keannu
VIP Member
- Joined
- Dec 27, 2010
- Member Type
- Student or Learner
- Native Language
- Korean
- Home Country
- South Korea
- Current Location
- South Korea
Source : Korean Education Broadcasting, English Reading 129p
Quite often, a party seeking to show statistical significance combines data from different sources to create larger numbers, and hence greater significance for a given disparity. Conversely, a party seeking to avoid finding significance disaggregates data insofar as possible. In a discrimination suit brought by female faculty members of a medical school, plaintiffs aggregated faculty data over several years, while the school based its statistics on separate departments and separate years. The argument for disaggregation is that pooled data may be quite misleading. A well-known study showed that at the University of California at Berkeley female applicants for graduate admissions were accepted at a lower rate than male applicants. When the figures were broken down by department, however, it appeared that in most departments the women’s acceptance rate was higher than the men’s. The reason for the reversal was that women applied in greater numbers to departments with lower acceptance rates than to the departments to which men predominantly applied. The departments were therefore variables that evidenced the association between sex and admission.
The two lines seem to be contradictory to each other. Does the former consider relative rate such as "accepted No of females/applied No of females", while the latter only "accepted No of females"?
Quite often, a party seeking to show statistical significance combines data from different sources to create larger numbers, and hence greater significance for a given disparity. Conversely, a party seeking to avoid finding significance disaggregates data insofar as possible. In a discrimination suit brought by female faculty members of a medical school, plaintiffs aggregated faculty data over several years, while the school based its statistics on separate departments and separate years. The argument for disaggregation is that pooled data may be quite misleading. A well-known study showed that at the University of California at Berkeley female applicants for graduate admissions were accepted at a lower rate than male applicants. When the figures were broken down by department, however, it appeared that in most departments the women’s acceptance rate was higher than the men’s. The reason for the reversal was that women applied in greater numbers to departments with lower acceptance rates than to the departments to which men predominantly applied. The departments were therefore variables that evidenced the association between sex and admission.
The two lines seem to be contradictory to each other. Does the former consider relative rate such as "accepted No of females/applied No of females", while the latter only "accepted No of females"?