Cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; <doi:10.1007/s11336-009-9125-0>). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.
|Author||Chia-Yi Chiu (Rutgers, the State University of New Jersey) and Wenchao Ma (Rutgers, the State University of New Jersey)|
|Date of publication||2016-12-17 10:48:08|
|Maintainer||Wenchao Ma <firstname.lastname@example.org>|
|License||GPL (>= 2)|
ACTCD-package: ACTCD: Asymptotic Classification Theory for Cognitive...
alpha: All possible attribute patterns
cd.cluster: Cluster analysis for cognitive diagnosis based on the...
eta: Ideal Response Patterns for all possible attribute profiles
labeling: labeling for clusters
npar.CDM: Main function for ACTCD package
perm.data: The partial orders of the attribute patterns for 'labeling'
print.output: The function prints outputs obtained from the functions in...
sim.dat: Simulated data
sim.Q: A complete Q-matrix used to generate 'sim.dat'.