1.5k citations · 3k across the 6 of their papers we have counts for
9 papers
Faster Estimates from Binned Test Score Data---and How Accurate They Are
Paul T. von Hippel
Education agencies often summarize test score distributions by counting how many students scored in 3 to 5 different \textit{bins}. The HETOP model transforms bin counts into estim…
Better estimates from binned income data: Interpolated CDFs and mean-matching
Paul T. von Hippel, David J. Hunter, McKalie Drown
Researchers often estimate income statistics from summaries that report the number of incomes in bins such as $0-10,000, $10,001-20,000,...,$200,000+. Some analysts assign incom…
Regression with missing Ys: An improved strategy for analyzing multiply imputed data
Paul T. von Hippel
When fitting a generalized linear model -- such as a linear regression, a logistic regression, or a hierarchical linear model -- analysts often wonder how to handle missing values…
Estimates of heterogeneity (I2) can be biased in small meta-analyses
Paul T. von Hippel
In meta-analysis, the fraction of variance that is due to heterogeneity is known as I2. We show that the usual estimator of I2 is biased. The bias is largest when a meta-analysis h…
Robust estimation of inequality from binned incomes
Paul T. von Hippel, Samuel V. Scarpino, Igor Holas
Researchers must often estimate income inequality using data that give only the number of cases (e.g., families or households) whose incomes fall in "bins" such as 10,00…
Efficiency Gains from Using Auxiliary Variables in Imputation
Paul von Hippel, Jamie Lynch
Imputation models sometimes use auxiliary variables that, though not part of the planned analysis, can improve the accuracy of imputed values and the efficiency of point estimates.…