Enhancing Survey Quality: Continuous Data Processing Systems

Dinkelmann Karl (), Granda Peter () and Shove Michael ()
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Dinkelmann Karl: University of Michigan, USA, Institute for Social Research, 330 Packard Street, Ann Arbor, MI, 48104, U.S.A.
Granda Peter: University of Michigan, USA, Institute for Social Research, 330 Packard Street, Ann Arbor, MI, 48104, U.S.A.
Shove Michael: University of Michigan, USA, Institute for Social Research, 330 Packard Street, Ann Arbor, MI, 48104, U.S.A.

Journal of Official Statistics, 2019, vol. 35, issue 2, 337-352

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.

Keywords: Data quality; curation; tools; dissemination (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:35:y:2019:i:2:p:337-352:n:3

DOI: 10.2478/jos-2019-0016

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