| cdi | R Documentation |
The cognitive diagnostic index (CDI) is a measure of how well an assessment is able to distinguish between attribute profiles. The index was originally proposed by Henson & Douglas (2005) for item- and test-level discrimination, and then expanded by Henson et al. (2008) to include attribute-level discrimination indices.
cdi(model, weight_prevalence = TRUE)
model |
The estimated model to be evaluated. |
weight_prevalence |
Logical indicating whether the discrimination indices should be weighted by the prevalence of the attribute profiles. See details for additional information. |
Henson et al. (2008) described two attribute-level discrimination indices,
\mathbf{d}_{(A)\mathbf{\cdot}} (Equation 8) and
\mathbf{d}_{(B)\mathbf{\cdot}} (Equation 13), which are similar in that
both are the sum of item-level discrimination indices.
In both cases, item-level discrimination indices are calculated as the
average of Kullback-Leibler information for all pairs of attributes profiles
for the item.
The item-level indices are then summed to achieve the test-level
discrimination index for each attribute, or the test overall.
However, whereas \mathbf{d}_{(A)\mathbf{\cdot}} is an unweighted
average of the Kullback-Leibler information,
\mathbf{d}_{(B)\mathbf{\cdot}} is a weighted average, where the weight
is defined by the prevalence of each profile (i.e.,
measr_extract(model, what = "strc_param")).
A list with two elements:
item_discrimination: A tibble with one row
per item containing the CDI for the item and any relevant
attributes.
test_discrimination: A tibble with one row
containing the total CDI for the assessment and for each
attribute.
Henson, R., & Douglas, J. (2005). Test construction for cognitive diagnosis. Applied Psychological Measurement, 29(4), 262-277. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0146621604272623")}
Henson, R., Roussos, L., Douglas, J., & Xuming, H. (2008). Cognitive diagnostic attribute-level discrimination indices. Applied Psychological Measurement, 32(4), 275-288. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0146621607302478")}
rstn_ecpe_lcdm <- dcm_estimate(
dcm_specify(dcmdata::ecpe_qmatrix, identifier = "item_id"),
data = dcmdata::ecpe_data,
missing = NA,
identifier = "resp_id",
method = "optim",
seed = 63277,
backend = "rstan"
)
cdi(rstn_ecpe_lcdm)
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