SGER: Examining the Feasibility of Developing a "Scalability Index"

本研究将探讨开发量化指标的可行性，以衡量创新的相对可扩展性。研究结果通常表明，背景变量在形成干预措施的可取性、实用性和有效性方面具有重大影响。制定一项量化战略，以评估在理想条件下成功的创新在相对不利的环境中推广的相对容易程度，这将有助于许多类型的教育改进。正如在已发表的实验中估计标准化效应大小，这些实验使用了各种统计技术来确定其原始发现，已被证明是比较不同干预措施的相对教育功效的有用方法，因此，估计创新的相对可扩展性的类似跨项目指数对个人研究者、从业者和政策制定者具有潜在价值。识别干预措施背景中代表成功的重要条件的因素，并总结干预措施的效果对每个因素变化的敏感程度，将使创新的潜在采用者更好地了解其在自己特定情况下的可能效果。创建一个可行的可扩展性指数的关键的第一步是仔细地说明一个环境因素的合理框架，这些因素代表了教育创新成功的可能的一般条件。对于许多类型的创新，一组相对较小的背景因素往往在决定有效性方面非常有影响力。本项目将创建所有可能的上下文因素的子集，并研究其在实际数据中的效用。其核心是，评估干预对选择上下文条件的影响的敏感性是一个统计相互作用的问题。在一项研究中，这些问题通常通过纳入治疗与其成功条件之间相互作用的统计模型来解决。因此，创建真正的可扩展性指数的一种方法是，确保将这种相互作用包括在统计模型中，这些模型支持用于评估教育干预实施情况的数据分析。在每种相互作用条件下预期的干预措施的几个效应值可以汇集成一个全局可扩展性指数，该指数捕获了干预措施的效应值对成功条件变化的敏感程度。然而，在实践中实施这种方法的重要技术挑战可能使这种方法不可行。这个活动的智力价值在于它检验了量化可伸缩性的统计方法。无论可伸缩性索引是否可行，通过尝试生成此指标所获得的见解都将告知其他计划，以便根据实现数据开发可推广的指标。如果可扩展性指数被证明是有效的，并且在实现创新所产生的数据集类型下是实用的，那么它对该领域的广泛影响和意义是三重的：一项创新的潜在采用者将对其在自己的特定情况下可能产生的效果有更好的认识。政策制定者将更好地了解哪些创新在适应广泛环境的潜力方面最有希望；研究人员可以对比不同类型创新的可扩展性，从而获得如何改进设计和实施的见解。

This study will examine the feasibility of developing a quantitative index that measures the relative scalability of an innovation. Research findings typically show substantial influence of contextual variables in shaping the desirability, practicality, and effectiveness of interventions. Developing a quantitative strategy for assessing the relative ease with which innovations successful under ideal conditions can scale up into relatively inhospitable settings will aid in many types of educational improvement.Just as estimating standardized effect sizes across published experiments that have used all manner of statistical techniques for ascertaining their original findings has proven a useful way of comparing the relative educational efficacy of different interventions, so a similar cross project index that estimated the relative scalability of innovations is of potential value to individual investigators, to practitioners and to policy makers. Identifying factors within the intervention's context that represent important conditions for success and summarizing the extent to which the effect of the intervention is sensitive to variation in each will provide prospective adopters of the innovation a better sense of what its likely effectiveness would be in their own particular circumstances.An initial step that is essential to creating a viable scalability index is the careful specification of a sensible framework of contextual factors that represent possible general conditions for success of educational innovations. For many types of innovations, a relatively small set of contextual factors are often very influential in determining effectiveness. This project will create such a subset of all possible contextual factors and investigate its utility in real data. At its core, the evaluation of the sensitivity of an intervention's impact to select contextual conditions is a question of statistical interactions. In a single study, such questions are usually addressed by the inclusion in the statistical models of interactions between the treatment and its conditions for success. One approach, then, to the creation of a true scalability index is to ensure that such interactions are included in the statistical models that underpin the data-analyses conducted to assess the implementation of educational interventions. The several effect sizes anticipated for the intervention under each of the interacting conditions can be pooled into a global index of scalability that captures the extent to which the intervention's effect size is sensitive to variation in the conditions for success. However, important technical challenges to implementing this approach in practice may render this method infeasible.The intellectual merit of this proposed activity is its examination of statistical approaches to quantifying scalability. Whether or not a scalability index is feasible, the insights gained by attempting to produce this metric will inform other initiatives to develop generalizable metrics from implementation data. If the scalability index proves valid and is practical given the types of datasets produced by implementations of innovations, its broader impact and significance to the field is threefold:1. prospective adopters of an innovation will have a better sense of what its likelyeffectiveness would be in their own particular circumstances, 2. policymakers will have a better sense of which innovations are most promising in their potential for adaptability to a wide range of settings, and3. researchers can contrast the scalability of various types of innovations, thereby gaininginsights into how to improve design and implementation.