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008 100301s2008 xxu| s |||| 0|eng d
020 _a9780387731865
024 7 _a10.1007/978-0-387-73186-5
_2doi
035 _a978-0-387-73186-5
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
090 _amg
100 1 _aLeeuw, Jan de
_925375
245 1 0 _aHandbook of Multilevel Analysis
_h[electronic resource]/
_cedited by Jan de Leeuw, Erik Meijer.
260 _aNew York:
_bSpringer New York,
_c2008.
300 _bdigital.
505 0 _aIntroduction to multilevel analysis, Jan de Leeuw, Erik Meijer -- Bayesian multilevel analysis and MCMC, David Draper -- Diagnostic checks for multilevel models, Tom A.B. Snijders, Johannes Berkhof -- Optimal designs for multilevel studies, Mirjam Moerbeek, Gerard J.P. Van Breukelen, Martijn P.F. Berger -- Many small groups, Stephen W. Raudenbush -- Multilevel models for ordinal and nominal variables, Donald Hedeker -- Multilevel and related models for longitudinal data, Anders Skrondal, Sophia Rabe-Hesketh -- Non-hierarchical multilevel models, Jon Rasbash, William J. Browne -- Multilevel generalized linear models, Germán Rodríguez -- Missing Data, Nicholas T. Longford -- Resampling multilevel models, Rien van der Leeden, Erik Meijer, Frank M.T.A. Busing -- Multilevel structural equation modeling, Stephen H.C. du Toit, Mathilda du Toit .
520 _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 .
650 0 _aStatistics.
_943790
650 0 _aEpidemiology.
_937814
650 0 _aMathematical statistics
_943558
650 0 _aEconometrics
_94014
650 0 _aSocial sciences
_xMethodology
_931380
650 0 _aPsychometrics.
_922831
697 _aMatemáticas Gerais-
_x(inclusive alguns textos elementares sobre assuntos específicos)
_923752
700 1 _aMeijer, Erik
_99201
710 1 _aSpringerLink (Online service).
_98857
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387731834
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-387-73186-5
942 _2impa
_cEBK
999 _aLEEUW, Jan de; MEIJER, Erik. <b> Handbook of Multilevel Analysis. </b> New York: Springer New York, 2008. ISBN 9780387731865. Disponível em: <http://dx.doi.org/10.1007/978-0-387-73186-5 >
_c38479
_d38479