BackgroundEconomists are making increasing use of measures of student achievement obtained through large-scale survey assessments such as NAEP, TIMSS, and PISA. The construction of these measures, employing plausible value (PV) methodology, is quite different from that of the more familiar test scores associated with assessments such as the SAT or ACT. These differences have important implications both for utilization and interpretation. Although much has been written about PVs, it appears that there are still misconceptions about whether and how to employ them in secondary analyses.MethodsWe address a range of technical issues, including those raised in a recent article that was written to inform economists using these databases. First, an extensive review of the relevant literature was conducted, with particular attention to key publications that describe the derivation and psychometric characteristics of such achievement measures. Second, a simulation study was carried out to compare the statistical properties of estimates based on the use of PVs with those based on other, commonly used methods.ResultsIt is shown, through both theoretical analysis and simulation, that under fairly general conditions appropriate use of PV yields approximately unbiased estimates of model parameters in regression analyses of large scale survey data. The superiority of the PV methodology is particularly evident when measures of student achievement are employed as explanatory variables.ConclusionsThe PV methodology used to report student test performance in large scale surveys remains the state-of-the-art for secondary analyses of these databases.

中文翻译：

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大规模评估调查中考试分数的使用：心理和统计方面的考虑

背景技术经济学家越来越多地使用通过大规模调查评估（例如NAEP，TIMSS和PISA）获得的学生成绩衡量标准。这些采用合理值（PV）方法的度量的构建与与诸如SAT或ACT之类的评估相关的更熟悉的测试分数的构建完全不同。这些差异对于使用和解释都具有重要意义。尽管关于PV的文献很多，但似乎仍存在关于是否以及如何在二次分析中使用PV的误解方法我们解决了一系列技术问题，包括最近写给那些使用这些数据库告知经济学家的文章中提出的技术问题。 。首先，对相关文献进行了广泛的回顾，特别要注意描述此类成就指标的派生和心理计量特征的主要出版物。其次，进行了仿真研究，以比较基于PV的使用和其他常用方法的估计值的统计属性。结果通过理论分析和模拟表明，在相当普遍的条件下适当使用在大规模调查数据的回归分析中，PV产生的模型参数近似无偏估计。当采用学生成绩的度量作为解释变量时，PV方法论的优越性尤其明显。结论结论在大规模调查中用于报告学生测试成绩的PV方法论仍是这些数据库二次分析的最新技术。