Log Linear Modeling

Autor: Alexander von Eye
Publisher: John Wiley & Sons
ISBN: 1118391764
File Size: 46,58 MB
Format: PDF, ePub
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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.

Log Linear Modeling Concepts Interpretation And Application

Autor: CTI Reviews
Publisher: Cram101 Textbook Reviews
ISBN: 1497033322
File Size: 58,39 MB
Format: PDF, ePub, Mobi
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Facts101 is your complete guide to Log-Linear Modeling, Concepts, Interpretation, and Application. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.

Categorical Statistics For Communication Research

Autor: Bryan E. Denham
Publisher: John Wiley & Sons
ISBN: 1118927079
File Size: 75,95 MB
Format: PDF, Docs
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Categorical Statistics for CommunicationResearch presents scholars with a discipline-specific guide to categorical data analysis. The text blends necessary background information and formulas for statistical procedures with data analyses illustrating techniques such as log- linear modeling and logistic regression analysis. Provides techniques for analyzing categorical data from a communication studies perspective Provides an accessible presentation of techniques for analyzing categorical data for communication scholars and other social scientists working at the advanced undergraduate and graduate teaching levels Illustrated with examples from different types of communication research such as health, political and sports communication and entertainment Includes exercises at the end of each chapter and a companion website containing exercise answers and chapter-by-chapter PowerPoint slides

Vector Generalized Linear And Additive Models

Autor: Thomas W. Yee
Publisher: Springer
ISBN: 149392818X
File Size: 48,71 MB
Format: PDF
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This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully operable, the framework allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole. Since their advent in 1972, GLMs have unified important distributions under a single umbrella with enormous implications. However, GLMs are not flexible enough to cope with the demands of practical data analysis. And data-driven GLMs, in the form of generalized additive models (GAMs), are also largely confined to the exponential family. The methodology here and accompanying software (the extensive VGAM R package) are directed at these limitations and are described comprehensively for the first time in one volume. This book treats distributions and classical models as generalized regression models, and the result is a much broader application base for GLMs and GAMs. The book can be used in senior undergraduate or first-year postgraduate courses on GLMs or categorical data analysis and as a methodology resource for VGAM users. In the second part of the book, the R package VGAM allows readers to grasp immediately applications of the methodology. R code is integrated in the text, and datasets are used throughout. Potential applications include ecology, finance, biostatistics, and social sciences. The methodological contribution of this book stands alone and does not require use of the VGAM package.

Developmental Psychopathology Theory And Method

Autor: Dante Cicchetti
Publisher: John Wiley & Sons
ISBN: 1119125448
File Size: 32,30 MB
Format: PDF
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The seminal reference for the latest research in developmental psychopathology Developmental Psychopathology is a four-volume compendium of the most complete and current research on every aspect of the field. Volume One: Theory and Method focuses on the theoretical and empirical work that has contributed to dramatic advancements in understanding of child and adult development, including findings in the areas of genetics and neurobiology, as well as social and contextual factors. Now in its third edition, this comprehensive reference has been fully updated to reflect the current state of the field and its increasingly multilevel and interdisciplinary nature and the increasing importance of translational research. Contributions from expert researchers and clinicians provide insight into how multiple levels of analysis may influence individual differences, the continuity or discontinuity of patterns, and the pathways by which the same developmental outcomes may be achieved. Advances in developmental psychopathology have burgeoned since the 2006 publication of the second edition ten years ago, and keeping up on the latest findings in multiple avenues of investigation can be burdensome to the busy professional and researcher from psychology and related fields. This reference solves the problem by collecting the best of the best, as edited by Dante Cicchetti, a recognized leader in the field, into one place, with a logical organization designed for easy reference. Get up to date on the latest research from the field Explore new models, emerging theory, and innovative approaches Learn new technical analysis and research design methods Understand the impact of life stage on mental health The complexity of a field as diverse as developmental psychopathology deepens with each emerging theory and new area of study, as made obvious by the exciting findings coming out of institutions and clinics around the world. Developmental Psychopathology Volume One: Theory and Method brings these findings together into a cohesive, broad-reaching reference.

Log Linear Models

Autor: Ronald Christensen
Publisher: Springer Science & Business Media
ISBN: 1475741111
File Size: 27,34 MB
Format: PDF, Mobi
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This book examines log-linear models for contingency tables. Logistic re gression and logistic discrimination are treated as special cases and gener alized linear models (in the GLIM sense) are also discussed. The book is designed to fill a niche between basic introductory books such as Fienberg (1980) and Everitt (1977) and advanced books such as Bishop, Fienberg, and Holland (1975), Haberman (1974), and Santner and Duffy (1989). lt is primarily directed at advanced Masters degree students in Statistics but it can be used at both higher and lower levels. The primary theme of the book is using previous knowledge of analysis of variance and regression to motivate and explicate the use of log-linear models. Of course, both the analogies and the distinctions between the different methods must be kept in mind. The book is written at several levels. A basic introductory course would take material from Chapters I, II (deemphasizing Section II. 4), III, Sec tions IV. 1 through IV. 5 (eliminating the material on graphical models), Section IV. lü, Chapter VII, and Chapter IX. The advanced modeling ma terial at the end of Sections VII. 1, VII. 2, and possibly the material in Section IX. 2 should be deleted in a basic introductory course. For Mas ters degree students in Statistics, all the material in Chapters I through V, VII, IX, and X should be accessible. For an applied Ph. D.

Lectures On Categorical Data Analysis

Autor: Tamás Rudas
Publisher: Springer
ISBN: 1493976931
File Size: 57,32 MB
Format: PDF, ePub, Mobi
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This book offers a relatively self-contained presentation of the fundamental results in categorical data analysis, which plays a central role among the statistical techniques applied in the social, political and behavioral sciences, as well as in marketing and medical and biological research. The methods applied are mainly aimed at understanding the structure of associations among variables and the effects of other variables on these interactions. A great advantage of studying categorical data analysis is that many concepts in statistics become transparent when discussed in a categorical data context, and, in many places, the book takes this opportunity to comment on general principles and methods in statistics, addressing not only the “how” but also the “why.” Assuming minimal background in calculus, linear algebra, probability theory and statistics, the book is designed to be used in upper-undergraduate and graduate-level courses in the field and in more general statistical methodology courses, as well as a self-study resource for researchers and professionals. The book covers such key issues as: higher order interactions among categorical variables; the use of the delta-method to correctly determine asymptotic standard errors for complex quantities reported in surveys; the fundamentals of the main theories of causal analysis based on observational data; the usefulness of the odds ratio as a measure of association; and a detailed discussion of log-linear models, including graphical models. The book contains over 200 problems, many of which may also be used as starting points for undergraduate research projects. The material can be used by students toward a variety of goals, depending on the degree of theory or application desired.

Learning Statistics Using R

Autor: Randall E. Schumacker
Publisher: SAGE Publications
ISBN: 148332477X
File Size: 62,78 MB
Format: PDF, Docs
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Providing easy-to-use R script programs that teach descriptive statistics, graphing, and other statistical methods, Learning Statistics Using R shows readers how to run and utilize R, a free integrated statistical suite that has an extensive library of functions. Schumacker’s comprehensive book describes the processing of variables in statistical procedures. Covering a wide range of topics, from probability and sampling distribution to statistical theorems and chi-square, this introductory book helps readers learn not only how to use formulae to calculate statistics, but also how specific statistics fit into the overall research process. Each chapter includes discussion and explanations, tables and graphs, and R functions and outputs to enrich readers' understanding of statistics through statistical computing and modeling.

Marginal Models

Autor: Wicher Bergsma
Publisher: Springer Science & Business Media
ISBN: 0387096108
File Size: 55,92 MB
Format: PDF
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Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data provides a comprehensive overview of the basic principles of marginal modeling and offers a wide range of possible applications. Marginal models are often the best choice for answering important research questions when dependent observations are involved, as the many real world examples in this book show. In the social, behavioral, educational, economic, and biomedical sciences, data are often collected in ways that introduce dependencies in the observations to be compared. For example, the same respondents are interviewed at several occasions, several members of networks or groups are interviewed within the same survey, or, within families, both children and parents are investigated. Statistical methods that take the dependencies in the data into account must then be used, e.g., when observations at time one and time two are compared in longitudinal studies. At present, researchers almost automatically turn to multi-level models or to GEE estimation to deal with these dependencies. Despite the enormous potential and applicability of these recent developments, they require restrictive assumptions on the nature of the dependencies in the data. The marginal models of this book provide another way of dealing with these dependencies, without the need for such assumptions, and can be used to answer research questions directly at the intended marginal level. The maximum likelihood method, with its attractive statistical properties, is used for fitting the models. This book has mainly been written with applied researchers in mind. It includes many real world examples, explains the types of research questions for which marginal modeling is useful, and provides a detailed description of how to apply marginal models for a great diversity of research questions. All these examples are presented on the book's website (www.cmm.st), along with user friendly programs.