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Open Access Occupation coding during the interview

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This article is Open Access under the terms of the Creative Commons CC BY-NC licence.

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.
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Keywords: Coding; Interview coding; Measurement error; Occupation; Open‐ended questions; Supervised learning

Document Type: Research Article

Publication date: February 1, 2018

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