Modeling nonignorable missing data in speeded tests

Cornelis A.W. Glas, Jonald Pimentel

Research output: Contribution to journalArticleAcademicpeer-review

64 Citations (Scopus)
17 Downloads (Pure)


In tests with time limits, items at the end are often not reached. Usually, the pattern of missing responses depends on the ability level of the respondents; therefore, missing data are not ignorable in statistical inference. This study models data using a combination of two item response theory (IRT) models: one for the observed response data and one for the missing data indicator. The missing data indicator is modeled using a sequential model with linear restrictions on the item parameters. The models are connected by the assumption that the respondents’ latent proficiency parameters have a joint multivariate normal distribution. Model parameters are estimated by maximum marginal likelihood. Simulations show that treating missing data as ignorable can lead to considerable bias in parameter estimates. Including an IRT model for the missing data indicator removes this bias. The method is illustrated with data from an intelligence test with a time limit.
Original languageEnglish
Pages (from-to)907-922
Number of pages15
JournalEducational and psychological measurement
Issue number6
Publication statusPublished - 2008


  • Missing data
  • Item Response Theory
  • nonignorable missing data
  • maximum marginal likelihood
  • IR-104270
  • steps model
  • sequential model
  • METIS-252721
  • ignorability


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