SayPro Preliminary data analysis notes

✅ SayPro Preliminary Data Analysis Notes

Project Name: Youth Skills Empowerment – SCLMR-1
Reporting Period: June 2025
Analyst: [Your Name]
Data Sources: Beneficiary registration (CSV), Training attendance (Excel), Youth satisfaction survey (KoBo export), M&E monthly indicators

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📋 1. Data Overview

Dataset	Total Records	Collection Tool	Notes
Beneficiary Register	1,214	Excel/Forms	Cleaned and validated
Attendance Sheets	1,004	Manual + ODK	Some IDs mismatched
Feedback Survey	875	KoBoToolbox	94% response rate
Indicator Tracker	N/A	Excel	Submitted by all 8 regional teams

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📊 2. Preliminary Quantitative Insights

• Gender Breakdown:
  • Female: 58%, Male: 41%, Other/Not specified: 1%
  • Slight increase in female participation vs. last quarter (52%).
• Age Distribution:
  • Median age: 22
  • Most participants (70%) are aged 18–25
• Training Attendance Rates:
  • Average session attendance: 76%
  • Highest attendance in Eastern Cape (84%)
  • Limpopo and Free State show lower consistency (<65%)
• Satisfaction Scores (Scale 1–5):
  • Mean: 4.2
  • Most common feedback: “Relevant,” “Engaging facilitators,” and “More practicals needed”
• Completion Rate of Training:
  • 72% completed full modules
  • Dropouts mainly occur after Module 2

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🧠 3. Preliminary Qualitative Observations

• Common Suggestions:
  • Increase time for hands-on training
  • Add job linkage sessions at the end of training
  • Provide transport stipends
• Themes in Open-Ended Feedback:
  • Motivation: Youth felt “empowered” and “confident”
  • Challenges: Digital skills gap in rural areas
  • Expectations: More frequent mentorship check-ins

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🛠️ 4. Initial Data Quality Issues

Issue	Affected Records	Action Taken
Missing gender values	17	Backfilled from registration sheet
Duplicate IDs	4	Removed older entries
Mismatched IDs in attendance vs. registration	28	Flagged for field team confirmation

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📈 5. Early Trends to Explore Further

• Relationship between attendance and satisfaction
• Gender-based completion rate disparities
• Dropout triggers around Module 2 (needs more investigation)
• Stronger engagement in urban vs. rural sites—explore infrastructural link

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📎 6. Pending Tasks

• Conduct deeper correlation analysis (attendance vs. employment outcomes)
• Run regression on satisfaction scores vs. demographics
• Map dropout trends by session and location
• Request follow-up data on transport support access

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🧾 7. Attachments/Files

• Cleaned Training Dataset: training_attendance_cleaned_June2025.xlsx
• Survey Output: youth_feedback_June2025.csv
• Notes Log: SCLMR_PreAnalysis_Notes.docx