Same goes for age when age is transformed to a qualitative ordinal variable with levels such as minors, adults and seniors. It is also often the case especially in surveys that the variable salary quantitative continuous is transformed into a qualitative ordinal variable with different range of salaries e.
The reason why we often class variables into different types is because not all statistical analyses can be performed on all variable types. On the other hand, finding the mode of a continuous variable does not really make any sense because most of the time there will not be two exact same values, so there will be no mode.
And even in the case there is a mode, there will be very few observations with this value. As an example, try finding the mode of the height of the students in your class. If you are lucky, a couple of students will have the same size. However, most of the time, every student will have a different size especially if heights have been measured in millimeters and thus there will be no mode.
Similarly, some statistical tests can only be performed on certain type of variables. For example, a correlation can only be computed on quantitative variables, while a Chi-square test of independence is done with qualitative variables, and a Student t-test or ANOVA requires a mix of quantitative and qualitative variables. Last but not least, in datasets it is very often the case that numbers are used for qualitative variables.
Despite the numerical classification, the variable gender is still a qualitative variable and not a discrete variable as it may look. The numerical classification is only used to facilitate data collection and data management. If you face this kind of setup, do not forget to transform your variable into the right type before performing any statistical analyses.
Usually, a basic descriptive analysis and knowledge about the variables which have been measured prior to the main statistical analyses is enough to check that all variable types are correct. Thanks for reading. Give an example of each: continuous data and discrete data. Which of the following would be classified as categorical data? How do you determine whether the quantitative variable is discrete or continuous given the See all questions in What is Statistics?
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Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions. Overview Pearson Education, Inc. Values are obtained by counting. They are represented by isolated points on the graph. They can whole number values in given range.
Catelogical Variable:. Categorical variables fall into mutually exclusive in one category or in another and exhaustive include all possible options categories. They tend to be represented by a non-numeric value. The data collected for a categorical variable are qualitative data. They may be further described as either ordinal or nominal:.
Ordinal Variable:. An ordinal variable is a categorical variable which can take a value that can be logically ordered or ranked. There is a clear ordering of the variables. The categories associated with ordinal variables can be ranked higher or lower than another, but do not necessarily establish a numeric difference between each category.
Examples of ordinal categorical variables include academic grades i. A, B, C , clothing size i. If these categories were equally spaced, then the variable would be an interval variable. Nominal Variable:.
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