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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.
Category | Quantitative | Qualitative |
---|---|---|
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 |