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: 8306
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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: 711
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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: 5016
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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: 632
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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.

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: 3235
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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: 2741
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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: 8746
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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.

Quantile Regression

Quantile Regression

  • Author: Lingxin Hao,Daniel Q. Naiman
  • Publisher: SAGE Publications
  • ISBN: 1483316904
  • Category: Social Science
  • Page: 136
  • View: 4070
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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

Explanatory Item Response Models

Explanatory Item Response Models

A Generalized Linear and Nonlinear Approach

  • Author: Paul de Boeck
  • Publisher: Springer Science & Business Media
  • ISBN: 9780387402758
  • Category: Education
  • Page: 382
  • View: 3461
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This edited volume gives a new and integrated introduction to item response models (predominantly used in measurement applications in psychology, education, and other social science areas) from the viewpoint of the statistical theory of generalized linear and nonlinear mixed models. The new framework allows the domain of item response models to be co-ordinated and broadened to emphasize their explanatory uses beyond their standard descriptive uses. The basic explanatory principle is that item responses can be modeled as a function of predictors of various kinds. The predictors can be (a) characteristics of items, of persons, and of combinations of persons and items; (b) observed or latent (of either items or persons); and they can be (c) latent continuous or latent categorical. In this way a broad range of models is generated, including a wide range of extant item response models as well as some new ones. Within this range, models with explanatory predictors are given special attention in this book, but we also discuss descriptive models. Note that the term "item responses" does not just refer to the traditional "test data," but are broadly conceived as categorical data from a repeated observations design. Hence, data from studies with repeated observations experimental designs, or with longitudinal designs, may also be modelled. The book starts with a four-chapter section containing an introduction to the framework. The remaining chapters describe models for ordered-category data, multilevel models, models for differential item functioning, multidimensional models, models for local item dependency, and mixture models. It also includes a chapter on the statistical background and one on useful software. In order to make the task easier for the reader, a unified approach to notation and model description is followed throughout the chapters, and a single data set is used in most examples to make it easier to see how the many models are related. For all major examples, computer commands from the SAS package are provided that can be used to estimate the results for each model. In addition, sample commands are provided for other major computer packages. Paul De Boeck is Professor of Psychology at K.U. Leuven (Belgium), and Mark Wilson is Professor of Education at UC Berkeley (USA). They are also co-editors (along with Pamela Moss) of a new journal entitled Measurement: Interdisciplinary Research and Perspectives. The chapter authors are members of a collaborative group of psychometricians and statisticians centered on K.U. Leuven and UC Berkeley.

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: 1513
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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.

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: 9312
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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.

The general linear model

The general linear model

data analysis in the social and behavioral sciences

  • Author: Raymond L. Horton
  • Publisher: McGraw-Hill Companies
  • ISBN: N.A
  • Category: Psychology
  • Page: 274
  • View: 510
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Statistics Applied to Clinical Trials

Statistics Applied to Clinical Trials

  • Author: Ton J. Cleophas,A.H. Zwinderman,T.F. Cleophas,Toine F. Cleophas,Eugene P. Cleophas
  • Publisher: Springer Science & Business Media
  • ISBN: 9781402046506
  • Category: Medical
  • Page: 366
  • View: 965
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In 1948 the first randomized controlled trial was published by the English Medical Research Council in the British Medical Journal. Until then, observations had been uncontrolled. Initially, trials frequently did not confirm hypotheses to be tested. This phenomenon was attributed to low sensitivity due to small samples, as well as inappropriate hypotheses based on biased prior trials. Additional flaws were recognized and subsequently were better accounted for: carryover effects due to insufficient washout from previous treatments, time effects due to external factors and the natural history of the condition under study, bias due to asymmetry between treatment groups, lack of sensitivity due to a negative correlation between treatment responses, etc. Such flaws, mainly of a technical nature, have been largely corrected and led to trials after 1970 being of significantly better quality than before. The past decade has focused, in addition to technical aspects, on the need for circumspection in planning and conducting of clinical trials. As a consequence, prior to approval, clinical trial protocols are now routinely scrutinized by different circumstantial bodies, including ethics committees, institutional and federal review boards, national and international scientific organizations, and monitoring committees charged with conducting interim analyses. This book not only explains classical statistical analyses of clinical trials, but addresses relatively novel issues, including equivalence testing, interim analyses, sequential analyses, and meta-analyses, and provides a framework of the best statistical methods currently available for such purposes. The book is not only useful for investigators involved in the field of clinical trials, but also for all physicians who wish to better understand the data of trials as currently published.

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: 4931
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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: 1499
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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.

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: 4797
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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.

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: 3136
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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.

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: 5090
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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: 6551
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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.

New Developments in Classification and Data Analysis

New Developments in Classification and Data Analysis

Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, University of Bologna, September 22-24, 2003

  • Author: Classification Group of SIS. Meeting,Società Italiana di Statistica Classification and Data Analysis Group
  • Publisher: Springer Science & Business Media
  • ISBN: 9783540238096
  • Category: Business & Economics
  • Page: 369
  • View: 1154
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The volume presents new developments in data analysis and classification. Particular attention is devoted to clustering, discrimination, data analysis and statistics, as well as applications in biology, finance and social sciences. The reader will find theory and algorithms on recent technical and methodological developments and many application papers showing the empirical usefulness of the newly developed solutions.