The Q-Matrix Validation Methods Framework
Iterative Attribute Hierarchy Exploration Methods for Cognitive Diagno...
Parameter Estimation for Cognitive Diagnosis Models (CDMs) by MMLE/EM ...
Extract Components from Qval Package Objects
Calculate Fit Indices
Calculate
Calculate matrix
Priority of Attribute
Calculate PVAF
Calculate McFadden Pseudo-R2
Restriction Matrix
Check Whether a Q-Matrix is Identifiable
Plot Methods for Various Qval Objects
Print Methods for Various Objects
Generate Response
Simulate Mis-specifications in Q-matrix
Generate a Random Q-matrix
Summary Methods for Various Objects
Update Method for Various Objects
Perform Q-matrix Validation Methods
Wald Test for Two Q-vectors
Calculate Over-Specification Rate (OSR)
Calculate Q-matrix Recovery Rate (QRR)
Calculate True-Negative Rate (TNR)
Calculate True-Positive Rate (TPR)
Calculate Under-Specification Rate (USR)
Calculate Vector Recovery Ratio (VRR)
Provide a variety of Q-matrix validation methods for the generalized cognitive diagnosis models, including the method based on the generalized deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the Hull method by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the stepwise Wald test method (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the multiple logistic regression‑based Q‑matrix validation method (the MLR-B method) by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>, the beta method based on signal detection theory by Li and Chen (2024) <DOI:10.1111/bmsp.12371> and Q-matrix validation based on relative fit index by Chen et al. (2013) <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative procedures during Q-matrix validating are available <DOI:10.3758/s13428-024-02547-5>.