CDM (version 4.8-0)

CDM-package: Cognitive Diagnosis Modeling: The RPackage CDM

Description

Functions for cognitive diagnosis modeling and multidimensional item response modeling for dichotomous and polytomous data. This package enables the estimation of the DINA and DINO model, the multiple group (polytomous) GDINA model, the multiple choice DINA model, the general diagnostic model (GDM), the multidimensional linear compensatory item response model and the structured latent class model (SLCA).

concept

cognitive diagnosis modeling

RFunction Versions

anova.din__1.10.R, calc_posterior__1.01.R, cdi.kli__0.05.R, cdm.est.calc.accuracy__2.15.R, check.input__1.07.R, coef__0.03.R, confint.din__0.06.R, din.deterministic__1.01.R, din.deterministic_alg__0.06.R, din.partable__0.14.R, din.validate.qmatrix__1.04.R, din__2.28.R, entropy.lca__0.08.R, equivalent.dina__1.02.R, equivalent.skillclasses__0.06.R, gdd__0.02.R, gdina.dif__1.04.R, gdina.dif_aux__1.09.R, gdina.partable__0.02.R, gdina.postproc__0.03.R, gdina.wald__0.16.R, gdina.wald_aux__0.05.R, gdina__8.934.R, gdina_aux__0.01.R, gdina_designmatrices__0.03.R, gdina_hogdina_alg__1.08.R, gdina_reduced_skillspace__0.04.R, gdm__8.33.R, gdm_algorithm__8.09.R, gdm_postproc__3.05.R, gdm_preproc__2.12.R, ideal.response.pattern__0.03.R, IRT.anova__0.02.R, IRT.compareModels__0.12.R, IRT.data__0.04.R, IRT.derivedParameters__0.02.R, IRT.expectedCounts__0.05.R, IRT.factor.scores__0.04.R, IRT.IC__0.03.R, IRT.irfprob__0.04.R, IRT.irfprobPlot__1.04.R, IRT.itemfit__0.02.R, IRT.jackknife.gdina__0.13.R, IRT.jackknife__0.01.R, IRT.likelihood__0.07.R, IRT.modelfit__0.08.R, IRT.parameterTable__0.02.R, IRT.posterior__0.01.R, IRT.predict__1.14.R, IRT.repDesign__0.07.R, IRT.se.din__0.04.R, IRT.se__0.01.R, itemfit.rmsea__0.17.R, itemfit.sx2__3.06.R, itemfit.sx2_aux__3.05.R, jkestimates__0.04.R, label_significance_level__0.01.R, logLik_CDM__0.07.R, mcdina.est.item__0.07.R, mcdina.postproc__0.08.R, mcdina.simul__0.10.R, mcdina__0.59.R, mcdina_alg_cppcall__0.01.R, mcdina_prepare__0.18.R, modelfit.cor.din__2.10.R, modelfit.cor__1.17.R, modelfit.cor2__3.18.R, numerical_Hessian__0.06.R, osink__1.02.R, personfit.appropriateness__1.05.R, plot.din__2.14.R, plot.gdina__0.02.R, plot.gdm__0.06.R, plot.slca__0.02.R, predict.CDM__0.02.R, print.din__2.03.R, print.gdina__0.03.R, print.gdm__0.02.R, print.mcdina__0.03.R, print.slca__0.03.R, print.summary.din__1.14.R, rowMaxs__1.05.R, rowProds__1.01.R, rrum.param__0.01.R, sequential.items__1.01.R, sim.din__1.04.R, sim.gdina__2.05.R, skill.cor__1.13.R, skillspace.approximation__0.02.R, skillspace.hierarchy__0.08.R, slca.algorithm__1.15.R, slca.postprocess__0.04.R, slca__1.36.R, summary.din__1.15.R, summary.gdina.wald__0.01.R, summary.gdina__1.23.R, summary.gdm__1.17.R, summary.mcdina__0.11.R, summary.slca__1.16.R, vcov.din__1.54.R, vcov.loglike.din__0.01.R, WaldTest__0.02.R, zzz__1.11.R,

<em>Rcpp</em> Function Versions

calc_posterior__1.01.c, cdm_kli_id_c__2.02.cpp, din.deterministic.devcrit_c__2.02.cpp, din.jml.devcrit_c__2.02.cpp, gdd_c__2.02.cpp, irt_predict_c__2.02.cpp, itemfit_sx2_calc_scoredistribution_cdm__2.01.cpp, modelfit_cor2_c__2.02.cpp, probs_multcat_items_counts_c__2.03.cpp, slca_cfunctions__2.02.cpp,

<em>Rd</em> Documentation Versions

anova.din__1.21.Rd, cdi.kli__0.10.Rd, CDM-package__2.52.Rd, cdm.est.class.accuracy__1.12.Rd, coef__0.15.Rd, Data-sim__1.12.Rd, data.cdm__0.06.Rd, data.dcm__0.16.Rd, data.dtmr__0.10.Rd, data.ecpe__0.13.Rd, data.fraction1__0.11.Rd, data.fraction2__0.14.Rd, data.hr__0.08.Rd, data.jang__0.09.Rd, data.melab__0.08.Rd, data.mg__0.18.Rd, data.pgdina__0.13.Rd, data.sda6__0.06.Rd, data.Students__0.12.Rd, data.timss03.G8.su__0.04.Rd, data.timss07.G4.lee__0.04.Rd, data.timss11.G4.AUT__0.06.Rd, din.deterministic__0.14.Rd, din.equivalent.class__0.16.Rd, din.validate.qmatrix__1.16.Rd, din__2.29.Rd, entropy.lca__0.16.Rd, equivalent.dina__1.11.Rd, fraction.subtraction.data__2.01.Rd, fraction.subtraction.qmatrix__1.03.Rd, gdd__0.08.Rd, gdina.dif__0.10.Rd, gdina.wald__0.18.Rd, gdina__2.54.Rd, gdm__4.25.Rd, ideal.response.pattern__0.04.Rd, IRT.anova__0.02.Rd, IRT.compareModels__0.11.Rd, IRT.data__0.04.Rd, IRT.expectedCounts__0.03.Rd, IRT.factor.scores__0.05.Rd, IRT.IC__0.05.Rd, IRT.irfprob__0.07.Rd, IRT.irfprobPlot__0.08.Rd, IRT.itemfit__0.03.Rd, IRT.jackknife__0.13.Rd, IRT.likelihood__0.06.Rd, IRT.modelfit__0.12.Rd, IRT.parameterTable__0.02.Rd, IRT.repDesign__0.11.Rd, itemfit.rmsea__1.07.Rd, itemfit.sx2__1.24.Rd, logLik__0.12.Rd, mcdina__0.18.Rd, modelfit.cor__1.48.Rd, numerical_Hessian__0.12.Rd, osink__0.05.Rd, personfit.appropriateness__0.13.Rd, plot.din__2.07.Rd, predict__0.12.Rd, print.summary.din__1.04.Rd, sequential.items__0.07.Rd, sim.din__2.04.Rd, sim.gdina__1.15.Rd, skill.cor__2.05.Rd, skillspace.approximation__0.06.Rd, skillspace.hierarchy__0.12.Rd, slca__1.47.Rd, summary.din__2.04.Rd, vcov__0.26.Rd, WaldTest__0.06.Rd,

Details

ll{ Package: CDM Type: Package Version: 4.8 Publication Year: 2016 License: GPL (>= 2) } Cognitive diagnosis models (CDMs) are restricted latent class models. They represent model-based classification approaches, which aim at assigning respondents to different attribute profile groups. The latent classes correspond to the possible attribute profiles, and the conditional item parameters model atypical response behavior in the sense of slipping and guessing errors. The core CDMs in particular differ in the utilized condensation rule, conjunctive / non-compensatory versus disjunctive / compensatory, where in the model structure these two types of response error parameters enter and what restrictions are imposed on them. The confirmatory character of CDMs is apparent in the Q-matrix, which can be seen as an operationalization of the latent concepts of an underlying theory. The Q-matrix allows incorporating qualitative prior knowledge and typically has as its rows the items and as the columns the attributes, with entries 1 or 0, depending on whether an attribute is measured by an item or not, respectively. CDMs as compared to common psychometric models (e.g., IRT) contain categorical instead of continuous latent variables. The results of analyses using CDMs differ from the results obtained under continuous latent variable models. CDMs estimate in a direct manner the probabilistic attribute profile of a respondent, that is, the multivariate vector of the conditional probabilities for possessing the individual attributes, given her / his response pattern. Based on these probabilities, simplified deterministic attribute profiles can be derived, showing whether an individual attribute is essentially possessed or not by a respondent. As compared to alternative two-step discretization approaches, which estimate continuous scores and discretize the continua based on cut scores, with CDMs the classification error can generally be reduced. The package CDM implements parameter estimation procedures for the DINA and DINO model (e.g.,de la Torre & Douglas, 2004; Junker & Sijtsma, 2001; Templin & Henson, 2006; the generalized DINA model for dichotomous attributes (GDINA, de la Torre, 2011) and for polytomous attributes (pGDINA, Chen & de la Torre, 2013); the general diagnostic model (GDM, von Davier, 2008) and its extension to the multidimensional latent class IRT model (Bartolucci, 2007), the structure latent class model (Formann, 1992), and tools for analyzing data under the models. These and related concepts are explained in detail in the book about diagnostic measurement and CDMs by Rupp, Templin and Henson (2010), and in such survey articles as DiBello, Roussos and Stout (2007) and Rupp and Templin (2008). The package CDM is implemented based on the S3 system. It comes with a namespace and consists of several external functions (functions the package exports). The package contains a utility method for the simulation of artificial data based on a CDM model (sim.din). It also contains seven internal functions (functions not exported by the package): this are plot, print, and summary methods for objects of the class din (plot.din, print.din, summary.din), a print method for objects of the class summary.din (print.summary.din), and three functions for checking the input format and computing intermediate information. The features of the package CDM are illustrated with an accompanying real dataset and Q-matrix (fraction.subtraction.data and fraction.subtraction.qmatrix) and artificial examples (Data-sim).

References

Bartolucci, F. (2007). A class of multidimensional IRT models for testing unidimensionality and clustering items. Psychometrika, 72, 141-157. Chen, J., & de la Torre, J. (2013). A general cognitive diagnosis model for expert-defined polytomous attributes. Applied Psychological Measurement, 37, 419-437. de la Torre, J., & Douglas, J. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69, 333--353. de la Torre, J. (2011). The generalized {DINA} model framework. Psychometrika, 76, 179--199. DiBello, L. V., Roussos, L. A., & Stout, W. F. (2007). Review of cognitively diagnostic assessment and a summary of psychometric models. In C. R. Rao and S. Sinharay (Eds.), Handbook of Statistics, Vol. 26 (pp. 979--1030). Amsterdam: Elsevier. Formann, A. K. (1992). Linear logistic latent class analysis for polytomous data. Journal of the American Statistical Association, 87, 476-486. Junker, B. W., & Sijtsma, K. (2001). Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 25, 258--272. Rupp, A. A., & Templin, J. (2008). Unique characteristics of diagnostic classification models: A comprehensive review of the current state-of-the-art. Measurement: Interdisciplinary Research and Perspectives, 6, 219--262. Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic Measurement: Theory, Methods, and Applications. New York: The Guilford Press. Templin, J., & Henson, R. (2006). Measurement of psychological disorders using cognitive diagnosis models. Psychological Methods, 11, 287--305. von Davier, M. (2008). A general diagnostic model applied to language testing data. British Journal of Mathematical and Statistical Psychology, 61, 287-307.

See Also

See also the ACTCD and NPCD packages for nonparametric cognitive diagnostic models. See dcmr for MCMC based estimation methods. See dina for estimating the DINA model with a Gibbs sampler.

Examples

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##   ** CDM 2.5-16 (2013-11-29)      **
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