Automated Organization Profile

College of Education, Inha University

Current S-Index

0.2

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.2

Average Dataset Index per dataset

Total Datasets

1

Total datasets in this organization

Average FAIR Score

15.4%

Average FAIR Score per dataset

Total Citations

0

Total citations to the organization's datasets

Total Mentions

0

Total mentions of the organization's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

The irtQ R Package: a user-friendly tool for item response theory-based test data analysis and calibration (Version: 1.0)

This paper introduces the irtQ R package, which simplifies IRT-based analysis and item calibration under unidimensional IRT models. While it does not directly simulate CAT, it provides essential tools to support CAT development, including parameter estimation using marginal maximum likelihood estimation via the expectation-maximization algorithm, pretest item calibration through fixed item parameter calibration and fixed ability parameter calibration methods, and examinee ability estimation. The package also enables users to compute item and test characteristic curves and information functions necessary for evaluating the psychometric properties of a test. This paper illustrates the key features of the irtQ package through examples using simulated datasets, demonstrating its utility in IRT applications such as test data analysis and ability scoring. By providing a user-friendly environment for IRT analysis, irtQ significantly enhances the capacity for efficient adaptive testing research and operations. Finally, the paper highlights additional core functionalities of irtQ, emphasizing its broader applicability to the development and operation of IRT-based assessments.

Authors

  • Hwanggyu Lim ;
  • Kang, Kyung Seok
0 Citations0 Mentions15% FAIR0.2 Dataset Index
10.7910/dvn/w8ppdhJanuary 2024