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Different denominators, different results: reanalyzing CS degrees by gender, race, and ethnicity. Author: Valerie Barr.
The numerator should be the number of degrees earned in CS by a cohort, while the denominator should be all degrees earned by that cohort, not all degrees ...
This chapter shows that preferences do not differ greatly when we separate students out by their race/ethnicity, gender, or socioeconomic background. All groups ...
Different denominators, different results: reanalyzing CS degrees by gender, race, and ethnicity. V Barr. ACM Inroads 9 (3), 40-47, 2018. 13, 2018. Cs+ x meets ...
Mar 18, 2024 · Different Denominators, Different Results: Reanalyzing CS Degrees by Gender, Race, and Ethnicity. ACM Inroads 9, 3 (September 2018), 40–47 ...
Different denominators, different results: reanalyzing CS degrees by gender, race, and ethnicity. Authors. Valerie Barr. Source Information. August 2018, Volume ...
Largest differences for groups which have a higher percentage in the computing higher education compared to the college age US population exist for Asian men, ...
Missing: denominators, reanalyzing
Demographic data can identify disparities that hinder participation [175], such as differences in access, retention, and achievement by gender and ethnicity [ ...
Mar 3, 2022 · Different denominators, different results: Reanalyzing CS degrees by gender, race, and ethnicity. ACM Inroads 9, 3 (Aug. 2018), 40–47. https ...
This paper explores these and other findings that ... Different denominators, different results: Reanalyzing CS degrees by gender, race, and ethnicity.