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*.

## Software

- The
**how_many_imputations**command for Stata - The
*mlmi*package for R (forthcoming).

## Articles

- 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
*Y*s: 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.