Occupation Coding During the Interview
提出一种在访谈过程中使用监督学习算法预测职业类别并让受访者选择的方法，测试中72.4%的受访者选择了职业，可节省成本并提高编码质量。
Summary 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.