## 4 Results
First, identifying and removing rapid guesses _purposefully_ based on RT values improves not only the model fit but also the overall reliability attained by a given assessment. Second, using empirical reliability (or another measure of reliability) as an additional optimization criterion in the iterative search process for RT thresholds can help maintain the psychometric quality of the assessment while identifying and removing rapid guesses effectively from the response dataset. Our final comparison focused on the ability estimates for the sample of 5088 students from the baseline model and the adjusted models based on the threshold-setting models. Figure 5 shows a histogram of the ability estimates on the diagonal part, the Pearson's correlation coefficient between the ability estimates obtained from different models in the upper-diagonal part, and the scatterplots of the ability estimates in the lower-diagonal part. The results show that the correlations among the ability estimates were very high (\(r>0.99\), \(p<0.001\)), suggesting that the overall ranking of the students did not change significantly after removing the rapid guessing cases based on the six threshold-setting methods. The ability estimates generated by the adjusted models with random search and genetic algorithm were perfectly and positively correlated, \(r=1\), \(p<0.001\). As the percentage values for the NT methods increased from 10% to 25%, the NT methods could produce ability estimates more comparable to those produced by random search and genetic algorithm. Consistent with the results of correlation coefficients, the scatter plots on the lower-diagonal part also showed that the random search and genetic algorithms had a very close alignment. Another important finding is that there was a large disparity between the ability estimates from the baseline model and those from the threshold-setting methods in the lower-ability region (i.e., \(\theta<-1\)), and this was more pronounced for the random search and genetic algorithms. This finding broadly supports the work of other studies in this area linking having a lower ability and the tendency to rapidly guess the items [54]. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **AIC** & **BIC** & **SABIC** & **HQ** & **Log-likelihood** & **Reliability** \\ \hline Baseline & 95,735.81 & 96,258.58 & 96,004.37 & 95,918.87 & \(-\)47,787.91 & 0.859 \\ Random Deletion & 72,970.21 & 73,492.98 & 73,238.77 & 73,153.27 & \(-\)36,405.10 & 0.849 \\ NT10 & 94,429.20 & 94,951.97 & 94,697.75 & 94,612.26 & \(-\)47,134.60 & 0.861 \\ NT15 & 93,727.81 & 94,250.55 & 93,996.33 & 93,910.86 & \(-\)46,783.90 & 0.862 \\ NT20 & 93,031.28 & 93,553.99 & 93,299.77 & 93,214.32 & \(-\)46,435.64 & 0.863 \\ NT25 & 92,271.10 & 92,793.77 & 92,539.56 & 92,454.14 & \(-\)46,055.55 & 0.864 \\ Random Search & 91,603.80 & 92,126.50 & 91,872.28 & 91,786.85 & \(-\)45,721.90 & 0.864 \\ Genetic Algorithm & 91,318.34 & 91,840.99 & 91,586.78 & 91,501.37 & \(-\)45,579.17 & 0.864 \\ \hline \hline \end{tabular} Note.: AIC: Akaike Information Criterion; BIC: Bayesian information criterion; SABIC: Sample Size Adjusted BIC; HQ: Hannan–Quinn (HQ) Criterion.  
\end{table} 
Table 3: Model–data fit indices for the threshold-setting methods.