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.
- The how_many_imputations command for Stata
- The mlmi package for R (forthcoming).
- von Hippel, P.T. (2017). “Maximum likelihood multiple imputation: Faster imputations without posterior draws.” arXiv:1210.0870
- 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.
- 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.
- von Hippel, P.T., & Lynch, J.L. (2013). “Efficiency gains from using auxiliary variables in imputation.” arXiv e-print 1311.5249
- 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.
- von Hippel, P. T. (2013). “Should a normal imputation model be modified to impute skewed variables?” Sociological Methods and Research, 42(1), 105-138.
- von Hippel, P. T. (2009). “How to impute interactions, squares, and other transformed variables.” Sociological Methodology 39, 265-291.
- 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.
- von Hippel, P.T. (2004). “Biases in SPSS 12.0 Missing Values Analysis.” The American Statistician 58(2), 160-164.