Using Attributes of Survey Items to Predict Response Times May Benefit Survey Research. Issue 2 (May 2023)

• Record Type: Journal Article
• Authors: Schneider, Stefan; Jin, Haomiao; Orriens, Bart; Junghaenel, Doerte U.; Kapteyn, Arie; Meijer, Erik; Stone, Arthur A.
• Is Part Of: Field methods. Volume 35:Issue 2(2023)
• Page Start: 87
• Page End: 99
• Publication Date: 2023-05
• Subjects: Ethnology -- Methodology -- Periodicals; Anthropology -- Methodology -- Periodicals; Anthropologie -- Méthodologie -- Périodiques; 306.072
• DOI: 10.1177/1525822X221100904
• Languages: English
• ISSNs: 1525-822X

Abstract

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.