Nonlinear Contingency Analysis


Book Description

Nonlinear Contingency Analysis is a guide to treating clinically complex behavior problems such as delusions and hallucinations. It’s also a framework for treating behavior problems, one that explores solutions based on the creation of new or alternative consequential contingencies rather than the elimination or deceleration of old or problematic thoughts, feelings, or behaviors. Chapters present strategies, analytical tools, and interventions that clinicians can use in session to think about clients’ problems using decision theory, experimental analysis of behavior, and clinical research and practice. By treating thoughts and emotions not as causes of behavior but as indicators of the environmental conditions that are responsible for them, patients can use that knowledge to make changes that not only result in changes in behavior, but in the thoughts and feelings themselves.




Multidimensional Nonlinear Descriptive Analysis


Book Description

Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines. Exploring data analysis in various areas of research, such as the social sciences and biology, Multidimensional Nonlinear Descriptive Analysis presents methods for analyzing categorical data that are not necessarily sam




Clinical Behavior Analysis


Book Description

Clinical behavior analysis uses verbally based interventions to treat a range of psychological problems in an outpatient context. This volume offers a collection of current research in this rapidly expanding field, with a special focus on acceptance issues in therapy and the importance of the therapeutic relationship.







Analysis of Time Series Structure


Book Description

Over the last 15 years, singular spectrum analysis (SSA) has proven very successful. It has already become a standard tool in climatic and meteorological time series analysis and well known in nonlinear physics and signal processing. However, despite the promise it holds for time series applications in other disciplines, SSA is not widely known among statisticians and econometrists, and although the basic SSA algorithm looks simple, understanding what it does and where its pitfalls lay is by no means simple. Analysis of Time Series Structure: SSA and Related Techniques provides a careful, lucid description of its general theory and methodology. Part I introduces the basic concepts, and sets forth the main findings and results, then presents a detailed treatment of the methodology. After introducing the basic SSA algorithm, the authors explore forecasting and apply SSA ideas to change-point detection algorithms. Part II is devoted to the theory of SSA. Here the authors formulate and prove the statements of Part I. They address the singular value decomposition (SVD) of real matrices, time series of finite rank, and SVD of trajectory matrices. Based on the authors' original work and filled with applications illustrated with real data sets, this book offers an outstanding opportunity to obtain a working knowledge of why, when, and how SSA works. It builds a strong foundation for successfully using the technique in applications ranging from mathematics and nonlinear physics to economics, biology, oceanology, social science, engineering, financial econometrics, and market research.




Analysis of Incomplete Multivariate Data


Book Description

The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering datasets in which the variables are continuous, categorical, or both. The focus is applied, where necessary, to help readers thoroughly understand the statistical properties of those methods, and the behavior of the accompanying algorithms. All techniques are illustrated with real data examples, with extended discussion and practical advice. All of the algorithms described in this book have been implemented by the author for general use in the statistical languages S and S Plus. The software is available free of charge on the Internet.




Theory-Based Data Analysis for the Social Sciences


Book Description

This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.




A Programing Contingency Analysis of Mental Health


Book Description

A Programing Contingency Analysis of Mental Health presents Dr. Israel Goldiamond’s reflections on various ways we formulate behavioral and emotional problems, most often in traditional terms of mental health disorders, mental diseases or illnesses, psychopathological disorders, and so on – what he calls a pathological orientation. Here, Goldiamond argues for a groundbreaking alternative view from the vantage point of radical behaviorism. The book begins by discussing contingency relations between behavior and its past and present consequences, along with other environmental events. It reminds us that this approach sits comfortably alongside other consequential systems in the social and biological sciences, particularly decision theory and evolution. This behaviorist system regards most important human behaviors as being emitted rather than stimulus-elicited. Described are some of the diverse origins of behavior, including the effects of environmental consequences and the programing procedures of social and cultural inheritance. The exposition includes decision matrices which rationalize some of the programed patterns and the accompanying thoughts and emotions commonly found in mental illness. As a result of this nonlinear contingency analysis, such patterns may be considered adaptive rather than maladaptive. The book describes programs based on those matrices and outlines how they might be applied to mitigate any problems or costs associated with those patterns. The book concludes by moving from individual analysis to social analysis, with particular reference to some societal contingencies that may maintain the pathological orientation and others that might shift our gaze in the direction proposed here. Alongside Dr. Goldiamond’s original work, this volume features a new introduction from Dr. Paul Thomas Andronis and Dr. T. V. Joe Layng, as well as an article tracing the history of the non-linear thinking of Dr. Goldiamond, first published in The Behavior Analyst. It will be a must-read for anyone working in the analysis of and clinical intervention in problems associated with mental health, or those more generally interested in the work of Israel Goldiamond.




Statistical Power Analysis for the Behavioral Sciences


Book Description

Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.




The Matching Law


Book Description

This impressive collection features Richard Herrnstein's most important and original contributions to the social and behavioral sciences--his papers on choice behavior in animals and humans and on his discovery and elucidation of a general principle of choice called the matching law. In recent years, the most popular theory of choice behavior has been rational choice theory. Developed and elaborated by economists over the past hundred years, it claims that individuals make choices in such a way as to maximize their well-being or utility under whatever constraints they face; that is, people make the best of their situations. Rational choice theory holds undisputed sway in economics, and has become an important explanatory framework in political science, sociology, and psychology. Nevertheless, its empirical support is thin. The matching law is perhaps the most important competing explanatory account of choice behavior. It views choice not as a single event or an internal process of the organism but as a rate of observable events over time. It states that instead of maximizing utility, the organism allocates its behavior over various activities in exact proportion to the value derived from each activity. It differs subtly but significantly from rational choice theory in its predictions of how people exert self-control, for example, how they decide whether to forgo immediate pleasures for larger but delayed rewards. It provides, through the primrose path hypothesis, a powerful explanation of alcohol and narcotic addiction. It can also be used to explain biological phenomena, such as genetic selection and foraging behavior, as well as economic decision making.