Richard Valliant

Nonresident Senior Statistical Fellow

Richard Valliant, PhD, is a Research Professor Emeritus at the University of Michigan and the Joint Program for Survey Methodology at the University of Maryland. He has over 40 years of experience in survey sampling, estimation theory, and statistical computing. He was formerly an Associate Director at Westat and a mathematical statistician with the Bureau of Labor Statistics. He has a range of applied experience in survey estimation and sample design on a variety of establishment, institutional, and household surveys.

Valliant is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has been an editor of several statistical journals.

Valliant recently coauthored two books—Survey Weights: A Step-by-Step Guide to Calculation and Practical Tools for Designing and Weighting Survey Samples, 2nd edition— and a chapter on Mixed‐Mode Surveys: Design, Estimation, and Adjustment Methods in Advances in Comparative Survey Methods.

In recognition of his contributions to the field, Valliant has been named a Westat nonresident Senior Statistical Fellow and serves on the Statistical Fellows Committee, which provides consultation on important survey statistics issues and addresses recent advances in applied statistics.


  • PhD, Biostatistics, Johns Hopkins University
  • MS, Statistics, Cornell University
  • BS, Mathematics, University of Arkansas

Areas of Expertise

Selected Publications

Han D. and Valliant R. (2020).

Effects of outcome and response models on single-step calibration estimators.

Valliant R. and Dever J.A. (2018).

Survey weights: A step-by-step guide to calculation.

Valliant R., Dever J.A., and Kreuter F. (2018).

Practical tools for designing and weighting survey samples, 2nd edition.

Suzer-Gurtekin T., Valliant R., Heeringa S.G., and de Leeuw E.D. (2018).

Mixed-mode surveys: Design, estimation and adjustment methods.

Chen J.K.T., Valliant R., and Elliott M.R. (2018).

Model-assisted calibration of non-probability sample survey data using adaptive LASSO.

Elliott M.R. and Valliant R. (2017).

Inference for nonprobability samples.

Lee S., Suzer-Gurtekin T., Wagner J., and Valliant R. (2017).

Total survey error and respondent driven sampling: Focus on nonresponse and measurement errors in the recruitment process and the network size reports and implications for inferences.

Li J. and Valliant R. (2015).

Linear regression diagnostics in cluster samples.

Henry K.A. and Valliant R. (2015).

A design effect measure for calibration weighting in single-stage samples.

Valliant R., Dever J.A., and Kreuter F. (2015).

Effects of cluster sizes on variance components in two-stage sampling.

Valliant R., Dever J.A., and Kreuter F. (2015).

PracTools: Computations for design of finite population samples.

Dever J.A. and Valliant R. (2014).

Estimation with non-probability surveys and the question of external validity.

Gambacorta R., Iannario M., and Valliant R. (2014).

Design-based inference in a mixture model for ordinal variables for a two stage stratified design.

Valliant R., Hubbard F., Lee S., and Chang C. (2014).

Efficient use of commercial lists in U.S. household sampling.

Wagner J., Valliant R., Hubbard F., and Jiang L. (2014).

Level-of-effort paradata and nonresponse adjustment models for a national face-to-face survey.

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