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05444nam a2200685 4500 |
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ocn825767800 |
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OCoLC |
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20170124071718.9 |
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m o d |
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cr cnu---unuuu |
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130129s2013 njua ob s001 0 eng d |
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|z 2012009791
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|a MAIN
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|a Eye, Alexander von.
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|a Log-linear modeling :
|b concepts, interpretation, and application /
|c Alexander von Eye, Michigan State University, Department of Psychology, East Lansing, MI, Eun-Young Mun, Rutgers, the State University of New Jersey, Center for Alcohol Studies, Piscataway, New Jersey.
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264 |
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|a Hoboken, New Jersey :
|b Wiley,
|c [2013]
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300 |
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|a 1 online resource (xv, 450 pages) :
|b illustrations
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336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a data file
|2 rda
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|a Bibliography
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|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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|a Includes bibliographical references and indexes.
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|a Print version record.
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|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.
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650 |
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|a Log-linear models.
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650 |
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7 |
|a MATHEMATICS
|x Probability & Statistics
|x General.
|2 bisacsh
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650 |
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7 |
|a MATHEMATICS
|x Probability & Statistics
|x Regression Analysis.
|2 bisacsh
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650 |
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7 |
|a Log-linear models.
|2 fast
|0 (OCoLC)fst01001918
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655 |
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|a Electronic books.
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655 |
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7 |
|a Electronic books.
|2 local
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700 |
1 |
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|a Mun, Eun Young.
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776 |
0 |
8 |
|i Print version:
|a Eye, Alexander von.
|t Log-linear modeling.
|d Hoboken, New Jersey : Wiley, [2013]
|z 9781118146408
|w (DLC) 2012009791
|w (OCoLC)779259318
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856 |
4 |
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|u https://doi.org/10.1002/9781118391778
|z Full Text via HEAL-Link
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994 |
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|a 92
|b DG1
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