Roderick Little
Roderick J.A. Little, Ph.D.
Richard D. Remington Distinguished University Professor, Biostatistics Department
Professor, Statistics Department
Research Professor, Institute for Social Research
University of Michigan

BA in Mathematics, Cambridge University, 1971

MSc in Statistics and Operational Research, London University, 1972

PhD in Statistics, London University, 1974

Research Interests: Incomplete Data, Sample Surveys, Bayesian Statistics, Applied Statistics.

A primary research interest is the analysis of data sets with missing values. Many statistical techniques are designed for complete, rectangular data sets, but in practice biostatistical data sets contain missing values, either by design or accident. As detailed in my book with Rubin, initial statistical approaches were relatively ad-hoc, such as discarding incomplete cases or substituting means, but modern methods are increasingly based on models for the data and missing-data mechanism, using likelihood-based inferential techniques. For a recent talk on this topic presented at the SAS SUGI conference in 2005, see

Another interest is the analysis of data collected by complex sampling designs involving stratification and clustering of units. Since working as a statistician for the World Fertility Survey, I have been interested in the development of model-based methods for survey analysis that are robust to misspecification, reasonably efficient, and capable of implementation in applied settings.

Statistics is philosophically fascinating and diverse in application. My inferential philosophy is model-based and Bayesian, although the effects of model misspecification need careful attention. My applied interests are broad, including mental health, demography, environmental statistics, biology, economics and the social sciences as well as biostatistics.

Selected Highly-Cited Publications {Number of citations in Google Scholar, October 2019}

Statistical analysis with missing data {26230}
RJA Little, DB Rubin


Modeling the drop-out mechanism in repeated-measures studies {1471}
RJA Little
Journal of the American Statistical Association, 1112-1121


Robust statistical modeling using the t distribution {1380}
KL Lange, RJA Little, JMG Taylor
Journal of the American Statistical Association, 881-896


Regression with missing X's: a review {1282}
RJA Little
Journal of the American Statistical Association, 1227-1237


Pattern-mixture models for multivariate incomplete data {1003}
RJA Little
Journal of the American Statistical Association, 125-134


Survey nonresponse adjustments {586}
RJA Little
International statistical review 54, 139-157


Survey nonresponse {624}
RM Groves, D Dillman, JL Eltinge, RJA Little


Models for nonresponse in sample surveys {422}
RJA Little
Journal of the American Statistical Association, 237-250


Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches {640}
RJ Little, DB Rubin
Annual Review of Public Health 21 (1), 121-145


Intent-to-treat analysis for longitudinal studies with drop-outs {390}
R Little, L Yau
Biometrics, 1324-1333


(revised May, 2016)