387-73186-5 035 $a(MiAaPQ)EBC338520 035 $a(PPN)123736803 035 $a(Au-PeEL)EBL338520 035 $a(CaPaEBR)ebr10222955 035 $a(CaONFJC)MIL114854 035 $a(EXLCZ)991000000000401391 100 $a20070927d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aHandbook of multilevel analysis$b[electronic resource] /$fJan de Leeuw, Erik Meijer, editors ; foreword by Harvey Goldstein 205 $a1st ed. 2008. 210 $aNew York $cSpringer$dc2008 215 $a1 online resource (504 p.) 300 $aDescription based upon print version of record. 311 $a0-387-73183-0 320 $aIncludes bibliographical references and indexes. 327 $ato Multilevel Analysis -- Bayesian Multilevel Analysis and MCMC -- Diagnostic Checks for Multilevel Models -- Optimal Designs for Multilevel Studies -- Many Small Groups -- Multilevel Models for Ordinal and Nominal Variables -- Multilevel and Related Models for Longitudinal Data -- Non-Hierarchical Multilevel Models -- Multilevel Generalized Linear Models -- Missing Data -- Resampling Multilevel Models -- Multilevel Structural Equation Modeling. 330 $aMultilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the bio-medical sciences. The models used for this type of data are linear and nonlinear regression models that account for observed and unobserved heterogeneity at the various levels in the data. This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. The authors of the chapters are the leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is useful for empirical researchers in these fields. Prior knowledge of multilevel analysis is not required, but a basic knowledge of regression analysis, (asymptotic) statistics, and matrix algebra is assumed. Jan de Leeuw is Distinguished Professor of Statistics and Chair of the Department of Statistics, University of California at Los Angeles. He is former president of the Psychometric Society, former editor of the Journal of Educational and Behavioral Statistics, founding editor of the Journal of Statistical Software, and editor of the Journal of Multivariate Analysis. He is coauthor (with Ita Kreft) of Introducing Multilevel Modeling and a member of the Albert Gifi team who wrote Nonlinear Multivariate Analysis. Erik Meijer is Economist at the RAND Corporation and Assistant Professor of Econometrics at the University of Groningen. He is coauthor (with Tom Wansbeek) of the highly acclaimed book Measurement Error and Latent Variables in Econometrics. 606 $aSocial sciences$xResearch$xMathematical models 606 $aMultilevel models (Statistics) 608 $aElectronic books. 615 0$aSocial sciences$xResearch$xMathematical models. 615 0$aMultilevel models (Statistics) 676 $a519.536 701 $aLeeuw$b Jan de$0367571 701 $aMeijer$b Erik$f1963-$0145462 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910458196603321 996 $aHandbook of multilevel analysis$91990350 997 $aUNINA