Nature: This data is subjective and rich in detail. It is typically gathered through open-ended interviews, focus groups, or case studies where participants can express themselves freely.
Strengths: High external validity. It captures the complexity of human experience in real-world settings, providing 'thick description' that numbers cannot convey.
Weaknesses: It is difficult to generalize. Because the data is unique to the individual or small group, it is hard to apply the findings to a wider population with statistical confidence.
| Feature | Qualitative Data | Quantitative Data |
|---|---|---|
| Goal | To understand underlying reasons | To quantify and generalize |
| Sample | Small, non-representative | Large, representative |
| Analysis | Thematic or Content Analysis | Statistical Analysis |
| Outcome | Exploratory / Hypothesis generating | Conclusive / Hypothesis testing |
The 'Counting vs. Measuring' Rule: To distinguish between discrete and continuous data, ask if the value is counted (Discrete) or measured (Continuous). If you can have of the unit, it is likely continuous.
Identifying Data Types: In exam scenarios, look at the output. If the result is a transcript or a diary entry, it is qualitative. If the result is a score, a percentage, or a time, it is quantitative.
Evaluation Logic: Remember that the strength of one is usually the weakness of the other. If asked to evaluate quantitative data, mention its lack of depth; for qualitative, mention its difficulty in replication.
