Loglinear Regression Analysis in jamovi

Analyzing relationships among multiple categorical variables often requires going beyond standard contingency tests. This text-based course introduces you to loglinear regression in jamovi, providing a clear path from foundational statistical principles to practical model estimation and interpretation. Through structured readings and applied examples, you will explore how to formulate hypotheses, structure contingency data, select appropriate model terms, and evaluate goodness-of-fit indicators. What you'll learn: - Understand the core concepts, assumptions, and terminology behind loglinear analysis - Structure and import categorical datasets ready for modeling in jamovi - Fit saturated and unsaturated loglinear models to analyze complex interaction effects - Interpret model coefficients, parameter estimates, and expected cell counts - Evaluate model adequacy using likelihood ratio tests, deviance, and AIC metrics - Apply standardized reporting conventions to communicate categorical analysis results The course starts with essential statistical definitions and categorical data foundations before guiding you step-by-step through model specification, diagnostics, and result interpretation. Designed for beginners and emerging data practitioners, this guide requires no prior experience with advanced regression modeling. Begin reading to build confidence in categorical data analysis today.