Quantitative Research Methods: Logit, Count, and Event History Models

To conduct rigorous research in social sciences, business, or economics, basic linear regression is often not enough. Understanding how to model binary outcomes, count data, and events over time is essential for producing high-quality empirical analysis. This text-only course guides you from foundational statistical concepts to implementing advanced regression models. You will learn how to select, specify, and interpret the right quantitative methods for complex observational data. What you'll learn: • Understand the foundations of non-linear models and maximum likelihood estimation • Analyze binary and categorical outcomes using logit and probit models • Model event counts and rates using Poisson and negative binomial regressions • Apply event history and survival analysis to study the timing of events • Handle panel data and pooled cross-sections with fixed and random effects • Interpret model coefficients, odds ratios, and marginal effects accurately. The course begins with key terminology and foundational theory before diving into specific model families. You will progress through practical scenarios, learning how to structure your data and interpret output for real-world research. This course is designed for aspiring researchers, data analysts, and graduate students who have a basic understanding of linear regression and want to expand their quantitative toolkit. Start mastering advanced quantitative research methods today through clear, step-by-step written explanations.