Applying Kaplan-Meier to Item Response Data

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)308-324
Number of pages17
JournalJournal of Experimental Education
Volume86
Issue number2
DOIs
StatePublished - Apr 3 2018
Externally publishedYes

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Logistic Models
logistics
regression
event
scenario
time

Keywords

  • IRT
  • item difficulty
  • Kaplan-Meier
  • nonparametric
  • survival analysis
  • time-to-event

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology

Cite this

Applying Kaplan-Meier to Item Response Data. / McNeish, Daniel.

In: Journal of Experimental Education, Vol. 86, No. 2, 03.04.2018, p. 308-324.

Research output: Contribution to journalArticle

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