Survey Research Methods Section News for October 2017

Survey Research Methods Section (SRMS) members gathered during JSM 2017 in Baltimore at the section’s business meeting and mixer to celebrate the following award winners:

• Wayne Fuller–2017 Samuel S. Wilks Award for Outstanding Contributions to Statistics (ASA)
• Wendy Martinez, Nick Horton, and John Eltinge–2017 Founders Award (ASA)
• Michael Brick–2017 Monroe G. Sirken Award in Interdisciplinary Survey Methods Research (ASA)
• Jon Rao–2017 Award for Outstanding Contribution to Small Area Estimation (ISI Satellite Meeting on Small Area Estimation)
• Donald Rubin–2017 Waksberg Award for Statistical Contributions to Survey Methodology (Survey Methodology)
• Roderick Little and Donald Rubin–2017 Karl Pearson Prize for their book, Statistical Analysis with Missing Data (ISI)
• Peter Miller–2017 AAPOR Award for Exceptionally Distinguished Achievement (AAPOR)
• Don Dillman, Jolene Smyth, and colleagues–2017 Warren J. Mitofsky Innovators Award (AAPOR)
• Donald Rubin–2017 C. R. and Bhargavi Rao Prize for Outstanding Research in Statistics (Penn State)
• Hejian Sang–2017 Edward C. Bryant Scholarship Award

This year’s JSM poster award recipients are the following:

• “Challenges in Linking Demographic Data at Different Geographic Levels” — Edward Mulrow, Rebecca Curtis, Ned English, Yongheng Lin, and Ilana Ventura, NORC at the University of Chicago
• “Sample Design and Weighting for Estimating a Dose-Response Curve” — Sharon Lohr, Pamela Broene, and Eric Jodts, Westat

And the JSM 2017 speed session award recipients are the following:

• “The Heckman Selection Model with Complex Survey Data” — Michael Machiorlatti, Sixia Chen, and Sara Vesely, University of Oklahoma Health Sciences Center
• “Imputation as Unifying Framework for Inferences from Nonrandom Samples” — Vladislav Beresovsky, National Center for Health Statistics
• “Comparing Alternative Methods for the Random Selection of a Respondent Within a Household for Online Surveys” — Geneviève Vézina and Pierre Caron, Statistics Canada
• “Highly Robust Multiple Imputation Models Using BART” — Michael R. Elliott, Vincent Tan, and Carol Flannagan, University of Michigan