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. (2016). “The number of imputations should increase quadratically with the fraction of missing information.” arXiv:1608.05406
  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. Also available as
  9. von Hippel, P.T. (2004). “Biases in SPSS 12.0 Missing Values Analysis.” The American Statistician 58(2), 160-164.

Binned data

  1. von Hippel, P.T., Scarpino, S.V., & †Holas, I. (2016). “Robust estimation of inequality from binned incomes.” Sociological Methodology 46(1), 212-251. Also available as arXiv e-print 1402.4061.

Estimating heterogeneous effects

  1. von Hippel, P. T., Bellows. L., Osborne, C., Lincove, J., & Mills, N. (2016). “Teacher quality differences between teacher preparation programs: How big? How reliable? Which programs are different?” Economics of Education Review 53: 31-45. Also available as SSRN working paper 2506935.
  2. von Hippel, P.T. (2015). “The heterogeneity statistic I2 can be biased in small meta-analyses.” BMC Medical Research Methodology 15:35.

Linear and logistic probability models

  1. von Hippel, P.T. (2015) “Linear vs. logistic probability models: Which is better, and when?Statistical Horizons.
  2. von Hippel, P.T. (2017) “When can you fit a linear probability model? More often than you thinkStatistical Horizons.


  1. von Hippel, P.T. (2005). “Mean, median, and skew: Correcting a textbook rule.” Journal of Statistics Education 13(2).
    • Comment by Lawrence Lesser, Journal of Statistics Education 13(3).