Categorical variables are used to quantify phenomena, group membership, and frequency of events
For example, mathematical equations cannot work using the "levels" of a categorical variable denoted as "male" and "female." Algebra and statistical software programs have NO IDEA what a "male" or a "female" is in real life. However, algebra and computers do understand numbers and logic.
Thus, for this gender variable, researchers can categorize everyone that is a "male" as a "0" and everyone that is a "female" as a "1."
The "0" and the "1" are known as "levels" of the categorical in that they are independent of each other (or different).
The software program will now be able to differentiate between groups. This is especially important when conducting between-subjects statistics.
Second, categorical variables are statistically analyzed using less powerful non-parametric statistics, which lead to less precise and accurate inferences that can be made back to the population.
Third, measurement error increases with categorical variables because participants may be mis-classified into one of the categories due to systematic or unsystematic error associated with categorization.
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