Log-Linear Modeling : Concepts, Interpretation, and Application

Content: Chapter 1 Basics of Hierarchical Log?Linear Models (pages 1–11): Chapter 2 Effects in a Table (pages 13–22): Chapter 3 Goodness?of?Fit (pages 23–54): Chapter 4 Hierarchical Log?Linear Models and Odds Ratio Analysis (pages 55–97): Chapter 5 Computations I: Basic Log?Linear Modeling (pages 99–113): Chapter 6 The Design Matrix Approach (pages 115–132): Chapter 7 Parameter Interpretation and Significance Tests (pages 133–160): Chapter 8 Computations II: Design Matrices and Poisson GLM (pages 161–183): Chapter 9 Nonhierarchical and Nonstandard Log?Linear Models (pages 185–253): Chapter 10 Computations III: Nonstandard Models (pages 255–275): Chapter 11 Sampling Schemes and Chi?Square Decomposition (pages 277–292): Chapter 12 Symmetry Models (pages 293–311): Chapter 13 Log?Linear Models of Rater Agreement (pages 313–330): Chapter 14 Comparing Associations in Subtables: Homogeneity of Associations (pages 331–343): Chapter 15 Logistic Regression and Other Logit Models (pages 345–369): Chapter 16 Reduced Designs (pages 371–385): Chapter 17 Computations IV: Additional Models (pages 387–424): "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 An easily accessible introduction to log-linear modeling for non-statisticians Highlighting advances that have lent to the topic's distinct, coherent methodology over the past decade, Log-Linear Modeling: Concepts, Interpretation, and Application provides an essential, introductory treatment of the subject, featuring many new and advanced log-linear methods, models, and applications. The book begins with basic coverage of categorical data, and goes on to describe the basics of hierarchical log-linear models as well as decomposing effects in cross-classifications and goodness-of-fit tests. Additional topics include: The generalized linear model (GLM) along with popular methods of coding such as effect coding and dummy coding Parameter interpretation and how to ensure that the parameters reflect the hypotheses being studied Symmetry, rater agreement, homogeneity of association, logistic regression, and reduced designs models Throughout the book, real-world data illustrate the application of models and understanding of the related results. In addition, each chapter utilizes R, SYSTAT®, and §¤EM software, providing readers with an understanding of these programs in the context of hierarchical log-linear modeling. Log-Linear Modeling is an excellent book for courses on categorical data analysis at the upper-undergraduate and graduate levels. It also serves as an excellent reference for applied researchers in virtually any area of study, from medicine and statistics to the social sciences, who analyze empirical data in their everyday work.