Search Results for "interaction-effects-in-linear-and-generalized-linear-models"

Interaction Effects in Linear and Generalized Linear Models

Interaction Effects in Linear and Generalized Linear Models

Examples and Applications Using Stata

  • Author: Robert L. Kaufman
  • Publisher: SAGE Publications
  • ISBN: 1506365361
  • Category: Social Science
  • Page: 608
  • View: 9575
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Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approach to interpreting interaction effects. The book develops the statistical basis for the general principles of interpretive tools and applies them to a variety of examples, introduces the ICALC Toolkit for Stata (downloadable from the Robert L. Kaufman’s website), and offers a series of start-to-finish application examples to show students how to interpret interaction effects for a variety of different techniques of analysis, beginning with OLS regression. The data sets and the Stata code to reproduce the results of the application examples are available online.

Multivariate General Linear Models

Multivariate General Linear Models

  • Author: Richard F. Haase
  • Publisher: SAGE Publications
  • ISBN: 1483342115
  • Category: Mathematics
  • Page: 224
  • View: 4926
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Multivariate General Linear Models is an integrated introduction to multivariate multiple regression analysis (MMR) and multivariate analysis of variance (MANOVA). Beginning with an overview of the univariate general linear model, this volume defines the key steps in analyzing linear model data, and introduces multivariate linear model analysis as a generalization of the univariate model. The author focuses on multivariate measures of association for four common multivariate test statistics, presents a flexible method for testing hypotheses on models, and emphasizes the multivariate procedures attributable to Wilks, Pillai, Hotelling, and Roy. The volume concludes with a discussion of canonical correlation analysis that is shown to subsume all the multivariate procedures discussed in previous chapters. The analyses are illustrated throughout the text with three running examples drawing from several disciples, including personnel psychology, anthropology, environmental epidemiology, and neuropsychology.

The Association Graph and the Multigraph for Loglinear Models

The Association Graph and the Multigraph for Loglinear Models

  • Author: Harry J. Khamis
  • Publisher: SAGE
  • ISBN: 1452238952
  • Category: Mathematics
  • Page: 136
  • View: 4193
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The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.

Internet Data Collection

Internet Data Collection

  • Author: Samuel J. Best,Brian S. Krueger
  • Publisher: SAGE
  • ISBN: 9780761927105
  • Category: Computers
  • Page: 91
  • View: 7722
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Designed for researchers and students alike, the volume describes how to perform each stage of the data collection process on the Internet, including sampling, instrument design, and administration. Through the use of non-technical prose and illustrations, it details the options available, describes potential dangers in choosing them, and provides guidelines for sidestepping them. In doing so, though, it does not simply reiterate the practices of traditional communication modes, but approaches the Internet as a unique medium that necessitates its own conventions.

Applied Regression Analysis and Generalized Linear Models

Applied Regression Analysis and Generalized Linear Models

  • Author: John Fox
  • Publisher: SAGE Publications
  • ISBN: 1483321312
  • Category: Social Science
  • Page: 816
  • View: 8962
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Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.

Interaction Effects in Multiple Regression

Interaction Effects in Multiple Regression

  • Author: James Jaccard,Jim Jaccard,Robert Turrisi
  • Publisher: SAGE
  • ISBN: 9780761927426
  • Category: Mathematics
  • Page: 92
  • View: 3380
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Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis. Learn more about "The Little Green Book" - QASS Series! Click Here

Foundations of Linear and Generalized Linear Models

Foundations of Linear and Generalized Linear Models

  • Author: Alan Agresti
  • Publisher: John Wiley & Sons
  • ISBN: 1118730305
  • Category: Mathematics
  • Page: 480
  • View: 8562
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A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Interaction Effects in Factorial Analysis of Variance

Interaction Effects in Factorial Analysis of Variance

  • Author: James Jaccard,Jim Jaccard
  • Publisher: SAGE
  • ISBN: 9780761912217
  • Category: Mathematics
  • Page: 103
  • View: 9582
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Although factorial analysis is widely used in the social sciences, there is some confusion as to how to use the technique's most powerful feature - the evaluation of interaction effects. Written to remedy this situation, this book explores the issues underlying the effective analysis of interaction in factorial designs. It includes discussion of: different ways of characterizing interactions in ANOVA; interaction effects using traditional hypothesis testing approaches; and alternative analytic frameworks that focus on effect size methodology and interval estimation.

Generalized Linear Models

Generalized Linear Models

with Applications in Engineering and the Sciences

  • Author: Raymond H. Myers,Douglas C. Montgomery,G. Geoffrey Vining,Timothy J. Robinson
  • Publisher: John Wiley & Sons
  • ISBN: 0470556978
  • Category: Mathematics
  • Page: 544
  • View: 2821
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Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities." —Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include: A new chapter on random effects and designs for GLMs A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights Illustrations of R code to perform GLM analysis The authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets. Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.

Quantile Regression

Quantile Regression

  • Author: Lingxin Hao,Daniel Q. Naiman
  • Publisher: SAGE Publications
  • ISBN: 1483316904
  • Category: Social Science
  • Page: 136
  • View: 6162
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Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality. Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well. Major software packages have also gradually implemented quantile regression. Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features: Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to empirical research

Interaction Effects in Logistic Regression

Interaction Effects in Logistic Regression

  • Author: James Jaccard,Jim Jaccard
  • Publisher: SAGE
  • ISBN: 9780761922070
  • Category: Mathematics
  • Page: 70
  • View: 3312
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This book provides an introduction to the analysis of interaction effects in logistic regression by focusing on the interpretation of the coefficients of interactive logistic models for a wide range of situations encountered in the research literature. The volume is oriented toward the applied researcher with a rudimentary background in multiple regression and logistic regression and does not include complex formulas that could be intimidating to the applied researcher.

An Introduction to Generalized Linear Models

An Introduction to Generalized Linear Models

  • Author: George H. Dunteman,Moon-Ho R. Ho,Moon-Ho R.. Ho
  • Publisher: SAGE
  • ISBN: 9780761920847
  • Category: Mathematics
  • Page: 72
  • View: 4052
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Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts to GLM and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets. The book provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation; includes discussion on checking model adequacy and description on how to use SAS to fit GLM; and describes the connection between survival analysis and GLM. It is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.

Statistics Applied to Clinical Trials

Statistics Applied to Clinical Trials

  • Author: Ton J. Cleophas,A.H. Zwinderman,Toine F. Cleophas,Eugene P. Cleophas
  • Publisher: Springer Science & Business Media
  • ISBN: 1402095236
  • Category: Mathematics
  • Page: 562
  • View: 2881
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In clinical medicine appropriate statistics has become indispensable to evaluate treatment effects. Randomized controlled trials are currently the only trials that truly provide evidence-based medicine. Evidence based medicine has become crucial to optimal treatment of patients. We can define randomized controlled trials by using Christopher J. Bulpitt’s definition “a carefully and ethically designed experiment which includes the provision of adequate and appropriate controls by a process of randomization, so that precisely framed questions can be answered”. The answers given by randomized controlled trials constitute at present the way how patients should be clinically managed. In the setup of such randomized trial one of the most important issues is the statistical basis. The randomized trial will never work when the statistical grounds and analyses have not been clearly defined beforehand. All endpoints should be clearly defined in order to perform appropriate power calculations. Based on these power calculations the exact number of available patients can be calculated in order to have a sufficient quantity of individuals to have the predefined questions answered. Therefore, every clinical physician should be capable to understand the statistical basis of well performed clinical trials. It is therefore a great pleasure that Drs. T. J. Cleophas, A. H. Zwinderman, and T. F. Cleophas have published a book on statistical analysis of clinical trials. The book entitled “Statistics Applied to Clinical Trials” is clearly written and makes complex issues in statistical analysis transparant.

Testing Research Hypotheses with the General Linear Model

Testing Research Hypotheses with the General Linear Model

  • Author: Keith A. McNeil,Isadore Newman,Francis J. Kelly
  • Publisher: SIU Press
  • ISBN: 9780809320196
  • Category: Mathematics
  • Page: 372
  • View: 4224
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Because the technique of multiple linear regression has been accepted by the research community since 1975, Keith McNeil, Isadore Newman, and Francis J. Kelly devote little space to defending the equivalence of correlational and ANOVA procedures with multiple linear regression. Instead, they show how the multiple linear regression technique frees the researcher from wondering if an analysis can be done and refocuses him or her back to the central concern: the research question itself. The first three sections of chapter 1 provide a conceptual, research, and statistical orientation to the entire text. The remainder of chapter 1 furnishes the rationale for the utility of a conceptual model of behavior, along with one such model that can be used to identify predictor variables. The authors strongly suggest that readers familiar with the general linear model read these three sections before delving into the more advanced material. Readers who are relatively unfamiliar with the general linear model should read the first eight chapters before branching off into topics that are of immediate interest. Examples are provided throughout the text, all using the same data in the same widely available statistical analysis package. Although the technique can be taught with matrix algebra, the authors use the simpler approach of vector algebra, an approach more in line with the way data are conceptualized and entered into the computer. All of the correlational statistical techniques are shown to be subsets of the general linear model. Of more importance, however, researchers are encouraged to think beyond these limitations and to ask the research questions they are interested in. Thus, the common researcher is freed from the shackles of the "right" statistical procedure and its associated "right" computer analysis.

Handbook of Data Analysis

Handbook of Data Analysis

  • Author: Melissa A Hardy,Alan Bryman
  • Publisher: SAGE
  • ISBN: 1446203441
  • Category: Social Science
  • Page: 728
  • View: 7463
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Electronic Inspection Copy available for instructors here 'This book provides an excellent reference guide to basic theoretical arguments, practical quantitative techniques and the methodologies that the majority of social science researchers are likely to require for postgraduate study and beyond' - Environment and Planning 'The book provides researchers with guidance in, and examples of, both quantitative and qualitative modes of analysis, written by leading practitioners in the field. The editors give a persuasive account of the commonalities of purpose that exist across both modes, as well as demonstrating a keen awareness of the different things that each offers the practising researcher' - Clive Seale, Brunel University 'With the appearance of this handbook, data analysts no longer have to consult dozens of disparate publications to carry out their work. The essential tools for an intelligent telling of the data story are offered here, in thirty chapters written by recognized experts. ' - Michael Lewis-Beck, F Wendell Miller Distinguished Professor of Political Science, University of Iowa 'This is an excellent guide to current issues in the analysis of social science data. I recommend it to anyone who is looking for authoritative introductions to the state of the art. Each chapter offers a comprehensive review and an extensive bibliography and will be invaluable to researchers wanting to update themselves about modern developments' - Professor Nigel Gilbert, Pro Vice-Chancellor and Professor of Sociology, University of Surrey This is a book that will rapidly be recognized as the bible for social researchers. It provides a first-class, reliable guide to the basic issues in data analysis, such as the construction of variables, the characterization of distributions and the notions of inference. Scholars and students can turn to it for teaching and applied needs with confidence. The book also seeks to enhance debate in the field by tackling more advanced topics such as models of change, causality, panel models and network analysis. Specialists will find much food for thought in these chapters. A distinctive feature of the book is the breadth of coverage. No other book provides a better one-stop survey of the field of data analysis. In 30 specially commissioned chapters the editors aim to encourage readers to develop an appreciation of the range of analytic options available, so they can choose a research problem and then develop a suitable approach to data analysis.

Parameter Estimation and Hypothesis Testing in Linear Models

Parameter Estimation and Hypothesis Testing in Linear Models

  • Author: Karl-Rudolf Koch
  • Publisher: Springer Science & Business Media
  • ISBN: 9783540652571
  • Category: Mathematics
  • Page: 333
  • View: 7419
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The necessity to publish the second edition of this book arose when its third German edition had just been published. This second English edition is there fore a translation of the third German edition of Parameter Estimation and Hypothesis Testing in Linear Models, published in 1997. It differs from the first English edition by the addition of a new chapter on robust estimation of parameters and the deletion of the section on discriminant analysis, which has been more completely dealt with by the author in the book Bayesian In ference with Geodetic Applications, Springer-Verlag, Berlin Heidelberg New York, 1990. Smaller additions and deletions have been incorporated, to im prove the text, to point out new developments or to eliminate errors which became apparent. A few examples have been also added. I thank Springer-Verlag for publishing this second edition and for the assistance in checking the translation, although the responsibility of errors remains with the author. I also want to express my thanks to Mrs. Ingrid Wahl and to Mrs. Heidemarlen Westhiiuser who prepared the second edition. Bonn, January 1999 Karl-Rudolf Koch Preface to the First Edition This book is a translation with slight modifications and additions of the second German edition of Parameter Estimation and Hypothesis Testing in Linear Models, published in 1987.

Hierarchical Linear Models

Hierarchical Linear Models

Applications and Data Analysis Methods

  • Author: Stephen W. Raudenbush,Anthony S. Bryk
  • Publisher: SAGE
  • ISBN: 9780761919049
  • Category: Mathematics
  • Page: 485
  • View: 7078
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Popular in its first edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been updated to include: an intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication; a new section on multivariate growth models; a discussion of research synthesis or meta-analysis applications; aata analytic advice on centering of level-1 predictors, and new material on plausible value intervals and robust standard estimators.

Methods and Applications of Linear Models

Methods and Applications of Linear Models

Regression and the Analysis of Variance

  • Author: Ronald R. Hocking
  • Publisher: John Wiley & Sons
  • ISBN: 1118593022
  • Category: Mathematics
  • Page: 720
  • View: 7900
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Praise for the Second Edition "An essential desktop reference book . . . it should definitely be on your bookshelf." —Technometrics A thoroughly updated book, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition features innovative approaches to understanding and working with models and theory of linear regression. The Third Edition provides readers with the necessary theoretical concepts, which are presented using intuitive ideas rather than complicated proofs, to describe the inference that is appropriate for the methods being discussed. The book presents a unique discussion that combines coverage of mathematical theory of linear models with analysis of variance models, providing readers with a comprehensive understanding of both the theoretical and technical aspects of linear models. With a new focus on fixed effects models, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition also features: Newly added topics including least squares, the cell means model, and graphical inspection of data in the AVE method Frequent conceptual and numerical examples for clarifying the statistical analyses and demonstrating potential pitfalls Graphics and computations developed using JMP® software to accompany the concepts and techniques presented Numerous exercises presented to test readers and deepen their understanding of the material An ideal book for courses on linear models and linear regression at the undergraduate and graduate levels, the Third Edition of Methods and Applications of Linear Models: Regression and the Analysis of Variance is also a valuable reference for applied statisticians and researchers who utilize linear model methodology.

Logistic Regression Models for Ordinal Response Variables

Logistic Regression Models for Ordinal Response Variables

  • Author: Ann A. O'Connell
  • Publisher: SAGE
  • ISBN: 9780761929895
  • Category: Mathematics
  • Page: 107
  • View: 5731
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Logistic Regression Models for Ordinal Response Variables provides applied researchers in the social, educational, and behavioral sciences with an accessible and comprehensive coverage of analyses for ordinal outcomes. The content builds on a review of logistic regression, and extends to details of the cumulative (proportional) odds, continuation ratio, and adjacent category models for ordinal data. Description and examples of partial proportional odds models are also provided. This book is highly readable, with lots of examples and in-depth explanations and interpretations of model characteristics.

Generalized Linear Models, Second Edition

Generalized Linear Models, Second Edition

  • Author: P. McCullagh,John A. Nelder
  • Publisher: CRC Press
  • ISBN: 9780412317606
  • Category: Mathematics
  • Page: 532
  • View: 6142
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The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications. The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables. The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions. Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.