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[null,"1.","Record Nr.",null,"UNINA9910141472403321"]
[null,null,"Autore",null,"Eye Alexander von"]
[null,null,"Titolo",null,"Log-linear modeling : concepts, interpretation, and application / / Alexander von Eye, Eun-Young Mun"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, N.J., : Wiley, 2013"]
[null,null,"ISBN",null,"1-118-39176-4"]
[{"$nulls":4},"1-118-39177-2"]
[{"$nulls":4},"1-283-97796-6"]
[{"$nulls":4},"1-118-39174-8"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (468 p.)"]
[null,null,"Classificazione",null,"MAT029000"]
[null,null,"Disciplina",null,"519.5/36"]
[null,null,"Soggetti",null,"Log-linear models"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and indexes."]
[null,null,"Nota di contenuto",null,"Cover; Title Page; Copyright Page; CONTENTS; Preface; Acknowledgments; 1 Basics of Hierarchical Log-linear Models; 1.1 Scaling: Which Variables Are Considered Categorical?; 1.2 Crossing Two or More Variables; 1.3 Goodman's Three Elementary Views of Log-linear Modeling; 1.4 Assumptions Made for Log-linear Modeling; 2 Effects in a Table; 2.1 The Null Model; 2.2 The Row Effects-Only Model; 2.3 The Column Effects-Only Model; 2.4 The Row- and Column-Effects Model; 2.5 Log-Linear Models; 3 Goodness-of-Fit; 3.1 Goodness-of-Fit I: Overall Fit Statistics; 3.1.1 Selecting between X2 and G2"]
[{"$nulls":4},"3.1.2 Degrees of Freedom3.2 Goodness-of-Fit II: R2 Equivalents and Information Criteria; 3.2.1 R2 Equivalents; 3.2.2 Information Criteria; 3.3 Goodness-of-Fit III: Null Hypotheses Concerning Parameters; 3.4 Goodness-of-fit IV: Residual Analysis; 3.4.1 Overall Goodness-of-Fit Measures and Residuals; 3.4.2 Other Residual Measures; 3.4.3 Comparing Residual Measures; 3.4.4 A Procedure to Identify Extreme Cells; 3.4.5 Distributions of Residuals; 3.5 The Relationship between Pearson's X2 and Log-linear Modeling; 4 Hierarchical Log-linear Models and Odds Ratio Analysis"]
[{"$nulls":4},"6.1.2 Poisson Models6.1.3 GLM for Continuous Outcome Variables; 6.2 Design Matrices: Coding; 6.2.1 Dummy Coding; 6.2.2 Effect Coding; 6.2.3 Orthogonality of Vectors in Log-linear Design Matrices; 6.2.4 Design Matrices and Degrees of Freedom; 7 Parameter Interpretation and Significance Tests; 7.1 Parameter Interpretation Based on Design "]
[{"$nulls":4},"Matrices; 7.2 The Two Sources of Parameter Correlation: Dependency of Vectors and Data Characteristics; 7.3 Can Main Effects Be Interpreted?; 7.3.1 Parameter Interpretation in Main Effect Models; 7.3.2 Parameter Interpretation in Models with Interactions"]
[{"$nulls":4},"7.4 Interpretation of Higher Order Interactions8 Computations II: Design Matrices and Poisson GLM; 8.1 GLM-Based Log-linear Modeling in R; 8.2 Design Matrices in SYSTAT; 8.3 Log-linear Modeling with Design Matrices in lEM; 8.3.1 The Hierarchical Log-linear Modeling Option in lEM; 8.3.2 Using lEM'S Command cov to Specify Hierarchical Log-linear Models; 8.3.3 Using lEM'S Command fac to Specify Hierarchical Log-linear Models; 9 Nonhierarchical and Nonstandard Log-linear Models; 9.1 Defining Nonhierarchical and Nonstandard Log-linear Models"]
[{"$nulls":4},"9.2 Virtues of Nonhierarchical and Nonstandard Log-linear Models"]
[null,null,null,"Sommario/riassunto","\"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\"--"]