ABSTRACT

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes ea

chapter 1|26 pages

Basic Concepts

chapter 2|24 pages

Weighting Methods

chapter 3|26 pages

Imputation

chapter 4|22 pages

Multiple Imputation

chapter 5|22 pages

Regression Analysis

chapter 6|24 pages

Longitudinal Analysis with Missing Values

chapter 7|10 pages

Nonignorable Missing Data Mechanisms

chapter 8|20 pages

Other Applications

chapter 9|12 pages

Other Topics