Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages. The book provides broad, but unified, coverage, and the authors integrate the concepts and ideas shared across models and types of data, especially regarding conceptual links between discrete and continuous limited dependent variables. The authors argue that these dependent variables are, if anything, more common throughout the human sciences than the kind that suit linear regression. They cover special cases or extensions of models, estimation methods, model diagnostics, and, of course, software. They also discuss bounded continuous variables, boundary-inflated models, and methods for modeling heteroscedasticity. Wherever possible, the authors have illustrated concepts, models, and techniques with real or realistic datasets and demonstrations in R and Stata, and each chapter includes several exercises at the end. The illustrations and exercises help readers build conceptual understanding and fluency in using these techniques. At several points the authors bring together material that has been previously scattered across the literature in journal articles, software package documentation files, and blogs. These features help students learn to choose the appropriate models for their purpose.

This is a major, and deeply thoughtful, contribution to understanding uncertainty and risk. Our world and its unprecedented challenges need such ways of thinking! Much more than a set of contributions from different disciplines, this book leads you to explore your own way of perceiving your own area of work. An outstanding contribution that will stay on my shelves for many years. Dr Neil T. M. Hamilton, Director, WWF International Arctic Programme This collection of essays provides a unique and fascinating overview of perspectives on uncertainty and risk across a wide variety of disciplines. It is a valuable and accessible sourcebook for specialists and laypeople alike. Professor Renate Schubert, Head of the Institute for Environmental Decisions and Chair of Economics at the Swiss Federal Institute of Technology This comprehensive collection of disciplinary perspectives on uncertainty is a definitive guide to contemporary insights into this Achilles heel of modernity and the endemic hubris of institutional science in its role as public authority. It gives firm foundations to the fundamental historic shift now underway in the world, towards normalizing acceptance of the immanent condition of ignorance and of its practical corollaries: contingency, uncontrol, and respect for difference. Brian Wynne, Professor of Science Studies, Lancaster University Bammer and Smithson have assembled a fascinating, important collection of papers on uncertainty and its management. The integrative nature of Uncertainty and Risk makes it a landmark in the intellectual history of this vital cross-disciplinary concept. George Cvetkovich, Director, Center for Cross-Cultural Research, Western Washington University Uncertainty governs our lives. From the unknowns of living with the risks of terrorism to developing policies on genetically modified foods, or disaster planning for catastrophic climate change, how we conceptualize, evaluate and cope with uncertainty drives our actions and deployment of resources, decisions and priorities. In this thorough and wide-ranging volume, theoretical perspectives are drawn from art history, complexity science, economics, futures, history, law, philosophy, physics, psychology, statistics and theology. On a practical level, uncertainty is examined in emergency management, intelligence, law enforcement, music, policy and politics. Key problems that are a subject of focus are environmental management, communicable diseases and illicit drugs. Opening and closing sections of the book provide major conceptual strands in uncertainty thinking and develop an integrated view of the nature of uncertainty, uncertainty as a motivating or de-motivating force, and strategies for coping and managing under uncertainty.

First published in 1999. Routledge is an imprint of Taylor & Francis, an informa company.

Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages. The book provides broad, but unified, coverage, and the authors integrate the concepts and ideas shared across models and types of data, especially regarding conceptual links between discrete and continuous limited dependent variables. The authors argue that these dependent variables are, if anything, more common throughout the human sciences than the kind that suit linear regression. They cover special cases or extensions of models, estimation methods, model diagnostics, and, of course, software. They also discuss bounded continuous variables, boundary-inflated models, and methods for modeling heteroscedasticity. Wherever possible, the authors have illustrated concepts, models, and techniques with real or realistic datasets and demonstrations in R and Stata, and each chapter includes several exercises at the end. The illustrations and exercises help readers build conceptual understanding and fluency in using these techniques. At several points the authors bring together material that has been previously scattered across the literature in journal articles, software package documentation files, and blogs. These features help students learn to choose the appropriate models for their purpose.

This is a major, and deeply thoughtful, contribution to understanding uncertainty and risk. Our world and its unprecedented challenges need such ways of thinking! Much more than a set of contributions from different disciplines, this book leads you to explore your own way of perceiving your own area of work. An outstanding contribution that will stay on my shelves for many years. Dr Neil T. M. Hamilton, Director, WWF International Arctic Programme This collection of essays provides a unique and fascinating overview of perspectives on uncertainty and risk across a wide variety of disciplines. It is a valuable and accessible sourcebook for specialists and laypeople alike. Professor Renate Schubert, Head of the Institute for Environmental Decisions and Chair of Economics at the Swiss Federal Institute of Technology This comprehensive collection of disciplinary perspectives on uncertainty is a definitive guide to contemporary insights into this Achilles heel of modernity and the endemic hubris of institutional science in its role as public authority. It gives firm foundations to the fundamental historic shift now underway in the world, towards normalizing acceptance of the immanent condition of ignorance and of its practical corollaries: contingency, uncontrol, and respect for difference. Brian Wynne, Professor of Science Studies, Lancaster University Bammer and Smithson have assembled a fascinating, important collection of papers on uncertainty and its management. The integrative nature of Uncertainty and Risk makes it a landmark in the intellectual history of this vital cross-disciplinary concept. George Cvetkovich, Director, Center for Cross-Cultural Research, Western Washington University Uncertainty governs our lives. From the unknowns of living with the risks of terrorism to developing policies on genetically modified foods, or disaster planning for catastrophic climate change, how we conceptualize, evaluate and cope with uncertainty drives our actions and deployment of resources, decisions and priorities. In this thorough and wide-ranging volume, theoretical perspectives are drawn from art history, complexity science, economics, futures, history, law, philosophy, physics, psychology, statistics and theology. On a practical level, uncertainty is examined in emergency management, intelligence, law enforcement, music, policy and politics. Key problems that are a subject of focus are environmental management, communicable diseases and illicit drugs. Opening and closing sections of the book provide major conceptual strands in uncertainty thinking and develop an integrated view of the nature of uncertainty, uncertainty as a motivating or de-motivating force, and strategies for coping and managing under uncertainty.

Ignorance and Uncertainty overviews a variety of approaches to the problem of indeterminacies in human thought and behavior. This book examines, in depth, trends in the psychology of judgment and decision-making under uncertainty or ignorance. Research from the fields of cognitive psychology, social psychology, organizational studies, sociology, and social anthroplogy are reviewed here in anticipation of what Dr. Smithson characterizes as the beginning of a "creative dialogue between these researchers". Ignorance and Uncertainty offers the conceptual framework for understanding the paradigms associated with current research. It discusses the ways in which attitudes toward ignorance and uncertainty are changing, and addresses issues previously ignored.

This textbook offers an accessible and comprehensive introduction to statistics for all undergraduate psychology students, but particularly those in their second and third years who have already covered an initial introductory course. It covers all of the key areas in quantitative methods including sampling, significance tests, regression, and multivariate techniques and incorporates a range of exercises and problems at the end of each chapter for the student to follow.

The free CD-ROM with tutorial modules complements and enhances the exercises in the text, offers scope for distance learning, and makes both the traditional and non-traditional approaches much more accessible.

Key points of the book are: an emphasis on measurement, data summaries and graphs; a clear explanation of statistical inference using sampling distributions and confidence intervals, making significance tests much easier to understand; and help for students to understand and judge the use of particular tests in the research context beyond simple recipe following.

This book introduces researchers and students to the concepts and generalized linear models for analyzing quantitative random variables that have one or more bounds. Examples of bounded variables include the percentage of a population eligible to vote (bounded from 0 to 100), or reaction time in milliseconds (bounded below by 0). The human sciences deal in many variables that are bounded. Ignoring bounds can result in misestimation and improper statistical inference. Michael Smithson and Yiyun Shou's book brings together material on the analysis of limited and bounded variables that is scattered across the literature in several disciplines, and presents it in a style that is both more accessible and up-to-date. The authors provide worked examples in each chapter using real datasets from a variety of disciplines. The software used for the examples include R, SAS, and Stata. The data, software code, and detailed explanations of the example models are available on an accompanying website.

Fuzzy set theory deals with sets or categories whose boundaries are blurry or, in other words, "fuzzy." This book presents an accessible introduction to fuzzy set theory, focusing on its applicability to the social sciences. Unlike most books on this topic, Fuzzy Set Theory: Applications in the Social Sciences provides a systematic, yet practical guide for researchers wishing to combine fuzzy set theory with standard statistical techniques and model-testing. Key Features: Addresses Basic Concepts: Fuzzy set theory is an analytic framework for handling concepts that are simultaneously categorical and dimensional. Starting with a rationale for fuzzy sets, this book introduces readers with an elementary knowledge of statistics to the necessary concepts and techniques of fuzzy set theory and fuzzy logic. Introduces Novel Ways of Analyses: Researchers are shown alternative methods to conventional models, especially for testing theories that are expressed in set-wise terms. Issues of operationalizing graded membership in a fuzzy set and the measurement of the properties of such sets are a few of the topics addressed. Illustrates Techniques and Applications: Real examples and data-sets from various disciplines in the social sciences are used to demonstrate the connections between fuzzy sets and other data analytic techniques, empirical applications of the technique, and the critiques of fuzzy set theory. Intended Audience: Ideal for researchers in the social sciences, education, and behavioral sciences; as well as graduate students in the applied social sciences

Contents:
M. Foddy, M. Smithson, Theories and Strategies for Studying Social Dilemmas. Part I. Formal Models and Dynamic Systems Approaches. M. Smithson, Taking Exogenous Dynamics Seriously in Public Goods and Resource Dilemmas. Y. Watanabe, T. Yamagishi, Emergence of Strategies in a Selective Play Environment with Geographic Mobility. E. Takagi, Generalized Exchange and the Emergence of Social Order. A. Rapoport, W. Almadoss, Social Dilemmas Embedded in Between-group Competitions: Effects of Contest and Distribution Rates. W. Au, D. Budescu, Sequential Effects in Give-some and Take-some Social Dilemmas. Part II. Control Systems and Structural Solutions. N. Kerr, Anonymity and Social Control in Social Dilemmas. M. van Vugt, Managing Natural Resource Dilemmas Through Structural Change. A. Franzen, The Volunteer's Dilemma: Theoretical Models and Empirical Evidence. S. Suleiman, K. Or-Chen, Providing Step-level Public Goods Under Uncertainty: The Case of Probably External Supply. M. Beckenkamp, Sanctioning as an Ambiguous Structural Solution. Xiao-Ping Chen, Work Team Cooperation: A Longitudinal Study on the Effects of Reward Allocation Rules. J. Webb, Structural Change Decision-Making in Social Dilemmas: A Preliminary Framework. Part III. Linking Individual and Group Processes. D. Messick, Models of Decision Making in Social Dilemmas. T. Garling, A. Biel, M. Gustafsson, Managing Uncertain Common Resources. G. Hertel, Mood Effects in Social Dilemmas. A. Biel, C. von Borgstede, U. Dahlstrand, Norm Perception and Cooperation in Large-scale Social Dilemmas. S. Schneider, J. Sundali, Curbside Recycling: Does it Promote Environmental Responsibility? J. Garvill, Factors Influencing Elementary and Instrumental Cooperation in Choice of Transportation Mode. J. Schopler, C.A. Insko, The Role of Future Consequences in the Reduction of the Interindividual-intergroup Discontinuity Effect. B. Morrison, Interdependence, the Group, and Social Cooperation: A New Look at an Old Problem. M. Foddy, M. Hogg, Leaders and Social Dilemmas: The Intergroup Context. S. Schneider, M. Brewer, Social Dilemmas and Social Evolution.

Smithson first introduces the basis of the confidence interval framework and then provides the criteria for "best" confidence intervals, along with the tradeoffs between confidence and precision. Next, using a reader-friendly style with lots of worked out examples from various disciplines, he covers such pertinent topics as: the transformation principle whereby a confidence interval for a parameter may be used to construct an interval for any monotonic transformation of that parameter; confidence intervals on distributions whose shape changes with the value of the parameter being estimated; and, the relationship between confidence interval and significance testing frameworks, particularly regarding power.

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