Bivariate Analysis 101 South Asian Development Research - Handwritten Notes Template
What is Bivariate Analysis in Development Context?
Definition: Statistical analysis of relationships between two variables to understand how development indicators relate to each other in South Asian contexts - such as education and income, distance and health access, or policy interventions and outcomes.
Why Essential: Most development questions involve understanding how one factor influences another. Bivariate analysis helps identify these relationships before moving to more complex multivariate models.
South Asian Development Context
Rural-urban relationships: How distance affects service access
Gender and outcomes: How women's education relates to child health
Policy evaluation: Does program participation improve outcomes?
Economic relationships: Income and consumption patterns
Types of Bivariate Analysis
Choosing the Right Method
Variable 1 Type
Variable 2 Type
Analysis Method
Example
Continuous
Continuous
Correlation & Regression
Income vs. Education years
Categorical
Continuous
T-test or ANOVA
Gender vs. Income level
Categorical
Categorical
Chi-square test
Caste vs. Program participation
Binary
Continuous
Logistic Regression
School enrollment (yes/no) vs. Distance
CORRELATION ANALYSIS
Purpose: Measure strength and direction of linear relationships between continuous variables
Pearson Correlation Coefficient (r)
Range: -1 to +1
r = 0: No linear relationship
r = +1: Perfect positive relationship
r = -1: Perfect negative relationship
Interpretation Guide:
|r| < 0.3: Weak relationship
0.3 ≤ |r| < 0.7: Moderate relationship
|r| ≥ 0.7: Strong relationship
South Asian Examples:
Rural Indian Villages: Maternal education years vs. child malnutrition rate: r =
Bangladesh Households: Distance to health center vs. vaccination coverage: r =
Pakistani Farmers: Rainfall (mm) vs. crop yield: r =
Key Assumptions:
Linearity: Relationship should be linear
Normality: Both variables should be normally distributed
When to use: Both variables are categorical Tests: Whether two categorical variables are independent H₀: Variables are independent H₁: Variables are associated
Example: Caste and Educational Access in Rural India
Higher Secondary Completed
Not Completed
Total
General Caste
OBC
SC/ST
Chi-square result: χ² = , p =
Interpretation:
T-tests for Comparing Groups
Independent samples t-test:
Compares means of continuous variable across two groups
Example: Male vs Female income levels
Example: Gender Wage Gap in Bangladesh Garment Industry
Male workers: Mean wage = ₹, SD = , n =
Female workers: Mean wage = ₹, SD = , n =
t-statistic = , p-value =
Interpretation:
Personal Notes & Examples:
South Asian Development Applications
Case Study 1: Maternal Education and Child Health
Context: Analyzing relationship between maternal education and child malnutrition in rural Indian villages
Variables:
X = Maternal education (years of schooling)
Y = Child malnutrition rate (% of children under 5)
Your Analysis:
1. What type of bivariate analysis would you use?
2. What would you expect the correlation to be (positive or negative)? Why?
3. What other factors might influence this relationship?
4. How would you present findings to policy makers?
Case Study 2: Digital Financial Inclusion
Context: Studying mobile banking adoption across different regions in Pakistan
Variables:
X = Region (Urban/Rural)
Y = Mobile banking usage (Yes/No)
Your Analysis:
1. What analysis method is appropriate?
2. Set up null and alternative hypotheses:
H₀:
H₁:
3. How would you interpret a significant result?
Data Quality and Assumptions
Common Issues in South Asian Development Data
Missing Data Patterns
Not random: Often reflects social exclusion or access barriers
Gender bias: Women's data may be missing or reported by male household heads
Seasonal migration: Affects data collection timing
Rural-urban differences: Different data collection challenges
Measurement Challenges
Income reporting: Informal economy income hard to measure
Social desirability bias: Responses influenced by perceived "correct" answers
Unit conversions: Different units across regions (local measures vs. standard)