Case Study: “What If” Scenario Analysis for a Quasi-Experimental Educational Intervention

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1. Study Context & Rationale

What if scenario analysis case study based on a quasi-experimental PhD research with 217 participants (120 children, 97 adolescents), standard inferential analyses confirmed the intervention’s overall efficacy . However, decision-makers often ask, “What if the pilot targeted only rural participants?” or “What if we raised the knowledge-gain threshold?” To answer these questions, we implemented a suite of “What If” variation analyses—sensitivity tests, subgroup re-sampling, and alternative threshold scenarios—to guide scalable program design.

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2. Defining “What If” Scenarios

We focused on three primary variations:

1. Subgroup Focus: Restrict analysis to rural schools only (n_rural ≈ 110) to assess localized impact.
2. Threshold Adjustment: Evaluate how raising the “successful gain” cutoff (e.g., Δknowledge ≥ 8 instead of ≥ 5) changes responder rates.
3. Sample-Size Sensitivity: Simulate smaller/larger cohorts (n = 150–300) to estimate statistical power and required sample for future rollouts.

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3. Software & Simulation Tools for what if scenario analysis

• R (tidyverse + simstudy): Generating “what if” datasets via Monte Carlo sampling and simstudy scenarios.
• Excel → R Integration: Masterchart filtered by subgroup, then exported for simulation.
• R Markdown: Parameterized templates produce dynamic tables and plots for each scenario.

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4. Workflow & Code Snippets for what if scenario analysis

4.1 Subgroup Resampling

# Filter for rural participants
rural_df <- df %>% filter(residence == \"Rural\")
# Paired t-test on rural subset
t.test(rural_df$post_knowledge, rural_df$pre_knowledge, paired=TRUE)

• Insight: Rural-only t(54)=11.72, p<0.001—intervention remains highly effective in non-urban settings.

4.2 Threshold Variation

# Define multiple gain thresholds
thresholds <- c(5, 8, 10)
results <- map_df(thresholds, function(th){
  df %>%
    mutate(success = ifelse((post_knowledge - pre_knowledge) >= th, 1, 0)) %>%
    summarize(threshold = th, success_rate = mean(success))
})

• Finding: Success rate drops from 78 % at Δ≥5 to 54 % at Δ≥10, guiding realistic program goals.

4.3 Sample-Size Power Simulation

library(simstudy)
def <- genData(1e3, def = data.frame(varname=\"delta\", formula=mean(df$post_knowledge - df$pre_knowledge), dist=\"normal\", variance=var(df$post_knowledge - df$pre_knowledge)))
# Simulate varying sample sizes
power_results <- getPower(def, test=\"t\", n=c(150, 200, 250, 300), alpha=0.05)

• Outcome: Achieving 80 % power requires n≈180 per cohort; informs future trial sizing.

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5. Key Takeaways from what if scenario analysis

• Localized Insights: Subgroup analyses ensure interventions suit diverse contexts.
• Realistic Benchmarks: Threshold variation quantifies trade-offs between rigor and feasibility.
• Data-Driven Planning: Power simulations provide concrete sample-size targets for scaling.

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This “What If Variations” case study demonstrates how sensitivity and scenario analyses—leveraging R simulations and dynamic workflows—empower data-driven decisions beyond standard hypothesis tests.

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Want to explore more PhD-level case studies like this what if scenario analysis? Check out our Comprehensive Case Studies on PhD Statistical Analysis guide page.