Course Materials

Important readings
Optional readings

# Module 4

Sampling & Estimation

Learning Objectives: Prepare and manage datasets for analysis by applying principles of tidy data and data merging; understand when to use multinomial logistic regression for outcomes with more than two categories; specify the model and reference category; interpret model coefficients and predicted probabilities; apply multinomial logistic regression to real-world categorical data

Before the Class
  • Remler & Van Ryzin. Chapter 4. Measurement: Focus on the concepts in the following sections: "What is Measurements?", "Validity" (No need to read criterion-related validity), "Measurement Error", "Reliability", and "Validity and Reliability: Contrasted and Compared"
  • Remler & Van Ryzin. Chapter 5. Sampling
  • The New York Times "Bunnies, Dragons and the 'Normal' World: Central Limit Theorem."