Stemmler M., von Eye A., Wiedermann W. (eds.) Dependent Data in Social Sciences Research: Forms, Issues, and Methods of Analysis

New York: Springer, 2015. — 385 p. — (Springer Proceedings in Mathematics & Statistics 145). — ISBN: 978-3-319-20584-7/.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.The Observed Dependency of Longitudinal Data
Nonlinear Growth Curve Models
Stage-Sequential Growth Mixture Modeling of Criminological Panel Data
Developmental Pathways of Externalizing Behavior from Preschool Age to Adolescence: An Application of General Growth Mixture Modeling
A Generalization of Nagin’s Finite Mixture ModelGranger Causality: Linear Regression and Logit Models
Decisions Concerning the Direction of Effects in Linear Regression Models Using Fourth Central MomentsAnalyzing Dyadic Data with IRT Models
Longitudinal Analysis of Dyads Using Latent Variable Models: Current Practices and Constraints
Can Psychometric Measurement Models Inform Behavior Genetic Models? A Bayesian Model Comparison ApproachItem Response Models for Dependent Data: Quasi-exact Tests for the Investigation of Some Preconditions for Measuring Change
Measuring Competencies across the Lifespan - Challenges of Linking Test Scores
Mixed Rasch Models for Analyzing the Stability of Response Styles Across Time: An Illustration with the Beck Depression Inventory (BDI-II)Studying Behavioral Change: Growth Analysis via Multidimensional Scaling Model
A Nonparametric Approach to Modeling Cross-Section Dependence in Panel Data: Smart Regions in Germany
MANOVA Versus Mixed Models: Comparing Approaches to Modeling Within-Subject Dependence