Alexander von Eye – författare

Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data from the social and behavioral sciences as well as biology, making the book useful for readers with biological and biometrical backgrounds. Sample command and result files for SYSTAT are included in the text.

These edited volumes present new statistical methods in a way that bridges the gap between theoretical and applied statistics. The volumes cover general problems and issues and more specific topics concerning the structuring of change, the analysis of time series, and the analysis of categorical longitudinal data. The book targets students of development and change in a variety of fields - psychology, sociology, anthropology, education, medicine, psychiatry, economics, behavioural sciences, developmental psychology, ecology, plant physiology, and biometry - with basic training in statistics and computing.

Structural equation modelling (SEM) is a technique that is used to estimate, analyse and test models that specify relationships among variables. The ability to conduct such analyses is essential for many problems in ecology and evolutionary biology. This book begins by explaining the theory behind the statistical methodology, including chapters on conceptual issues, the implementation of an SEM study and the history of the development of SEM. The second section provides examples of analyses on biological data including multi-group models, means models, P-technique and time-series. The final section of the book deals with computer applications and contrasts three popular SEM software packages. Aimed specifically at biological researchers and graduate students, this book will serve as valuable resource for both learning and teaching the SEM methodology. Moreover, data sets and programs that are presented in the book can also be downloaded from a website to assist the learning process.

Structural equation modelling (SEM) is a technique that is used to estimate, analyse and test models that specify relationships among variables. The ability to conduct such analyses is essential for many problems in ecology and evolutionary biology. This book begins by explaining the theory behind the statistical methodology, including chapters on conceptual issues, the implementation of an SEM study and the history of the development of SEM. The second section provides examples of analyses on biological data including multi-group models, means models, P-technique and time-series. The final section of the book deals with computer applications and contrasts three popular SEM software packages. Aimed specifically at biological researchers and graduate students, this book will serve as valuable resource for both learning and teaching the SEM methodology. Moreover, data sets and programs that are presented in the book can also be downloaded from a website to assist the learning process.

A comprehensive resource for analyzing a variety of categorical data, this book emphasizes the application of many recent advances of longitudinal categorical statistical methods. Each chapter provides basic methodology, helpful applications, examples using data from all fields of the social sciences, computer tutorials, and exercises. Written for social scientists and students, no advanced mathematical training is required. Step-by-step command files are given for both the CDAS and the SPSS software programs.

A comprehensive resource for analyzing a variety of categorical data, this book emphasizes the application of many recent advances of longitudinal categorical statistical methods. Each chapter provides basic methodology, helpful applications, examples using data from all fields of the social sciences, computer tutorials, and exercises. Written for social scientists and students, no advanced mathematical training is required. Step-by-step command files are given for both the CDAS and the SPSS software programs.

Configural Frequency Analysis (CFA) provides an up-to-the-minute comprehensive introduction to its techniques, models, and applications. Written in a formal yet accessible style, actual empirical data examples are used to illustrate key concepts. Step-by-step program sequences are used to show readers how to employ CFA methods using commercial software packages, such as SAS, SPSS, SYSTAT, S-Plus, or those written specifically to perform CFA. CFA is an important method for analyzing results involved with categorical and longitudinal data. It allows one to answer the question of whether individual cells or groups of cells of cross-classifications differ significantly from expectations. The expectations are calculated using methods employed in log-linear modeling or a priori information. It is the only statistical method that allows one to make statements about empty areas in the data space. Applied and or person-oriented researchers, statisticians, and advanced students interested in CFA and categorical and longitudinal data will find this book to be a valuable resource. Developed since 1969, this method is now used by a large number of researchers around the world in a variety of disciplines, including psychology, education, medicine, and sociology. Configural Frequency Analysis will serve as an excellent text for courses on configural frequency analysis, categorical variable analysis, or analysis of contingency tables. Prerequisites include an understanding of descriptive statistics, hypothesis testing, statistical model fitting, and some understanding of categorical data analysis and matrix algebra.

Configural Frequency Analysis (CFA) provides an up-to-the-minute comprehensive introduction to its techniques, models, and applications. Written in a formal yet accessible style, actual empirical data examples are used to illustrate key concepts. Step-by-step program sequences are used to show readers how to employ CFA methods using commercial software packages, such as SAS, SPSS, SYSTAT, S-Plus, or those written specifically to perform CFA. CFA is an important method for analyzing results involved with categorical and longitudinal data. It allows one to answer the question of whether individual cells or groups of cells of cross-classifications differ significantly from expectations. The expectations are calculated using methods employed in log-linear modeling or a priori information. It is the only statistical method that allows one to make statements about empty areas in the data space. Applied and or person-oriented researchers, statisticians, and advanced students interested in CFA and categorical and longitudinal data will find this book to be a valuable resource. Developed since 1969, this method is now used by a large number of researchers around the world in a variety of disciplines, including psychology, education, medicine, and sociology. Configural Frequency Analysis will serve as an excellent text for courses on configural frequency analysis, categorical variable analysis, or analysis of contingency tables. Prerequisites include an understanding of descriptive statistics, hypothesis testing, statistical model fitting, and some understanding of categorical data analysis and matrix algebra.

Agreement among raters is of great importance in many domains. For example, in medicine, diagnoses are often provided by more than one doctor to make sure the proposed treatment is optimal. In criminal trials, sentencing depends, among other things, on the complete agreement among the jurors. In observational studies, researchers increase reliability by examining discrepant ratings. This book is intended to help researchers statistically examine rater agreement by reviewing four different approaches to the technique.The first approach introduces readers to calculating coefficients that allow one to summarize agreements in a single score. The second approach involves estimating log-linear models that allow one to test specific hypotheses about the structure of a cross-classification of two or more raters' judgments. The third approach explores cross-classifications or raters' agreement for indicators of agreement or disagreement, and for indicators of such characteristics as trends. The fourth approach compares the correlation or covariation structures of variables that raters use to describe objects, behaviors, or individuals. These structures can be compared for two or more raters. All of these methods operate at the level of observed variables.This book is intended as a reference for researchers and practitioners who describe and evaluate objects and behavior in a number of fields, including the social and behavioral sciences, statistics, medicine, business, and education. It also serves as a useful text for graduate-level methods or assessment classes found in departments of psychology, education, epidemiology, biostatistics, public health, communication, advertising and marketing, and sociology. Exposure to regression analysis and log-linear modeling is helpful.

Agreement among raters is of great importance in many domains. For example, in medicine, diagnoses are often provided by more than one doctor to make sure the proposed treatment is optimal. In criminal trials, sentencing depends, among other things, on the complete agreement among the jurors. In observational studies, researchers increase reliability by examining discrepant ratings. This book is intended to help researchers statistically examine rater agreement by reviewing four different approaches to the technique.The first approach introduces readers to calculating coefficients that allow one to summarize agreements in a single score. The second approach involves estimating log-linear models that allow one to test specific hypotheses about the structure of a cross-classification of two or more raters' judgments. The third approach explores cross-classifications or raters' agreement for indicators of agreement or disagreement, and for indicators of such characteristics as trends. The fourth approach compares the correlation or covariation structures of variables that raters use to describe objects, behaviors, or individuals. These structures can be compared for two or more raters. All of these methods operate at the level of observed variables.This book is intended as a reference for researchers and practitioners who describe and evaluate objects and behavior in a number of fields, including the social and behavioral sciences, statistics, medicine, business, and education. It also serves as a useful text for graduate-level methods or assessment classes found in departments of psychology, education, epidemiology, biostatistics, public health, communication, advertising and marketing, and sociology. Exposure to regression analysis and log-linear modeling is helpful.

General Linear Model methods are the most widely used in data analysis in applied empirical research. Still, there exists no compact text that can be used in statistics courses and as a guide in data analysis. This volume fills this void by introducing the General Linear Model (GLM), whose basic concept is that an observed variable can be explained from weighted independent variables plus an additive error term that reflects imperfections of the model and measurement error. It also covers multivariate regression, analysis of variance, analysis under consideration of covariates, variable selection methods, symmetric regression, and the recently developed methods of recursive partitioning and direction dependence analysis. Each method is formally derived and embedded in the GLM, and characteristics of these methods are highlighted. Real-world data examples illustrate the application of each of these methods, and it is shown how results can be interpreted.

General Linear Model methods are the most widely used in data analysis in applied empirical research. Still, there exists no compact text that can be used in statistics courses and as a guide in data analysis. This volume fills this void by introducing the General Linear Model (GLM), whose basic concept is that an observed variable can be explained from weighted independent variables plus an additive error term that reflects imperfections of the model and measurement error. It also covers multivariate regression, analysis of variance, analysis under consideration of covariates, variable selection methods, symmetric regression, and the recently developed methods of recursive partitioning and direction dependence analysis. Each method is formally derived and embedded in the GLM, and characteristics of these methods are highlighted. Real-world data examples illustrate the application of each of these methods, and it is shown how results can be interpreted.

While regression analysis is widely understood, it falls short in determining the causal direction of relationships in observational data. In this groundbreaking volume, Wiedermann and von Eye introduce Direction Dependence Analysis (DDA), a novel method that leverages variable information often overlooked by traditional techniques, such as higher-order moments like skewness and kurtosis. DDA reveals the asymmetry properties of regression and correlation, enabling researchers to evaluate competing causal hypotheses, assess the roles of variables in causal flows, and develop statistical methods for testing causal direction. This book provides a comprehensive formal description of DDA, illustrated with both artificial and real-world data examples. Additionally, readers will find free software implementations of DDA, making this an essential resource for researchers seeking to enhance their understanding of causal relationships in data analysis.

While regression analysis is widely understood, it falls short in determining the causal direction of relationships in observational data. In this groundbreaking volume, Wiedermann and von Eye introduce Direction Dependence Analysis (DDA), a novel method that leverages variable information often overlooked by traditional techniques, such as higher-order moments like skewness and kurtosis. DDA reveals the asymmetry properties of regression and correlation, enabling researchers to evaluate competing causal hypotheses, assess the roles of variables in causal flows, and develop statistical methods for testing causal direction. This book provides a comprehensive formal description of DDA, illustrated with both artificial and real-world data examples. Additionally, readers will find free software implementations of DDA, making this an essential resource for researchers seeking to enhance their understanding of causal relationships in data analysis.

>STATISTICS AND CAUSALITY A one-of-a-kind guide to identifying and dealing with modern statistical developments in causality Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses. The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes: New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theoriesEnd-of-chapter bibliographies that provide references for further discussions and additional research topicsDiscussions on the use and applicability of software when appropriateStatistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic.

Covers the latest developments in direction dependence research Direction Dependence in Statistical Modeling: Methods of Analysis incorporates the latest research for the statistical analysis of hypotheses that are compatible with the causal direction of dependence of variable relations. Having particular application in the fields of neuroscience, clinical psychology, developmental psychology, educational psychology, and epidemiology, direction dependence methods have attracted growing attention due to their potential to help decide which of two competing statistical models is more likely to reflect the correct causal flow. The book covers several topics in-depth, including: A demonstration of the importance of methods for the analysis of direction dependence hypothesesA presentation of the development of methods for direction dependence analysis together with recent novel, unpublished software implementationsA review of methods of direction dependence following the copula-based tradition of Sungur and KimA presentation of extensions of direction dependence methods to the domain of categorical dataAn overview of algorithms for causal structure learningThe book's fourteen chapters include a discussion of the use of custom dialogs and macros in SPSS to make direction dependence analysis accessible to empirical researchers.

No detailed description available for "Growing up in Times of Social Change".

This volume contains the following five sections: growth curve modeling, directional dependence, dyadic data modeling, item response modeling (IRT), and other methods for the analysis of dependent data (e.g., approaches for modeling cross-section dependence, multidimensional scaling techniques, and mixed models).

Insgesamt stellt sich die KFA als statistische Methode dar, mit der für kategoriale Daten wichtige und interessante Fragen bearbeitet werden können, die im Kontext der Anwendung von Routinemethoden der Statistik nicht zugänglich sind.

This unique book provides a comprehensive and detailed coverage of configural frequency analysis (CFA), the most useful method of analysis of categorical data in person-oriented research.