February 2005
Intermediate to advanced
430 pages
15h 6m
English
5.3 Data Setting and Modeling Framework
5.4 Analysis of Complete Growth Data
5.7 Likelihood-Based Ignorable Analyses
5.11 MNAR and Sensitivity Analysis
A large number of empirical studies are prone to incompleteness. Over the last decades, a number of methods have been developed to handle incomplete data. Many of these methods are relatively simple, but their validity can be questioned. With increasing computational power and software tools available, more flexible methods have come within reach. This chapter sketches a general taxonomy ...
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