Adaptive Intervention Methodology for Reduction of Respondent Contact Burden in the American Community Survey

Author:

Ashmead Robert1,Slud Eric12,Hughes Todd3

Affiliation:

1. U.S. Census Bureau, 4600 Silver Hill Road, Washington, DC, 20233 United States of America .

2. Mathematics Department , Kirwan Hall Room 2314, University of Maryland , College Park 20742

3. UCLA Center for Health Policy Research , 10960 Wilshire Blvd, Suite 1550, Los Angeles , CA 90024 , United States of America

Abstract

Abstract The notion of respondent contact burden in sample surveys is defined, and a multi-stage process to develop policies for curtailing nonresponse follow-up is described with the goal of reducing this burden on prospective survey respondents. The method depends on contact history paradata containing information about contact attempts both for respondents and for sampled nonrespondents. By analysis of past data, policies to stop case follow-up based on control variables measured in paradata can be developed by calculating propensities to respond for paradata-defined subgroups of sampled cases. Competing policies can be assessed by comparing outcomes (lost interviews, numbers of contacts, patterns of reluctant participation, or refusal to participate) as if these stopping policies had been followed in past data. Finally, embedded survey experiments may be used to assess contact-burden reduction policies when these are implemented in the field. The multi-stage method described here abstracts the stages followed in a series of research studies aimed at reducing contact burden in the Computer Assisted Telephone Interview (CATI) and Computer Assisted Personal Interview (CAPI) modes of the American Community Survey (ACS), which culminated in implementation of policy changes in the ACS.

Publisher

Walter de Gruyter GmbH

Reference33 articles.

1. Bates, N., J. Dahlhamer, P. Phipps, A. Safir, and L. Tan. 2010. “Assessing Contact History Paradata Quality Across Several Federal Surveys.” In JSM Proceedings, Survey Research Methods Section, American Statistical Association, Vancouver, BC, July 31-August 5, 2010. Alexandria, VA: American Statistical Association. 91–105. Available at: http://ww2.amstat.org/sections/SRMS/Proceedings/y2010/Files/306005_55654.pdf (accessed February 2017).

2. Bates, N., J. Dahlhamer, and E. Singer. 2008. “Privacy Concerns, Too Busy, or Just Not Interested: Using Doorstep Concerns to Predict Survey Nonresponse.” Journal of Official Statistics 24: 591–612.

3. Biemer, P.P., P. Chen, and K. Wang. 2013. “Using Level-of-Effort Paradata in Non-Response Adjustments with Application to Field Surveys.” Journal of the Royal Statistical Society: Series A 176: 147–168. Doi: http://dx.doi.org/10.1111/j.1467-985X.2012.01058.x.10.1111/j.1467-985X.2012.01058.x

4. Bothwell, L. and S. Podolsky. 2016. “The Emergence of the Randomized, Controlled Trial.” New England Journal of Medicine 375: 501–504. Doi http://dx.doi.org/10.1056/NEJMp1604635.10.1056/NEJMp1604635

5. Bradburn, N. 1978. “Respondent Burden.” In JSM Proceedings, Survey Research Methods Section, American Statistical Association, San Diego, California, August 14–17, 1978. Alexandria, VA: American Statistical Association. 35–40. Available at: http://ww2.amstat.org/sections/SRMS/Proceedings/papers/1978_007.pdf (accessed February 2017).

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