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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

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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