Case Study: Lessons Learned & Best Practices from a Quasi-Experimental Educational Intervention

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1. Project Snapshot of this quasi-experimental educational intervention

In this quasi-experimental educational intervention PhD study assessed an instructional intervention’s impact on knowledge (0–20) and attitude (0–72) among 217 participants (120 children; 97 adolescents) across urban and rural schools. The mixed-methods analytic pipeline combined R scripting, Excel VBA automation, and inferential tests (chi-square, paired t-tests, repeated measures ANOVA), yielding robust findings on both efficacy and durability .

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2. Challenge #1: Data Version Drift

Issue: Multiple CSV exports (pre-, post-, follow-up) risked inconsistent variable naming and missing-value coding, jeopardizing reproducibility.
Lesson Learned: Enforce a single source of truth—the “masterchart”—standardized via an Excel VBA macro and validated in R before any analysis .

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3. Challenge #2: Manual Dashboard Fatigue

Issue: Creating 30 descriptive dashboards by hand (tables + charts + narrative abstracts) consumed over 50 % of project time.
Lesson Learned: Automate descriptive analytics with parameterized R scripts and Excel macros, reducing manual effort by 70 % and ensuring uniform formatting across outputs .

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4. Challenge #3: Hidden Assumption Violations

Issue: Initial paired t-tests and ANOVAs were run without systematic checks for normality, homoscedasticity, or sphericity—leading to borderline p-values that risked misinterpretation.
Lesson Learned: Integrate automated assumption diagnostics into the inferential pipeline:

• Normality: Shapiro-Wilk tests and QQ-plots
• Variance Homogeneity: Levene’s test
• Sphericity (ANOVA): Mauchly’s test with Greenhouse–Geisser correction if violated
  This practice surfaced two variables requiring non-parametric alternatives, improving result validity .

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5. Challenge #4: Code Reusability & Readability

Issue: Monolithic R scripts quickly became unwieldy, hampering debugging and collaboration.
Lesson Learned:

• Modularize Functions: Encapsulate each statistical method (e.g., run_chisq(), run_paired_t(), run_rm_anova()) in its own script.
• Adopt Clear Naming Conventions: Use descriptive function and variable names.
• Version Control Discipline: Feature branches and pull-request reviews in Git enforced quality and traceability .

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6. Challenge #5: Stakeholder Communication

Issue: Non-technical stakeholders found raw tables and code outputs opaque, delaying feedback loops.
Lesson Learned:

• Executive Summaries: Embed high-level insights at the top of each report, using lay terminology (e.g., “The intervention increased knowledge by an average of 7 points, p < 0.001”).
• Visual Storytelling: Include annotated plots (effect-size bar charts, trend lines with confidence intervals) directly in R Markdown reports.
• Interactive Dashboards: Provide an Excel workbook with filterable pivot tables so users can explore subgroups without needing R .

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7. Best Practices Checklist from this quasi-experimental educational intervention case

1. Masterchart Governance:
   • Automate schema validation (validate_schema()) before analysis.
   • Store codebook metadata with data.
2. Automated Pipelines:
   • Use R scripts for inferential tests; Excel macros for descriptive dashboards.
   • Parameterize scripts to handle new cohorts or variables seamlessly.
3. Assumption Diagnostics:
   • Integrate normality, variance, and sphericity checks.
   • Automatically switch to non-parametric tests when assumptions fail.
4. Code Modularity & Versioning:
   • Break logic into discrete, reusable functions.
   • Maintain a Git workflow with code reviews for each analytical module.
5. Stakeholder-Friendly Reporting:
   • Prepend executive summaries.
   • Use R Markdown to weave narrative with visuals.
   • Deliver interactive Excel dashboards for hands-on exploration.
6. Ethical & Audit Readiness:
   • Document all data-audit steps in a final PDF (missing-data, outliers, IRB compliance).
   • Timestamp and sign off each deliverable to ensure accountability .

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8. Outcome & Impact from this quasi-experimental educational intervention case

By adopting these lessons and practices, the project achieved:

• Enhanced Reproducibility: Zero data-version errors in final analyses.
• Operational Efficiency: 50 % reduction in manual dashboard creation time.
• Analytic Integrity: Rigorous assumption checks bolstered stakeholder confidence in p-values and effect sizes.
• Stakeholder Engagement: Faster approval cycles through clearer, interactive deliverables.

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This Lessons Learned & Best Practices case study highlights the critical adjustments and protocols that transform a PhD-level quasi-experimental analysis into a scalable, transparent, and stakeholder-aligned research workflow.

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Want to explore more PhD-level case studies? Check out our Comprehensive Case Studies on PhD Statistical Analysis guide page.