This function calculate test-, pattern- and attribute-level classification accuracy indices based on GDINA estimates from the GDINA function using approaches in Iaconangelo (2017) and Wang, Song, Chen, Meng, and Ding (2015). It is only applicable for dichotomous attributes.

CA(GDINA.obj, what = "MAP")

Arguments

GDINA.obj

estimated GDINA object returned from GDINA

what

what attribute estimates are used? Default is "MAP".

Value

a list with elements

tau

estimated test-level classification accuracy, see Iaconangelo (2017, Eq 2.2)

tau_l

estimated pattern-level classification accuracy, see Iaconangelo (2017, p. 13)

tau_k

estimated attribute-level classification accuracy, see Wang, et al (2015, p. 461 Eq 6)

CCM

Conditional classification matrix, see Iaconangelo (2017, p. 13)

References

Iaconangelo, C.(2017). Uses of Classification Error Probabilities in the Three-Step Approach to Estimating Cognitive Diagnosis Models. (Unpublished doctoral dissertation). New Brunswick, NJ: Rutgers University.

Wang, W., Song, L., Chen, P., Meng, Y., & Ding, S. (2015). Attribute-Level and Pattern-Level Classification Consistency and Accuracy Indices for Cognitive Diagnostic Assessment. Journal of Educational Measurement, 52 , 457-476.

Examples

if (FALSE) { dat <- sim10GDINA$simdat Q <- sim10GDINA$simQ fit <- GDINA(dat = dat, Q = Q, model = "GDINA") fit CA(fit) }