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Occupation coding during the interview
Author(s) -
Schierholz Malte,
Gensicke Miriam,
Tschersich Nikolai,
Kreuter Frauke
Publication year - 2018
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12297
Subject(s) - respondent , ask price , coding (social sciences) , psychology , interview , applied psychology , computer science , social psychology , statistics , mathematics , business , sociology , political science , finance , anthropology , law
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.