Multinomial Logistic Regression in Jamovi

Analyzing categorical outcome variables with more than two groups requires moving beyond standard binary logistic models. This practical text-based guide equips you with the statistical understanding and step-by-step methodology needed to execute multinomial logistic regression seamlessly in Jamovi. You will gain the confidence to structure multi-class datasets, estimate parameters relative to baseline categories, and translate statistical outputs into meaningful analytical insights. What you'll learn: - Understand the core concepts, probability theory, and terminology behind multinomial logistic regression - Set up and clean multi-categorical variables properly within the Jamovi interface - Configure baseline reference groups and interpret odds ratios and relative risk ratios - Evaluate overall model fit using deviance, AIC, BIC, and pseudo R-squared measures - Check statistical assumptions, identify potential multicollinearity, and review diagnostic indicators - Report and document regression findings according to modern academic and research reporting standards The course begins with foundational statistical definitions and data preparation rules before walking systematically through model configuration, parameter estimation, and result interpretation. Designed for beginners, students, and researchers, this course requires no advanced mathematical background. Start reading today to master multi-category regression modeling with Jamovi.