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.
Author(s): Mark Stemmler, Alexander von Eye, Wolfgang Wiedermann (eds.)
Series: Springer Proceedings in Mathematics & Statistics 145
Edition: 1
Publisher: Springer International Publishing
Year: 2015
Language: English
Pages: XIII, 385
Tags: Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law; Statistical Theory and Methods; Psychometrics
Front Matter....Pages i-xiii
Front Matter....Pages 1-1
The Observed Dependency of Longitudinal Data....Pages 3-45
Nonlinear Growth Curve Models....Pages 47-66
Stage-Sequential Growth Mixture Modeling of Criminological Panel Data....Pages 67-89
Developmental Pathways of Externalizing Behavior from Preschool Age to Adolescence: An Application of General Growth Mixture Modeling....Pages 91-106
A Generalization of Nagin’s Finite Mixture Model....Pages 107-123
Front Matter....Pages 125-125
Granger Causality: Linear Regression and Logit Models....Pages 127-148
Decisions Concerning the Direction of Effects in Linear Regression Models Using Fourth Central Moments....Pages 149-169
Front Matter....Pages 171-171
Analyzing Dyadic Data with IRT Models....Pages 173-202
Longitudinal Analysis of Dyads Using Latent Variable Models: Current Practices and Constraints....Pages 203-229
Can Psychometric Measurement Models Inform Behavior Genetic Models? A Bayesian Model Comparison Approach....Pages 231-259
Front Matter....Pages 261-261
Item Response Models for Dependent Data: Quasi-exact Tests for the Investigation of Some Preconditions for Measuring Change....Pages 263-279
Measuring Competencies across the Lifespan - Challenges of Linking Test Scores....Pages 281-308
Mixed Rasch Models for Analyzing the Stability of Response Styles Across Time: An Illustration with the Beck Depression Inventory (BDI-II)....Pages 309-324
Front Matter....Pages 325-325
Studying Behavioral Change: Growth Analysis via Multidimensional Scaling Model....Pages 327-343
A Nonparametric Approach to Modeling Cross-Section Dependence in Panel Data: Smart Regions in Germany....Pages 345-367
MANOVA Versus Mixed Models: Comparing Approaches to Modeling Within-Subject Dependence....Pages 369-385
