Researchers have developed a variety of effective Q-matrix validation methods,but almost all of them have the problem that their performance is not very well when the proportion of misspecified elements in the Q-matrix is high.The column permutation method based on cosine similarity or Tucker's congruence coefficient is proposed for Q-matrix validation.The Monte Carlo simulation study is conducted under different conditions and four Q-matrix validation methods(GDI,Hull,MLR-B and stepwise).The mean percentage of correct vector or entries of Q-matrix is calculated as evaluation criteria of recovery rates.The simulation results show that under various conditions com-bination,the column permutation for Q-matrix validation method can accurately identify misspecified Q-entries,es-pecially when the high proportion of misspecified entries is involved in the Q-matrix.
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