A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data

Inhan Kang (), Dylan Molenaar and Roger Ratcliff
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Inhan Kang: Yonsei University
Dylan Molenaar: University of Amsterdam
Roger Ratcliff: The Ohio State University

Psychometrika, 2023, vol. 88, issue 3, No 10, 940-974

Abstract: Abstract This article presents a joint modeling framework of ordinal responses and response times (RTs) for the measurement of latent traits. We integrate cognitive theories of decision-making and confidence judgments with psychometric theories to model individual-level measurement processes. The model development starts with the sequential sampling framework which assumes that when an item is presented, a respondent accumulates noisy evidence over time to respond to the item. Several cognitive and psychometric theories are reviewed and integrated, leading us to three psychometric process models with different representations of the cognitive processes underlying the measurement. We provide simulation studies that examine parameter recovery and show the relationships between latent variables and data distributions. We further test the proposed models with empirical data measuring three traits related to motivation. The results show that all three models provide reasonably good descriptions of observed response proportions and RT distributions. Also, different traits favor different process models, which implies that psychological measurement processes may have heterogeneous structures across traits. Our process of model building and examination illustrates how cognitive theories can be incorporated into psychometric model development to shed light on the measurement process, which has had little attention in traditional psychometric models.

Keywords: response time; psychological process; measurement; psychometric process modeling; decision-making; confidence judgments (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11336-023-09902-z

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