For diagnostic classification models, reliability can be estimated at the pattern or attribute level. Pattern-level reliability represents the classification consistency and accuracy of placing students into an overall mastery profile. Rather than an overall profile, attributes can also be scored individually. In this case, classification consistency and accuracy should be evaluated for each individual attribute, rather than the overall profile. This is referred to as the maximum a posteriori (MAP) reliability. Finally, it may be desirable to report results as the probability of proficiency or mastery on each attribute instead of a proficient/not proficient classification. In this case, the reliability of the posterior probability should be reported. This is the expected a posteriori (EAP) reliability.
Usage
reliability(model, ...)
# S3 method for class 'measrdcm'
reliability(model, ..., threshold = 0.5, force = FALSE)Arguments
- model
The estimated model to be evaluated.
- ...
Unused. For future extensions.
- threshold
For
map_reliability, the threshold applied to the attribute-level probabilities for determining the binary attribute classifications.- force
If reliability information has already been added to the model object with
add_reliability(), should it be recalculated. Default isFALSE.
Value
For class measrdcm, a list with 3 elements:
pattern_reliability: The pattern-level accuracy (p_a) and consistency (p_c) described by Cui et al. (2012).map_reliability: A list with 2 elements:accuracyandconsistency, which include the attribute-level classification reliability statistics described by Johnson & Sinharay (2018).eap_reliability: The attribute-level posterior probability reliability statistics described by Johnson & Sinharay (2020).
Details
The pattern-level reliability (pattern_reliability) statistics are
described in Cui et al. (2012). Attribute-level classification reliability
statistics (map_reliability) are described in Johnson & Sinharay (2018).
Reliability statistics for the posterior mean of the skill indicators (i.e.,
the mastery or proficiency probabilities; eap_reliability) are described in
Johnson & Sinharay (2019).
Methods (by class)
reliability(measrdcm): Reliability measures for diagnostic classification models.
References
Cui, Y., Gierl, M. J., & Chang, H.-H. (2012). Estimating classification consistency and accuracy for cognitive diagnostic assessment. Journal of Educational Measurement, 49(1), 19-38. doi:10.1111/j.1745-3984.2011.00158.x
Johnson, M. S., & Sinharay, S. (2018). Measures of agreement to assess attribute-level classification accuracy and consistency for cognitive diagnostic assessments. Journal of Educational Measurement, 55(4), 635-664. doi:10.1111/jedm.12196
Johnson, M. S., & Sinharay, S. (2020). The reliability of the posterior probability of skill attainment in diagnostic classification models. Journal of Educational and Behavioral Statistics, 45(1), 5-31. doi:10.3102/1076998619864550
Examples
rstn_mdm_lcdm <- measr_dcm(
data = mdm_data, missing = NA, qmatrix = mdm_qmatrix,
resp_id = "respondent", item_id = "item", type = "lcdm",
method = "optim", seed = 63277, backend = "rstan"
)
reliability(rstn_mdm_lcdm)
#> $pattern_reliability
#> p_a p_c
#> 0.9122250 0.8401031
#>
#> $map_reliability
#> $map_reliability$accuracy
#> # A tibble: 1 × 8
#> attribute acc lambda_a kappa_a youden_a tetra_a tp_a tn_a
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 multiplication 0.912 0.820 0.823 0.824 0.962 0.923 0.901
#>
#> $map_reliability$consistency
#> # A tibble: 1 × 10
#> attribute consist lambda_c kappa_c youden_c tetra_c tp_c tn_c gammak
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 multiplication 0.840 0.666 0.821 0.680 0.876 0.847 0.833 0.870
#> # ℹ 1 more variable: pc_prime <dbl>
#>
#>
#> $eap_reliability
#> # A tibble: 1 × 5
#> attribute rho_pf rho_bs rho_i rho_tb
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 multiplication 0.740 0.740 0.613 0.918
#>