Time Course Data
Chapter overview
What are "time course data"?
Key challenges in analyzing time course data
Visualizing time course data
Formatting data for analysis and plotting
Conceptual Overview of Growth Curve Analysis
Chapter overview
Structure of a growth curve model
A simple growth curve analysis
Another example: Visual search response times
When Change over Time Is Not Linear
Chapter overview
Choosing a functional form
Using higher-order polynomials
Example: Word learning
Parameter-specific p-values
Reporting growth curve analysis results
Structuring Random Effects
Chapter overview
"Keep it maximal"
Within-participant effects
Participants as random vs. fixed effects
Visualizing effects of polynomial time terms
Categorical Predictors
Chapter overview
Coding categorical predictors
Multiple comparisons
Binary Outcomes: Logistic GCA
Chapter overview
Why binary outcomes need logistic analyses
Logistic GCA
Quasi-logistic GCA: Empirical logit
Plotting model fits
Individual Differences
Chapter overview
Individual differences as fixed effects
Individual differences as random effects
Complete Examples
Linear change
Orthogonal polynomials
Within-subject manipulation
Logistic GCA
Quasi-logistic GCA
Individual differences as fixed effects
Individual differences as random effects
References
Index
Exercises appear at the end of each chapter.
Biography
Mirman, Daniel
"… an up-to-date, practical introduction to visualizing and modeling time course and multilevel data. It is particularly well suited to applied researchers in the fields of cognitive science, neuroscience, and linguistics. Virtually no familiarity with R is required (although it helps). … Detailed code examples are given using lme4 for linear and logistic growth curve models and ggplot2 for graphing. … The writing is clear and easy to follow, without jargon …"
—Joshua F. Wiley, Journal of Statistical Software, June 2014
