Research Study of the LSAMP Program

美国国家科学基金会（NSF）拥有多样化的项目组合，旨在通过促进科学、技术、工程和数学（STEM）教育，在推进发现和知识方面产生更广泛的社会影响；扩大弱势群体对STEM的参与；通过院校、工业界和其他方面的合作，加强研究和教育的基础设施；传播知识。路易斯·斯托克斯少数民族参与联盟（LSAMP）计划和LSAMP通往博士学位的桥梁（BD）完全符合这些目标，本项目中提出的活动也是如此。LSAMP旨在通过建立合作来扩大STEM的参与，以增加获得STEM学位和攻读研究生学习的少数民族学生的代表性。这项研究产生的证据和知识将有助于NSF制定计划决策，并将使LSAMP和类似的计划受益。首先，这项研究将帮助NSF确定今天的LSAMP计划与当前NSF优先事项一致的程度。第二，通过比较LSAMP监测数据系统和国家学生信息交换中心（NSC）的毕业数据，本项目将衡量通过监测数据系统收集的数据所产生的估计的质量和有用性。这将有助于LSAMP向受资助者提供指导并改进其数据收集方法。最后，鉴于缺乏严格的证据证明奖学金是否有助于增加STEM中代表性不足群体的参与，LSAMP-BD的子群体分析将有助于为NSF的决策提供信息并建立知识库。该研究的发现可能还会对NSF以外的机构和基金会产生影响，特别是通过提供类似项目有效性的证据，以及使用监测数据系统和对受助者实施报告要求的指导，来建立和多样化STEM劳动力。这项研究将扩展LSAMP和LSAMP- bd的早期评价。它包括三个不同的组成部分。首先，LSAMP纵向数据分析将关注LSAMP项目随时间的演变和特征，包括提供的活动和联盟、机构、教师和学生的特征。这种主要是描述性和时间序列分析将利用人口普查方案监测数据系统的人口数据。第二个评估部分将侧重于在LSAMP计划参与者中验证毕业数据的质量和这些数据的准确性。这将涉及比较各院校通过LSAMP监测数据系统报告的毕业结果与向国家安全委员会报告的结果。该分析将基于LSAMP参与者的分层随机样本，并将依靠标准t-检验和卡方检验来估计两个数据源产生的估计值之间的显著差异。第三个评价组成部分，即BD分组影响分析，将以最近的BD评价为基础，按性别分析影响，并为研究生水平的研究做准备。将通过测量参与者组（接受双相障碍奖学金的组）与匹配对照组（未提供奖学金并使用倾向评分匹配技术匹配的组）的平均结果（经回归调整）差异来评估影响。这些影响将使用线性概率模型进行估计，以模拟在获得博士学位的道路上达到关键里程碑的概率，包括毕业。

The National Science Foundation (NSF) has a diverse portfolio of programs geared toward having broader societal impacts in terms of advancing discovery and knowledge by promoting education in science, technology, engineering, and mathematics (STEM); broadening participation of underrepresented groups in STEM; enhancing infrastructure for research and education through collaborations between institutions, industry, and others; and disseminating knowledge. The Louis Stokes Alliances for Minority Participation (LSAMP) Program and the LSAMP Bridge to the Doctorate (BD) fit perfectly within these goals, as do the activities proposed in this project. LSAMP aims to broaden STEM participation by building collaborations to increase the representation of minority students receiving STEM degrees and pursuing graduate study. The evidence and knowledge generated by this study will help NSF make programmatic decisions and should benefit LSAMP and similar programs. First, this study will help NSF determine the extent to which today's LSAMP Program aligns with current NSF priorities. Second, by comparing graduation data from the LSAMP monitoring data system and the National Student Clearinghouse (NSC), this project will gauge the quality and usefulness of estimates generated from data collected through the monitoring data system. This will help LSAMP provide guidance to grantees and improve its approach to data collection. Last, the subgroup analysis of LSAMP-BD will help inform NSF's decisions and build the knowledge base, given the lack of rigorous evidence on whether fellowships help increase the participation of underrepresented groups in STEM. The study's findings may also have an impact beyond NSF,particularly in agencies and foundations working to build and diversify the STEM workforce by providing evidence on the effectiveness of similar programs and guidance on using monitoring data systems and implementing reporting requirements for grantees. This study will expand on earlier evaluations of LSAMP and LSAMP-BD. It includes three distinct components. First, the LSAMP longitudinal data analysis will focus on the evolution and characteristics of the LSAMP Program over time, including activities offered and characteristics of the alliances, institutions, faculty, and students. This mostly descriptive and time-series analysis will draw on population data from the LSAMP monitoring data system. The second evaluation component will focus on verifying the quality of graduation data and the accuracy of these data among LSAMP Program participants. This will involve comparing graduation outcomes reported by institutions through the LSAMP monitoring data system with those reported to the NSC. This analysis will be based on a stratified random sample of LSAMP participants and will rely on standard t- and chi-squared tests to estimate significant differences between estimates generated from the two data sources. The third evaluation component, the BD subgroup impact analyses, will build on the recent BD evaluation by analyzing impacts by gender and prior preparation for graduate-level studies. Impacts will be estimated by measuring the (regression-adjusted) difference in average outcomes for the participant group (those receiving the BD fellowship) versus a matched comparison group (those not offered the fellowship and matched using propensity-score matching techniques). These impacts will be estimated using linear probability models to model the probability of reaching key milestones on the path to a Ph.D., including graduation.