The Spectrum from Qualitative to Quantitative Data

Data are information in the form of numbers, words, symbols, sounds, and/or images. Data are usually organised in such a way that they may be stored, summarised, communicated, and interpreted. The source of data is some type of research, be it experimentation, survey research, or observation.

There are three categories of data used to create statistics and indicators to inform policy processes: story data, categorical data, and numeric data.

Story data are data in the form of stories, statements, written documents, or other non-numeric forms. Such data are the most "qualitative" of the three categories of data given above, and qualitative methods must be used to analyze them. However, it is possible to convert story data into categorical data through a controlled vocabulary and coding procedure.

Categorical data are data that take a finite set of values that can be either numeric or categorical. For example, eye color, ethnicity, and country of residence are all possible types of categorical data that are not numeric (the categories can not be ordered meaningfully from a "lesser" to a "greater" value). Statisticians often refer to categorical data as "qualitative data," while social scientists might consider categorical data to be "quantitative."

Numeric data are data that exist in numeric form, such as height, the number of children in a household, and annual income. Numeric data is most definitely quantitative, and can be either “continuous,” such as time, or income, or “discrete,” such as number of children. It is almost always possible to convert numeric data into categorical data. For example, the number of children in a household can be binned into three categories: none, 1-4, or 5 or more.

Both categorical data and numeric data can be used to create statistics.

In analysing a complex concept or situation, a combination of story data and categorical/numeric data can be more effective than either by itself. While categorical and numeric data tend to illustrate and summarise overall trends and patterns, story data can add detail to the "picture" and explain the causes of some of those trends and patterns. In that way, a researcher can view both the "forest," the overall trends given by the categorical and numeric data, and the "trees," the details and richness provided by the story data, and create the best possible analysis.