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020 _a9780387731865
_99780387731865
024 7 _a10.1007/9780387731865
_2doi
035 _avtls000332350
039 9 _a201509030735
_bVLOAD
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_dVLOAD
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040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA276-280
100 1 _aLeeuw, Jan de.
_eeditor.
_9302376
245 1 0 _aHandbook of Multilevel Analysis /
_cedited by Jan de Leeuw, Erik Meijer.
264 1 _aNew York, NY :
_bSpringer New York,
_c2008.
300 _brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
500 _aSpringer eBooks
505 0 _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.
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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aMeijer, Erik.
_eeditor.
_9302377
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9780387731834
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-73186-5
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c278628
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