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Evaluating Psychometric Differences Between Fast Versus Slow Responses on Rating Scale Items

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  • Nana Kim

    (University of Minnesota-Twin Cities)

  • Daniel M. Bolt

    (University of Wisconsin-Madison)

Abstract

Some previous studies suggest that response times (RTs) on rating scale items can be informative about the content trait, but a more recent study suggests they may also be reflective of response styles. The latter result raises questions about the possible consideration of RTs for content trait estimation, as response styles are generally viewed as nuisance dimensions in the measurement of noncognitive constructs. In this article, we extend previous work exploring the simultaneous relevance of content and response style traits on RTs in self-report rating scale measurement by examining psychometric differences related to fast versus slow item responses. Following a parallel methodology applied with cognitive measures, we provide empirical illustrations of how RTs appear to be simultaneously reflective of both content and response style traits. Our results demonstrate that respondents may exhibit different response behaviors for fast versus slow responses and that both the content trait and response styles are relevant to such heterogeneity. These findings suggest that using RTs as a basis for improving the estimation of noncognitive constructs likely requires simultaneously attending to the effects of response styles.

Suggested Citation

  • Nana Kim & Daniel M. Bolt, 2024. "Evaluating Psychometric Differences Between Fast Versus Slow Responses on Rating Scale Items," Journal of Educational and Behavioral Statistics, , vol. 49(4), pages 565-594, August.
    • Handle: RePEc:sae:jedbes:v:49:y:2024:i:4:p:565-594
    DOI: 10.3102/10769986231195260
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    References listed on IDEAS

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    1. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    2. De Boeck, Paul & Partchev, Ivailo, 2012. "IRTrees: Tree-Based Item Response Models of the GLMM Family," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(c01).
    3. Esther Ulitzsch & Steffi Pohl & Lale Khorramdel & Ulf Kroehne & Matthias Davier, 2022. "A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 593-619, June.
    4. Maria Bolsinova & Paul Boeck & Jesper Tijmstra, 2017. "Modelling Conditional Dependence Between Response Time and Accuracy," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1126-1148, December.
    5. R. Darrell Bock, 1972. "Estimating item parameters and latent ability when responses are scored in two or more nominal categories," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 29-51, March.
    6. Dylan Molenaar & Paul Boeck, 2018. "Response Mixture Modeling: Accounting for Heterogeneity in Item Characteristics across Response Times," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 279-297, June.
    7. Esther Ulitzsch & Steffi Pohl & Lale Khorramdel & Ulf Kroehne & Matthias Davier, 2022. "Erratum to: A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 798-798, June.
    8. Weicong Lyu & Daniel M. Bolt & Samuel Westby, 2023. "Exploring the Effects of Item-Specific Factors in Sequential and IRTree Models," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 745-775, September.
    9. Wim Linden & Fanmin Guo, 2008. "Bayesian Procedures for Identifying Aberrant Response-Time Patterns in Adaptive Testing," Psychometrika, Springer;The Psychometric Society, vol. 73(3), pages 365-384, September.
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