Dependent Data in Social Sciences Research

Forms, Issues, and Methods of Analysis de

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Éditeur :

Springer


Paru le : 2024-10-21

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Description

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.
Pages
786 pages
Collection
n.c
Parution
2024-10-21
Marque
Springer
EAN papier
9783031563171
EAN PDF
9783031563188

Informations sur l'ebook
Nombre pages copiables
7
Nombre pages imprimables
78
Taille du fichier
28477 Ko
Prix
142,93 €
EAN EPUB
9783031563188

Informations sur l'ebook
Nombre pages copiables
7
Nombre pages imprimables
78
Taille du fichier
45926 Ko
Prix
142,93 €

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