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

本研究将探讨制定一个量化指标来衡量创新的相对可扩展性的可行性。研究结果通常表明，背景变量在塑造干预措施的可取性，实用性和有效性方面具有重大影响。开发一种定量策略来评估在理想条件下成功的创新在相对不友好的环境中推广的相对容易程度，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一样，这些实验使用了各种统计技术来确定其原始发现，这已被证明是比较不同实验的相对教育功效的有用方法。因此，一个类似的跨项目指数，估计创新的相对可扩展性，对个人调查人员，从业人员和决策者具有潜在的价值。在干预的背景下确定代表成功的重要条件的因素，并总结干预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有效性。背景因素的框架，代表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对确定有效性非常有影响。这个项目将创建这样一个子集的所有可能的上下文因素，并调查其效用在真实的数据。其核心是，评估干预措施对特定背景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，这些问题通常是通过在统计模型中纳入治疗与其成功条件之间的相互作用来解决的。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纳入统计模型，这些模型支持为评估教育干预措施的实施情况而进行的数据分析。在每个相互作用的条件下，预期的干预措施的几个效应量可以汇集到一个全球可扩展性指数中，该指数可以捕捉干预措施的效应量对成功条件变化的敏感程度。然而，在实践中实施这一方法所面临的重大技术挑战可能使这一方法不可行，这一拟议活动的智力价值在于审查量化可伸缩性的统计方法。无论可伸缩性指标是否可行，通过尝试产生此指标所获得的见解将为其他计划提供信息，以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，并且考虑到创新实施所产生的数据集的类型，它对该领域的更广泛的影响和意义是三方面的：1。创新的潜在采用者将更好地了解其在自己特定环境中的可能有效性，2.政策制定者将更好地了解哪些创新在适应各种环境方面最有希望，以及3.研究人员可以对比不同类型创新的可扩展性，从而深入了解如何改进设计和实施。

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.