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Dropout Rates and Response Times of an Occupation Search Tree in a Web Survey

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  • Tijdens Kea

    (University of Amsterdam, Amsterdam Institute for Advanced Labour Studies (AIAS), Postbus 94025 1090 GA, Amsterdam, The Netherlands)

Abstract

Occupation is key in socioeconomic research. As in other survey modes, most web surveys use an open-ended question for occupation, though the absence of interviewers elicits unidentifiable or aggregated responses. Unlike other modes, web surveys can use a search tree with an occupation database. They are hardly ever used, but this may change due to technical advancements. This article evaluates a three-step search tree with 1,700 occupational titles, used in the 2010 multilingual WageIndicator web survey for UK, Belgium and Netherlands (22,990 observations). Dropout rates are high; in Step 1 due to unemployed respondents judging the question not to be adequate, and in Step 3 due to search tree item length. Median response times are substantial due to search tree item length, dropout in the next step and invalid occupations ticked. Overall the validity of the occupation data is rather good, 1.7-7.5% of the respondents completing the search tree have ticked an invalid occupation.

Suggested Citation

  • Tijdens Kea, 2014. "Dropout Rates and Response Times of an Occupation Search Tree in a Web Survey," Journal of Official Statistics, Sciendo, vol. 30(1), pages 23-43, March.
    • Handle: RePEc:vrs:offsta:v:30:y:2014:i:1:p:23-43:n:2
    DOI: 10.2478/jos-2014-0002
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    1. repec:aia:aiaswp:wp102 is not listed on IDEAS
    2. Elias, Peter & McKnight, Abigail, 2001. "Skill Measurement in Official Statistics: Recent Developments in the UK and the Rest of Europe," Oxford Economic Papers, Oxford University Press, vol. 53(3), pages 508-540, July.
    3. Peter Elias, 1997. "Occupational Classification (ISCO-88): Concepts, Methods, Reliability, Validity and Cross-National Comparability," OECD Labour Market and Social Policy Occasional Papers 20, OECD Publishing.
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    Cited by:

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    2. Magdalena Smyk & Joanna Tyrowicz & Lucas van der Velde, 2021. "A Cautionary Note on the Reliability of the Online Survey Data: The Case of Wage Indicator," Sociological Methods & Research, , vol. 50(1), pages 429-464, February.
    3. Brian Fabo & Miroslav BEBLAVY & Karolien LENAERTS & Zachary KILHOFFER, 2017. "An overview of European Platforms: Scope and Business Models," JRC Research Reports JRC109190, Joint Research Centre.
    4. Schierholz, Malte & Brenner, Lorraine & Cohausz, Lea & Damminger, Lisa & Fast, Lisa & Hörig, Ann-Kathrin & Huber, Anna-Lena & Ludwig, Theresa & Petry, Annabell & Tschischka, Laura, 2018. "Eine Hilfsklassifikation mit Tätigkeitsbeschreibungen für Zwecke der Berufskodierung : Leitgedanken und Dokumentation," IAB-Discussion Paper 201813, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    5. Gweon Hyukjun & Schonlau Matthias & Steiner Stefan & Kaczmirek Lars & Blohm Michael, 2017. "Three Methods for Occupation Coding Based on Statistical Learning," Journal of Official Statistics, Sciendo, vol. 33(1), pages 101-122, March.
    6. Schierholz, Malte & Gensicke, Miriam & Tschersich, Nikolai, 2016. "Occupation coding during the interview," IAB-Discussion Paper 201617, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    7. Massing Natascha & Wasmer Martina & Wolf Christof & Zuell Cornelia, 2019. "How Standardized is Occupational Coding? A Comparison of Results from Different Coding Agencies in Germany," Journal of Official Statistics, Sciendo, vol. 35(1), pages 167-187, March.

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