Why cut into thirds or fourths instead of dichotomizing?

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'''Why cut into thirds or fourths instead of dichotomizing?''' * When you cut the variable into thirds (or fourths, or whatever), your new categorical variable only includes the top and bottom third. Why is this advantageous? For one, sometimes you are only interested in the more polarized decisions, and not those undecided people in the middle. * For another, sometimes you can strengthen the relationship between your variables by only including the polarized judgments. * Plus, think about why dichotomizing continuous variables results in reduced information and reduced statistical power. In the newly created categorical variable, subjects in the continuous variable who are near the middle are now the same as subjects near the top/bottom after you dichotomize the variable. [[Image:Fe40.png]] - In a 100 point scale for example, the subjects who respond 49 and 51 are treated the same as the subjects who respond 0 and 100, respectively. Thus, you are reducing your ability to detect true relationships in the study because the subjects close to the middle may be masking relationships amongst your variables by diluting the strength of the high/low categories in the variable. Eliminating the middle third when you cut the continuous variable in thirds is one way to create a categorical variable while minimizing your loss of power. ---- ◄ Back to [[Analyzing Data]] page

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