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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 Us National Comorbidity Survey Replication (Ncs-R) Design and Field Procedures

Publication Abstract

Kessler, R.C., P. Berglund, W.T. Chiu, O. Demler, Steven Heeringa, E. Hiripi, R. Jin, B.E. Pennell, E.E. Walters, A. Zaslavsky, and H. Zheng. 2004. "The Us National Comorbidity Survey Replication (Ncs-R) Design and Field Procedures." International Journal of Methods in Psychiatric Research, 13:69-92.

The National Comorbidity Survey Replication (NCS-R) is a survey of the prevalence and correlates of mental disorders in the US that was carried out between February 2001 and April 2003. Interviews were administered face-to-face in the homes of respondents, who were selected from a nationally representative multi-stage clustered area probability sample of households. A total of 9,282 interviews were completed in the main survey and an additional 554 short non-response interviews were completed with initial non-respondents. This paper describes the main features of the NCS-R design and field procedures, including information on fieldwork organization and procedures, sample design, weighting and considerations in the use of design-based versus model-based estimation. Empirical information is presented on non-response bias, design effect, and the trade-off between bias and efficiency in minimizing total mean-squared error of estimates by trimming weights.

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