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Surprising findings on what influences unintended pregnancy from Wise, Geronimus and Smock

Recommendations on how to reduce discrimination resulting from ban-the-box policies cite Starr's work

Brian Jacob on NAEP scores: "Michigan is the only state in the country where proficiency rates have actually declined over time."

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Call for papers: Conference on computational social science, April 2017, U-M

Sioban Harlow honored with 2017 Sarah Goddard Power Award for commitment to women's health

Post-doc fellowship in computational social science for summer or fall 2017, U-Penn

ICPSR Summer Program scholarships to support training in statistics, quantitative methods, research design, and data analysis

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Mon, March 13, 2017, noon:
Rachel Best

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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