03240nam a2200337 a 4500001001200000003000800012006001900020007001500039008004100054010001700095020002900112020002700141040002100168035002100189050002300210082001700233100002400250245012900274246002400403260003500427300002300462504005300485520198000538533015202518650002302670655002902693700002002722710001702742856012602759999001702885ebr10648815CaPaEBRm     o  u        cr cn|||||||||120306s2013    njuad   sb   s001 0 eng d  z  2012009791  z9781118146408 (hardback)  z9781118391747 (e-book)  aCaPaEBRcCaPaEBR  a(OCoLC)82576780014aQA278b.E95 2013eb04a519.5/362231 aEye, Alexander von.10aLog-linear modelingh[electronic resource] :bconcepts, interpretation, and application /cAlexander von Eye, Eun-Young Mun.3 aLog linear modeling  aHoboken, N.J. :bWiley,c2013.  axv, 450 p. :bill.  aIncludes bibliographical references and indexes.  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.  aElectronic reproduction.bPalo Alto, Calif. :cebrary,d2015.nAvailable via World Wide Web.nAccess may be limited to ebrary affiliated libraries. 0aLog-linear models. 7aElectronic books.2local1 aMun, Eun Young.2 aebrary, Inc.40uhttp://site.ebrary.com/lib/rucke/Doc?id=10648815zAn electronic book accessible through the World Wide Web; click to view  c34782d34782