Missing data

Paul Allison and I run the site missingdata.org, which publicizes new research and software for analyzing data with missing values. We are in contract to revise the Sage textbook Missing Data.



  1. von Hippel, P.T. (2017). “Maximum likelihood multiple imputation: Faster imputations without posterior draws.” arXiv:1210.0870
  2. von Hippel, P.T. (2017). “How many imputations do you need? A two-step recipe using a quadratic rule.” Sociological Methods and Research, accepted. arXiv e-print. SAS macros and replication materials.
  3. von Hippel, P.T. (2015). “New confidence intervals and bias calculations show that maximum likelihood can beat multiple imputation in small samples.” Structural Equation Modeling, 23(3): 423-437. Also available as arXiv e-print 5835.
  4. von Hippel, P.T., & Lynch, J.L. (2013). “Efficiency gains from using auxiliary variables in imputation.” arXiv e-print 1311.5249
  5. von Hippel, P.T. (2013). “The bias and efficiency of incomplete-data estimators in small univariate normal samples.” Sociological Methods and Research, 42(4): 531-558. Also available as arXiv e-print 3132.
  6. von Hippel, P. T. (2013). “Should a normal imputation model be modified to impute skewed variables?Sociological Methods and Research, 42(1), 105-138.
  7. von Hippel, P. T. (2009). “How to impute interactions, squares, and other transformed variables.Sociological Methodology 39, 265-291.
  8. von Hippel, P.T. (2007). “Regression with missing Ys: An improved strategy for analyzing multiply imputed data” Sociological Methodology 37, 83-117. arXiv e-print. SAS macro implemention.
  9. von Hippel, P.T. (2004). “Biases in SPSS 12.0 Missing Values Analysis.” The American Statistician 58(2), 160-164.