is a powerful tool for estimating impact in evaluation studies. It models relationships between variables, allowing researchers to isolate the effects of interventions while controlling for other factors. This technique helps uncover causal links and quantify program impacts.
Key considerations include selecting appropriate variables, addressing assumptions, and interpreting results correctly. Advanced methods like fixed effects and can strengthen causal inference. Proper analysis and interpretation of regression coefficients provide valuable insights into program effectiveness.
Principles of Regression Analysis
Fundamentals of Regression
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Regression analysis models relationship between dependent variable and one or more independent variables
method estimates regression coefficients by minimizing sum of squared residuals
assumes linear relationship between variables represented by straight line (simple regression) or hyperplane (multiple regression)
requires constant variance of residuals across all levels of independent variables
means each data point not influenced by other observations in dataset
Key Assumptions and Considerations
allows for valid hypothesis testing and confidence interval estimation
(high correlation between independent variables) can lead to unreliable coefficient estimates
Assess using
Address by removing correlated variables or using regularization techniques (ridge regression)
can significantly impact regression results
Identify using scatter plots or Cook's distance
Consider techniques (M-estimators, least trimmed squares)
Non-linear relationships may require transformation of variables or non-linear regression models
for quadratic relationships
for exponential relationships
Regression Models for Impact Evaluation
Treatment and Control Variables
Impact evaluation models include to capture effect of intervention on outcome
account for other factors influencing outcome, reducing