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.

"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.

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.

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.

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.

Social change, such as the consequences of German unification, is likely to impact normative as well as maladaptive development during adolescence. Beyond documenting effects by comparing adolesecents' psychosocial development at various time periods of the unification process, this book offers insights into the macro-and-micro-level mechanisms that bring about the changes, such as the demands by new social insitutions or challenges facing families.

Structural Equation Modeling (SEM) is a technique that is used to estimate, analyze and test models that specify relationships among variables. This book explains 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. It provides examples of analyses on biological data including multi-group models, means models, p-technique and time-series. In addition, the book discusses computer applications and contrasts three popular SEM software packages. Data sets and programs in the book can be downloaded from http://nrmsc.usgs.gov/products/Pugesek_SEM.htm.

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. It presents the foundations, methods, and models of CFA and features numerous empirical data examples from a range of disciplines that can be reproduced by the readers. It also addresses computer applications, including relevant R packages and modules. Configural frequency analysis is a statistical method that allows the processing of important and interesting questions in categorical data. The perspective of CFA differs from the usual perspective of relations among variables; its focus is on patterns of variable categories that stand out with respect to specific hypotheses, and as such, CFA allows for testing numerous substantive hypotheses. The book describes the origins of CFA and their relation to chi-square analysis as well as the developments that are based on log-linear modeling. The models covered range from simple models of variable independence to complex models that are needed when causal hypotheses are tested. Empirical data examples are provided for each model. New models are introduced for person-oriented mediation analysis and locally optimized time series analysis, and new results concerning the characteristics of CFA methods are bolstered using Monte Carlo simulations. Primarily intended for researchers and students in the social and behavioral sciences, the book will also appeal to anyone who deals with categorical data from a person-centered perspective.

This volume presents contributions on handling data in which the postulate of independence in the data matrix is violated. When this postulate is violated and when the methods assuming independence are still applied, the estimated parameters are likely to be biased, and statistical decisions are very likely to be incorrect. Problems associated with dependence in data have been known for a long time, and led to the development of tailored methods for the analysis of dependent data in various areas of statistical analysis. These methods include, for example, methods for the analysis of longitudinal data, corrections for dependency, and corrections for degrees of freedom. 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). Researchers and graduate students in the social and behavioral sciences, education, econometrics, and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful.

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.

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.
Key Features
* Presents accessible methods of regression analysis
* Includes a broad spectrum of methods
* Techniques are explained step-by-step
* Provides sample command and result files for SYSTAT

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.

Dieser Band stellt umfassend die Methoden der Konfigurationsfrequenzanalyse (KFA) vor, eines von G.A. Lienert erstmals eingebrachten Verfahrens zur Testung von Hypothesen in Bezug auf Haufigkeiten in individuellen Zellen oder Gruppen einer Kreuzklassifikation. Die Autoren, die die Methode weiterentwickelt haben, bieten eine umfassende Darstellung der Grundlagen, Modelle und konkreten Anwendungsfalle in der psychologischen und sozialwissenschaftlichen, personen-orientierten Forschung. Dabei werden die Anfange der KFA und ihr Bezug zur Chi-Quadrat Analyse ebenso beschrieben wie die Entwicklungen, die auf log-linearen Modellen basieren. Fur jedes Modell und fur jede Fragestellung, die mit der KFA untersucht werden koennen, werden empirische Datenbeispiele prasentiert. Neue Ergebnisse werden durch Monte-Carlo Simulationen untermauert sowie neue Modelle entwickelt und vorgestellt.Das Buch richtet sich zum einen an Leser*innen, die uber grundlegendes Hintergrundwissen in der angewandten Statistik aus einfuhrenden Kursen und Kursen uber log-lineare Modelle verfugen. Aber auch Leserinnen und Leser ohne diese Kenntnisse koennen von diesem Buch profitieren, weil alle noetigen technischen Elemente eigens eingefuhrt und erklart werden. Computerprogramme werden vorgestellt und in Beispielen angewendet. Insgesamt stellt sich die KFA als statistische Methode dar, mit der fur kategoriale Daten wichtige und interessante Fragen bearbeitet werden koennen, die im Kontext der Anwendung von Routinemethoden der Statistik nicht zuganglich sind.

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.

This book presents an introduction to the methodology of structural equation modeling, illustrates its use, and goes on to argue that it has revolutionary implications for the study of natural systems. A major theme of this book is that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for considering individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses. Structural equation modeling (SEM) has such capabilities. It also possesses many other traits that add strength to its utility as a means of making scientific progress. In light of the capabilities of SEM, it can be argued that much of ecological theory is currently locked in an immature state that impairs its relevance. It is further argued that the principles of SEM are capable of leading to the development and evaluation of multivariate theories of the sort vitally needed for the conservation of natural systems. Supplementary information can be found at the authors website, http: //www.jamesbgrace.com/. Details why multivariate analyses should be used to study ecological systems Exposes unappreciated weakness in many current popular analyses Emphasises the future methodological developments needed to advance our understanding of ecological systems