Data Literacy

Qualitative and Quantitative Data Explained

Since data can take various forms, understanding the differences and intersections of qualitative and quantitative data can help you produce data-informed insights. 

Qualitative data are data that have no numerical value but rather represent categories or concepts. For example, someone's affiliation to the university (student, staff, faculty) would be considered qualitative information. Though mathematical calculations cannot be used on qualitative data, descriptive statistics such as frequency can describe qualitative data. Nominal data and ordinal data are the two qualitative data types.

Nominal Data

Nominal data are data that represent categories and have no quantitative (numeric) value or natural ordering. For example, someone's nationality would be considered nominal data: American, British, French, etc. This data type can be described statistically in terms of frequency. Other examples of  nominal data include hair color, religion, sexual orientation, college major, and preferred cell phone carrier.

Ordinal Data

Ordinal data are data that can be categorized and has a natural ordering but it has no quantitative (numeric) value. For example, someone's final place in a race would be considered ordinal data: first, second, third, etc. This data type can be described statistically in terms of frequency. Other examples of ordinal data include academic grades (A,B,C,D,F), education degree level (Bachelor's, Master's, Doctoral), and satisfaction rating (extremely dissatisfied, dissatisfied, neutral, satisfied, extremely satisfied). 

Qualitative data are data that have numerical value and can be processed using statistical methods. For example, someone's shoe size would be considered qualitative information. Qualitative data can be further divided into discrete data, continuous interval data, and continuous ratio data.

Discrete Data

Discrete data are data that has quantitative (numeric) value and can be counted, not measured. For example, the total number of cars parked on campus would be considered discrete data. This data type can be described statistically in aggregate -- as a single number, for example, there may be 50 cars parked on campus. This aggregate number can be subdivided based on a categorical feature -- there are 10 blue cars, 30 red cars, and 10 green cars. You can run more advanced statistical tests on discrete data if you observe the data over time. With this longitudinal view, you may ask the average cars parked on campus over a given temporal period (day, week, etc.). Other examples of discrete data include the number of students in a classroom, the number of computers in a given space, and the number of light fixtures in a commercial property.

Continuous Data

Continuous data are data that have quantitative (numeric) value but are measured instead of counted. For example, someone's height would be considered continuous data. When continuous data is observed and collected in a dataset, statistical procedures can be used on the determine the mean, mode, range, standard deviation, and other statistical characteristics of the data. Other examples of continuous data include temperature in a given space, time taken to complete a task, and length of a film. Continuous data can be converted without changing the value itself --> 3 feet is the same as 1 yard.

Continuous data can also be further divided into interval and ratio data. The difference between these types is that interval data can be represented by values less than zero (temperature, for example) while ratio data cannot. Temperature is an example of interval data whereas height is an example of ratio data.