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Occupation Coding During the Interview in a Web-First Sequential Mixed-Mode Survey

Author

Listed:
  • Peycheva Darina N.
  • Calderwood Lisa

    (Centre for Longitudinal Studies, UCL Institute of Education, 55–59 Gordon Square, London WC1H 0NT, United Kingdom.)

  • Sakshaug Joseph W.

    (Institute for Employment Research, 104 Regensburger Straße, Nuremberg 90478, Germany.)

Abstract

Coding respondent occupation is one of the most challenging aspects of survey data collection. Traditionally performed manually by office coders post-interview, previous research has acknowledged the advantages of coding occupation during the interview, including reducing costs, processing time and coding uncertainties that are more difficult to address post-interview. However, a number of concerns have been raised as well, including the potential for interviewer effects, the challenge of implementing the coding system in a web survey, in which respondents perform the coding procedure themselves, or the feasibility of implementing the same standardized coding system in a mixed-mode self- and interviewer-administered survey. This study sheds light on these issues by presenting an evaluation of a new occupation coding method administered during the interview in a large-scale sequential mixed-mode (web, telephone, face-to-face) cohort study of young adults in the UK. Specifically, we assess the take-up rates of this new coding method across the different modes and report on several other performance measures thought to impact the quality of the collected occupation data. Furthermore, we identify factors that affect the coding of occupation during the interview, including interviewer effects. The results carry several implications for survey practice and directions for future research.

Suggested Citation

  • Peycheva Darina N. & Calderwood Lisa & Sakshaug Joseph W., 2021. "Occupation Coding During the Interview in a Web-First Sequential Mixed-Mode Survey," Journal of Official Statistics, Sciendo, vol. 37(4), pages 981-1007, December.
  • Handle: RePEc:vrs:offsta:v:37:y:2021:i:4:p:981-1007:n:12
    DOI: 10.2478/jos-2021-0042
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    References listed on IDEAS

    as
    1. Belloni Michele & Brugiavini Agar & Meschi Elena & Tijdens Kea, 2016. "Measuring and Detecting Errors in Occupational Coding: an Analysis of SHARE Data," Journal of Official Statistics, Sciendo, vol. 32(4), pages 917-945, December.
    2. Brooke Helppie-McFall & Amanda Sonnega, 2018. "Feasibility and Reliability of Automated Coding of Occupation in the Health and Retirement Study," Working Papers wp392, University of Michigan, Michigan Retirement Research Center.
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