Let’s get to know some of the descriptive statistics. The first challenge is determining what kind of data you are dealing with. There are generally two main types of data, qualitative and quantitative.

Qualitative data is typically words, but could also be images or other media, we will refer to this data in this course as categorical. Qualitative data may be labeled with numbers allowing this type of data to be analyzed using some of the techniques in the course. Maria might encounter some qualitative data in her work by labeling some of the mental health diagnoses (depression might be a “1”; anxiety a “2”). Note how these numerical labels are arbitrary. On the other hand, quantitative data is the focus of this course and is numerical. If Maria counts the number of patients seen each day, this data is quantitative.

Quantitative variables may be discrete or continuous. Discrete variables can only take on a limited number of values (e.g., only whole numbers) while continuous variables can take on any value and any value between two values (e.g., out to an infinite number of decimal places).

Before we get too far along, let’s take a moment to think about what the word “variable” means. A variable, notice this is a noun, not a verb, is an element or a feature. In statistics, this is typically something that is measured or recorded. In Maria’s case, the “number of patients” is a variable, the mental health diagnosis is a variable.

To summarize:

If a child admitted to Maria’s program is weighed upon admission, this weight is a quantitative variable because it takes on numerical values with meaningful magnitudes. It is a continuous variable because, theoretically, weight could take on any value. Any value between any two values is a possibility.