Analysis of Incomplete Data     5

 

 

5.1 Introduction

5.2 Case Studies

5.3 Data Setting and Modeling Framework

5.4 Analysis of Complete Growth Data

5.5 Simple Methods and MCAR

5.6 Available Case Methods

5.7 Likelihood-Based Ignorable Analyses

5.8 Multiple Imputation

5.9 The EM Algorithm

5.10 Categorical Data

5.11 MNAR and Sensitivity Analysis

5.12 Summary

 

 

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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