Project Description
Currently, most surveys ask for occupation with open-ended questions. The verbatim responses are coded afterwards into a classification with hundreds of categories and thousands of jobs, which is error-prone, time-consuming, and costly. The project investigates how to improve this process by asking response-dependent questions during the interview. We developed an instrument for interview coding of occupations and successfully tested it in a CATI and a CAPI survey. Results are promising: between 55 and 85 percent of the text responses can be coded with the newly developed tool, and there is no evidence that the use of this tool is an additional burden to interviewers and respondents. During the second funding phase, we aim to further develop this method of occupation coding. An in-depth analysis of various error sources is required to inform and improve the quality and the validity of measurement. The improvements to the instrument will reflect the specific requirements of computer-assisted personal interviews, computer-assisted telephone interviews, and web surveys. Results from analyzing interviewer behavior will inform the improvement of training documents to minimize the influence of interviewers on the survey results. The new version of the instrument allows for continuous improvement of the suggested answer options. The results will be made available in the form of an open-source software so that researchers can use our instrument for their own surveys.
Publications
- Schierholz, Malte. 2019. "New methods for job and occupation classification". Dissertation, Mannheim. https://madoc.bib.uni-mannheim.de/50617/.
- Schierholz, Malte, Miriam Gensicke, Nikolai Tschersich, and Frauke Kreuter. 2018. "Occupation Coding during the Interview." Journal of the Royal Statistical Society: Series A (Statistics in Society) 181 (2): 379–407. https://doi.org/10.1111/rssa.12297.
- Schierholz, Malte. 2018. "Eine Hilfsklassifikation mit Tätigkeitsbeschreibungen für Zwecke der Berufskodierung." AStA Wirtschafts- und Sozialstatistisches Archiv 12 (3–4): 285–98. https://doi.org/10.1007/s11943-018-0231-2.
- 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, 13/2018), Nürnberg, 43 S.
- Schierholz, Malte, and Matthias Schonlau. 2020. "Machine Learning for Occupation Coding—a Comparison Study." Journal of Survey Statistics and Methodology. https://doi.org/10.1093/jssam/smaa023.