Growth curve analysis and visualization using R

Table of Contents:

1. 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

2. 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

3. 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

4. Structuring Random Effects

Chapter overview
"Keep it maximal"
Within-participant effects
Participants as random vs. fixed effects
Visualizing effects of polynomial time terms

5. Categorical Predictors

Chapter overview
Coding categorical predictors
Multiple comparisons

6. Binary Outcomes: Logistic GCA

Chapter overview
Why binary outcomes need logistic analyses
Logistic GCA
Quasi-logistic GCA: Empirical logit
Plotting model fits

7. Individual Differences

Chapter overview
Individual differences as fixed effects
Individual differences as random effects

8. 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

Growth Curve Analysis and Visualization Using R provides a practical, easy-to-understand guide to carrying out multilevel regression/growth curve analysis (GCA) of time course or longitudinal data in the behavioral sciences, particularly cognitive science, cognitive neuroscience, and psychology. With a minimum of statistical theory and technical jargon, the author focuses on the concrete issue of applying GCA to behavioral science data and individual differences.

The book begins with discussing problems encountered when analyzing time course data, how to visualize time course data using the ggplot2 package, and how to format data for GCA and plotting. It then presents a conceptual overview of GCA and the core analysis syntax using the lme4 package and demonstrates how to plot model fits. The book describes how to deal with change over time that is not linear, how to structure random effects, how GCA and regression use categorical predictors, and how to conduct multiple simultaneous comparisons among different levels of a factor. It also compares the advantages and disadvantages of approaches to implementing logistic and quasi-logistic GCA and discusses how to use GCA to analyze individual differences as both fixed and random effects. The final chapter presents the code for all of the key examples along with samples demonstrating how to report GCA results.

Throughout the book, R code illustrates how to implement the analyses and generate the graphs. Each chapter ends with exercises to test your understanding. The example datasets, code for solutions to the exercises, and supplemental code and examples are available on the author’s website.


(https://www.crcpress.com/Growth-Curve-Analysis-and-Visualization-Using-R/Mirman/9781466584327)