The relationship of race, class, and gender with mathematics achievement for fifth‐, eighth‐, and eleventh‐grade students in Pennsylvania schools

Kohr, Richard L.; Masters, James R.; Coldiron, J. Robert; Blust, R.; Skiffington, Eugene W.

doi:10.1080/01619568909538643

Cited by 6 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consistent with previous research (Backman, 1979;Dulaney & Bethune, 1995;Entwisle & Alexander, 1992;Kohr et al, 1989), a Caucasian superiority was found in performance on test questions measuring the understanding of CONCPT. Inconsistent with previous findings, though, an African American superiority was found in the COMPUT skill area.…”

Section: Discussionsupporting

confidence: 90%

Computational Mathematical Abilities of African American Girls

Bauer

1999

Journal of Black Studies

View full text Add to dashboard Cite

The role of ethnicity in predicting students' academic performance continues to attract attention from scholars and policy makers. One reason for the continued interest in whether there is a racial gap in mathematical performance relates to minorities'underrepresentation in highly technical professions (Campbell, 1989; Grandy, 1994). The underrepresentation of minorities in mathematicsrelated careers is believed to have a close relationship to their low academic achievement in mathematics (Campbell, 1989; Maxwell, 1994). However, when racial differences are investigated without taking socioeconomic status (SES) into consideration, there is a likelihood that race and SES are confounded (Kohr, Masters, Coldiron, Blust, & Skiffington, 1989). This is reflected by a disproportionate number of minority group members who are low in SES and, similarly, a disproportionate number of majority group members who are high in SES. The end result of ignoring SES when comparing the behavior of Caucasians and African Americans can be the unintentional comparison of economically advantaged Caucasian students with economically disadvantaged African American students. Thus, a simple comparison based on the number of majority and the number of minority of the science and engineering workforce (e.g., African Americans accounted for about 2% of all employed scientists and engineers) (Campbell, 1989) can certainly lead to misconceptions about racial differences. To respond more properly to the racial difference questions, the racial gaps on mathematics performance were examined after subjects were 204

show abstract

Section: Discussionsupporting

confidence: 90%