R-package LNIRT for joint modeling of response accuracy and times

Jean-Paul Fox, Konrad Klotzke, Ahmet Salih Simsek

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
31 Downloads (Pure)

Abstract

In computer-based testing it has become standard to collect response accuracy (RA) and response times (RTs) for each test item. IRT models are used to measure a latent variable (e.g., ability, intelligence) using the RA observations. The information in the RTs can help to improve routine operations in (educational) testing, and provide information about speed of working. In modern applications, the joint models are needed to integrate RT information in a test analysis. The R-package LNIRT supports fitting joint models through a user-friendly setup which only requires specifying RA, RT data, and the total number of Gibbs sampling iterations. More detailed specifications of the analysis are optional. The main results can be reported through the summary functions, but output can also be analysed with Markov chain Monte Carlo (MCMC) output tools (i.e., coda, mcmcse). The main functionality of the LNIRT package is illustrated with two real data applications.
Original languageEnglish
Article numbere1232
Pages (from-to)1-33
Number of pages33
JournalPeerJ computer science
Volume9
Issue numbere1232
DOIs
Publication statusPublished - 10 Jan 2023

Keywords

  • R-code
  • R-package LNIRT
  • IRT models
  • RT models
  • Joint models
  • MCMC
  • Model-fit tools
  • Variable working-speed

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