Mark Stemmler

Wolfgang Wiedermann

Alexander von Eye

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.

Forms, Issues, and Methods of Analysis

Dependent Data in Social Sciences Research

This book takes an easy-to-understand look at the statistical approach called the person-centered method. Instead of analyzing means, variances and covariances of scale scores as in the common variable-centered approach, the person-centered approach analyzes persons or objects grouped according to their characteristic patterns or configurations in contingency tables. The main focus of the book will be on Configural Frequency Analysis (CFA; Lienert and Krauth, 1975) which is a statistical method that looks for over and under-frequented cells or patterns. Over frequented means that the observations in this cell or configuration are observed more often than expected, under-frequented means that this cell or configuration is observed less often than expected. In CFA a pattern or configuration that contains more observed cases than expected is called a type; similarly, a pattern or configuration that is less observed than expected are called an antitype. CFA is similar to log-linear modeling. In log-linear modeling the goal is to come up with a fitting model including all important variables. Instead of fitting a model, CFA looks at the significant residuals of a log-linear model. The book describes the use of an R-package called confreq (derived from Configural Frequency Analysis). The use of the software package is described and demonstrated with data examples.

Configural Frequency Analysis (CFA) and Other Methods for the Analysis of Contingency Tables

Person-Centered Methods

Thomas Bliesener

Andreas Beelmann

Leading experts review the latest findings and recommendations concerning the roots and prevention of antisocial behavior and crime The developmental perspective is becoming increasingly significant in criminology and in research on antisocial and criminal behavior. Developmental criminology can explain the stability of antisocial behavior as well as how it emerges and disappears across the life span. This volume brings together many of the most renowned scientists in the field. Theyreport the latest findings, explain various phenomena and trajectories of antisocial behavior, and present new research methods. In a series of reviews, they analyze the significance of biological, psychological, and social risk factors in the development of antisocial behavior and show how these findings can be applied not only in crime prevention, criminal justice, and offender treatment, but also in the risk management of young offenders.

Mark Stemmler

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