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Frey's Scenario F simulation mentioned in account of the Democratic Party's tribulations

U-M Poverty Solutions funds nine projects

Dynarski says NY's Excelsior Scholarship Program could crowd out low-income and minority students

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Workshops on EndNote, NIH reporting, and publication altmetrics, Jan 26 through Feb 7, ISR

2017 PAA Annual Meeting, April 27-29, Chicago

NIH funding opportunity: Etiology of Health Disparities and Health Advantages among Immigrant Populations (R01 and R21), open Jan 2017

Russell Sage 2017 Summer Institute in Computational Social Science, June 18-July 1. Application deadline Feb 17.

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Next Brown Bag

Mon, Jan 23, 2017 at noon:
Decline of cash assistance and child well-being, Luke Shaefer

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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