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Titlebook: Dependent Data in Social Sciences Research; Forms, Issues, and M Mark Stemmler,Wolfgang Wiedermann,Francis L. Huang Book 2024Latest edition

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書目名稱Dependent Data in Social Sciences Research
副標(biāo)題Forms, Issues, and M
編輯Mark Stemmler,Wolfgang Wiedermann,Francis L. Huang
視頻videohttp://file.papertrans.cn/285/284526/284526.mp4
概述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 dep
圖書封面Titlebook: Dependent Data in Social Sciences Research; Forms, Issues, and M Mark Stemmler,Wolfgang Wiedermann,Francis L. Huang Book 2024Latest edition
描述.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
出版日期Book 2024Latest edition
關(guān)鍵詞analysis of longitudinal panel count data; close proximity data; clustered or paired data; corrections
版次2
doihttps://doi.org/10.1007/978-3-031-56318-8
isbn_softcover978-3-031-56320-1
isbn_ebook978-3-031-56318-8
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Exploration of Dependence Structures in Longitudinal Categorical Data with Ordinal Responsesrelationship with categorical covariates, the proposed approach consists of a set of SCCRAM-based strategies that take into account time dependence, data format, potential of asymmetric dependence, and model-free inference. The utility of the proposed method is demonstrated using two longitudinal ca
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Bayesian Network for Discovering the Potential Causal Structure in Observational Dataht on the factors that drive observed patterns and phenomena, facilitating a clear understanding of the intricate web of relationships, enabling researchers and practitioners to derive meaningful insights, and making informed decisions based on a nuanced understanding of the causal mechanisms at pla
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