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Dependent data in social sciences research = forms, issues, and methods of analysis /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dependent data in social sciences research/ edited by Mark Stemmler, Wolfgang Wiedermann, Francis L. Huang.
Reminder of title:	forms, issues, and methods of analysis /
other author:	Stemmler, M.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xxii, 786 p. :ill., digital ;24 cm.
[NT 15003449]:	Growth Curve Modeling -- Directional Dependence -- Dydatic Data Modeling -- Item Response Modeling -- Other Methods for the Analyses of Dependent Data.
Contained By:	Springer Nature eBook
Subject:	Social sciences - Research -
Online resource:	https://doi.org/10.1007/978-3-031-56318-8
ISBN:	9783031563188

Dependent data in social sciences research = forms, issues, and methods of analysis /
Dependent data in social sciences researchforms, issues, and methods of analysis /[electronic resource] :edited by Mark Stemmler, Wolfgang Wiedermann, Francis L. Huang. - Second edition. - Cham :Springer International Publishing :2024. - xxii, 786 p. :ill., digital ;24 cm.

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

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.

ISBN: 9783031563188

Standard No.: 10.1007/978-3-031-56318-8doiSubjects--Topical Terms:

524282
Social sciences
--Research

LC Class. No.: H62.A5 / D474 2024

Dewey Class. No.: 300.721

Dependent data in social sciences research = forms, issues, and methods of analysis /
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245 0 0 $a Dependent data in social sciences research $h [electronic resource] : $b forms, issues, and methods of analysis / $c edited by Mark Stemmler, Wolfgang Wiedermann, Francis L. Huang.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xxii, 786 p. : $b ill., digital ; $c 24 cm.
505 0 $a Growth Curve Modeling -- Directional Dependence -- Dydatic Data Modeling -- Item Response Modeling -- Other Methods for the Analyses of Dependent Data.
520 $a 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.
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