Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 808 pp. Englisch.

Bestandsnummer des Verk�ufers 9783031563171

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Bibliografische Details

Titel: Dependent Data in Social Sciences Research
Verlag: Springer International Publishing, Springer International Publishing Okt 2024
Ver�ffentlichungsdatum: 2024
Sprache: Englisch
ISBN-10: 3031563174
ISBN-13: 9783031563171
Bindung: Buch
Ausgabe: 2. Auflage
Zustand: Neu

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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.

�ber die Autorin bzw. den Autor

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).

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