data.cdm {CDM}R Documentation

Several Datasets for the CDM Package

Description

Several datasets for the CDM package

Usage

data(data.cdm01)
data(data.cdm02)
data(data.cdm03)
data(data.cdm04)
data(data.cdm05)
data(data.cdm06)
data(data.cdm07)
data(data.cdm08)
data(data.cdm09)
data(data.cdm10)

Format

References

Chen, H., & Chen, J. (2017). Cognitive diagnostic research on chinese students' English listening skills and implications on skill training. English Language Teaching, 10(12), 107-115. http://dx.doi.org/10.5539/elt.v10n12p107

Chen, J., & de la Torre, J. (2013). A general cognitive diagnosis model for expert-defined polytomous attributes. Applied Psychological Measurement, 37, 419-437. http://dx.doi.org/10.1177/0146621613479818

Chen, Y., Li, X., Liu, J., & Ying, Z. (2017). Regularized latent class analysis with application in cognitive diagnosis. Psychometrika, 82, 660-692. http://dx.doi.org/10.1007/s11336-016-9545-6

Chiu, C.-Y., Koehn, H.-F., & Wu, H.-M. (2016). Fitting the reduced RUM with Mplus: A tutorial. International Journal of Testing, 16(4), 331-351. http://dx.doi.org/10.1080/15305058.2016.1148038

Fang, G., Liu, J., & Ying, Z. (2017). On the identifiability of diagnostic classification models. arXiv, 1706.01240. https://arxiv.org/abs/1706.01240

Heller, J. and Wickelmaier, F. (2013). Minimum discrepancy estimation in probabilistic knowledge structures. Electronic Notes in Discrete Mathematics, 42, 49-56.
http://dx.doi.org/10.1016/j.endm.2013.05.145

Kuo, B.-C., Chen, C.-H., & de la Torre, J. (2018). A cognitive diagnosis model for identifying coexisting skills and misconceptions. Applied Psychological Measurement, 42(3), 179-191. http://dx.doi.org/10.1177/0146621617722791

Ma, W., & de la Torre, J. (2016). A sequential cognitive diagnosis model for polytomous responses. British Journal of Mathematical and Statistical Psychology, 69(3), 253-275.
https://doi.org/10.1111/bmsp.12070

Philipp, M., Strobl, C., de la Torre, J., & Zeileis, A. (2018). On the estimation of standard errors in cognitive diagnosis models. Journal of Educational and Behavioral Statistics, 43(1), 88-115. http://dx.doi.org/10.3102/1076998617719728

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Reduced RUM model, Chiu et al. (2016)
#############################################################################

data(data.cdm03, package="CDM")
dat <- data.cdm03$data
qmatrix <- data.cdm03$qmatrix

#*** Model 1: Reduced RUM
mod1 <- CDM::gdina( dat, q.matrix=qmatrix[,-1], rule="RRUM" )
summary(mod1)

#*** Model 2: Additive model with identity link function
mod2 <- CDM::gdina( dat, q.matrix=qmatrix[,-1], rule="ACDM" )
summary(mod2)

#*** Model 3: Additive model with logit link function
mod3 <- CDM::gdina( dat, q.matrix=qmatrix[,-1], rule="ACDM", linkfct="logit")
summary(mod3)

#############################################################################
# EXAMPLE 2: GDINA model - probability dataset from the pks package
#############################################################################

data(data.cdm05, package="CDM")
dat <- data.cdm05$data
Q <- data.cdm05$q.matrix

#* estimate model
mod1 <- CDM::gdina( dat, q.matrix=Q )
summary(mod1)

## End(Not run)

[Package CDM version 7.5-15 Index]