A Machine Learning Model Helps Process Interviewer Comments in Computer-Assisted Personal Interview Instruments: A Case Study

Autor/inn/en	Billington, Catherine; Rivero, Gonzalo; Jannett, Andrew; Chen, Jiating
Titel	A Machine Learning Model Helps Process Interviewer Comments in Computer-Assisted Personal Interview Instruments: A Case Study
Quelle	In: Field Methods, 34 (2022) 4, S.275-287 (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1525-822X
DOI	10.1177/1525822X221107053
Schlagwörter	Artificial Intelligence; Interviews; Data Collection; Notetaking; Computer Mediated Communication; Accuracy; Man Machine Systems; Natural Language Processing + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Interviewing; Interviewtechnik; Data capture; Datensammlung; Computerkonferenz; Mensch-Maschine-System; Natürliche Sprache
Abstract	During data collection, field interviewers often append notes or comments to a case in open text fields to request updates to case-level data. Processing these comments can improve data quality, but many are non-actionable, and processing remains a costly manual task. This article presents a case study using a novel application of machine learning tools to assist in the evaluation of these comments. Using over 5,000 comments from the Medical Expenditure Panel Survey, we built features that were fed to a machine learning model to predict a grouping category for each comment as previously assigned by data technicians to expedite processing. The model achieved high top-3 accuracy and was incorporated into a production tool for editing. A qualitative evaluation of the tool also provided encouraging results. This application of machine learning tools allowed a small but worthwhile increase in processing efficiency, while maintaining exacting standards for data quality. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01