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Log-linear modeling = concepts, inte...

von Eye, Alexander.

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Log-linear modeling = concepts, interpretation, and application /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Log-linear modeling/ Alexander von Eye, Eun-Young Mun.
其他題名:	concepts, interpretation, and application /
其他題名:	Log linear modeling
作者:	von Eye, Alexander.
其他作者:	Mun, Eun Young.
出版者:	Hoboken, New Jersey :Wiley, : 2013.,
面頁冊數:	1 online resource (xv, 450 p.) :ill.
內容註:	Basics of Hierarchical Log-Linear Models -- Effects in a Table -- Goodness-of-Fit -- Hierarchical Log-Linear Models and Odds Ratio Analysis -- Computations I: Basic Log-Linear Modeling -- The Design Matrix Approach -- Parameter Interpretation and Significance Tests -- Computations II: Design Matrices and Poisson GLM -- Nonhierarchical and Nonstandard Log-Linear Models -- Computations III: Nonstandard Models -- Sampling Schemes and Chi-Square Decomposition -- Symmetry Models -- Log-Linear Models of Rater Agreement -- Comparing Associations in Subtables: Homogeneity of Associations -- Logistic Regression and Other Logit Models -- Reduced Designs -- Computations IV: Additional Models.
標題:	Log-linear models. -
電子資源:	http://onlinelibrary.wiley.com/book/10.1002/9781118391778
ISBN:	9781118391747 (electronic bk.)

Log-linear modeling = concepts, interpretation, and application /
von Eye, Alexander.

Log-linear modelingconcepts, interpretation, and application /[electronic resource] :Log linear modelingAlexander von Eye, Eun-Young Mun. - Hoboken, New Jersey :Wiley,2013. - 1 online resource (xv, 450 p.) :ill.

Includes bibliographical references and indexes.

Basics of Hierarchical Log-Linear Models -- Effects in a Table -- Goodness-of-Fit -- Hierarchical Log-Linear Models and Odds Ratio Analysis -- Computations I: Basic Log-Linear Modeling -- The Design Matrix Approach -- Parameter Interpretation and Significance Tests -- Computations II: Design Matrices and Poisson GLM -- Nonhierarchical and Nonstandard Log-Linear Models -- Computations III: Nonstandard Models -- Sampling Schemes and Chi-Square Decomposition -- Symmetry Models -- Log-Linear Models of Rater Agreement -- Comparing Associations in Subtables: Homogeneity of Associations -- Logistic Regression and Other Logit Models -- Reduced Designs -- Computations IV: Additional Models.

"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"--

ISBN: 9781118391747 (electronic bk.)Subjects--Topical Terms:

565846
Log-linear models.

LC Class. No.: QA278 / .E95 2013eb

Dewey Class. No.: 519.5/36

Log-linear modeling = concepts, interpretation, and application /
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245 1 0 $a Log-linear modeling $h [electronic resource] : $b concepts, interpretation, and application / $c Alexander von Eye, Eun-Young Mun.
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505 0 $a Basics of Hierarchical Log-Linear Models -- Effects in a Table -- Goodness-of-Fit -- Hierarchical Log-Linear Models and Odds Ratio Analysis -- Computations I: Basic Log-Linear Modeling -- The Design Matrix Approach -- Parameter Interpretation and Significance Tests -- Computations II: Design Matrices and Poisson GLM -- Nonhierarchical and Nonstandard Log-Linear Models -- Computations III: Nonstandard Models -- Sampling Schemes and Chi-Square Decomposition -- Symmetry Models -- Log-Linear Models of Rater Agreement -- Comparing Associations in Subtables: Homogeneity of Associations -- Logistic Regression and Other Logit Models -- Reduced Designs -- Computations IV: Additional Models.
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"-- $c Provided by publisher.
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650 0 $a Log-linear models. $3 565846
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856 4 0 $u http://onlinelibrary.wiley.com/book/10.1002/9781118391778