Introduction to Mixed Modelling
Mixed modelling is central to modern statistical analysis and is often considered the go-to analysis for large health, social science, ecological, and biological data sets, particularly when there are repeated measurements for each subject or when there is clustering or multiple-levels apparent in the data. Mixed models can also be applied to longitudinal data with missing observations, a common hindrance to fitting ANOVA models. Able to be used across many data situations, mixed models are a form of regression analysis that is essential for any pioneering data analyst to have available to them.
This introductory in-person workshop covers the basics of building linear statistical models, covering standard regression analysis, interpreting categorical predictors and using interaction terms. We introduce exactly what mixed models are, what data is suited to mixed models, how to apply mixed models in R, and how to properly interpret the results. We will also cover the basics of using R and RStudio and give an overview of the tidyverse functions that can help get your data in the correct form for analysis. No prior knowledge of R is necessary. We will also show you how to create appealing data visualisations for clustered data using ggplot2.
Instructors: Prof Marijka Batterham and Brad Wakefield from the Statistical Consulting Centre
Contact Bradley Wakefield for any inquiries email@example.com