Qualitative Data

Qualitative data refers to non-numerical information that describes qualities or characteristics. It is often collected through interviews, observations, and open-ended survey questions. Unlike quantitative data, which focuses on numbers and statistics, qualitative data captures opinions, feelings, experiences, and motivations, providing rich insights into social phenomena and human behavior.

Data collected from the world around us can be categorized as either qualitative or quantitative. Quantitative data includes any information that can be quantified, counted, or measured and is expressed as numerical values. These quantitative values can be subjected to mathematical analysis. In contrast, qualitative data is descriptive in nature and typically conveyed through language rather than numbers. 

Qualitative data describes characteristics based on observation, such as appearance, texture, color, smell, and taste. Although numerical labels or scales can sometimes be assigned to qualitative data, these values cannot be used in mathematical operations like quantitative data.  Qualitative data is also referred to as categorical data since the researcher is categorizing observations based upon either nominal or ordinal scale.

Nominal Data 

Nominal data is a type of categorical data used for labeling or naming variables without any quantitative value. This type of qualitative data is used when labeling variables is required. Nominal data does not have any order, so you can rearrange it and nothing changes. These are observed variables, not measured.  There is no meaningful zero in nominal data. 

Examples of Nominal Data: 

• What language do you speak?  English, French, Punjabi …
• What is your nationality? American, Indian, Japanese …

Ordinal Data

This is similar to nominal data, but it can be ordered (1st, 2nd, …).  There is no continuity between the variables (they do not exist on a continuum).  Ordinal data is observed, not measured.  Ordinal data can be found in Likert Scale surveys measuring things like happiness, satisfaction, etc. 

Examples of Ordinal Data: 

• Option – agree, disagree, mostly agree, neutral, mostly disagree
• Time of Day – morning, noon, night …
• Education Levels – high school, bachelor’s, master’s, Ph.D.