ltm

ltm: an R package for latent variable modelling and item response theory analyses. The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum’s Three-Parameter models have been implemented, whereas for polytomous data Semejima’s Graded Response model is available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule. The capabilities and features of the package are illustrated using two real data examples.

This software is also peer reviewed by journal JSS.


References in zbMATH (referenced in 38 articles , 1 standard article )

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  1. Badih, Ghattas; Pierre, Michel; Laurent, Boyer: Assessing variable importance in clustering: a new method based on unsupervised binary decision trees (2019)
  2. Battauz, Michela: On Wald tests for differential item functioning detection (2019)
  3. Fariña, Paula; González, Jorge; San Martín, Ernesto: The use of an identifiability-based strategy for the interpretation of parameters in the 1PL-G and Rasch models (2019)
  4. 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)
  5. Noh, Maengseok; Lee, Youngjo; Oud, Johan H. L.; Toharudin, Toni: Hierarchical likelihood approach to non-Gaussian factor analysis (2019)
  6. Butler, Emily L.; Laber, Eric B.; Davis, Sonia M.; Kosorok, Michael R.: Incorporating patient preferences into estimation of optimal individualized treatment rules (2018)
  7. Lee, Sora; Bolt, Daniel M.: Asymmetric item characteristic curves and item complexity: insights from simulation and real data analyses (2018)
  8. Liu, Xiang; Han, Zhuangzhuang; Johnson, Matthew S.: The UMP exact test and the confidence interval for person parameters in IRT models (2018)
  9. Liu, Yang; Yang, Ji Seung: Bootstrap-calibrated interval estimates for latent variable scores in item response theory (2018)
  10. Mair, Patrick: Modern psychometrics with R (2018)
  11. Battauz, Michela: Multiple equating of separate IRT calibrations (2017)
  12. Ordóñez Galán, Celestino; Sánchez Lasheras, Fernando; de Cos Juez, Francisco Javier; Bernardo Sánchez, Antonio: Missing data imputation of questionnaires by means of genetic algorithms with different fitness functions (2017)
  13. Sulis, Isabella; Porcu, Mariano: Handling missing data in item response theory. Assessing the accuracy of a multiple imputation procedure based on latent class analysis (2017)
  14. Víctor Cervantes: DFIT: An R Package for Raju’s Differential Functioning of Items and Tests Framework (2017) not zbMATH
  15. Bartolucci, Francesco; Bacci, Silvia; Gnaldi, Michela: Statistical analysis of questionnaires. A unified approach based on R and Stata (2016)
  16. Ip, Edward H.; Chen, Shyh-Huei; Quandt, Sara A.: Analysis of multiple partially ordered responses to belief items with don’t know option (2016)
  17. Jorge Tendeiro and Rob Meijer and A. Niessen: PerFit: An R Package for Person-Fit Analysis in IRT (2016) not zbMATH
  18. Dylan Molenaar;Francis Tuerlinckx; Han van der Maas: Fitting Diffusion Item Response Theory Models for Responses and Response Times Using the R Package diffIRT (2015) not zbMATH
  19. Michela Battauz: equateIRT: An R Package for IRT Test Equating (2015) not zbMATH
  20. Nikoloulopoulos, Aristidis K.; Joe, Harry: Factor copula models for item response data (2015)

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