Analyzing #s for an External Evaluation

I’m conducting an external evaluation for a college’s NSF grant. This is in a high-demand field (automotive and diesel), so I found it puzzling that their reported job placement rate was only 43%. After discussing with their Workforce Development Coordinator, I discovered the calculation included non-graduates in the total count - adjusting to count only graduates revealed a 100% placement rate —a big difference.

The college's internship placement showed only 23% success. Upon analyzing the data, I suggested separating first-year and second-year student statistics, since the program gives preference to second-year students for work-based learning. This breakdown showed improved metrics: 40% placement for second-year students. While the program aims to provide internships for all students, it's important to note this grant is only in its second year.

Could your program's impact be understated due to incorrect data calculations? Throughout my career spanning over ten years, I've developed specialized data dashboards enabling schools to track enrollment, retention, persistence, and employment outcomes.

I've authored the WomenTech Gender Equity School Assessment, supported by the National Science Foundation. This tool allows schools to evaluate their capacity for recruiting and retaining female students in STEM/CTE programs in just 20 minutes.

During my Plan Revision workshops, I guide institutions in evaluating their successes and challenges to create data-driven improvement strategies.

As a National Science Foundation (NSF) Principal Investigator of five grants, my CalWomenTech Project earned NSF recognition for exceptional achievement and effectiveness, as determined by an expert panel review for the Committee for Government Performance and Results Act (GPRA) Performance Assessment.

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P.S.: I may be able to offer expedited evaluation services depending on your timeline.