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sirt (version 1.5-0)

sirt-package: Supplementary Item Response Theory Models

Description

Supplementary item response theory models to complement existing functions in R, including multidimensional compensatory and noncompensatory IRT models, MCMC for hierarchical IRT models and testlet models, NOHARM, faceted and hierarchical rater models, ordinal IRT model (ISOP), discrete grade of membership model, latent regression models, DETECT statistic.

Arguments

RFunction Versions

anova_sirt__0.08.R, ARb_utils__0.23.R, attach.environment.sirt__0.01.R, automatic.recode__1.04.R, brm.irf__0.02.R, brm.sim__0.02.R, class.accuracy.rasch__0.03.R, data.prep__1.03.R, data.wide2long__0.14.R, detect__0.06.R, dif.logisticregression__1.02.R, dif.variance__0.06.R, dirichlet__1.03.R, eigenvalues.manymatrices__0.03.R, eigenvalues.sirt__0.05.R, equating.rasch__0.02.R, f1d.irt__1.06.R, fit.adisop__2.09.R, fit.gradedresponse__1.05.R, fit.gradedresponse_alg__1.06.R, fit.isop__2.05.R, fit.logistic__2.03.R, fit.logistic_alg__0.04.R, fuzcluster__0.07.R, fuzcluster_alg__0.10.R, fuzdiscr__0.02.R, fuzirt__0.01.R, fuzlc__0.01.R, gom.em.alg__5.06.R, gom.em__5.12.R, gom.jml__0.08.R, gom.jml_alg__0.04.R, greenyang.reliability__1.03.R, invariance.alignment.aux__1.03.R, invariance.alignment__2.23.R, invariance.alignment2.aux__0.14.R, invariance.alignment2__3.25.R, IRT.factor.scores.sirt__0.04.R, IRT.irfprob.sirt__0.07.R, IRT.likelihood_sirt__0.10.R, IRT.mle__0.07.R, IRT.modelfit.sirt__0.09.R, IRT.posterior_sirt__0.07.R, isop.dich__3.08.R, isop.poly__2.06.R, isop.scoring__1.03.R, isop.test__0.05.R, latent.regression.em.normal__2.02.R, latent.regression.em.raschtype__2.46.R, lavaan2mirt__0.49.R, lavaanify.sirt__1.11.R, lc.2raters.aux__0.02.R, lc.2raters__0.11.R, linking.haberman.aux__0.02.R, linking.haberman__2.11.R, linking.robust__1.06.R, logLik_sirt__0.07.R, lsdm__1.07.R, lsdm_aux__0.01.R, marginal.truescore.reliability__0.01.R, matrix_functions__0.03.R, matrixfunctions_sirt__0.06.R, mcmc.2pno.ml__3.08.R, mcmc.2pno.ml_alg__3.12.R, mcmc.2pno.ml_output__1.02.R, mcmc.2pno__1.14.R, mcmc.2pno_alg__1.08.R, mcmc.2pnoh__1.02.R, mcmc.2pnoh_alg__0.05.R, mcmc.3pno.testlet__4.05.R, mcmc.3pno.testlet_alg__2.11.R, mcmc.3pno.testlet_output__1.06.R, mcmc.aux__0.02.R, mcmc.list.descriptives__0.04.R, mcmclist2coda__0.01.R, md.pattern.sirt__0.03.R, mirt.IRT.functions__0.03.R, mirt.model.vars__0.11.R, mirt.specify.partable__0.01.R, mirt.wrapper.calc.counts__0.01.R, mirt.wrapper.coef__0.07.R, mirt.wrapper.fscores__0.02.R, mirt.wrapper.itemplot__0.01.R, mirt.wrapper.posterior__0.16.R, mle.pcm.group__0.04.R, mle.reliability__0.02.R, modelfit.cor.poly__0.03.R, modelfit.cor__2.25.R, monoreg.rowwise__0.03.R, nedelsky.irf__0.04.R, nedelsky.latresp__0.01.R, nedelsky.sim__0.04.R, noharm.sirt.est.aux__4.04.R, noharm.sirt.preprocess__0.13.R, noharm.sirt__0.45.R, normal2.cw__0.04.R, np.dich__0.09.R, nr.numdiff__0.01.R, pbivnorm2__1.06.R, pcm.conversion__0.02.R, pcm.fit__0.05.R, personfit.stat__0.03.R, personfit__1.16.R, pgenlogis__1.01.R, plausible.values.raschtype__2.08.R, plot.invariance.alignment__0.01.R, plot.isop__1.05.R, plot.mcmc.sirt__0.07.R, plot.noharm.sirt__0.13.R, plot.rasch.mml__0.06.R, plot.rm.sdt__0.02.R, polychoric2__0.04.R, prmse.subscores__0.02.R, prob.guttman__1.05.R, qmc.nodes__0.02.R, R2noharm-utility__1.04.R, R2noharm.EAP__0.15.R, R2noharm.jackknife__1.03.R, R2noharm__2.14.R, rasch.conquest__1.20.R, rasch.copula__0.993.R, rasch.copula2__6.16.R, rasch.copula2_aux__1.06.R, rasch.copula3.covariance__0.05.R, rasch.copula3__6.39.R, rasch.copula3_aux__6.09.R, rasch.evm.pcm.methods__0.02.R, rasch.evm.pcm__1.11.R, rasch.evm.pcm_aux__0.02.R, rasch.jml.biascorr__0.03.R, rasch.jml__3.12.R, rasch.mirtlc__91.27.R, rasch.mirtlc_aux__91.12.R, rasch.mml.npirt__2.03.R, rasch.mml.ramsay__2.03.R, rasch.mml.raschtype__2.39.R, rasch.mml__2.02.R, rasch.mml2.missing1__0.08.R, rasch.mml2__6.921.R, rasch.pairwise.itemcluster__0.02.R, rasch.pairwise__0.14.R, rasch.pml__2.14.R, rasch.pml_aux__1.03.R, rasch.pml2__4.10.R, rasch.pml2_aux__3.17.R, rasch.pml3__6.02.R, rasch.pml3_aux__5.01.R, rasch.prox__1.03.R, rasch.va__0.01.R, reliability.nonlinear.sem__1.06.R, rm.facets__3.25.R, rm.facets_alg__3.18.R, rm.facets_IC__0.02.R, rm.facets_PP__0.04.R, rm.hrm.calcprobs__0.02.R, rm.hrm.est.tau.item__0.03.R, rm.sdt__8.24.R, rm.sdt_alg__8.06.R, rm.smooth.distribution__0.02.R, rm_proc__0.03.R, sia.sirt__0.10.R, sim.rasch.dep__0.06.R, smirt__7.14.R, smirt_alg_comp__1.05.R, smirt_alg_noncomp__2.27.R, smirt_alg_partcomp__0.05.R, smirt_postproc__0.02.R, smirt_preproc__1.04.R, stratified.cronbach.alpha__0.02.R, summary.fuzcluster__0.03.R, summary.gom.em__0.05.R, summary.invariance.alignment__0.09.R, summary.isop__0.04.R, summary.latent.regression__0.01.R, summary.mcmc.sirt__1.02.R, summary.noharm.sirt__1.05.R, summary.R2noharm.jackknife__1.01.R, summary.R2noharm__0.04.R, summary.rasch.copula__2.03.R, summary.rasch.evm.pcm__0.04.R, summary.rasch.mirtlc__7.03.R, summary.rasch.mml2__1.03.R, summary.rasch.pml__0.06.R, summary.rm.facets__0.06.R, summary.rm.sdt__1.02.R, summary.smirt__0.06.R, tam2mirt.aux__0.03.R, tam2mirt__0.12.R, testlet.marginalized__0.04.R, tetrachoric2__1.13.R, truescore.irt__0.08.R, unidim.csn__0.05.R, wle.rasch__1.07.R, yen.q3__1.01.R, zzz__1.07.R,

<em>Rcpp</em> Function Versions

eigenvaluessirt__3.06.cpp, evm_comp_matrix_poly__1.25.cpp, evm_eigaux_fcts__4.07.h, evm_eigenvals2__0.01.h, first_eigenvalue_sirt__2.18.h, gooijer_isop__4.02.cpp, gooijercsntableaux__1.08.h, invariance_alignment__0.03.cpp, matrixfunctions_sirt__1.07.cpp, mle_pcm_group_c__1.02.cpp, noharm_sirt_auxfunctions__2.09.cpp, pbivnorm_rcpp_aux__0.51.h, polychoric2_tetrachoric2_rcpp_aux__2.03.cpp, probs_multcat_items_counts_csirt__2.03.cpp, rm_smirt_mml2_code__4.07.cpp,

<em>Rd</em> Documentation Versions

automatic.recode__0.07.Rd, brm.sim__0.30.Rd, ccov.np__0.09.Rd, class.accuracy.rasch__0.12.Rd, conf.detect__1.24.Rd, data.activity.itempars__0.04.Rd, data.big5__0.36.Rd, data.bs__0.05.Rd, data.eid__0.15.Rd, data.ess2005__0.05.Rd, data.g308__0.08.Rd, data.inv4gr__0.04.Rd, data.liking.science__0.04.Rd, data.long__0.21.Rd, data.math__0.09.Rd, data.mcdonald__0.12.Rd, data.mixed1__0.07.Rd, data.ml__1.07.Rd, data.noharm__2.07.Rd, data.pars1.rasch__0.08.Rd, data.pirlsmissing__0.06.Rd, data.pisaMath__0.08.Rd, data.pisaPars__0.06.Rd, data.pisaRead__0.06.Rd, data.ratings1__0.16.Rd, data.raw1__0.04.Rd, data.read__1.88.Rd, data.reck__0.18.Rd, data.si__0.21.Rd, data.timss__0.06.Rd, data.timss07.G8.RUS__0.02.Rd, data.wide2long__0.12.Rd, detect.index__0.11.Rd, dif.logistic.regression__0.18.Rd, dif.strata.variance__0.06.Rd, dif.variance__0.07.Rd, dirichlet.mle__0.11.Rd, dirichlet.simul__0.07.Rd, eigenvalues.manymatrices__0.08.Rd, eigenvalues.sirt__0.04.Rd, equating.rasch.jackknife__0.13.Rd, equating.rasch__1.22.Rd, expl.detect__1.07.Rd, f1d.irt__1.16.Rd, fit.isop__1.10.Rd, fuzcluster__0.11.Rd, fuzdiscr__0.11.Rd, gom.em__1.54.Rd, gom.jml__0.12.Rd, greenyang.reliability__1.19.Rd, invariance.alignment__1.32.Rd, IRT.mle__0.08.Rd, isop.scoring__1.13.Rd, isop.test__0.11.Rd, isop__3.16.Rd, latent.regression.em.raschtype__1.28.Rd, lavaan2mirt__0.31.Rd, lc.2raters__0.11.Rd, linking.haberman__0.33.Rd, linking.robust__0.14.Rd, lsdm__2.05.Rd, marginal.truescore.reliability__0.15.Rd, matrixfunctions.sirt__1.10.Rd, mcmc.2pno.ml__0.28.Rd, mcmc.2pno__1.20.Rd, mcmc.2pnoh__0.15.Rd, mcmc.3pno.testlet__1.13.Rd, mcmc.list.descriptives__0.11.Rd, mcmclist2coda__0.09.Rd, md.pattern.sirt__0.10.Rd, mirt.specify.partable__0.02.Rd, mirt.wrapper__1.63.Rd, mle.pcm.group__0.15.Rd, modelfit.sirt__0.47.Rd, monoreg.rowwise__0.08.Rd, nedelsky.sim__0.06.Rd, noharm.sirt__0.21.Rd, np.dich__0.12.Rd, pbivnorm2__0.11.Rd, pcm.conversion__0.09.Rd, pcm.fit__0.11.Rd, person.parameter.rasch.copula__1.09.Rd, personfit.stat__0.13.Rd, pgenlogis__0.15.Rd, plausible.value.imputation.raschtype__1.19.Rd, plot.mcmc.sirt__0.05.Rd, plot.np.dich__0.11.Rd, polychoric2__0.06.Rd, prmse.subscores.scales__0.15.Rd, prob.guttman__1.17.Rd, qmc.nodes__0.07.Rd, R2conquest__3.09.Rd, R2noharm.EAP__0.09.Rd, R2noharm.jackknife__1.08.Rd, R2noharm__2.19.Rd, rasch.copula__1.51.Rd, rasch.evm.pcm__0.28.Rd, rasch.jml.biascorr__0.14.Rd, rasch.jml.jackknife1__2.10.Rd, rasch.jml__1.22.Rd, rasch.mirtlc__2.77.Rd, rasch.mml__3.82.Rd, rasch.pairwise.itemcluster__0.22.Rd, rasch.pairwise__0.15.Rd, rasch.pml__2.49.Rd, rasch.prox__1.09.Rd, rasch.va__0.06.Rd, reliability.nonlinearSEM__0.12.Rd, rm.facets__0.41.Rd, rm.sdt__1.17.Rd, sia.sirt__0.08.Rd, sim.qm.ramsay__0.27.Rd, sim.rasch.dep__0.18.Rd, sim.raschtype__0.11.Rd, sirt-package__2.10.Rd, smirt__2.16.Rd, stratified.cronbach.alpha__0.17.Rd, summary.mcmc.sirt__0.06.Rd, tam2mirt__0.12.Rd, testlet.marginalized__0.12.Rd, testlet.yen.q3__1.03.Rd, tetrachoric2__1.25.Rd, truescore.irt__0.12.Rd, unidim.test.csn__1.11.Rd, wle.rasch.jackknife__1.14.Rd, wle.rasch__1.09.Rd, yen.q3__1.06.Rd,

Details

ll{ Package: sirt Type: Package Version: 1.5 Publication Year: 2015 License: GPL (>= 2) URL: https://sites.google.com/site/alexanderrobitzsch/software } This package enables the estimation of following models:
    %% M-dim generalized item response model
  • Multidimensional marginal maximum likelihood estimation (MML) of generalized logistic Rasch type models using the generalized logistic link function (Stukel, 1988) can be conducted withrasch.mml2and the argumentitemtype="raschtype". This model also allows the estimation of the 4PL item response model (Loken & Rulison, 2010). Multiple group estimation, latent regression models and plausible value imputation are supported. In addition, pseudo-likelihood estimation for fractional item response data can be conducted. %% M-dim noncompensatory and compensatory IRT model
  • Multidimensional noncompensatory, compensatory and partially compensatory item response models for dichotomous item responses (Reckase, 2009) can be estimated with thesmirtfunction and the optionsirtmodel="noncomp",irtmodel="comp"andirtmodel="partcomp". %% 1-dim Ramsay type model
  • The unidimensional quotient model (Ramsay, 1989) can be estimated usingrasch.mml2withitemtype="ramsay.qm". %% 1-dim nonparametric IRT models
  • Unidimensional nonparametric item response models can be estimated employing MML estimation (Rossi, Wang & Ramsay, 2002) by making use ofrasch.mml2withitemtype="npirt". Kernel smoothing for item response function estimation (Ramsay, 1991) is implemented innp.dich. %% 1-dim Copula model
  • The multidimensional IRT copula model (Braeken, 2011) can be applied for handling local dependencies, seerasch.copula3. %% 1-dim JML
  • Unidimensional joint maximum likelihood estimation (JML) of the Rasch model is possible with therasch.jmlfunction. Bias correction methods for item parameters are included inrasch.jml.jackknife1andrasch.jml.biascorr. %% M-dim LC Rasch model
  • The multidimensional latent class Rasch and 2PL model (Bartolucci, 2007) which employs a discrete trait distribution can be estimated withrasch.mirtlc. %% Rater Models
  • The unidimensional 2PL rater facets model (Lincare, 1994) can be estimated withrm.facets. A hierarchical rater model based on signal detection theory (DeCarlo, Kim & Johnson, 2011) can be conducted withrm.sdt. A simple latent class model for two exchangeable raters is implemented inlc.2raters. %% Grade of membership model
  • The discrete grade of membership model (Erosheva, Fienberg & Joutard, 2007) and the Rasch grade of membership model can be estimated bygom.em. %% MCMC estimation multilevel IRT models
  • Some hierarchical IRT models and random item models for dichotomous and normally distributed data (van den Noortgate, de Boeck & Meulders, 2003; Fox & Verhagen, 2010) can be estimated withmcmc.2pno.ml. %% 1-dim PCML
  • Unidimensional pairwise conditional likelihood estimation (PCML; Zwinderman, 1995) is implemented inrasch.pairwiseorrasch.pairwise.itemcluster. %% 1-dim PMML
  • Unidimensional pairwise marginal likelihood estimation (PMML; Renard, Molenberghs & Geys, 2004) can be conducted usingrasch.pml3. In this function local dependence can be handled by imposing residual error structure or omitting item pairs within a dependent item cluster from the estimation. The functionrasch.evm.pcmestimates the mutiple group partial credit model based on the pairwise eigenvector approach which avoids iterative estimation. %% MCMC estimation of some models
  • Some item response models insirtcan be estimated via Markov Chain Monte Carlo (MCMC) methods. Inmcmc.2pnothe two-parameter normal ogive model can be estimated. A hierarchical version of this model (Janssen, Tuerlinckx, Meulders & de Boeck, 2000) is implemented inmcmc.2pnoh. The 3PNO testlet model (Wainer, Bradlow & Wang, 2007; Glas, 2012) can be estimated withmcmc.3pno.testlet. Some hierarchical IRT models and random item models (van den Noortgate, de Boeck & Meulders, 2003) can be estimated withmcmc.2pno.ml. %% NOHARM
  • For dichotomous response data, the free NOHARM software (McDonald, 1997) estimates the multidimensional compensatory 3PL model and the functionR2noharmruns NOHARM from withinR. Note that NOHARM must be downloaded fromhttp://noharm.niagararesearch.ca/nh4cldl.htmlat first. A pureRimplementation of the NOHARM model with some extensions can be found innoharm.sirt. %% Nonparametric item response theory / ISOP model
  • The measurement theoretic founded nonparametric item response models of Scheiblechner (1995, 1999) -- the ISOP and the ADISOP model -- can be estimated withisop.dichorisop.poly. Item scoring within this theory can be conducted withisop.scoring. %% Functional unidimensional item response model
  • The functional unidimensional item response model (Ip et al., 2013) can be estimated withf1d.irt. %% 1-dim Rasch model variational approximation
  • The Rasch model can be estimated by variational approximation (Rijmen & Vomlel, 2008) usingrasch.va. %% 1-dim Guttman model
  • The unidimensional probabilistic Guttman model (Proctor, 1970) can be specified withprob.guttman. %% jackknife WLE
  • A jackknife method for the estimation of standard errors of the weighted likelihood trait estimate (Warm, 1989) is available inwle.rasch.jackknife. %% reliability
  • Model based reliability for dichotomous data can be calculated by the method of Green and Yang (2009) withgreenyang.reliabilityand the marginal true score method of Dimitrov (2003) using the functionmarginal.truescore.reliability. %% DETECT
  • Essential unidimensionality can be assessed by the DETECT index (Stout, Habing, Douglas & Kim, 1996), see the functionconf.detect. %% linking / alignment
  • Item parameters from several studies can be linked using the Haberman method (Haberman, 2009) inlinking.haberman. See alsoequating.raschandlinking.robust. The alignment procedure (Asparouhov & Muthen, 2013)invariance.alignmentis originally for comfirmatory factor analysis and aims at obtaining approximate invariance. %% Person Fit
  • Some person fit statistics in the Rasch model (Meijer & Sijtsma, 2001) are included inpersonfit.stat. %% LSDM
  • An alternative to the linear logistic test model (LLTM), the so called least squares distance model for cognitive diagnosis (LSDM; Dimitrov, 2007), can be estimated with the functionlsdm.

References

Asparouhov, T., & Muthen, B. (2014). Multiple-group factor analysis alignment. Structural Equation Modeling, 21, 1-14. Bartolucci, F. (2007). A class of multidimensional IRT models for testing unidimensionality and clustering items. Psychometrika, 72, 141-157. Braeken, J. (2011). A boundary mixture approach to violations of conditional independence. Psychometrika, 76, 57-76. DeCarlo, T., Kim, Y., & Johnson, M. S. (2011). A hierarchical rater model for constructed responses, with a signal detection rater model. Journal of Educational Measurement, 48, 333-356. Dimitrov, D. (2003). Marginal true-score measures and reliability for binary items as a function of their IRT parameters. Applied Psychological Measurement, 27, 440-458. Dimitrov, D. M. (2007). Least squares distance method of cognitive validation and analysis for binary items using their item response theory parameters. Applied Psychological Measurement, 31, 367-387. Erosheva, E. A., Fienberg, S. E., & Joutard, C. (2007). Describing disability through individual-level mixture models for multivariate binary data. Annals of Applied Statistics, 1, 502-537. Fox, J.-P., & Verhagen, A.-J. (2010). Random item effects modeling for cross-national survey data. In E. Davidov, P. Schmidt, & J. Billiet (Eds.), Cross-cultural Analysis: Methods and Applications (pp. 467-488), London: Routledge Academic. Glas, C. A. W. (2012). Estimating and testing the extended testlet model. LSAC Research Report Series, RR 12-03. Green, S.B., & Yang, Y. (2009). Reliability of summed item scores using structural equation modeling: An alternative to coefficient alpha. Psychometrika, 74, 155-167. Haberman, S. J. (2009). Linking parameter estimates derived from an item respone model through separate calibrations. ETS Research Report ETS RR-09-40. Princeton, ETS. Ip, E. H., Molenberghs, G., Chen, S. H., Goegebeur, Y., & De Boeck, P. (2013). Functionally unidimensional item response models for multivariate binary data. Multivariate Behavioral Research, 48, 534-562. Janssen, R., Tuerlinckx, F., Meulders, M., & de Boeck, P. (2000). A hierarchical IRT model for criterion-referenced measurement. Journal of Educational and Behavioral Statistics, 25, 285-306. Linacre, J. M. (1994). Many-Facet Rasch Measurement. Chicago: MESA Press. Loken, E. & Rulison, K. L. (2010). Estimation of a four-parameter item response theory model. British Journal of Mathematical and Statistical Psychology, 63, 509-525. McDonald, R. P. (1997). Normal-ogive multidimensional model. In W. van der Linden & R. K. Hambleton (1997): Handbook of modern item response theory (pp. 257-269). New York: Springer. Meijer, R. R., & Sijtsma, K. (2001). Methodology review: Evaluating person fit. Applied Psychological Measurement, 25, 107-135. Proctor, C. H. (1970). A probabilistic formulation and statistical analysis for Guttman scaling. Psychometrika, 35, 73-78. Ramsay, J. O. (1989). A comparison of three simple test theory models. Psychometrika, 54, 487-499. Ramsay, J. O. (1991). Kernel smoothing approaches to nonparametric item characteristic curve estimation. Psychometrika, 56, 611-630. Reckase, M.D. (2009). Multidimensional item response theory. New York: Springer. Rijmen, F., & Vomlel, J. (2008). Assessing the performance of variational methods for mixed logistic regression models. Journal of Statistical Computation and Simulation, 78, 765-779. Renard, D., Molenberghs, G., & Geys, H. (2004). A pairwise likelihood approach to estimation in multilevel probit models. Computational Statistics & Data Analysis, 44, 649-667. Rossi, N., Wang, X. & Ramsay, J. O. (2002). Nonparametric item response function estimates with the EM algorithm. Journal of Educational and Behavioral Statistics, 27, 291-317. Rusch, T., Mair, P., & Hatzinger, R. (2013). Psychometrics with R: A Review of CRAN Packages for Item Response Theory. http://epub.wu.ac.at/4010/1/resrepIRThandbook.pdf. Scheiblechner, H. (1995). Isotonic ordinal probabilistic models (ISOP). Psychometrika, 60, 281-304. Scheiblechner, H. (1999). Additive conjoint isotonic probabilistic models (ADISOP). Psychometrika, 64, 295-316. Stout, W., Habing, B., Douglas, J., & Kim, H. R. (1996). Conditional covariance-based nonparametric multidimensionality assessment. Applied Psychological Measurement, 20, 331-354. Stukel, T. A. (1988). Generalized logistic models. Journal of the American Statistical Association, 83, 426-431. Uenlue, A., & Yanagida, T. (2011). Ryou ready for R?: The CRAN psychometrics task view. British Journal of Mathematical and Statistical Psychology, 64, 182-186. van den Noortgate, W., De Boeck, P., & Meulders, M. (2003). Cross-classification multilevel logistic models in psychometrics. Journal of Educational and Behavioral Statistics, 28, 369-386. Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54, 427-450. Wainer, H., Bradlow, E. T., & Wang, X. (2007). Testlet response theory and its applications. Cambridge: Cambridge University Press. Zwinderman, A. H. (1995). Pairwise parameter estimation in Rasch models. Applied Psychological Measurement, 19, 369-375.

See Also

For estimating multidimensional models for polytomous item resonses see the mirt, flirt (http://faculty.psy.ohio-state.edu/jeon/lab/flirt.php) and TAM packages. For conditional maximum likelihood estimation see the eRm package. For pairwise estimation likelihood methods (also known as composite likelihood methods) see pln or lavaan. The estimation of cognitive diagnostic models is possible using the CDM package. For the multidimensional latent class IRT model see the MultiLCIRT package which also allows the estimation IRT models with polytomous item responses. Latent class analysis can be carried out with covLCA, poLCA, BayesLCA or randomLCA packages. Markov Chain Monte Carlo estimation for item response models can also be found in the MCMCpack package (see the MCMCirt functions therein). See Rusch, Mair and Hatzinger (2013) and Uenlue and Yanagida (2011) for reviews of psychometrics packages in R.

Examples

Run this code
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##   | sirt 0.40-4 (2013-11-26)                                        |
##   | Supplementary Item Response Theory                              |
##   | Maintainer: Alexander Robitzsch <a.robitzsch at bifie.at >      |
##   | https://sites.google.com/site/alexanderrobitzsch/software       |
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