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Using Data Mining to Predict the Occurrence of Respondent Retrieval Strategies in Calendar Interviewing: The Quality of Retrospective Reports

Author

Listed:
  • Belli Robert F.

    (University of Nebraska, Department of Psychology, Lincoln, NE 68588-0308, United States of America)

  • Miller L. Dee

    (University of Nebraska, 2343 Stone Creek Loop South, Lincoln, NE 68512, United States of America)

  • Baghal Tarek Al

    (University of Essex, ISER, Colchester, UK CO4 3SQ.)

  • Soh Leen-Kiat

    (University of Nebraska, 122E Avery Hall, Lincoln, NE 68588-0115, United States of America)

Abstract

Determining which verbal behaviors of interviewers and respondents are dependent on one another is a complex problem that can be facilitated via data-mining approaches. Data are derived from the interviews of 153 respondents of the Panel Study of Income Dynamics (PSID) who were interviewed about their life-course histories. Behavioral sequences of interviewer-respondent interactions that were most predictive of respondents spontaneously using parallel, timing, duration, and sequential retrieval strategies in their generation of answers were examined. We also examined which behavioral sequences were predictive of retrospective reporting data quality as shown by correspondence between calendar responses with responses collected in prior waves of the PSID. The verbal behaviors of immediately preceding interviewer and respondent turns of speech were assessed in terms of their co-occurrence with each respondent retrieval strategy. Interviewers’ use of parallel probes is associated with poorer data quality, whereas interviewers’ use of timing and duration probes, especially in tandem, is associated with better data quality. Respondents’ use of timing and duration strategies is also associated with better data quality and both strategies are facilitated by interviewer timing probes. Data mining alongside regression techniques is valuable to examine which interviewer-respondent interactions will benefit data quality.

Suggested Citation

  • Belli Robert F. & Miller L. Dee & Baghal Tarek Al & Soh Leen-Kiat, 2016. "Using Data Mining to Predict the Occurrence of Respondent Retrieval Strategies in Calendar Interviewing: The Quality of Retrospective Reports," Journal of Official Statistics, Sciendo, vol. 32(3), pages 579-600, September.
  • Handle: RePEc:vrs:offsta:v:32:y:2016:i:3:p:579-600:n:2
    DOI: 10.1515/jos-2016-0030
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    Cited by:

    1. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.

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