Advanced Psychological Statistics: The General Linear Model and Beyond


The General(ized) Linear Model (GLM) refers to a common family of statistical techniques popular in the social sciences such as ANOVA, regression, and logistic regression. The primary goal of this course is for students to understand the basics and nuances of the GLM. We will also explore and introduce students to extensions of the GLM common in psychology and neuroscience research. These frameworks include multilevel modeling, generalized linear models, Bayesian analyses, machine learning, etc. Students will analyze data using each of these methods using R statistical software, therefore improving their advanced computing skills. By the end of the course, students will understand the general framework used in most statistical tests employed by psychological and neuroscientific researchers. Students will not be expected to know every detail of every test, but will instead have a conceptual understanding of these tools and practice in deploying these tools. PREREQ: L33 Psych 300, Mth 2200, Mth 3200, or DAT 120 and Fluency or proficiencey with R statistical software (e.g., Psych 4175).
Course Attributes: AS NSM; FA NSM; AS AN

Section 01

Advanced Psychological Statistics: The General Linear Model and Beyond
INSTRUCTOR: Jackson, Cooper
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