Functions for cognitive diagnosis modeling and multidimensional item response modeling for dichotomous and polytomous item responses. This package enables the estimation of the DINA and DINO model (Junker & Sijtsma, 2001, <doi:10.1177/01466210122032064>), the multiple group (polytomous) GDINA model (de la Torre, 2011, <doi:10.1007/s11336-011-9207-7>), the multiple choice DINA model (de la Torre, 2009, <doi:10.1177/0146621608320523>), the general diagnostic model (GDM; von Davier, 2008, <doi:10.1348/000711007X193957>), the structured latent class model (SLCA; Formann, 1992, <doi:10.1080/01621459.1992.10475229>) and regularized latent class analysis (Chen, Li, Liu, & Ying, 2017, <doi:10.1007/s11336-016-9545-6>). See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> for further details on estimation and the package structure. For tutorials on how to use the CDM package see George and Robitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as Ravand and Robitzsch (2015).
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.
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).
CDM is implemented based on the S3 system. It comes
with a namespace and consists of several external functions (functions the
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
summary methods for objects of the class
and three functions for checking the input format and computing intermediate
information. The features of the package
illustrated with an accompanying real dataset and Q-matrix
and artificial examples (
See George et al. (2016) for an overview and some computational details of the CDM package.
Alexander Robitzsch [aut, cre], Thomas Kiefer [aut], Ann Cathrice George [aut], Ali Uenlue [aut]
Maintainer: Alexander Robitzsch <[email protected]>
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.
Chen, Y., Li, X., Liu, J., & Ying, Z. (2017). Regularized latent class analysis with application in cognitive diagnosis. Psychometrika, 82, 660-692.
de la Torre, J., & Douglas, J. (2004). Higher-order latent trait models for cognitive diagnosis. Psychometrika, 69, 333–353.
de la Torre, J. (2009). A cognitive diagnosis model for cognitively based multiple-choice options. Applied Psychological Measurement, 33, 163-183.
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.
George, A. C., & Robitzsch, A. (2015) Cognitive diagnosis models in R: A didactic. The Quantitative Methods for Psychology, 11, 189-205. doi:10.20982/tqmp.11.3.p189
George, A. C., Robitzsch, A., Kiefer, T., Gross, J., & Uenlue, A. (2016). The R package CDM for cognitive diagnosis models. Journal of Statistical Software, 74(2), 1-24.
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.
Ravand, H., & Robitzsch, A.(2015). Cognitive diagnostic modeling using R. Practical Assessment, Research & Evaluation, 20(11). Available online: http://pareonline.net/getvn.asp?v=20&n=11
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 the GDINA package for comprehensive functions for the GDINA model.
See also the ACTCD and NPCD packages for nonparametric cognitive diagnostic models.
See the dina package for estimating the DINA model with a Gibbs sampler.
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