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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

Cell collapsing in poststratification

Archived Abstract of Former PSC Researcher

Kim, Jay J., Jianzhu Li, and Richard L. Valliant. 2007. "Cell collapsing in poststratification." Survey Methodology, 33(2): 139-150.

Poststratification is a common method of estimation in household surveys. Cells are formed based on characteristics that are known for all sample respondents and for which external control counts are available from a census or another source. The inverses of the poststratification adjustments are usually referred to as coverage ratios. Coverage of some demographic groups may be substantially below 100 percent, and poststratifying serves to correct for biases due to poor coverage. A standard procedure in poststratification is to collapse or combine cells when the sample sizes fall below some minimum or the weight adjustments are above some maximum. Collapsing can either increase or decrease the variance of an estimate but may simultaneously increase its bias. We study the effects on bias and variance of this type of dynamic cell collapsing theoretically and through simulation using a population based on the 2003 National Health Interview Survey. Two alternative estimators are also proposed that restrict the size of weight adjustments when cells are collapsed.

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