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plink: An R Package for Linking Mixed-Format Tests Using IRT-Based Methods

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  • Weeks, Jonathan P.

Abstract

The R package plink has been developed to facilitate the linking of mixed-format tests for multiple groups under a common item design using unidimensional and multidimensional IRT-based methods. This paper presents the capabilities of the package in the context of the unidimensional methods. The package supports nine unidimensional item response models (the Rasch model, 1PL, 2PL, 3PL, graded response model, partial credit and generalized partial credit model, nominal response model, and multiple-choice model) and four separate calibration linking methods (mean/sigma, mean/mean, Haebara, and Stocking-Lord). It also includes functions for importing item and/or ability parameters from common IRT software, conducting IRT true-score and observed-score equating, and plotting item response curves and parameter comparison plots.

Suggested Citation

  • Weeks, Jonathan P., 2010. "plink: An R Package for Linking Mixed-Format Tests Using IRT-Based Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i12).
    • Handle: RePEc:jss:jstsof:v:035:i12
    DOI: http://hdl.handle.net/10.18637/jss.v035.i12
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    Cited by:

    1. González, Jorge, 2014. "SNSequate: Standard and Nonstandard Statistical Models and Methods for Test Equating," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i07).
    2. Han-Yuan Zhang & Zi-Xia Zhao & Jian Xu & Peng Xu & Qing-Li Bai & Shi-Yong Yang & Li-Kun Jiang & Bao-Hua Chen, 2018. "Population genetic analysis of aquaculture salmonid populations in China using a 57K rainbow trout SNP array," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-12, August.
    3. Daniel M. Bolt & Xiangyi Liao, 2022. "Item Complexity: A Neglected Psychometric Feature of Test Items?," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1195-1213, December.
    4. Alexander Robitzsch, 2020. "L p Loss Functions in Invariance Alignment and Haberman Linking with Few or Many Groups," Stats, MDPI, vol. 3(3), pages 1-38, August.
    5. Brzezińska Justyna, 2018. "Item Response Theory Models in the Measurement Theory with the Use of ltm Package in R," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(1), pages 11-25, March.
    6. Yang Liu & Jan Hannig & Abhishek Pal Majumder, 2019. "Second-Order Probability Matching Priors for the Person Parameter in Unidimensional IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 701-718, September.
    7. Rikkert M. van der Lans & Ridwan Maulana & Michelle Helms-Lorenz & Carmen-María Fernández-García & Seyeoung Chun & Thelma de Jager & Yulia Irnidayanti & Mercedes Inda-Caro & Okhwa Lee & Thys Coetze, 2021. "Student Perceptions of Teaching Quality in Five Countries: A Partial Credit Model Approach to Assess Measurement Invariance," SAGE Open, , vol. 11(3), pages 21582440211, August.
    8. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).

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