Qualitative data is any set of data, including text, images, and video, that expresses the subjective and interpretive qualities of an item or process. Unlike quantitative data, which can be easily measured and expressed numerically, qualitative data often results in data sets with fewer straightforward methods of measurement and analysis.
For example, the training programs that most interest employees and the typical career background of a retailer’s top buyers are considered qualitative data. Obtaining this information can be straightforward, but determining how it can be used or how it affects business processes will take further analysis.
Portions of this definition originally appeared on Datamation.com and are excerpted here with permission.
How is it collected?
Qualitative data can be collected in many different ways—sometimes even unintentionally. From interview responses to conversation topics, qualitative data can take on various forms that can then be collected and interpreted based on a set of parameters for analysis.
Some of the most common ways to collect qualitative data include:
- Asking open-ended survey questions or questionnaires
- Asking questions during an interview
- Forming focus and discussion groups through Socratic seminars
- Reviewing existing information, particularly about customers and prospects in a CRM database.
How are qualitative and quantitative data different?
Where quantitative data involves a set of data that can be measured and expressed in numerical values, qualitative data involves a varying range of data types that express more subjective and interpretive qualities often dealing with a surveyed population or data set.
Quantitative data can be limited in its ability to convey important nuance in that it must be expressed as a quantity rather than a word or open-ended response. However, not all numerical data can be considered quantitative data.
For example, while phone numbers contain numerical data, the meaning of the data is still subjective and would need to be analyzed from a qualitative perspective. It relates to only one person and can fluctuate or be interpreted differently by a different collector of this data, depending on who you ask and when or how you ask them to obtain the data.
Moreover, a phone number contains no real “value” to count, measure, or include in statistical analysis. Analyzing data from a quantitative perspective won’t necessarily produce valuable insight into what those numbers mean.
Some types of data can also be confused with quantitative data. For example, categorical data involves thematically grouping collected qualitative data into categories. While the categories and data within can now be counted, it would still be considered qualitative since the groups are measured and categorized based on an interpretation of open-ended responses. The data is still “words,” or qualities rather than numbers.
Learn more about qualitative data’s role in business, including the crucial role it plays in mapping the user journey at Datamation.com.