# Why transform continuous variables into categorical variables?

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 Revision as of 02:55, 28 May 2008 (view source)Doug (Talk | contribs)← Older edit Revision as of 02:56, 28 May 2008 (view source)Doug (Talk | contribs) Newer edit → Line 1: Line 1: - + '''It is possible to transform continuous variables into categorical variables''' - + * For example, imagine a study about happiness where your happiness question (or composite) ranges from 1 to 7. You might be interested in categorizing the subjects as either high happiness (4 through 7 on the scale) or low happiness (1 through 4 on the scale). This is called "dichotomizing" the variable because you are creating a new variable that has only two options. - + * Another example of why you would want to transform a continuous variable into a categorical variable is if there are only a few responses on some of the answer choices in the continuous variable. For example, imagine a scale range from 1-11 in which answer choice 4 and/or answer choice 9 received only 1 response each. 1 response is not enough data for meaningful interpretation. You may want to collapse the 11 point scale into 3 or 4 categories. As another example, look at the “rel_category” in our dataset which measures the religious category memberships of the subjects. The frequency distribution is listed on the next page. Hindu received only 6 responses, and Jewish received only 9 responses. You may want to merge those responses into “other” and/or merge all the data into “Christian” versus “other”. Notice that creating the new categorical variable is answering a different research question than the original categorical variable. ---- ---- ◄ Back to [[Research_Tools |Research Tools mainpage]] ◄ Back to [[Research_Tools |Research Tools mainpage]]

## Revision as of 02:56, 28 May 2008

It is possible to transform continuous variables into categorical variables

• For example, imagine a study about happiness where your happiness question (or composite) ranges from 1 to 7. You might be interested in categorizing the subjects as either high happiness (4 through 7 on the scale) or low happiness (1 through 4 on the scale). This is called "dichotomizing" the variable because you are creating a new variable that has only two options.
• Another example of why you would want to transform a continuous variable into a categorical variable is if there are only a few responses on some of the answer choices in the continuous variable. For example, imagine a scale range from 1-11 in which answer choice 4 and/or answer choice 9 received only 1 response each. 1 response is not enough data for meaningful interpretation. You may want to collapse the 11 point scale into 3 or 4 categories. As another example, look at the “rel_category” in our dataset which measures the religious category memberships of the subjects. The frequency distribution is listed on the next page. Hindu received only 6 responses, and Jewish received only 9 responses. You may want to merge those responses into “other” and/or merge all the data into “Christian” versus “other”. Notice that creating the new categorical variable is answering a different research question than the original categorical variable.

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