| Yazarlar |
Jean-Paul Fox
|
|
Konrad Kloztke
|
Ahmet Salih ŞİMŞEK
Kırşehir Ahi Evran Üniversitesi, Türkiye |
| Özet |
| 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. |
| Anahtar Kelimeler |
| IRT models | Joint models | MCMC | Model-fit tools | R-code | R-package LNIRT | RT models | Variable working-speed |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | PEERJ COMPUTER SCIENCE |
| Dergi ISSN | 2376-5992 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 03-2023 |
| Cilt No | 9 |
| Sayı | 1 |
| Sayfalar | 1 / 33 |
| Doi Numarası | 10.7717/peerj-cs.1232 |
| Makale Linki | https://peerj.com/articles/cs-1232/ |
| Atıf Sayıları | |
| WoS | 3 |
| SCOPUS | 3 |
| Google Scholar | 3 |
