000			03380nam a2200397 a 4500
001			EBC918213
003			MiAaPQ
005			20240120133526.0
006			m o d |
007			cr cn|||||||||
008			120306s2013 njuad sb s001 0 eng d
010			_z 2012009791
020			_z9781118146408 (hardback)
020			_a9781118391747 (electronic bk.)
035			_a(MiAaPQ)EBC918213
035			_a(Au-PeEL)EBL918213
035			_a(CaPaEBR)ebr10648815
035			_a(CaONFJC)MIL429046
035			_a(OCoLC)826853571
040			_aMiAaPQ _cMiAaPQ _dMiAaPQ
050		4	_aQA278 _b.E95 2013
082	0	4	_a519.5/36 _223
100	1		_aEye, Alexander von.
245	1	0	_aLog-linear modeling _h[electronic resource] : _bconcepts, interpretation, and application / _cAlexander von Eye, Eun-Young Mun.
246	3		_aLog linear modeling
260			_aHoboken, N.J. : _bWiley, _c2013.
300			_axv, 450 p. : _bill.
504			_aIncludes bibliographical references and indexes.
520			_a"Over the past ten years, there have been many important advances in log-linear modeling, including the specification of new models, in particular non-standard models, and their relationships to methods such as Rasch modeling. While most literature on the topic is contained in volumes aimed at advanced statisticians, Applied Log-Linear Modeling presents the topic in an accessible style that is customized for applied researchers who utilize log-linear modeling in the social sciences. The book begins by providing readers with a foundation on the basics of log-linear modeling, introducing decomposing effects in cross-tabulations and goodness-of-fit tests. Popular hierarchical log-linear models are illustrated using empirical data examples, and odds ratio analysis is discussed as an interesting method of analysis of cross-tabulations. Next, readers are introduced to the design matrix approach to log-linear modeling, presenting various forms of coding (effects coding, dummy coding, Helmert contrasts etc.) and the characteristics of design matrices. The book goes on to explore non-hierarchical and nonstandard log-linear models, outlining ten nonstandard log-linear models (including nonstandard nested models, models with quantitative factors, logit models, and log-linear Rasch models) as well as special topics and applications. A brief discussion of sampling schemes is also provided along with a selection of useful methods of chi-square decomposition. Additional topics of coverage include models of marginal homogeneity, rater agreement, methods to test hypotheses about differences in associations across subgroup, the relationship between log-linear modeling to logistic regression, and reduced designs. Throughout the book, Computer Applications chapters feature SYSTAT, Lem, and R illustrations of the previous chapter's material, utilizing empirical data examples to demonstrate the relevance of the topics in modern research"-- _cProvided by publisher.
533			_aElectronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.
650		0	_aLog-linear models.
655		4	_aElectronic books.
700	1		_aMun, Eun Young.
710	2		_aProQuest (Firm)
856	4	0	_uhttps://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=918213 _zClick to View
999			_c78811 _d78811