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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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11#
發(fā)表于 2025-3-23 11:03:14 | 只看該作者
Jaitri Das,Buddhadeb Chattopadhyayn models for longitudinal data, focusing on continuous-time (CT) models. Unlike the more widely used discrete-time (DT) models, CT models do not require the time intervals between measurements to be equal and, therefore, can adapt effortlessly to irregular sampling schemes. Thus, our resulting appro
12#
發(fā)表于 2025-3-23 17:38:47 | 只看該作者
13#
發(fā)表于 2025-3-23 20:43:53 | 只看該作者
14#
發(fā)表于 2025-3-23 23:28:05 | 只看該作者
Mauro Ferrario,Maria Clelia Righiearly related to each other, and errors may be multiplicative. Thus, the present chapter discusses linearizable non-linear models for which distributional and independence-based direction dependence measures are applicable. Simulation results suggest that direction of dependence properties of linear
15#
發(fā)表于 2025-3-24 03:34:23 | 只看該作者
16#
發(fā)表于 2025-3-24 10:07:18 | 只看該作者
17#
發(fā)表于 2025-3-24 13:39:30 | 只看該作者
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發(fā)表于 2025-3-24 16:52:05 | 只看該作者
19#
發(fā)表于 2025-3-24 20:28:56 | 只看該作者
20#
發(fā)表于 2025-3-24 23:14:39 | 只看該作者
Engineered Fe-Based Nanocolumnar Films,n 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 t
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