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Thompson says America must "unchoose" policies that have led to mass incarceration

Axinn says new data on campus rape will "allow students to see for themselves the full extent of this problem"

Frey says white population is growing in Detroit and other large cities


Susan Murphy to speak at U-M kickoff for data science initiative, Oct 6, Rackham

Andrew Goodman-Bacon, former trainee, wins 2015 Nevins Prize for best dissertation in economic history

Deirdre Bloome wins ASA award for work on racial inequality and intergenerational transmission

Bob Willis awarded 2015 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics

Next Brown Bag

Monday, Oct 5 at noon, 6050 ISR
Colter Mitchell: Biological consequences of poverty

The 2010 Morris Hansen Lecture Dealing with Survey Nonresponse in Data Collection, in Estimation Discussion

Archived Abstract of Former PSC Researcher

Tourangeau, Roger. 2011. "The 2010 Morris Hansen Lecture Dealing with Survey Nonresponse in Data Collection, in Estimation Discussion." Journal of Official Statistics, 27(1): 29-32.

In dealing with survey nonresponse, statisticians need to consider (a) measures to be taken at the data collection stage, and (b) measures to be taken at the estimation stage. One may employ some form of responsive design. In the later stages of the data collection in particular, one tries to achieve an ultimate set of responding units that is "better balanced" or "more representative" than if no special effort is made. The concept of "balanced response set" introduced in this article extends the well-known idea of "balanced sample." A measure of "lack of balance" is proposed; it is a quadratic form relating to a multivariate auxiliary vector; its statistical properties are explored. But whether or not good balance has been achieved in the data collection, a compelling question remains at the estimation stage: How to achieve the most effective reduction of nonresponse bias in the survey estimates. Balancing alone may not help. The nonresponse adjustment effort is aided by a bias indicator, a product of three factors involving selected powerful auxiliary variables.

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