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An Evaluation of Five Linear Equating Methods for the NEAT Design

机译:NEAT设计的五种线性方程评估方法的评估

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This study uses the results of two previous papers (Kane, Mroch, Suh, & Ripkey, this issue; Suh, Mroch, Kane, & Ripkey, this issue) and the literature on linear equating to evaluate five linear equating methods along several dimensions, including the plausibility of their assumptions and their levels of bias and root mean squared difference (RMSD). The methods all employ non-equivalent groups anchor test (NEAT) design, but make different assumptions about the empirical relationship to be generalized across groups. The analyses indicate that the assumptions employed in Levine Observed-score and Levine True-score methods are more plausible than those for a Tucker, Tucker-like, and Chained Linear method, and that the Levine methods generally have lower levels of bias and RMSD than the other three methods. Furthermore, the methods that employed a chained linear relationship (CLR) approach, in which observed relationships between total test scores and anchor test scores are generalized across groups taking various tests, are found to be more consistent with programs in which a series of test forms administered over a period of years are equated to each other, than a parameter substitution (PS) approach, which estimates results for specific synthetic populations. It is argued that the Levine Observed-score and Levine True-score methods have strong advantages over the other methods studied, unless the groups taking the tests to be equated are known to be very similar.
机译:这项研究使用了前两篇论文的结果(本期的凯恩(Kane),Mroch,Suh,&Ripkey;本期的Suh,穆拉克(Kane),& Ripkey)和有关线性等式的文献,从多个维度评估了五种线性等式方法,包括假设的合理性,偏倚水平和均方根差(RMSD)。这些方法均采用非等效组锚定测试(NEAT)设计,但是对要在组之间推广的经验关系做出了不同的假设。分析表明,Levine观测分数法和Levine True分数法所采用的假设比Tucker,Tucker样和链式线性法更合理,并且Levine方法的偏倚和RMSD通常低于其他三种方法。此外,发现采用链线性关系(CLR)方法的方法与通过一系列测试形式的程序更加一致,在该方法中,观察到的总测验分数和锚定测验分数之间的关系可以在进行各种测验的组中进行概括。与使用参数替代(PS)方法估算了特定合成人群的结果相比,在数年内进行的管理彼此等同。有人认为,Levine Observed-score和Levine True-score方法比其他研究方法具有强大的优势,除非已知接受相等测试的组非常相似。

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