Dependent Data in Social Sciences Research

Beschreibung

This second edition presents a variety of up-to-date statistical issues with regard to dependent or longitudinal data such as continuous time modeling, growth curve modeling, dynamic modeling, network analysis, Bayesian network analysis, directional dependence, multilevel analysis, item response modeling (IRT), estimation of missing data of longitudinal data and other methods for the analysis of dependent data (e.g., configural frequency analysis, ecological momentary assessment, and unobserved within-group individual differences). It 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. In addition, R-scripts to recapture the presented content are provided.

Researchers and graduate students in the social and behavioral sciences, education, econometrics, mathematics, biology, physics and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful.

This book covers the following subjects: 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). It 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 include, for example, methods for the analysis of longitudinal data, corrections for dependency, and corrections for degrees of freedom. 

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.

Autor*in

Mark Stemmler

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analysis of longitudinal panel count data close proximity data clustered or paired data corrections for dependency dependent data directional dependence dyadic data modeling growth curve modeling item response modeling psychometrics statistical analysis for dependence in data

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