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Data Types

Data Types

Differences Between Quantitative and Qualitative Data

Quantitative and Qualitative Data

I find the breakdown between quantitative and qualitative data really helpful—it’s something I think about often when planning evaluations. Quantitative data is great when I need solid numbers to answer questions like “how much” or “to what extent,” especially when I’m reporting to stakeholders who want clear, measurable results. But I also rely on qualitative data to get the deeper story—what’s really happening behind the numbers. Those open-ended responses and interviews often reveal things I wouldn’t catch otherwise. Using both together gives me a more complete picture of what’s working and what needs adjustment.

CategoryQuantitativeQualitative
Purpose- To answer questions of “What?”, “How much?”, “How often?”, “To what extent?”, or “For whom?”
- To provide information that can be analyzed and represented statistically, with validity and reliability
- To answer questions of “How?” or “Why?”
- To provide rich and nuanced details on programs, processes, and perspectives
- To solicit different perspectives and opinions
- To contextualize and complement quantitative findings
Approach- More objective, closed-ended
- Large numbers of participants
- Structured
- More subjective, open-ended
- Not dependent on sample size; sample sizes tend to be small
- Semi-structured or unstructured
Analysis- Statistical methods—for example:
• Descriptive analysis (means, medians, distributions)
• Correlation analysis
• Regression analysis
- Nonstatistical methods—for example:
• Coding
• Recursive abstraction
• Content analysis
Resulting Data- Quantified averages and measures of variation
- Values that can be represented statistically
- May be generalizable if study sample is similar to a larger population
- Open-ended text summarizing main themes
- Description of contexts, processes, variations, and relationships
- Not generalizable—specific to study setting and participants