measr_extract {measr}

Extract components of a measrfit object.

Description

Extract components of a measrfit object.

Extract components of an estimated diagnostic classification model

Usage

measr_extract(model, ...)

## S3 method for class 'measrdcm'
measr_extract(model, what, ...)

Arguments

model

The estimated to extract information from.

...

Additional arguments passed to each extract method.

• ppmc_interval:

  For what = "odds_ratio_flags" and what = "conditional_prob_flags", the compatibility interval used for determining model fit flags to return. For example, a ppmc_interval of 0.95 (the default) will return any PPMCs where the posterior predictive p-value (ppp) is less than 0.025 or greater than 0.975.

• agreement:

  For what = "classification_reliability", additional measures of agreement to include. By default, the classification accuracy and consistency metrics defined Johnson & Sinharay (2018) are returned. Additional metrics that can be specified to agreement are Goodman & Kruskal's lambda (lambda), Cohen's kappa (kappa), Youden's statistic (youden), the tetrachoric correlation (tetra), true positive rate (tp), and the true negative rate (tn).

  For what = "probability_reliability", additional measures of agreement to include. By default, the informational reliability index defined by Johnson & Sinharay (2020) is returned. Additional metrics that can be specified to agreement are the point biserial reliability index (bs), parallel forms reliability index (pf), and the tetrachoric reliability index (tb), which was originally defined by Templin & Bradshaw (2013).

what

Character string. The information to be extracted. See details for available options.

Details

For diagnostic classification models, we can extract the following information:

• item_param: The estimated item parameters. This shows the name of the parameter, the class of the parameter, and the estimated value.

• strc_param: The estimated structural parameters. This is the base rate of membership in each class. This shows the class pattern and the estimated proportion of respondents in each class.

• prior: The priors used when estimating the model.

• classes: The possible classes or profile patterns. This will show the class label (i.e., the pattern of proficiency) and the attributes included in each class.

• class_prob: The probability that each respondent belongs to class (i.e., has the given pattern of proficiency).

• attribute_prob: The proficiency probability for each respondent and attribute.

• m2: The M₂ fit statistic. See fit_m2() for details. Model fit information must first be added to the model using add_fit().

• rmsea: The root mean square error of approximation (RMSEA) fit statistic and associated confidence interval. See fit_m2() for details. Model fit information must first be added to the model using add_fit().

• srmsr: The standardized root mean square residual (SRMSR) fit statistic. See fit_m2() for details. Model fit information must first be added to the model using add_fit().

• ppmc_raw_score: The observed and posterior predicted chi-square statistic for the raw score distribution. See fit_ppmc() for details. Model fit information must first be added to the model using add_fit().

• ppmc_conditional_prob: The observed and posterior predicted conditional probabilities of each class providing a correct response to each item. See fit_ppmc() for details. Model fit information must first be added to the model using add_fit().

• ppmc_conditional_prob_flags: A subset of the PPMC conditional probabilities where the ppp is outside the specified ppmc_interval.

• ppmc_odds_ratio: The observed and posterior predicted odds ratios of each item pair. See fit_ppmc() for details. Model fit information must first be added to the model using add_fit().

• ppmc_odds_ratio_flags: A subset of the PPMC odds ratios where the ppp is outside the specified ppmc_interval.

• loo: The leave-one-out cross validation results. See loo::loo() for details. The information criterion must first be added to the model using add_criterion().

• waic: The widely applicable information criterion results. See loo::waic() for details. The information criterion must first be added to the model using add_criterion().

• pattern_reliability: The accuracy and consistency of the overall attribute profile classification, as described by Cui et al. (2012). Reliability information must first be added to the model using add_reliability().

• classification_reliability: The classification accuracy and consistency for each attribute, using the metrics described by Johnson & Sinharay (2018). Reliability information must first be added to the model using add_reliability().

• probability_reliability: Reliability estimates for the probability of proficiency on each attribute, as described by Johnson & Sinharay (2020). Reliability information must first be added to the model using add_reliability().

Value

The extracted information. The specific structure will vary depending on what is being extracted, but usually the returned object is a tibble with the requested information.

Methods (by class)

• measr_extract(measrdcm): Extract components of an estimated diagnostic classification model.

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

Templin, J., & Bradshaw, L. (2013). Measuring the reliability of diagnostic classification model examinee estimates. Journal of Classification, 30(2), 251-275. doi:10.1007/s00357-013-9129-4

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"
)

measr_extract(rstn_mdm_lcdm, "strc_param")