Selecting an appropriate cognitive diagnostic model (CDM) for data analysis is always challenging. Studies have explored several model fit indices for CDMs. The common results of these studies indicate that Qmatrix misspecifications lead to poor performance of the model fit indices in the context of CDMs. Thus, this study explored whether model fit indices improve performance with a modified Q-matrix. The average class size has reduced to 23 students in Taiwan because of the low birth rate; therefore, the study sought the effect of sample size on the performance of model fit indices. The results showed that Akaike's information criterion (AIC) was an excellent model fit index in small samples. Model fit indices with the modified Q-matrix presented superior performance.
The unprecedented global pandemic known as COVID-19 caused by SARS-CoV-2 has a profound impact on human life and health, economy and society. Since the outbreak, tremendous effort has been put forth to expand our capacity to diagnose this deadly virus, because accurate detection is essential to effectively combat the epidemic. Numbers of reports have focused on whether the detection methods are sensitive and accurate enough [1]. Little information on the critical first step of detection, sample preservation, is available. At present, there are various of virus Preservation Solutions (PS) on the market, which are used for the preservation of swab samples in nucleic acid detection of SARS-CoV-2. When screening or testing for SARS-CoV-2, pharyngeal swab or nasal swab should be put into PS. Before nucleic acid extraction, the sample may stay in the protective solution for several hours or days. If the sample is not effectively protected in the preservation solution, resulting in the degradation of virus nucleic acid then false negative will occur in the downstream detection process [2]. Therefore, the protection ability to virus RNA of PS is critical important to the reliability of nucleic acid detection results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.