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Exploring the Role of Response Time in Item Response Theory: Rethinking the PISA 2022 Creative Thinking Assessment

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Title: Exploring the Role of Response Time in Item Response Theory: Rethinking the PISA 2022 Creative Thinking Assessment
Language: English
Authors: Lihong Xie (ORCID 0000-0002-6706-3175), Xiaowen Liu (ORCID 0000-0002-4285-1124)
Source: Journal of Creative Behavior. 2025 59(4).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 7
Publication Date: 2025
Document Type: Journal Articles
Reports - Descriptive
Education Level: Secondary Education
Descriptors: Reaction Time, Item Response Theory, Creative Thinking, Creativity Tests, Achievement Tests, Foreign Countries, International Assessment, Secondary School Students, Accuracy, Classification
Assessment and Survey Identifiers: Program for International Student Assessment
DOI: 10.1002/jocb.70072
ISSN: 0022-0175
2162-6057
Abstract: This article explores the potential of response time-item response theory (RT-IRT) models to enhance creative thinking (CT) measurement in Programme for International Student Assessment (PISA) 2022, which employs traditional IRT models (2PL and generalized partial credit) excluding response time (RT). Given traditional IRT's limitations in capturing cognitive processes, we explored how numerous advanced IRT models, particularly those that incorporate RT as valuable information, have been developed for model testing and comparison for large-scale assessment. In addition, we discuss the critical role of RT as demonstrated in the research literature, linking it to key aspects of creativity. We also explore the possibilities of connecting two types of RTs (i.e., the total time spent on task completion and the time from start to first action spent on each task) to patterns in creative performance across domains and stages, using RT-IRT models. Benefits and types of RT-IRT models (e.g., joint, diffusion, mixture) are further examined as they integrate RT to model speed-accuracy trade-offs, detect aberrant response behaviors, and enhance result interpretability by reflecting engagement and creative processes. Lastly, we propose RT-IRT models to leverage PISA 2022's RT data and provide process-oriented insights, improve ability estimates, and potentially prevent misclassification of spontaneously creative responses as careless ones.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1494130
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Lihong+Xie%22">Lihong Xie</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6706-3175">0000-0002-6706-3175</externalLink>)<br /><searchLink fieldCode="AR" term="%22Xiaowen+Liu%22">Xiaowen Liu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4285-1124">0000-0002-4285-1124</externalLink>)
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: This article explores the potential of response time-item response theory (RT-IRT) models to enhance creative thinking (CT) measurement in Programme for International Student Assessment (PISA) 2022, which employs traditional IRT models (2PL and generalized partial credit) excluding response time (RT). Given traditional IRT's limitations in capturing cognitive processes, we explored how numerous advanced IRT models, particularly those that incorporate RT as valuable information, have been developed for model testing and comparison for large-scale assessment. In addition, we discuss the critical role of RT as demonstrated in the research literature, linking it to key aspects of creativity. We also explore the possibilities of connecting two types of RTs (i.e., the total time spent on task completion and the time from start to first action spent on each task) to patterns in creative performance across domains and stages, using RT-IRT models. Benefits and types of RT-IRT models (e.g., joint, diffusion, mixture) are further examined as they integrate RT to model speed-accuracy trade-offs, detect aberrant response behaviors, and enhance result interpretability by reflecting engagement and creative processes. Lastly, we propose RT-IRT models to leverage PISA 2022's RT data and provide process-oriented insights, improve ability estimates, and potentially prevent misclassification of spontaneously creative responses as careless ones.
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