What is Quantitative Analysis?
Quantitative analysis refers to the collection and interpretation of numerical data. It relies on measurable variables and is used to test hypotheses, identify patterns, and make predictions.
🔬 Example in Research:
A researcher studying the effectiveness of two antihypertensive drugs may use quantitative methods to compare mean systolic blood pressure readings between groups.
Tools Commonly Used:
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Statistical software (SPSS, R, Python)
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Structured surveys with closed-ended questions
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Data from electronic health records (EHRs), lab values, etc.
Typical Outcomes:
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Percentages
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Mean ± standard deviation
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Confidence intervals and p-values
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Graphs and regression models
🧠 What is Qualitative Analysis?
Qualitative analysis involves the collection of non-numeric data to explore attitudes, experiences, or concepts. It's used when the research aims to understand phenomena, not just measure them.
💬 Example in Research:
A study on why patients with diabetes do not adhere to insulin therapy may involve interviewing participants and analyzing themes from their responses.
Methods Include:
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In-depth interviews
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Focus groups
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Open-ended survey responses
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Thematic or content analysis using software like NVivo or ATLAS.ti
Typical Outcomes:
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Themes or categories
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Narrative descriptions
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Participant quotes
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Conceptual models
📊 Quantitative vs. Qualitative: Key Differences
| Aspect | Quantitative | Qualitative |
|---|---|---|
| Data Type | Numerical | Textual / Descriptive |
| Aim | Measure, quantify, test hypotheses | Explore, understand, generate ideas |
| Sample Size | Large, representative | Small, purposive |
| Analysis | Statistical | Thematic / narrative |
| Tools | SPSS, R, Excel | NVivo, ATLAS.ti, manual coding |
🧪 When to Use Quantitative Methods
Use quantitative methods when:
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You want to test a hypothesis (e.g., “Drug A reduces CRP levels more than Drug B”).
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You need generalizable results.
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Your data is measurable and structured.
🧾 When to Use Qualitative Methods
Use qualitative methods when:
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Exploring new or complex topics where little prior data exists.
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Understanding patient experiences or physician perspectives.
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You need detailed insight into motivations or barriers.
🧷 Mixed Methods: The Best of Both Worlds?
In many modern research designs, especially in health and social sciences, mixed-methods are used to combine statistical power with rich context.
Example:
A study on burnout in medical residents:
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Quantitative arm: Burnout scale scores and hours worked per week.
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Qualitative arm: Interviews exploring emotional experiences, systemic challenges.
