Missing Data

Mechanisms, Methods, and Applications

Author

Francisco Cardozo, PhD

Published

March 30, 2026

Overview

In this class, we will cover the theory and practice of handling missing data in quantitative research. We will learn how to identify different missingness mechanisms (MCAR, MAR, MNAR), understand their implications for statistical inference, and apply modern approaches—including full information maximum likelihood (FIML) and multiple imputation—to obtain valid estimates from incomplete datasets.

Learning Objectives

By the end of this lesson, you will be able to:

  1. Distinguish between MCAR, MAR, and MNAR mechanisms from descriptions and data patterns
  2. Explain why complete-case analysis can produce biased estimates under MAR and MNAR
  3. Apply FIML estimation in a CFA model using lavaan or Mplus
  4. Conduct and interpret results from multiple imputation
  5. Evaluate the assumptions and trade-offs of different missing data methods

Topics

  • Maximum Likelihood estimation
  • Full Information Maximum Likelihood (FIML)
  • Multiple imputation
  • Planned missingness designs
  • Machine Learning methods for missing data

Materials

TipSlides

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WarningAssignment

Complete the Missing Data assignment:

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Assignment Overview

In the assignment, you will:

  1. Classify missingness mechanisms from real-world scenarios
  2. Generate simulated data with MAR missingness
  3. Fit a CFA model using both listwise deletion and FIML
  4. Compare estimated parameters to the true values and reflect on the advantages of FIML

Readings

Required

  • Enders, C. K. (2023). Missing data: An update on the state of the art. Psychological Methods.

Supplemental

  • Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8(3), 206-213.
  • Little, R. J., Carpenter, J. R., & Lee, K. J. (2022). A Comparison of Three Popular Methods for Handling Missing Data: Complete-Case Analysis, Inverse Probability Weighting, and Multiple Imputation. Sociological Methods & Research, 53(3), 1105-1135.
  • Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual review of psychology, 60, 549-576.
  • Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48(1), 5-37.
  • Graham, J. W. (2012). Missing data: Analysis and Design. New York: Springer.
  • Graham, J. W., Hofer, S. M., Donaldson, S. I., MacKinnon, D. P., & Schafer, J. L. (1997). Analysis with missing data in prevention research. The science of prevention: Methodological advances from alcohol and substance abuse research, 1, 325-366.