Enhancing Survey Quality: Continuous Data Processing Systems

Open access

Abstract

Producers of large government-sponsored surveys regularly use Computer-Assisted Interviewing (CAI) software to design data collection instruments, monitor fieldwork operations, and evaluate data quality. When used in conjunction with responsive survey designs, last-minute modifications to problems in the field are quickly addressed. Complementing this strategy, but little discussed, is the need to implement similar changes in the post data collection stage of the survey data life cycle. We describe a continuous data processing system where completed interviews are carefully examined as soon as they are collected; editing, recode, and imputation programs are applied using CAI tools; and the results are reviewed to correct problematic cases. The goal: provide higher quality data and shorten the time between the conclusion of data collection and the appearance of public use data files.

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  • Bethlehem J. 1997. “Integrated Control Systems for Survey Processing.” In Survey Measurement and Process Quality edited by L. Lyberg P. Biemer M. Collins E. De leeuw C. Dippo N. Schwarz and D. Trewin 371–392. New York: Wiley and Sons Inc.

  • Beullens K. H. Matsuo G. Loosveldt and C. Vandenplas. 2014. Quality report for the European Social Survey Round 6. London: European Social Survey ERIC. Available at: http://www.europeansocialsurvey.org/docs/round6/methods/ESS6_quality_report.pdf (accessed September 2018).

  • Biemer P. 2010. “Total Survey Error: Design Implementation and Evaluation.” Public Opinion Quarterly 74(5): 817–848. Doi: https://doi.org/10.1093/poq/nfq058.

  • Biemer P. and L. Lyberg. 2003. Introduction to Survey Quality. Hoboken New Jersey: John Wiley & Sons.

  • Biemer P. D. Trewin H. Bergdahl and Y. Xie. 2017. “ASPIRE.” In Total Survey Error in Practice edited by P. Biemer E. de Leeuw S. Eckman B. Edwards F. Kreuter L.E. Lyberg N.C. Tucker and B.T. West 359–385. Hoboken New Jersey: John Wiley & Sons Inc. Doi: https://doi.org/10.1002/9781119041702.ch17.

  • De Waal T. 2013. “Selective Editing: A Quest for Efficiency and Data Quality.” Journal of Official Statistics 29(4): 473–488. Doi: https://doi.org/10.2478/jos-2013-0036.

  • Fellegi I.P. and D. Holt. 1976. “A Systematic Approach to Automatic Edit and Imputation.” Journal of the American Statistical Association 71: 17–35. Doi: https://doi.org/10.1080/01621459.1976.10481472.

  • Groves R.M. and L. Lyberg. 2010. “Total Survey Error: Past Present and Future.” Public Opinion Quarterly 74(5): 849–879. Doi: https://doi.org/10.1093/poq/nfq065.

  • Groves R.M. W.D. Mosher J. Lepkowski and N.G. Kirgis. 2009. Planning and Development of the Continuous National Survey of Family Growth. National Center for Health Statistics. Vital Health Stat 1(48). Available at: https://www.ncbi.nlm.nih.gov/pubmed/20141029 (accessed May 2019).

  • Sana M. and A.A. Weinreb. 2008. “Insiders Outsiders and the Editing of Inconsistent Survey Data.” Sociological Methods Research 36: 515–541.

  • Seiss M. E.A. Vance and R.P. Hall. 2014. “The Importance of Cleaning Data During Fieldwork: Evidence from Mozambique.” Survey Practice 7(4). E-ISSN: 2168-0094. Available at: http://www.surveypractice.org/article/2864-the-importance-of-cleaningdata-during-fieldwork-evidence-from-mozambique. (accessed September 2018).

  • Thalji L. C.A. Hill S. Mitchell R. Suresh H. Speizer and D. Pratt 2013. “The General Survey System Initiative at RTI International: An Integrated System for the Collection and Management of Survey Data.” Journal of Official Statistics 29(1): 29–48. Doi: https://doi.org/10.2478/jos-2013-0003.

  • Tourangeau R. J.M. Brick S. Lohr and J. Li. 2016. “Adaptive and Responsive Survey Designs: A Review and Assessment.” Journal of the Royal Statistical Society Series A 180(1): 203–223. Doi: https://doi.org/10.1111/rssa.12186.

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