Module 6

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Question 1 of 6

1. Question

Why does data collection matter in CSR impact measurement?

Data collection is mainly about collecting opinions without any link to measuring results.
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Data collection is the backbone of impact measurement – it turns activities into evidence (e.g. improved well-being or reduced CO2 emissions)
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Data collection is only useful for financial audits, not for CSR impact.
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Question 2 of 6

2. Question

What does quantitative data measure?

Personal stories only, collected through interviews and focus groups.
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How much or how many; objective, measurable data that is easy to track over time (useful for KPIs that require measurable progress).
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The how and why behind the numbers; it gives depth and context.
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Question 3 of 6

3. Question

Qualitative data is best described as:

Data that measures how much or how many.
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Data that is only collected through spreadsheets and dashboards.
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Data that explains the how and why behind the numbers and gives depth and context to results.
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Question 4 of 6

4. Question

Choose the correct methods for collecting qualitative data:

Tracking a percentage KPI over time.
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Interviews.
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Only using automated numeric reports.
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Focus groups.
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Open-ended surveys
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Counting the number of items produced per month.
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Question 5 of 6

5. Question

Choose the correct statements about combining quantitative and qualitative data:

Qualitative data is unnecessary, if you have enough numbers.
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Numbers show scale.
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You should collect only one type of data (either numbers or stories), never both.
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Stories show meaning.
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Together, numbers and stories create a fuller picture of CSR impact.
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For each major KPI, you should plan to collect both types of data.
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Question 6 of 6

6. Question

Data collection checklist – choose the correct items to plan in advance:

Competitor pricing analysis.
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Purpose of data collection: What will this data help you decide or prove?
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Frequency: once, weekly, monthly, after events.
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Choosing a marketing slogan for the campaign
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Collection method: survey, interview, observation, feedback loop.
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Ethical considerations: consent, privacy, data storage.
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Type of data needed: quantitative, qualitative, or both.
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Accessibility: language, format, inclusivity.
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Module 6 : Impact Measurement & Evaluation

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