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

Stefan Schneider; Haomiao Jin; Bart Orriens; Doerte U Junghaenel; Arie Kapteyn; Erik Meijer; Arthur A Stone

doi:10.1177/1525822x221100904

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

Field methods. 2023 May;35(2):87-99. doi: 10.1177/1525822x221100904. Epub 2022 May 20.

Authors

Stefan Schneider^{1

2

3}, Haomiao Jin⁴, Bart Orriens⁴, Doerte U Junghaenel^{1

2

3}, Arie Kapteyn⁴, Erik Meijer⁴, Arthur A Stone^{1

2}

Affiliations

¹ Center for Self-Report Science & Center for Economic and Social Research, University of Southern California, Los Angeles, CA 90089-3332, USA.
² Department of Psychology, University of Southern California, Los Angeles, CA, USA.
³ Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA.
⁴ Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA.

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.

Abstract

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