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

Jean-Paul Fox; Konrad Klotzke; Ahmet Salih Simsek

doi:10.7717/peerj-cs.1232

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

PeerJ Comput Sci. 2023 Mar 30:9:e1232. doi: 10.7717/peerj-cs.1232. eCollection 2023.

Authors

Jean-Paul Fox¹, Konrad Klotzke¹, Ahmet Salih Simsek²

Affiliations

¹ Faculty of Behavioral, Management, and Social Sciences, University of Twente, Enschede, Netherlands.
² Department of Measurement and Evaluation in Education, University of Kirsehir Ahi Evran, Kirsehir, Turkey.

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.

Keywords: IRT models; Joint models; MCMC; Model-fit tools; R-code; R-package LNIRT; RT models; Variable working-speed.

Grants and funding

Ahmet Salih Simsek was supported financially by The Scientific and Technological Research Council of Turkey (TUBITAK) through project number 1059B191800628. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.