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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Active Targeting of Nanomedicines are nested within classrooms in educational studies, or participants are repeatedly measured at different time points in longitudinal studies. Multilevel models (MLM), also known as hierarchical linear models and mixed effects models, have been widely used to account for clustered data.

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Understanding the Budget Process present when observations are nested within two higher-level structures which in turn are not nested within each other. To capture the variations from both clustering dimensions, cross-classified random effects models (CCREMs) were developed as an extension to standard multilevel modeling. Alternat

theta-waves

Xiaoyu Wang,Yufei Wang,Qi Liang,Yuning Zhangtion approach for score-based tests of mixed models, which addresses situations where there is dependence between scores. This differs from the traditional score-based tests, which require independence of scores. We first review traditional score-based tests and then propose a new, self-normalized s

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Mark Stemmler,Wolfgang Wiedermann,Francis L. HuangPresents 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

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Time in Latent Growth Curve Modelsn the construction of analytical models. In this chapter, we look at how longitudinal data are analyzed in latent growth curve models. We focus on the real-world problem of sampling-time variation, when individuals do not have exactly equal intervals between measurements, its consequences, and how to handle it.

cumulative

Bootstrap Methods for Robust Multilevel Analysis are nested within classrooms in educational studies, or participants are repeatedly measured at different time points in longitudinal studies. Multilevel models (MLM), also known as hierarchical linear models and mixed effects models, have been widely used to account for clustered data.

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https://doi.org/10.1007/978-3-031-56318-8analysis of longitudinal panel count data; close proximity data; clustered or paired data; corrections

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