# Categorical variables

## Categorical variables are used to quantify phenomena, group membership, and frequency of events

Variables measured at this scale of measurement are considered the "weakest" method to assess phenomena. Categorical variables are also often called

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 "

The software program will now be able to differentiate between groups. This is especially important when conducting between-subjects statistics.

**nominal**variables. When looking at the word itself, categorical, one can find the most important part of understanding this scale of measurement. All that researchers are doing is creating a unique number for each participant that dictates whether**they do or do not possess****a certain characteristic**. Thus, researchers are putting participants into numerical categories. The same holds true for "nominal," where numbers are used to give names to categories.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.

While categorical variables allow for researchers to measure for important variables in statistics, they have some inherent faults. First, categorical variables allow for very

Second, categorical variables are statistically analyzed using

Third, measurement error increases with categorical variables because participants may be mis-classified into one of the categories due to

**little variation**between your study participants. Researchers are essentially forcing them into different categories. When there is little chance for variation, or being in the "grey area" between two levels of a categorical variable, then the precision and accuracy of measurement decreases drastically.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.**Categorical measurement is used for group membership, characteristics, and outcomes**

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