多组DIF方法在国际大规模测评分数可比性评价中的应用
Performance of Multi-group DIF Methods in Assessing Cross-Country Score Comparability of International Large-Scale Assessments
摘要：
标准化的大规模测试是一个有争议的话题，其中测试公平性是其核心。这项研究发现，五个最近的多组DIF检测方法中有两个能够捕获影响测试公平性的均匀和非均匀DIF。尽管如此，以前的研究还没有证明这两种方法在相互比较时的相对性能。这两种方法是改进的Wald检验和广义逻辑斯谛回归方法。本研究利用最新的TIMSS数学成绩数据评估了这两种方法的两组实证结果之间的共性和差异。主要结论是，改进的Wald检验比广义逻辑斯谛回归程序更适用于多组DIF分析。本研究的实证结果可以为ILSA评分分析中多组DIF方法的选择提供信息。
英文摘要：
Standardized large-scale testing can be a debatable topic, in which test fairness sits at its very core. This study found that two out of five recent multi-group DIF detection methods are capable of capturing both the uniform and nonuniform DIF that affects test fairness. Still, no prior research has demonstrated the relative performance of these two methods when they are compared with each other. These two methods are the improved Wald test and the generalized logistic regression procedure. This study assessed the commonalities and differences between two sets of empirical results from these two methods with the latest TIMSS math score data. The primary conclusion was that the improved Wald test is relatively more established than the generalized logistic regression procedure for multi-group DIF analysis. Empirical results from this study may inform the selection of a multi-group DIF method in the ILSA score analysis.
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