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We briefly outline how association measures of segregation and their flip side of diversity can be useful in biology.
Positive applications of these indices are sometimes known as empirical measures of segregation, equality, or inequality [15].
Many of the above measures of segregation are normative, i.e. they describe what ought to be, such as equality in pay or employment across races, sexes, or other categories.
Second, when extending to multi-way indices, not only is the highest order index important, but the lower order indices – which are projections or aggregates of variables – also provide useful measures of segregation.
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One classic measure of segregation is the likelihood that a black student will have classmates who are white.
We show that the formation of iodine (S) under the influence of an acid (B) in an alkaline medium (A) is a measure of segregation.
While these methods allow for hypothesis testing of independence, they do not provide a single scalar measure of segregation or diversity.
Chiefly for this reason, we limited our measure of segregation to (un evenness.
We were also able to examine a measure of segregation in addition to household poverty as a contextual variable.
Because of racial disparities in income, there was a strong positive correlation (0.83) between household poverty rate and this measure of segregation.
We found, though, that due to the substantial correlations between household poverty rate and our measure of segregation that there was no advantage to including both community contextual measures in our final models.
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