Occupation coding during the interview

Schierholz, Malte ; Gensicke, Miriam ; Tschersich, Nikolai ; Kreuter, Frauke

DOI: https://doi.org/10.1111/rssa.12297
URL: http://onlinelibrary.wiley.com/doi/10.1111/rssa.12...
Additional URL: https://www.researchgate.net/publication/317569481...
Document Type: Article
Year of publication: 2018
The title of a journal, publication series: Journal of the Royal Statistical Society. Series A, Statistics in Society
Volume: 181
Issue number: 2
Page range: 379-407
Place of publication: Oxford
Publishing house: Wiley-Blackwell
ISSN: 0035-9238 , 1467-985X
Publication language: English
Institution: Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department A
School of Social Sciences > Statistik u. Sozialwissenschaftliche Methodenlehre (Kreuter 2014-2020)
Subject: 300 Social sciences, sociology, anthropology
Abstract: Currently, most surveys ask for occupation with open-ended questions. The verbal responses are coded afterwards, which is error prone and expensive. We present an alternative approach that allows occupation coding during the interview. Our new technique uses a supervised learning algorithm to predict candidate job categories. These suggestions are presented to the respondent, who in turn can choose the most appropriate occupation. 72.4% of the respondents selected an occupation when the new instrument was tested in a telephone survey, entailing potential cost savings. To aid further improvements, we identify some factors for how to increase quality and to reduce interview duration.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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