Applying Kaplan-Meier to Item Response Data |
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| Authors: | Daniel McNeish |
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| Institution: | Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA |
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| Abstract: | Some IRT models can be equivalently modeled in alternative frameworks such as logistic regression. Logistic regression can also model time-to-event data, which concerns the probability of an event occurring over time. Using the relation between time-to-event models and logistic regression and the relation between logistic regression and IRT, this article outlines how the nonparametric Kaplan-Meier estimator for time-to-event data can be applied to IRT data. Established Kaplan-Meier computational formulas are shown to aid in better approximating “parametric-type” item difficulty compared to methods from existing nonparametric methods, particularly for the less-well-defined scenario wherein the response function is monotonic but invariant item ordering is unreasonable. Limitations and the potential for Kaplan-Meier within differential item functioning are also discussed. |
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| Keywords: | IRT item difficulty Kaplan-Meier nonparametric survival analysis time-to-event |
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