Case Study Analysis Worksheets

Case Study 1: Educational Access in Rural Bangladesh

Research Context

You are analyzing factors affecting school enrollment among children aged 6-14 in rural Bangladesh. Your research question: "What are the primary barriers to educational access, and how do they differ for boys and girls?"

Study design: Cross-sectional survey of 1,200 households across 75 villages in three districts

Key variables: Enrollment status, distance to school, household income, parental education, child gender, household size

Sample Descriptive Statistics

Variable	Overall Mean (SD)	Boys Mean (SD)	Girls Mean (SD)
Enrollment rate (%)	68.4 (47.0)	72.8 (44.5)	63.9 (48.0)
Distance to school (km)	2.3 (1.8)	2.3 (1.8)	2.3 (1.8)
Household income (Taka/month)	12,450 (8,200)	12,380 (8,150)	12,520 (8,250)
Parental education (years)	3.2 (2.8)	3.1 (2.7)	3.3 (2.9)
Household size	5.8 (2.1)	5.9 (2.1)	5.7 (2.1)

Phase 1: Correlation Analysis

Calculate and interpret correlations between enrollment and potential barriers:

Given Correlation Results:

Enrollment vs. Distance to school: r = -0.58 (p < 0.001)
Enrollment vs. Household income: r = 0.34 (p < 0.001)
Enrollment vs. Parental education: r = 0.47 (p < 0.001)
Enrollment vs. Household size: r = -0.22 (p = 0.008)

Analysis Tasks:

Which factor shows the strongest relationship with enrollment? Interpret the correlation coefficient.
Are there any surprising findings in these correlations? Explain.
Which correlations suggest practical intervention points?

Your Correlation Analysis:

1. Strongest relationship:

2. Surprising findings:

3. Intervention implications:

Phase 2: ANOVA Analysis

Compare enrollment rates across distance categories and by gender:

Distance Categories Analysis:

Distance Category	n	Enrollment Rate (%)	Standard Deviation
< 1 km	320	82.5	38.0
1-2 km	480	71.2	45.3
2-3 km	280	58.6	49.3
> 3 km	120	41.7	49.5

ANOVA Results: F = 28.4, p < 0.001

Gender Comparison:

Boys enrollment: 72.8% (SD = 44.5)
Girls enrollment: 63.9% (SD = 48.0)
t-test: t = 2.89, p = 0.004

Analysis Tasks:

Interpret the ANOVA results for distance categories.
What does the gender comparison reveal?
Identify the critical distance threshold for enrollment.

Your ANOVA Analysis:

1. Distance ANOVA interpretation:

2. Gender differences:

3. Critical distance threshold:

Phase 3: Regression Analysis

Build a logistic regression model to predict enrollment probability:

Logistic Regression Results:

Variable	Coefficient	Odds Ratio	p-value	95% CI for OR
Intercept	0.68	1.97	0.023	-
Distance to school (km)	-0.51	0.60	< 0.001	0.52-0.69
Household income (Taka 1000s)	0.04	1.04	0.002	1.01-1.07
Parental education (years)	0.18	1.20	< 0.001	1.12-1.28
Female child	-0.42	0.66	0.006	0.49-0.88
Distance × Female	-0.28	0.76	0.041	0.58-0.99

Model Statistics: Pseudo R² = 0.23, AUC = 0.74

Analysis Tasks:

Interpret the distance coefficient and odds ratio.
What does the interaction term (Distance × Female) tell us?
Calculate enrollment probability for: Girl, 3km from school, parents have 5 years education, income = 15,000 Taka

Your Regression Analysis:

1. Distance coefficient interpretation:

2. Interaction effect interpretation:

3. Probability calculation:

Logit = 0.68 + (-0.51)(3) + (0.04)(15) + (0.18)(5) + (-0.42)(1) + (-0.28)(3×1)

Logit = 0.68 + ____ + ____ + ____ + ____ + ____

Logit = ____

Probability = e^(logit) / (1 + e^(logit)) = ____

Phase 4: Synthesis and Interpretation

Integrate your findings across all three analyses:

What is the complete story about educational barriers in rural Bangladesh?

How do findings differ for boys vs. girls?

Which analytical method provided the most useful insights?

Phase 5: Policy Recommendations

Based on your statistical analysis, provide three specific, evidence-based policy recommendations:

1. Primary recommendation:

2. Gender-specific intervention:

3. Household-level support:

What additional data would strengthen your recommendations?

Critical Reflection:

What are the key limitations of this analysis?

How might cultural factors affect your statistical interpretations?

Case Study 2: Climate-Smart Agriculture Adoption in South Asia

Research Context

You are evaluating factors affecting adoption of climate-smart agricultural practices across 400 smallholder farmers in India, Bangladesh, and Pakistan. Research question: "What predicts successful adoption of climate-smart farming techniques, and how do effects vary by country?"

Study design: Multi-country cross-sectional survey with 18-month follow-up

Key variables: Adoption rate (%), country, farm size, education, extension contact, previous yield loss, risk tolerance

Sample Overview

Country	n	Adoption Rate (%)	Avg Farm Size (ha)	Avg Education (years)
India	150	68.0	1.8	6.2
Bangladesh	125	72.8	0.9	4.6
Pakistan	125	45.6	2.4	5.8

Additional Variables (Overall Sample)

Extension contact: 58% had contact with extension agent in past year
Previous yield loss: 74% experienced significant loss due to weather in past 3 years
Risk tolerance: Scale 1-10, Mean = 5.8 (SD = 2.1)

Phase 1: Correlation Analysis

Examine relationships between adoption and potential predictors:

Correlation Results:

Variable	Correlation with Adoption	p-value
Farm size (hectares)	0.34	< 0.001
Education (years)	0.42	< 0.001
Extension contact (binary)	0.56	< 0.001
Previous yield loss (binary)	0.38	< 0.001
Risk tolerance	0.29	0.003

Analysis Tasks:

Fill in the interpretation column for each correlation
Rank the variables by importance based on correlation strength
Which finding is most actionable for policy makers?

Your Correlation Analysis:

1. Variable ranking by importance:

1st:

2nd:

3rd:

4th:

5th:

2. Most actionable finding:

Phase 2: ANOVA Analysis

Compare adoption rates across countries and by key categorical variables:

Country Comparison:

ANOVA Results: F = 18.7, p < 0.001, η² = 0.086

Extension Contact Comparison:

Extension Contact	n	Mean Adoption (%)	Standard Deviation
No contact	168	48.2	50.0
Had contact	232	73.7	44.1

t-test: t = 5.28, p < 0.001, Cohen's d = 0.53

Farm Size Categories:

Farm Size	n	Adoption Rate (%)	SD
< 1 hectare	158	52.5	50.1
1-2 hectares	142	68.3	46.7
> 2 hectares	100	74.0	44.1

ANOVA: F = 9.8, p < 0.001

Analysis Tasks:

Which country comparison is most significant? What might explain differences?
Calculate the effect size for extension contact. Is this practically significant?
What does the farm size analysis suggest about scalability?

Your ANOVA Analysis:

1. Country differences explanation:

2. Extension contact effect size:

Effect size (Cohen's d) = (73.7 - 48.2) / pooled SD

Given Cohen's d = 0.53, this is a ________ effect (small=0.2, medium=0.5, large=0.8)

3. Farm size scalability implications:

Phase 3: Regression Analysis

Build a comprehensive model predicting adoption:

Multiple Regression Results:

Variable	Coefficient	Std Error	t-value	p-value	Beta (standardized)
Intercept	15.2	8.4	1.81	0.071	-
Extension contact (1=yes)	22.8	4.2	5.43	< 0.001	0.32
Education (years)	2.1	0.8	2.63	0.009	0.16
Farm size (hectares)	6.8	2.1	3.24	0.001	0.19
Previous yield loss (1=yes)	14.6	5.1	2.86	0.004	0.17
Bangladesh (vs India)	8.9	4.8	1.85	0.065	0.11
Pakistan (vs India)	-18.4	5.2	-3.54	< 0.001	-0.22

Model Statistics: R² = 0.48, Adjusted R² = 0.46, F = 60.2, p < 0.001

Analysis Tasks:

Which variable has the largest impact on adoption (use standardized betas)?
Interpret the Pakistan coefficient in practical terms
Predict adoption rate for: Pakistani farmer, 1.5 ha farm, 8 years education, extension contact, previous yield loss

Your Regression Analysis:

1. Largest impact variable:

2. Pakistan coefficient interpretation:

3. Prediction calculation:

Adoption = 15.2 + 22.8(1) + 2.1(8) + 6.8(1.5) + 14.6(1) + 8.9(0) + (-18.4)(1)

= 15.2 + ____ + ____ + ____ + ____ + ____ + ____

= _____% adoption rate

Synthesis: What Drives Climate-Smart Agriculture Adoption?

Primary drivers (in order of importance):

Country-specific patterns:

Implications for scaling programs:

Evidence-Based Policy Recommendations

1. Immediate action (highest impact):

2. Country-specific strategies:

3. Supporting interventions:

Case Study 3: Urban Water Access Equity in Indian Cities

Research Context

You are analyzing water access inequality across 85 Indian cities. Research question: "What factors best predict equitable water access, and how can cities improve service delivery for low-income households?"

Study design: Multi-city comparative analysis using municipal data

Outcome variables: Overall water access (%), Low-income household access (%), Equity gap

Variable Summary (n=85 cities)

Variable	Mean	Std Dev	Range
Overall water access (%)	71.2	18.4	35.0 - 95.0
Low-income access (%)	52.8	22.6	15.0 - 88.0
Equity gap (percentage points)	18.4	9.2	3.0 - 42.0
Per capita investment (₹1000s)	8.7	4.2	2.1 - 22.8
Transparency index (1-10)	5.8	2.1	2.0 - 9.5
Population (millions)	2.4	3.1	0.2 - 18.5

Your Integrated Analysis Challenge

Complete the three-phase analysis independently, then synthesize findings:

Identify the strongest correlations and their policy implications
Compare water access across regions and city size categories
Build a predictive model for equitable water access
Integrate findings into actionable policy framework

Key Research Findings (for your analysis):

Strongest correlation: Transparency index vs overall access (r = 0.78)
Surprising finding: Investment vs access correlation only moderate (r = 0.43)
Regional differences: Significant variations across North, South, East, West (p < 0.001)
Best predictive model: R² = 0.71, with transparency, targeted programs, and governance as top predictors

Phase 1 - Your Correlation Analysis:

What do the correlation patterns tell us about water access drivers?

Why might transparency matter more than investment?

Phase 2 - Your ANOVA Analysis:

How would you set up ANOVA to test regional differences?

What city size categories would you create and why?

Phase 3 - Your Regression Strategy:

What would your regression model look like?

DV:

Key IVs:

Control variables:

How would you handle the equity gap vs overall access distinction?

Your Evidence-Based Policy Framework

Top 3 recommendations based on statistical evidence:

1. Governance-focused interventions:

2. Equity-specific programs:

3. Resource optimization strategies:

How would you convince city officials to prioritize transparency over just increasing investment?

Cross-Case Reflection:

How do patterns in urban water access compare to rural education and agricultural adoption?

What common themes emerge across all three South Asian development challenges?

Integrated Analysis Approach

Case Study 1: Educational Access in Rural Bangladesh

Research Context

Sample Descriptive Statistics

Phase 1: Correlation Analysis

Given Correlation Results:

Analysis Tasks:

Your Correlation Analysis:

Phase 2: ANOVA Analysis

Distance Categories Analysis:

Gender Comparison:

Analysis Tasks:

Your ANOVA Analysis:

Phase 3: Regression Analysis

Logistic Regression Results:

Analysis Tasks:

Your Regression Analysis:

Phase 4: Synthesis and Interpretation

Phase 5: Policy Recommendations

Critical Reflection:

Case Study 2: Climate-Smart Agriculture Adoption in South Asia

Research Context

Sample Overview

Additional Variables (Overall Sample)

Phase 1: Correlation Analysis

Correlation Results:

Analysis Tasks:

Your Correlation Analysis:

Phase 2: ANOVA Analysis

Country Comparison:

Extension Contact Comparison:

Farm Size Categories:

Analysis Tasks:

Your ANOVA Analysis:

Phase 3: Regression Analysis

Multiple Regression Results:

Analysis Tasks:

Your Regression Analysis:

Synthesis: What Drives Climate-Smart Agriculture Adoption?

Evidence-Based Policy Recommendations

Case Study 3: Urban Water Access Equity in Indian Cities

Research Context

Variable Summary (n=85 cities)

Your Integrated Analysis Challenge

Key Research Findings (for your analysis):

Phase 1 - Your Correlation Analysis:

Phase 2 - Your ANOVA Analysis:

Phase 3 - Your Regression Strategy:

Your Evidence-Based Policy Framework

Cross-Case Reflection:

Integration Activity: Cross-Case Analysis

Common Success Factors:

Common Barriers: