03240nam a2200337 a 4500 ebr10648815 CaPaEBR m o u cr cn||||||||| 120306s2013 njuad sb s001 0 eng d 2012009791 9781118146408 (hardback) 9781118391747 (e-book) CaPaEBR CaPaEBR (OCoLC)825767800 QA278 .E95 2013eb 519.5/36 23 Eye, Alexander von. Log-linear modeling [electronic resource] : concepts, interpretation, and application / Alexander von Eye, Eun-Young Mun. Log linear modeling Hoboken, N.J. : Wiley, 2013. xv, 450 p. : ill. Includes bibliographical references and indexes. "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"-- Provided by publisher. Electronic reproduction. Palo Alto, Calif. : ebrary, 2015. Available via World Wide Web. Access may be limited to ebrary affiliated libraries. Log-linear models. Electronic books. local Mun, Eun Young. ebrary, Inc. http://site.ebrary.com/lib/rucke/Doc?id=10648815 An electronic book accessible through the World Wide Web; click to view 34782 34782