CA.MI {cdmTools}

Calculate corrected classification accuracy with multiple imputation

Description

This function calculates the test-, pattern-, and attribute-level classification accuracy indices based on integrated posterior probabilities from multiple imputed item parameters (Kreitchmann et al., 2022). The classification accuracy indices are the ones developed by Iaconangelo (2017) and Wang et al. (2015). It is only applicable to dichotomous attributes. The function is built upon the CA function from the GDINA package (Ma & de la Torre, 2020).

Usage

CA.MI(fit, what = "EAP", R = 500, n.cores = 1, verbose = TRUE, seed = NULL)

Arguments

fit

An object of class RDINA or GDINA (Ma & de la Torre, 2020).

what

What attribute estimates are used? The default is "EAP".

R

Number of bootstrap samples and imputations. The default is 500.

n.cores

Number of processors to use to speed up multiple imputation. The default is 2.

verbose

Show progress. The default is TRUE.

seed

A seed for obtaining consistent results. If NULL, no seed is used. The default is NULL.

Value

CA.MI returns an object of class CA, with a list of elements:

tau

Estimated test-level classification accuracy, see Iaconangelo (2017, Eq 2.2) (vector).

tau_l

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

tau_k

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

CCM

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

Author(s)

Rodrigo S. Kreitchmann, Universidad Nacional de Educación a Distancia

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 (Unpublished doctoral dissertation).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.

Kreitchmann, R. S., de la Torre, J., Sorrel, M. A., Nájera, P., & Abad, F. J. (2022). Improving reliability estimation in cognitive diagnosis modeling. Behavior Research Methods. https://doi.org/10.3758/s13428-022-01967-5

Ma, W., & de la Torre, J. (2020). GDINA: An R package for cognitive diagnosis modeling. Journal of Statistical Software, 93(14). https://doi.org/10.18637/jss.v093.i14

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

library(GDINA)
dat <- sim10GDINA$simdat[1:100,]
Q <- sim10GDINA$simQ
fit <- GDINA(dat = dat, Q = Q, model = "GDINA")
ca.mi <- CA.MI(fit)
ca.mi