In data analytics, qualitative research often relies on what type of data?

Qualitative research in data analytics emphasizes understanding concepts, experiences, or phenomena that cannot be easily quantified. It often relies on nominal data because this type of data is used to categorize and label variables without a specific order or ranking. Examples of nominal data include categories such as gender, race, or types of cuisine.

In qualitative research, the focus is on the quality of information rather than numerical measurements, allowing researchers to gather insights into participant experiences and viewpoints through interviews, open-ended surveys, or observational data. Nominal data helps in segmenting this information into meaningful groups while maintaining its categorical nature, which aligns well with qualitative analysis. This can help researchers explore and interpret patterns in human behavior and social context, further enriching the understanding in a qualitative framework.

The other types of data mentioned have different roles and are more suitable for quantitative research. For instance, ordinal data indicates a ranked order but does not provide a precise measurement scale. Quantitative data involves numerical values that can be analyzed statistically, while statistical data refers to data that has been subjected to statistical analysis and typically involves quantitative metrics. These characteristics make them less appropriate for the qualitative research objectives, reinforcing why nominal data is preferred in such contexts.