David Morganstein

Vice President

David Morganstein is a Vice President and a senior statistician with 40+ years of experience in the design and application of sample surveys and systems of evaluation, quality control, statistical analysis, and estimation and quantification.

Mr. Morganstein was elected as the 110th President of the American Statistical Association (ASA), the world’s largest community of statisticians, for 2015. He has also served as its Treasurer and Vice President, as well as on many committees.

Mr. Morganstein is a Fellow of the ASA and is a recipient of the ASA Founders Award. An elected Member of the International Statistical Institute (ISI), he chaired its Ethics Committee and served on the ISI Ethics Advisory Board, and directed the re-writing of the ISI’s Declaration on Professional Ethics.

Mr. Morganstein has worked with more than 75 organizations in North America and overseas in the teaching and use of statistical methods for quality assurance and improvement. He is also Special Faculty in the Joint Program in Survey Methodology at the University of Maryland and serves on George Mason University’s Statistics Department Advisory Board.

In recognition of his contributions to the field, Mr. Morganstein has been named a Westat 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.

Education

Coursework and Preliminary Doctoral Examination Completed, University of Michigan
M.A., Statistics, University of Michigan
B.S., Electrical Engineering, Purdue University

Areas of Expertise

Selected Publications

Siegfried Y., Morganstein D., Piesse A., and Lohr S. (2017).

Why independent surveys with the same objective yield different estimates.

Morganstein D. (2017).

Mentoring in the ASA: A commentary.

Lohr S., Riddles M.K., and Morganstein D. (2016).

Tests for evaluating nonresponse bias in surveys.

Morganstein D. and Wasserstein R. (2014).

ASA statement on value-added models.

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