mirt

R package mirt: Multidimensional Item Response Theory. Analysis of dichotomous and polytomous response data using unidimensional and multidimensional latent trait models under the Item Response Theory paradigm. Exploratory and confirmatory models can be estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier analyses are available for modeling item testlets. Multiple group analysis and mixed effects designs also are available for detecting differential item functioning and modelling item and person covariates.


References in zbMATH (referenced in 39 articles , 1 standard article )

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  1. Bergner, Yoav; Halpin, Peter; Vie, Jill-Jênn: Multidimensional item response theory in the style of collaborative filtering (2022)
  2. Ul Hassan, Mahmood; Miller, Frank: Discrimination with unidimensional and multidimensional item response theory models for educational data (2022)
  3. Converse, Geoffrey; Curi, Mariana; Oliveira, Suely; Templin, Jonathan: Estimation of multidimensional item response theory models with correlated latent variables using variational autoencoders (2021)
  4. Hays, Ron D.; Spritzer, Karen L.; Reise, Steven P.: Using item response theory to identify responders to treatment: examples with the Patient-reported outcomes measurement information system (PROMIS\circledR) physical function scale and emotional distress composite (2021)
  5. Hong, Maxwell; Lin, Lizhen; Cheng, Ying: Asymptotically corrected person fit statistics for multidimensional constructs with simple structure and mixed item types (2021)
  6. Jeon, Minjeong; Jin, Ick Hoon; Schweinberger, Michael; Baugh, Samuel: Mapping unobserved item-respondent interactions: a latent space item response model with interaction map (2021)
  7. Paul-Christian Burkner: Bayesian Item Response Modeling in R with brms and Stan (2021) not zbMATH
  8. Plajner, Martin; Vomlel, Jiří: Bayesian networks for the test score prediction: a case study on a math graduation exam (2021)
  9. Reise, Steven P.; Du, Han; Wong, Emily F.; Hubbard, Anne S.; Haviland, Mark G.: Matching IRT models to patient-reported outcomes constructs: the graded response and log-logistic models for scaling depression (2021)
  10. Schalet, Benjamin D.; Lim, Sangdon; Cella, David; Choi, Seung W.: Linking scores with patient-reported health outcome instruments: a validation study and comparison of three linking methods (2021)
  11. Teresi, Jeanne A.; Wang, Chun; Kleinman, Marjorie; Jones, Richard N.; Weiss, David J.: Differential item functioning analyses of the Patient-reported outcomes measurement information system (PROMIS\circledR) measures: methods, challenges, advances, and future directions (2021)
  12. Wu, Qian; Vanerum, Monique; Agten, Anouk; Christiansen, Andrés; Vandenabeele, Frank; Rigo, Jean-Michel; Janssen, Rianne: Certainty-based marking on multiple-choice items: psychometrics meets decision theory (2021)
  13. Yuan, Ke-Hai; Liu, Hongyun; Han, Yuting: Differential item functioning analysis without a priori information on anchor items: QQ plots and graphical test (2021)
  14. Brzezińska, Justyna: Item response theory models in the measurement theory (2020)
  15. Gnaldi, Michela; Bacci, Silvia; Kunze, Thiemo; Greiff, Samuel: Students’ complex problem solving profiles (2020)
  16. Haaf, Julia M.; Merkle, Edgar C.; Rouder, Jeffrey N.: Do items order? The psychology in IRT models (2020)
  17. Chen, Yunxiao; Li, Xiaoou; Zhang, Siliang: Joint maximum likelihood estimation for high-dimensional exploratory item factor analysis (2019)
  18. Jiang, Zhehan; Templin, Jonathan: Gibbs samplers for logistic item response models via the Pólya-gamma distribution: a computationally efficient data-augmentation strategy (2019)
  19. Jin, Ick Hoon; Jeon, Minjeong: A doubly latent space joint model for local item and person dependence in the analysis of item response data (2019)
  20. Martínez-Plumed, Fernando; Prudêncio, Ricardo B. C.; Martínez-Usó, Adolfo; Hernández-Orallo, José: Item response theory in AI: analysing machine learning classifiers at the instance level (2019)

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