For example, the test scores on a standardized test are discrete because there are only so many values that can be obtained on a test. It would be impossible, for example, to obtain a 342.34 score on SAT.
Ordinal (ordered) variables, e.g., grade levels, income levels, school grades. Discrete interval variables with only a few values, e.g., number of times married. Continuous variables grouped into small number of categories, e.g., income grouped into subsets, blood pressure levels (normal, high-normal etc)
General rule of thumb: if you can add it, it's quantitative. For example, a G.P.A. of 3.3 and a G.P.A. of 4.0 can be added together (3.3 + 4.0 = 7.3), so that means it's quantitative.
Here are some examples of ordinal level data: Order of finish in a race or a contest. Letter grades: A, B, C, D, or F. Ranking of chili peppers on a scale of hot, hotter, hottest.
To my knowledge, GPA is generally considered an interval scale, so ordinal regression wouldn't be appropriate. There's no rule against conducting an analysis with both linear and nonlinear (polynomial) models and seeing which describes the data best.
For example, the variable " the number of children" is discrete and the variable " GPA" is continuous. Since GPA can take an infinite number of possible values, for example interval 0.0 to 4.0.
Ordinal, when there is a natural order among the categories, such as, ranking scales or letter grades.
At Five Below, the answer is YES!
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