In this chapter, I present older methods for handling missing data. I then turn to the major new approaches for handling missing data. In this chapter, I present methods that make the MAR assumption. Included in this introduction are the EM algorithm for covariance matrices, normal-model multiple imputation (MI), and what I will refer to as FIML (full information maximum likelihood) methods. Before getting to these methods, however, I talk about the goals of analysis.
CITATION STYLE
Graham, J. W. (2012). Analysis of Missing Data. In Missing Data (pp. 47–69). Springer New York. https://doi.org/10.1007/978-1-4614-4018-5_2
Mendeley helps you to discover research relevant for your work.