The Impact of Unbalanced Designs on the Performance of Parametric and Nonparametric DIF Procedures: A Comparison of Mantel Haenszel, Logistic Regression, SIBTEST, and IRTLR Procedures
| Jazyk: | angličtina |
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| Druh dokumentu: | Text<br />Doctoral Thesis<br />Text |
| Popis: | The current study examined the impact of unbalanced sample sizes between focal and reference groups on the Type I error rates and DIF detection rates (power) of five DIF procedures (MH, LR, general IRTLR, IRTLR-b, and SIBTEST). Five simulation factors were used in this study. Four factors were for generating simulation data and they were sample size, DIF magnitude, group mean ability difference (impact), and the studied item difficulty. The fifth factor was the DIF method factor that included MH, LR, general IRTLR, IRTLR-b, and SIBTEST. A repeated-measures ANOVA, where the DIF method factor was the within-subjects variable, was performed to compare the performance of the five DIF procedures and to discover their interactions with other factors. For each data generation condition, 200 replications were made. Type I error rates for MH and IRTLR DIF procedures were close to or lower than 5%, the nominal level for different sample size levels. On average, the Type I error rates for IRTLR-b and SIBTEST were 5.7%, and 6.4%, respectively. In contrast, the LR DIF procedure seems to have a higher Type I error rate, which ranged from 5.3% to 8.1% with 6.9% on average. When it comes to the rejection rate under DIF conditions, or the DIF detection rate, the IRTLR-b showed the highest DIF detection rate followed by SIBTEST with averages of 71.8% and 68.4%, respectively. Overall, the impact of unbalanced sample sizes between reference and focal groups on the performance of DIF detection showed a similar tendency for all methods, generally increasing DIF detection rates as the total sample size increased. In practice, IRTLR-b, which showed the best performance for DIF detection rates and controlled for the Type I error rates, should be the choice when the model-data fit is reasonable. If other non-IRT DIF methods are considered, MH or SIBTEST could be used, depending on which type of error (Type I or II) is more seriously considered. A Dissertation submitted to the Department of Educational Psychology and Learning Systems in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Fall Semester 2017. November 6, 2017. Includes bibliographical references. Insu Paek, Professor Directing Dissertation; Fred Huffer, University Representative; Betsy Jane Becker, Committee Member; Yanyun Yang, Committee Member. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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