00494nam a2200169 u 450099100425173180753620220922164527.0220922s2022 xx r 000 0 ita dBibl. Interfacoltà T. Pellegrinoita846.912Emery, Leon436953Correspondance 1 /Leon Emery[S.l.] :O.D.I.C.E. Carpentras,1982464 p. ;21 cm.991004251731807536Correspondance 13377709UNISALENTO04184nam 2200625 a 450 991078477200332120200520144314.01-281-14854-797866111485460-387-73186-510.1007/978-0-387-73186-5(CKB)1000000000401391(EBL)338520(OCoLC)233971361(SSID)ssj0000167901(PQKBManifestationID)11170242(PQKBTitleCode)TC0000167901(PQKBWorkID)10177803(PQKB)10654812(DE-He213)978-0-387-73186-5(MiAaPQ)EBC338520(Au-PeEL)EBL338520(CaPaEBR)ebr10222955(CaONFJC)MIL114854(PPN)123736803(EXLCZ)99100000000040139120070927d2008 uy 0engur|n|---|||||txtccrHandbook of multilevel analysis[electronic resource] /Jan de Leeuw, Erik Meijer, editors ; foreword by Harvey Goldstein1st ed. 2008.New York Springerc20081 online resource (504 p.)Description based upon print version of record.0-387-73183-0 Includes bibliographical references and indexes.to 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.Multilevel 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.Social sciencesResearchMathematical modelsMultilevel models (Statistics)Social sciencesResearchMathematical models.Multilevel models (Statistics)519.536Leeuw Jan de367571Meijer Erik1963-145462MiAaPQMiAaPQMiAaPQBOOK9910784772003321Handbook of multilevel analysis3720113UNINA