000			03865nam a22004935i 4500
001			978-0-387-73186-5
003			DE-He213
005			20250710084017.0
007			cr nn 008mamaa
008			100301s2008 xxu| s |||| 0|eng d
020			_a9780387731865 _a99780387731865
024	7		_a10.1007/978-0-387-73186-5 _2doi
082	0	4	_a519.5 _223
100	1		_aLeeuw, Jan de. _eeditor.
245	1	0	_aHandbook of Multilevel Analysis _h[recurso electrónico] / _cedited by Jan de Leeuw, Erik Meijer.
264		1	_aNew York, NY : _bSpringer New York, _c2008.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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.
650		0	_aSTATISTICS.
650		0	_aEPIDEMIOLOGY.
650		0	_aMATHEMATICAL STATISTICS.
650		0	_aECONOMETRICS.
650		0	_aSOCIAL SCIENCES _xMETHODOLOGY.
650		0	_aPSYCHOMETRICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
650	2	4	_aMETHODOLOGY OF THE SOCIAL SCIENCES.
650	2	4	_aPSYCHOMETRICS.
650	2	4	_aECONOMETRICS.
650	2	4	_aEPIDEMIOLOGY.
700	1		_aMeijer, Erik. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387731834
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-73186-5 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c58448 _d58448