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Normalizing Mix Variables in Financial Data
Suppose researchers studying obesity in college students find that engineering majors have a higher average body weight than liberal arts majors. Obviously math and engineering courses aren't more caloric than English and history courses, but it's tempting to start comparing donut consumption in the engineering and liberal arts cafeterias.
Not so fast. It turns out that 80% of the engineering students are male, while only 50% of the liberal arts majors are male. Since the average male weighs considerably more than the average female, the researchers' finding merits further analysis only if a weight difference still exists after factoring out gender.
These researchers have just encountered a 'mix' issue ' the possibility that an aggregate difference between two data groups stems from varying proportions of component subgroups rather than from changes in the individuals within those subgroups. If relevant subgroup variables are not identified and normalized, the corresponding apples-to-kiwis analysis may lead to wildly wrong conclusions.
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