Missing Data: Statistical Analysis of Data with Incomplete Observations (Hubert M. Blalock Memorial Lecture Series)


Missing data is a pervasive problem in almost all empirical scientific investigations involving human populations. Still, many statistical methods have been, and continue to be, developed for rectangular or complete datasets. Recently, there has been considerable research in the development of methods that acknowledge missing data -- largely due to the increase in the available computing power. The objectives of these lectures is to review various issues that the analyst needs to be aware of while dealing with missing data. The lectures will focus on issues related to the process that creates missing values and methods for analyzing data with missing values: weighting, maximum likelihood, and imputation techniques. All these aspects will be exemplified through a linear regression model in which either the dependent or independent variables have missing values.

The Hubert M. Blalock Memorial Lecture Series: Advanced Topics in Social Research -- Frontiers of Quantitative Methods

This is a special lecture series covering advanced topics on the frontier in quantitative methods of social research. Some of this material draws upon recent work in fields such as applied statistics, econometrics, computer science, and mathematical modeling.

This series is dedicated to the late Hubert "Tad" Blalock, whose scholarship, integrity, insight, and wit benefited all the social sciences through his work in applied statistics, causal modeling, theory construction, conceptualization, and measurement.

Fees: Consult the fee structure.

Tags: missing data, incomplete observations

Course Sections