Research Study of the LSAMP Program

美国国家科学基金会（NSF）有一个多元化的项目组合，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STEM中代表性不足的群体的参与;通过机构，行业和其他人之间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博士学位桥梁（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参与，以增加获得STEM学位和攻读研究生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做出计划决策，并将使LSAMP和类似计划受益。首先，这项研究将帮助NSF确定今天的LSAMP计划与当前NSF优先事项相一致的程度。第二，通过比较LSAMP监测数据系统和国家学生信息中心的毕业数据，该项目将衡量通过监测数据系统收集的数据产生的估计数的质量和有用性。这将有助于LSAMP向受赠方提供指导，并改进其数据收集方法。最后，LSAMP-BD的亚组分析将有助于为NSF的决策提供信息并建立知识库，因为缺乏严格的证据来证明奖学金是否有助于增加STEM中代表性不足的群体的参与。该研究的结果也可能对NSF以外的机构和基金会产生影响，特别是通过提供类似计划的有效性的证据和使用监测数据系统和实施受赠方报告要求的指导，努力建立和多样化STEM劳动力。本研究将扩展LSAMP和LSAMP-BD的早期评估。它包括三个不同的组成部分。首先，LSAMP纵向数据分析将重点关注LSAMP计划随时间的演变和特征，包括提供的活动以及联盟、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LSAMP监测数据系统的人口数据。第二个评估部分将侧重于核实毕业数据的质量以及LSAMP计划参与者中这些数据的准确性。这将涉及将各机构通过地方标准化监测和监测方案监测数据系统报告的毕业结果与向国家指导委员会报告的毕业结果进行比较。该分析将基于LSAMP参与者的分层随机样本，并将依赖于标准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，BD分组影响分析，将在最近的BD评价的基础上，通过分析性别和研究生一级研究的先前准备情况的影响。将通过测量参与者组（接受BD研究金的人）与匹配的对照组（未获得研究金并使用倾向评分匹配技术匹配的人）的平均结果（回归调整）差异来估计影响。这些影响将使用线性概率模型来估计，以模拟在通往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.