Detecting differential item functioning in a framework of cognitive diagnostic measurement

• Main Authors: Su-Pin Hung, 洪素蘋
• Other Authors: Po-Hsi Chen
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/92713730368329586923

博士 === 國立臺灣師範大學 === 教育心理與輔導學系 === 101 === Detection of Differential item functioning, DIF has been recognizing as an important procedure especially in test development. With the cognitive diagnostic measurements, CDMs continue to receive attention both in applied and methodological studies. DIF related issues in the framework of CDMs remain to concern. The purpose of the study had three objectives; first, to propose model based DIF detection method in dealing compensatory and non-compensatory cognitive diagnostic data; second, to address on the contaminated matching criterion issue that has be overlook in the past DIF study within the CDM framework; third, to investigate more possible factors that may affect DIF detection methods and introduced into the simulation design. An MCMC algorithm employing Gibbs sampling was used to estimate the two proposed models and simulation study was done to examine model recovery, Type I error rates, and power under different testing conditions. For DIF detection, the model based method was also compared with the MH method and LR method. Furthermore, the purification procedure is applied in the MH and LR methods and compared with the model based method to investigate the effectiveness of DIF detection methods. Finally, TIMSS 2007 fourth grade mathematics assessment was used to demonstrate and the results were used to illustrate the implementation of the new method. The parameter recovery of the proposed models yielded well. The simulation results of DIF methods comparison appeared to confirm that the model based method outperformed the MH and LR methods in Type I error control and power rate under comparable testing conditions. Moreover, the result revealed that the biased matching criterion may also determine the effectiveness of DIF detection in a framework of cognitive diagnostic measurement. With purification procedure, could improve the Type I errors and power rates for MH and LR under specific circumstance. Finally, the model based method had the strength of interpreting results more elaborately compared to the other DIF methods.