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.
Original language | English (US) |
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Pages (from-to) | 308-324 |
Number of pages | 17 |
Journal | Journal of Experimental Education |
Volume | 86 |
Issue number | 2 |
DOIs | |
State | Published - Apr 3 2018 |
Externally published | Yes |
Keywords
- IRT
- Kaplan-Meier
- item difficulty
- nonparametric
- survival analysis
- time-to-event
ASJC Scopus subject areas
- Education
- Developmental and Educational Psychology