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Example probabilities from a diagnostic classification model

Source: R/data.R
dcm_probs.Rd

Estimates of attribute mastery and true classification values for use in demonstrating the functionality of the threshold

Usage

dcm_probs

Format

dcm_probs is a list contain true attribute classifications and estimated probabilities of proficiency for the 3 attributes in the simulated data. Each element is itself a list containing a vector of attribute estimates and a vector of true classifications.

  • dcm_probs

    • att1

      • estimate: A double vector of length 500 containing the proficiency probabilities for attribute 1.

      • truth: An integer vector of length 500 containing the true classifications for attribute 1.

    • att2

      • estimate: A double vector of length 500 containing the proficiency probabilities for attribute 2.

      • truth: An integer vector of length 500 containing the true classifications for attribute 2.

    • att3

      • estimate: A double vector of length 500 containing the proficiency probabilities for attribute 3.

      • truth: An integer vector of length 500 containing the true classifications for attribute 3.

Details

The data was simulated from the loglinear cognitive diagnostic model (LCDM; Henson et al., 2009). In total, we simulated 500 respondents taking 15 items, which combined to measure 3 attributes. After simulating the data, an LCDM was estimated using measr (Thompson, 2023). This data contains the probabilities of attribute proficiency from the estimated model, as well as the true attribute classifications that were used to generate the data.

References

Henson, R. A., Templin, J. L., & Willse, J. T. (2009). Defining a family of cognitive diagnosis models using log-linear models with latent variables. Psychometrika, 74(2), 191-210. doi:10.1007/s11336-008-9089-5

Thompson, W. J. (2023). measr: Bayesian psychometric modeling using Stan. Journal of Open Source Software, 8(91). doi:10.21105/joss.05742