Quantitative Evaluation Based on Participants’ Qualitative Feedback -NSF’s I/UCRC Program, 1993-2009

Author(s):  
Hexuan Liu ◽  
Carl W. Roberts

Tucked away towards the end of survey instruments, open-ended questions are often only examined post hoc as researchers search for strategically-placed quotations in support of their otherwise quantitatively-derived conclusions. Yet program evaluations can benefit from participants’ spontaneous insights on how their program might be improved, because these insights commonly reveal previously unforeseen aspects needing support or discouragement. This paper introduces a semantic text analysis technique for the analysis of survey data generated from open-ended questions of the form, “How can our program improve?” The technique is illustrated using data from faculty-participants in a National Science Foundation (NSF) program for the development of greater cooperation between industrial and academic researchers.

2020 ◽  
Vol 6 (33) ◽  
pp. eaaz6300
Author(s):  
A. Lupia ◽  
S. Soroka ◽  
A. Beatty

The U.S. Congress writes the legislation that funds the National Science Foundation (NSF). Researchers who seek NSF support may benefit by understanding how Congress views the agency. To this end, we use text analysis to examine every statement in the Congressional Record made by any member of Congress about the NSF over a 22-year period. While we find broad bipartisan support for the NSF, there are notable changes over time. Republicans have become more likely to express concerns about accountability in how the NSF spends its funds. Democrats are more likely to focus on how NSF-funded activities affect education, technology, and students. We use these findings to articulate how researchers and scientific organizations can more effectively conduct transformative science that corresponds to long-term and broadly held Congressional priorities.


2000 ◽  
Author(s):  
Joseph L. Young ◽  
Rodney R. Cocking ◽  
Ann H. Bostrom ◽  
Fred Stollnitz

1997 ◽  
Author(s):  
Joseph Young ◽  
Fred Stollnitz ◽  
Michael McCloslcey

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