Qualitative data analysis is an essential aspect of many research projects. However, the term "qualitative data" can mean different things to different people, depending on their field of study and the methods they use.
Structured data is often used by statisticians and allows for the categorization and ranking of data. Unstructured data comes from sources like interviews and social media and is typically used by researchers. Learn more about both types of data, examples of qualitative data, and their uses below.
Unstructured Data
Subcategories: Textual data, video, audio, images
Examples: Interview transcripts, Observations, literature, social media
Qualitative data can first be defined as any unstructured data that can be collected through interviews, and open-ended questions in surveys, tweets, etc. as well as secondary data, such as journal articles, company reports, and webpages. What all these types of data have in common is that they are unstructured. There are a variety of methods that can be used for content analysis with this type of qualitative data such as thematic analysis, grounded theory, narrative analysis, conversational analysis, to name just a few.
Structured Data
Subcategories: Nominal data, ordinal data
Examples: Gender, hair color, groups, priority status (low, medium, high)
For statisticians, qualitative data is a synonym for categorical data which is structured and can only take a finite number of values – the categories. These categories can be nominal which means there is no inherent order to them, or ordinal, which means that the categories have a natural order. This type of qualitative data is usually analyzed or modeled using methods such as Multiple Correspondence Analysis (MCA) or Supervised Machine Learning tools in classification problems for example.
Two Worlds for a Same Name
Structured and unstructured qualitative data differ in terms of their organization and the methods used to analyze them. While they may be used together to gain a more comprehensive understanding of a phenomenon, it is important for researchers to understand the differences between them to choose the most appropriate methods for collecting and analyzing data.
Content Analysis with Qualitative Data Software
Whether you’re analyzing structured or unstructured data, qualitative data analysis software like NVivo can help streamline the process. With NVivo data analysis, you can upload unstructured data such as interview transcriptions to then autocode for themes and sentiment. Using frequency queries is another time-saver technique that both statisticians and researchers can apply with NVivo as it considers data like gender and demographic.
Plus, with the crosstab query, you can quickly check the spread of coding across cases and demographic variables. For example, you can use the crosstab query to see how often interview respondents refer to a particular topic or issue or compare what different demographic groups have said about a theme.
Listen to our podcast episode Navigating Inductive Content Analysis in Qualitative Research or read the article summary to learn more about content analysis with NVivo qualitative data analysis software (QDA).
Want to learn more about thematic analysis and grounded theory? Check out these articles!