探索性结构方程模型(ESEM)在项目微分功能检测中的性能

M. A. Ayanwale
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引用次数: 2

摘要

如果一个不相关的构念被考虑到考生的表现,那么标准化测试的有效性就会受到质疑,这被错误地建模为构念(测试项目)中的能力。测试必须确保考生能力的准确性,而不管他们在任何人口统计变量中的亚群。本文利用探索性结构方程模型(ESEM)探讨了性别和学校位置作为西非考试委员会(WAEC)考生(N=2,866)数学项目主要协变量的潜力。结果表明,检验具有多维度(六因素),其依从性确定指数分别为(χ2 (940)=4882.024, p < 0.05, CFI=0.962, TLI=0.930, RMSEA=0.038, SRMR=0.030, 90% CI=0.037 ~ 0.039,赤池信息准则(AIC)=147290.577,贝叶斯信息准则(BIC)=149585.436,调整样本容量的BIC=148362.154)。此外,WAEC中有10个(20%)项目对性别具有显著的DIF,而3个(6%)项目对学校地点具有显著的DIF。观察到的DIF项目使测试开发人员熟悉;DIF的存在会对具有相同能力水平的考生的成绩产生不同的影响。这次考试对考生来说意义重大。因此,准确和无偏见的评估应该是任何测试项目测量的基本原则,测试开发人员需要在心理测量学上测试项目以避免偏见。
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Performance of exploratory structural equation model (ESEM) in detecting differential item functioning
The validity of a standardised test is questioned if an irrelevant construct is accounted for the performance of examinees, which is wrongly modeled as the ability in the construct (test items). A test must ensure precision in the examinee's ability irrespective of their sub-population in any demographic variables. This paper explored the potentials of gender and school location as major covariates on the West African Examinations Council (WAEC) mathematics items among examinees (N=2,866) using Exploratory Structural Equation Modeling (ESEM). The results remarked that the test is multidimensional (six-factors) with compliance fix indices of (χ2 (940)=4882.024, p < 0.05, CFI=0.962, TLI=0.930, RMSEA=0.038, SRMR=0.030, 90 % CI=0.037-0.039, Akaike information criterion (AIC)=147290.577, Bayesian information criterion (BIC)=149585.436 and Sample-size adjusted BIC=148362.154) respectively. Also, there were 10 (20 %) significant DIF items in the WAEC to gender, while 3 (6 %) of the items indicated significant DIF to school location. Observed DIF items acquaint test developers; the existence of DIF may differentially affect the performance of examinees with the same ability level. The implications of the test are severe for the examinees. Hence, accurate and unbiased assessment should be the basic principles for any test item measurement, and test developers need to test the items to be free from biases psychometrically.
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