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英文文献：Econometric Assessment of Research Programs: A Bayesian Approach-研究计划的计量经济评估:贝叶斯方法
英文文献作者：Qin, Lin,Buccola, Steven T.
英文文献摘要：
Effective research-project assessment typically is impeded by project variety. In particular, bibliometric approaches to science assessment tend to offer little information about the content of the projects examined. We introduce here a new approach – based on Bayesian theory – of econometrically evaluating the factors affecting scientific discovery, and use the method to assess a biological research program comprised of numerous heterogeneous projects. Our knowledge metric not only flexibly accommodates project variety but accounts for information in “failed” as well as “successful” studies. Using a mean-absolute-deviation utility functional form to measure new scientific knowledge, we decompose knowledge gain into a mean-surprise and statistical-accuracy effect. The two effects are econometrically examined independently, and then combined into the net knowledge production function. Research FTE and distance to study site have statistically significant but moderate effects on the amount by which research shifts the prediction of scientific outcome. However, scientist education powerfully improves the research study’s predictive accuracy or precision, a one-percent boost in the average investigator’s formal schooling improving precision by 4.3 percent. Largely on the basis of that precision effect, increasing returns to research project scale are evident.
有效的研究项目评估通常受到项目多样性的阻碍。特别是，科学评估的文献计量方法往往无法提供有关所审查项目内容的信息。我们在这里介绍了一种新的方法-基于贝叶斯理论-计量评估影响科学发现的因素，并使用该方法来评估由许多异质项目组成的生物研究计划。我们的知识度量不仅灵活地适应了项目的变化，而且也解释了“失败”和“成功”研究中的信息。利用均值-绝对-偏差效用函数的形式来度量新的科学知识，将知识增益分解为均值-惊奇效应和统计-准确度效应。对这两种效应分别进行计量分析，并将其合并到网络知识生产函数中。研究时间和到研究地点的距离对研究改变科学结果预测的数量有统计学上显著但中等的影响。然而，科学家教育有力地提高了研究结果预测的准确性或精确度，研究者的正规教育水平每提高1%，预测的准确性就会提高4.3%。在很大程度上，基于这种精确效应，研究项目规模的增加回报是显而易见的。
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