413 research outputs found
Scalable and accurate algorithms for computational genomics and dna-based digital storage
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Accurate variant classification in tumour-only genomic data using interpretable tabular models
No full textEvaluating the robustness of FlexMIRT on DIF analysis
Get PDFSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01The student, Yiqing Liu, accepted the attached license on 2023-12-06 at 14:58.The student, Yiqing Liu, submitted this Thesis for approval on 2023-12-06 at 15:11.This Thesis was approved for publication on 2023-12-07 at 16:09.DSpace SAF Submission Ingestion Package generated from Vireo submission #20156 on 2024-03-01 at 13:32:42Item Response Theory (IRT) plays a crucial role in educational measurement, and accurately detecting Differential Item Functioning (DIF) items is critical to ensuring fairness and validity in tests. This study focused on flexMIRT, a software for multilevel and IRT analysis, performance in DIF analysis within the three-parameter logistic (3PL) and four-parameter logistic (4PL) models. Specifically, the aim was to evaluate the robustness of flexMIRT when applied to the 4PL model, which was not the software's true model. Simulation studies evaluated the ability to control Type I error rates and detect DIF items with varying sample sizes, test lengths, percentages of DIF items, and different levels of parameter change. The results of the simulation studies indicated that flexMIRT maintains low Type I error rates for discrimination (a) and difficulty (b) parameters across both models. An uptick in Type I error rates for the test of guessing parameter (g) was noted as the sample size increased. For Type II error rates, flexMIRT demonstrated enhanced detection capabilities in larger samples and with significant parameter changes, affirming its effectiveness in different IRT models. While flexMIRT showed adaptability in DIF detection, careful interpretation was warranted for the g parameter in extensive datasets. Future research directions include evaluating flexMIRT's robustness with IRT models, exploring a more comprehensive array of testing conditions, and establishing a more definitive framework for detecting significant DIF to provide precise usage guidelines for flexMIRT
Towards migration-free “just-in-case” data archival for future cloud data lakes using synthetic DNA
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