tdcm.score {TDCM}

DCM scoring function.

Description

Function to score responses with fixed item parameters from a previously calibrated LCDM.

Usage

tdcm.score(
  calibration.model,
  newdata,
  q.matrix,
  attr.prob.fixed = NULL,
  progress = TRUE
)

Arguments

calibration.model

the previously calibrated model; an object of class gdina.

newdata

a required N \times I matrix. Binary item responses are in the columns.

q.matrix

a required I \times A matrix indicating which items measure which attributes.

attr.prob.fixed

optional argument for attribute profile proportions. Default is uniform distribution of profiles.

progress

An optional logical indicating whether the function should print the progress of estimation.

Details

Obtain classifications for new responses to items that were previously calibrated. The calibrate-and-score approach is further detailed in Madison et al. (2023).

Value

An object of class gdina with entries as indicated in the CDM package.

References

George, A. C., Robitzsch, A., Kiefer, T., Gross, J., & Ünlü , A. (2016). The R package CDM for cognitive diagnosis models. Journal of Statistical Software, 74(2), 1-24.

Henson, R., Templin, J., & Willse, J. (2009). Defining a family of cognitive diagnosis models using log linear models with latent variables. Psychometrika, 74, 191-21.

Madison, M.J., Chung, S., Kim, J., & Bradshaw, L. (2023). Approaches to estimating longitudinal diagnostic classification models. Behaviormetrika.

Examples

## Example 1: T = 2, A = 4
data(data.tdcm01, package = "TDCM")
dat1 <- data.tdcm01$data
qmat1 <- data.tdcm01$q.matrix
pre <- dat1[, 1:20]
post <- dat1[, 21:40]

# calibrate LCDM with post-test data
m1 <- CDM::gdina(data = post, q.matrix = qmat1, linkfct = "logit", method = "ML")

# score pre-test responses
m2 <- TDCM::tdcm.score(m1, newdata = pre, q.matrix = qmat1)
summary(m2)
m2$pattern