A Machine Learning Model Helps Process Interviewer Comments in Computer-assisted Personal Interview Instruments

A Case Study

Bibliographic Data

ID7774126
AuthorsCatherine Billington (Westat (United States), corresponding author), Gonzalo Rivero (0000-0003-3452-7823, Westat (United States)), Andrew Jannett (Westat (United States)), Jiating Chen (Westat Inc., Rockville, MD, USA)
Year2022
Volume34
Issue4
Pages275-287
Publication date2022-11-01
Open AccessYes
TypeARTICLE
VenueCAM (JOURNAL)
Journal identifiersISSN: 1087-8513 • E-ISSN: 1552-3969
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/1525822x221107053
OpenAlexW4283263258
LanguageEN
References cited14

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

Data collection · Data Processing · Data science · Database · Field (mathematics) · Human–computer interaction · Interview · Machine learning · Process (computing) · Quality (philosophy) · Task (project management) · Artificial Intelligence · Computational and Text Analysis Methods · Computer Science · Survey Methodology and Nonresponse · Topic Modeling

Tools

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