R Workshop on Using Linear Models, Logistic Regression, and Growth Curve Analyses to Analyze Eye-tracking Data

A 4-part series culminating in using growth-curve analyses to model eye-tracking data.

Introduction to R

What are dataframes and vectors? How do R functions work? How do statistical tests in R work? How can I import and export data?

General Linear Models

How can I fit linear models in R? When should I use aov() and when should I use lm()? How can I interpret parameter estimates (without the help of SPSS...)?

Generalized Linear Models

How can I use generalized linear models (e.g., logistic regression) to do time-based eye-tracking analyses? How can I use empirical logit regression to the same end? And the arcsin-root transformation? How do mixed-effects models' random effects (intercepts and slopes) work in lmer()?

Growth Curve Analyses

How do I look at non-linear change over time? What are the differences between natural and orthogonal polynomials? How can interpret estimates in a growth curve model versus an empirical logit model? How can I visualize my raw data and model fits simultaneously?

Acknowledgments

• Dan Mirman for GCA techniques
• Dale Barr for empirical logit regression
• Florian Jaeger for mixed-effects models
• Mike Frank and the Wordbank team for vocabulary data in the first two tutorials