Improving Lives Through Research®

Publications

Lohr S.L., Riddles M.K., and Brick J.M. (2019).

Goodness-of-fit tests for distributions estimated from complex survey data.

Canadian Journal of Statistics, 47(3), 409-425.
DOI
Messier K.P., Wheeler D.C., Flory A.R., Jones R.R., Nolan B.T., and Ward M.H. (2019).

Modeling groundwater nitrate exposure in private wells of North Carolina for the Agricultural Health Study.

Science of the Total Environment, 655, 512-519.
DOI | PubMed
Mohadjer L. and Edwards B. (2018).

Paradata and dashboards in PIAAC.

Quality Assurance in Education, 26(2), 263-277.
DOI
Cooper M., Pacek L.R., Guy M.C., Barrington-Trimis J.L., Simon P., Stanton C., and Kong G. (2018).

Hookah use among U.S. youth: A systematic review of the literature from 2009-2017.

Nicotine and Tobacco Research nty135.
DOI | PubMed
Cohn A.M., Johnson A.L., Rose S.W., Pearson J.L., Villanti A.C., and Stanton C. (2018).

Population-level patterns and mental health and substance use correlates of alcohol, marijuana, and tobacco use and co-use in US young adults and adults: Results from the Population Assessment of Tobacco and Health.

American Journal on Addictions, 27(6), 491-500.
DOI | PubMed
Tourangeau R., Yan T., Sun H., Hyland A., and Stanton C.A. (2018).

Population Assessment of Tobacco and Health (PATH) reliability and validity study: Selected reliability and validity estimates.

Tobacco Control.
DOI | PubMed
Jones M., Brick J.M., and Piesse A. (2018).

The effects of address coverage enhancement on estimates from a study using an ABS frame.

Proceedings of the Survey Research Methods Section of the American Statistical Association (pp. 1586-1594). Alexandria, VA: American Statistical Association.
Source
Jiao R. and Piesse A. (2018).

An approach to predict final yield among interim cases.

Proceedings of the Government Statistics Section of the American Statistical Association (pp. 2045-2056). Alexandria, VA: American Statistical Association.
Source
Lin T.-H., Flores Cervantes I., Saito S., and Bain R. (2018).

Evaluating nonresponse weighting adjustment for the Population-Based HIV Impact Assessments Surveys: On incorporating survey outcomes.

Proceedings of the Survey Research Methods Section of the American Statistical Association (pp. 2515-2526). Alexandria, VA: American Statistical Association.
Source
Marker D.A., Mardon R., Jenkins F., Campione J., Nooney J., Li J., Saydeh S., Zhang X., Shrestha S., and Rolka D. (2018).

State-level estimation of diabetes and prediabetes prevalence: Combining national and local survey data and clinical data.

Statistics in Medicine, 37(27), 3975-3990.
DOI | PubMed
Sinha R., Ahsan H., Blaser M., Caporaso J.G., Carmical J.R., Chan A.T., Fodor A., Gail M.H., Harris C.C., Helzlsouer K., Huttenhower C., Knight R., Kong H.H., Lai G.Y., Hutchinson D.L., Le Marchand L., Li H., Orlich M.J., Shi J., Truelove A., Verma M., Vogtmann E., White O., Willett W., Zheng W., Mahabir S., and Abnet C. (2018).

Next steps in studying the human microbiome and health in prospective studies, Bethesda, MD, May 16-17, 2017.

Microbiome, 6, 210.
DOI | PubMed
Tipton E. and Olsen R.B. (2018).

A review of statistical methods for generalizing from evaluations of educational interventions.

Educational Researcher, 47(8), 516-524.
DOI
Williams D. and Brick J.M. (2018).

Trends in U.S. face-to-face household survey nonresponse and level of effort.

Journal of Survey Statistics and Methodology, 6(2), 186-211.
DOI
Suzer-Gurtekin T., Valliant R., Heeringa S.G., and de Leeuw E.D. (2018).

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

In T.P. Johnson, B.E. Pennell, I.A.L. Stroop, and B. Dorer (Eds.), Advances in comparative survey methods: Multinational, multiregional, and multicultural contexts (3MC) (pp. 409-430). Hoboken, NJ: John Wiley and Sons, Inc.
Source
Valliant R., Dever J.A., and Kreuter F. (2018).

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

New York, NY: Springer.
Source
Valliant R. and Dever J.A. (2018).

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

College Station, TX: Stata Press.
Source
Chen J.K.T., Valliant R., and Elliott M.R. (2018).

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

Survey Methodology, 44(1), 117-144.
Source (PDF)
Stanton C.A. and Halenar M.J. (2018).

Patterns and correlates of multiple tobacco product use in the United States.

Nicotine and Tobacco Research, 20(suppl. 1), S1-S4.
DOI | PubMed
Brick J.M. (2018).

Sampling to minimize nonresponse bias.

In D.L. Vannette and J.A. Krosnick (Eds.), The Palgrave handbook of survey research (pp. 23-28). Cham, Switzerland: Palgrave Macmillan.
Source
Brick J.M. (2018).

Optimizing response rates.

In D.L. Vannette and J.A. Krosnick (Eds.), The Palgrave handbook of survey research (pp. 383-391). Cham, Switzerland: Palgrave Macmillan.
Source
Maitland A., Tourangeau R., and Sun H. (2018).

Separating science knowledge from religious belief: Two approaches for reducing the effect of identity on survey responses.

Public Opinion Quarterly, 82(1), 34-62.
DOI
Noel V.A., Francis S.E., and Tilley M.A. (2018).

An adapted measure of sibling attachment: Factor structure and internal consistency of the Sibling Attachment Inventory in youth.

Child Psychiatry & Human Development, 49(2), 217-224.
DOI | PubMed
Metcalfe J.D., Riley J.K., McGurk S.R., Hale T.W., Drake R.E., and Bond G.R. (2018).

Comparing predictors of employment in Individual Placement and Support: A longitudinal analysis.

Psychiatry Research, 264, 85-90.
DOI | PubMed
Finster M. and Milanowski A. (2018).

Teacher perceptions of a new performance evaluation system and their influence on practice: A within- and between-level analysis.

Education Policy Analysis Archives, 26(41).
DOI
Yan T., Keusch F., and He L. (2018).

The impact of question and scale characteristics on scale direction effects.

Survey Practice, 11(2).
DOI
Raithel M.A. (2018).

Validating user-submitted data files with Base SAS.

Proceedings of the SAS Global Forum 2018 Conference. Cary, NC: SAS Institute, Inc.
Source (PDF)
Olsen R.B., Bell S.H., and Nichols A. (2018).

Using preferred applicant random assignment (PARA) to reduce randomization bias in randomized trials of discretionary programs.

Journal of Policy Analysis and Management, 37(1), 167-180.
DOI
Ma H., Ogawa T.K., Sminkey T.R., Breidt F.J., Lesser V.M., Opsomer J.D., Foster J.R., and Van Voorhees D.A. (2018).

Pilot surveys to improve monitoring of marine recreational fisheries in Hawaii.

Fisheries Research, 204, 197-208.
DOI
Mardon R., Marker D., Nooney J., Campione J., Jenkins F., Johnson M., Merrill L., Rolka D.B., Saydah S., Geiss L.S., Zhang X., and Shrestha S. (2017).

Novel methods and data sources for surveillance of state-level diabetes and prediabetes prevalence.

Preventing Chronic Disease, 14, 160572.
DOI | PubMed
Elliott M.R. and Valliant R. (2017).

Inference for nonprobability samples.

Statistical Science, 32(2), 249-264.
Source | DOI
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.

Journal of Official Statistics, 33(2), 335-366.
Source | DOI
Keith D.R., Stanton C., Gaalema D.E., Bunn J.Y., Doogan N.J., Redner R., Kurti A.N., Cepeda-Benito A., Lopez A.A., Morehead A.L., Roberts M.E., and Higgins S.T. (2017).

Disparities in US healthcare provider screening and advice for cessation across chronic medical conditions and tobacco products.

Journal of General Internal Medicine, 32(9), 974-980.
DOI | PubMed
Siegfried Y., Morganstein D., Piesse A., and Lohr S. (2017).

Why independent surveys with the same objective yield different estimates.

Proceedings of the Survey Research Methods Section of the American Statistical Association (pp. 935-952). Alexandria, VA: American Statistical Association.
Source (PDF)
Lin T., Weil N.V., Flores Cervantes I., and Saito S. (2017).

Developing nonresponse weighting adjustments for population-based HIV impact assessments surveys in three African countries.

Proceedings of the Survey Research Methods Section of the American Statistical Association (pp. 965-982). Alexandria, VA: American Statistical Association.
Source (PDF)
Breidt F.J. and Opsomer J.D. (2017).

Model-assisted survey estimation with modern prediction techniques.

Statistical Science, 32(2), 16.
DOI
Riddles J., Burke J., Rivero G., and Rust K.F. (2017).

Adjustments to student weights to account for student nonresponse in the National Assessment of Educational Progress.

Proceedings of the Survey Research Methods Section of the American Statistical Association (pp. 561-570). Alexandria, VA: American Statistical Association.
Source
Zhou C.K., Young D., Yeboah E.D., Coburn S.B., Tettey Y., Biritwum R.B., Adjei A.A., Tay E., Niwa S., Truelove A., Welsh J., Mensah J.E., Hoover R.N., Sesterhenn I.A., Hsing A.W., Srivastava S., and Cook M.B. (2017).

TMPRSS2:ERG gene fusions in prostate cancer of West African men and a meta-analysis of racial differences.

American Journal of Epidemiology, 186(12), 1352-1361.
DOI | PubMed
Conti D.V., Wang K., Sheng X., Bensen J.T., Hazelett D.J., Cook M.B., Ingles S.A., Kittles R.A., Strom S.S., Rybicki B.A., Nemesure B., Isaacs W.B., Stanford J.L., Zheng W., John E.M., Park J.Y., Xu J., Stevens V.L., Berndt S.I., Sanderson M., Huff C.D., Wang Z., Yeboah E.D., Tettey Y., Biritwum R.B., Adjei A.A., Tay E., Truelove A., Niwa S., Sellers T.A., Yamoah K., Murphy A.B., Crawford D.C., Gapstur S.M., Bush W.S., Aldrich M.C., Cussenot O., Petrovics G., Cullen J., Neslund-Dudas C., Stern M.C., Kote-Jarai Z., Govindasami K., Chokkalingam A.P., Hsing A.W., Goodman P.J., Hoffmann T., Drake B.F., Hu J.J., Clark P.E., Van Den Eeden S.K., Blanchet P., Fowke J.H., Casey G., Hennis A.J., Han Y., Lubwama A., Thompson I.M., Leach R., Easton D.F., Schumacher F., Van Den Berg D., Gundell S.M., Stram A., Wan P., Xia L., Pooler L.C., Mohler J.L., Fontham E.T.H., Smith G.J., Taylor J.A., Srivastava S., Eeles R.A., Carpten J., Kibel A.S., Multigner L., Parent M.E., Menegaux F., Cancel-Tassin G., Klein E.A., Brureau L., Stram D.O., Watya S., Chanock S.J., Witte J.S., Blot W.J., Henderson B.E., Haiman C.A., and for the PRACTICAL/ELLIPSE Consortium (2017).

Two novel susceptibility loci for prostate cancer in men of African ancestry.

Journal of the National Cancer Institute, 109(8).
DOI | PubMed
Noar S.M., Cappella J.N., and Price S.M. (2017).

Communication regulatory science: Mapping a new field.

Health Communication.
DOI | PubMed
Gonzales F.A., Willis G.B., Breen N., Yan T., Cronin K.A., Taplin S.H., and Yu M. (2017).

An exploration of changes in the measurement of mammography in the National Health Interview Survey.

Cancer Epidemiology, Biomarkers, and Prevention, 26(11), 1611-1618.
DOI | PubMed
Tourangeau R., Brick J.M., Lohr S., and Li J. (2017).

Adaptive and responsive survey designs: A review and assessment.

Journal of the Royal Statistical Society, A, 180(1), 203-223.
DOI
Krenzke T., Li J., and McKenna L. (2017).

Producing multiple tables for small areas with confidentiality protection.

Statistical Journal of the IAOS, 33(2), 469-485.
DOI
Funkhouser E., Vellala K., Baltuck C., Cacciato R., Durand E., McEdward D., Sowell E., Theisen S.E., and Gilbert G.H. (2017).

Survey methods to optimize response rate in the National Dental Practice-Based Research Network.

Evaluation and the Health Professions, 40(3), 332-358.
DOI | PubMed
Tourangeau R., Sun H., Conrad F.G., and Couper M.P. (2017).

Examples in open-ended survey questions.

International Journal of Public Opinion Research, 29(4), 690-702.
DOI
Rust K.F., Krawchuk S., and Monseur C. (2017).

Sample design, weighting, and calculation of sampling variance.

In P. Lietz, J.C. Cresswell, K.F. Rust, and R.J. Adams (Eds.), Implementation of large-scale education assessments. New York, NY: Wiley.
DOI
Lipman P.D., Loudon K., Dluzak L., Moloney R., Messner D., and Stoney C.M. (2017).

Framing the conversation: Use of PRECIS-2 ratings to advance understanding of pragmatic trial design domains.

Trials, 18(1), 532.
DOI | PubMed
Jones C.D., Milam A.J., Debnam K.J., and Bradshaw C.P. (2017).

School environments and obesity: The mediating role of personal stress.

Journal of Community Psychology.
DOI
Birch R.J., Gallagher J.E., Wilkie A.A., Cordner A., Hudgens E.E., and Wade T.J. (2017).

Gender and racial/ethnic disparities: Cumulative screening of health risk indicators in 20-50 year olds in the United States.

Journal of Health Disparities Research and Practice, 10(1), 126-142.
Source
Tatalovich Z., Stinchcomb D.G., Lyman J.A., Hunt Y., and Cucinelli J.E. (2017).

A geo-view into historical patterns of smoke-free policy coverage in the USA.

Tobacco Prevention & Cessation, 3(December), 134.
DOI
Chen S. and Rust K.F. (2017).

An extension of Kish's formula for design effects to two- and three-stage designs with stratification.

Journal of Survey Statistics and Methodology, 5(2), 111-130.
DOI

Want to work with us?
You’ll be in great company.

About Us Careers

Westat Employees.
Westat Employee.
Client List
Centers for Disease Control and Prevention
Centers for Medicare & Medicaid Services
Substance Abuse and Mental Health Services Administration
The National Institutes of Health
AAA Foundation for Traffic Safety
The Johns Hopkins University
University of Maryland Baltimore Campus
University of Denver
U.S. Department of Veterans Affairs
U.S. Department of Transportation
U.S. Department of Justice
U.S. Department of Health and Human Services
U.S. Department of Education
U.S. Department of Agriculture
Toyota
The Verizon Foundation
Texas Education Agency
Baltimore Metropolitan Council
Teach for America
Social Security Administration
SiriusXM
Robert Wood Johnson Foundation
Organization for Economic Cooperation and Development
NYC Mayor’s Office for Economic Opportunity
National Science Foundation
Michigan Department of Health and Human Services
Maryland Cancer Registry
Internal Revenue Service
Georgia Department of Transportation
DC Public Schools
ClearWay Minnesota
Chicago Metropolitan Agency for Planning
University of Michigan
Forbes 2019: The Best Employers for DiversityForbes 2019: The Best Employers for Women

Westat is an Equal Opportunity Employer and does not discriminate on the basis of race, creed, color, religion, sex, national origin, age, veteran status, disability, marital status, sexual orientation, citizenship status, genetic information, gender identity, or any other protected status under applicable law. Notices to Employees & Applicants.