Using Attributes of Survey Items to Predict Response Times May Benefit Survey Research

Bibliographic Data

ID7287703
AuthorsStefan Schneider (0000-0002-4562-0524, University of Southern California, corresponding author), Haomiao Jin (0000-0001-8908-1959, University of Southern California), Bart Orriens (0000-0002-1304-7735, University of Southern California), Doerte U Junghaenel (0000-0002-7733-571X, University of Southern California), Arie Kapteyn (0000-0002-1855-5528, University of Southern California), Erik Meijer (0000-0001-7959-4670, University of Southern California), Arthur A Stone (0000-0001-7490-1758, University of Southern California)
Year2022
Volume35
Issue2
Pages87-99
Publication date2022-05-20
Open AccessYes
TypeARTICLE
VenueCAM (JOURNAL)
Journal identifiersISSN: 1087-8513 • E-ISSN: 1552-3969
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/1525822x221100904
PMID37799827
OpenAlexW4280567304
LanguageEN
Citations received2
References cited30

Researchers have become increasingly interested in response times to survey items as a measure of cognitive effort. We used machine learning to develop a prediction model of response times based on 41 attributes of survey items (e.g., question length, response format, linguistic features) collected in a large, general population sample. The developed algorithm can be used to derive reference values for expected response times for most commonly used survey items

Data mining · Item response theory · Measure (data warehouse · Population · Psychometrics · Response bias · Sample (material · Sociology · Statistics · Survey data collection · Survey sampling · Computer Science · Demography · Mathematics · Psychology · Social Psychology · Survey Methodology and Nonresponse

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Open DOIOpen Access