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Asymmetric Item Characteristic Curves and Item Complexity: Insights from Simulation and Real Data Analyses

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  • Sora Lee

    (University of Wisconsin)

  • Daniel M. Bolt

    (University of Wisconsin)

Abstract

While item complexity is often considered as an item feature in test development, it is much less frequently attended to in the psychometric modeling of test items. Prior work suggests that item complexity may manifest through asymmetry in item characteristics curves (ICCs; Samejima in Psychometrika 65:319–335, 2000). In the current paper, we study the potential for asymmetric IRT models to inform empirically about underlying item complexity, and thus the potential value of asymmetric models as tools for item validation. Both simulation and real data studies are presented. Some psychometric consequences of ignoring asymmetry, as well as potential strategies for more effective estimation of asymmetry, are considered in discussion.

Suggested Citation

  • Sora Lee & Daniel M. Bolt, 2018. "Asymmetric Item Characteristic Curves and Item Complexity: Insights from Simulation and Real Data Analyses," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 453-475, June.
    • Handle: RePEc:spr:psycho:v:83:y:2018:i:2:d:10.1007_s11336-017-9586-5
    DOI: 10.1007/s11336-017-9586-5
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    References listed on IDEAS

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    1. Wim van der Linden, 2007. "A Hierarchical Framework for Modeling Speed and Accuracy on Test Items," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 287-308, September.
    2. Robert Jannarone, 1986. "Conjunctive item response theory kernels," Psychometrika, Springer;The Psychometric Society, vol. 51(3), pages 357-373, September.
    3. Fumiko Samejima, 1995. "Acceleration model in the heterogeneous case of the general graded response model," Psychometrika, Springer;The Psychometric Society, vol. 60(4), pages 549-572, December.
    4. Susan Embretson (Whitely), 1984. "A general latent trait model for response processes," Psychometrika, Springer;The Psychometric Society, vol. 49(2), pages 175-186, June.
    5. Rizopoulos, Dimitris, 2006. "ltm: An R Package for Latent Variable Modeling and Item Response Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i05).
    6. Heleno Bolfarine & Jorge Luis Bazan, 2010. "Bayesian Estimation of the Logistic Positive Exponent IRT Model," Journal of Educational and Behavioral Statistics, , vol. 35(6), pages 693-713, December.
    7. Fumiko Samejima, 2000. "Logistic positive exponent family of models: Virtue of asymmetric item characteristic curves," Psychometrika, Springer;The Psychometric Society, vol. 65(3), pages 319-335, September.
    8. Eric Maris, 1995. "Psychometric latent response models," Psychometrika, Springer;The Psychometric Society, vol. 60(4), pages 523-547, December.
    9. Dylan Molenaar & Conor Dolan & Paul Boeck, 2012. "The Heteroscedastic Graded Response Model with a Skewed Latent Trait: Testing Statistical and Substantive Hypotheses Related to Skewed Item Category Functions," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 455-478, July.
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    Cited by:

    1. Xiangyi Liao & Daniel M. Bolt, 2021. "Item Characteristic Curve Asymmetry: A Better Way to Accommodate Slips and Guesses Than a Four-Parameter Model?," Journal of Educational and Behavioral Statistics, , vol. 46(6), pages 753-775, December.
    2. Daniel M. Bolt & Xiangyi Liao, 2022. "Item Complexity: A Neglected Psychometric Feature of Test Items?," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1195-1213, December.

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