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Dependent Data in Social Sciences Research: Forms, Issues, and Methods of Analysis

Editat de Mark Stemmler, Wolfgang Wiedermann, Francis L. Huang
en Limba Engleză Hardback – 7 oct 2024
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.
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Specificații

ISBN-13: 9783031563171
ISBN-10: 3031563174
Pagini: 485
Ilustrații: Approx. 485 p.
Dimensiuni: 155 x 235 mm
Ediția:2nd ed. 2024
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Cuprins

Growth Curve Modeling.- Directional Dependence.- Dydatic Data Modeling.- Item Response Modeling.- Other Methods for the Analyses of Dependent Data.

Notă biografică

Mark Stemmler is Professor at Friedrich Alexander University Erlangen-Nuremberg (FAU), Department of Psychology

Wolfgang Wiedermann is Associate Professor, College of Education and Human Development, Co-Director of the Methodology Branch of the Missouri Prevention Science Institute, University of Missouri-Columbia (US).

Francis L. Huang is Associate Professor, College of Education and Human Development, Co-Director of the Methodology Branch of the Missouri Prevention Science Institute, University of Missouri-Columbia (US).

Textul de pe ultima copertă

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.

Caracteristici

Presents new developments and applications for dependent data Includs methods for the analysis of longitudinal data and corrections for degrees of freedom Covers growth curve modeling, directional dependence, dyadic data modeling, item response modelling and more