Growth Curve Analysis and Visualization Using R: 1st Edition (Hardback) book cover

Growth Curve Analysis and Visualization Using R

1st Edition

By Daniel Mirman

Chapman and Hall/CRC

188 pages | 14 B/W Illus.

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Hardback: 9781466584327
pub: 2014-02-24
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pub: 2017-09-07
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Learn How to Use Growth Curve Analysis with Your Time Course Data

An increasingly prominent statistical tool in the behavioral sciences, multilevel regression offers a statistical framework for analyzing longitudinal or time course data. It also provides a way to quantify and analyze individual differences, such as developmental and neuropsychological, in the context of a model of the overall group effects. To harness the practical aspects of this useful tool, behavioral science researchers need a concise, accessible resource that explains how to implement these analysis methods.

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.


"… 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

Table of Contents

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



Exercises appear at the end of each chapter.

About the Originator

About the Series

Chapman & Hall/CRC The R Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General