Growth Curve Analysis and Visualization Using R

Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Growth Curve Analysis and Visualization Using R by Daniel Mirman, CRC Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Daniel Mirman ISBN: 9781315360331
Publisher: CRC Press Publication: September 7, 2017
Imprint: Chapman and Hall/CRC Language: English
Author: Daniel Mirman
ISBN: 9781315360331
Publisher: CRC Press
Publication: September 7, 2017
Imprint: Chapman and Hall/CRC
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from CRC Press

Cover of the book Cereals in Breadmaking by Daniel Mirman
Cover of the book Extreme Environment Electronics by Daniel Mirman
Cover of the book Smart Grids by Daniel Mirman
Cover of the book Naturally Occurring Phorbol Esters by Daniel Mirman
Cover of the book Strength, Cuttability, and Workability of Coal by Daniel Mirman
Cover of the book Image Sensors and Signal Processing for Digital Still Cameras by Daniel Mirman
Cover of the book Aircraft Communications and Navigation Systems, 2nd ed by Daniel Mirman
Cover of the book Power System Protective Relaying by Daniel Mirman
Cover of the book Mathematical Theory of Optimal Processes by Daniel Mirman
Cover of the book Developing a Narrative Approach to Healthcare Research by Daniel Mirman
Cover of the book Quasi-Exactly Solvable Models in Quantum Mechanics by Daniel Mirman
Cover of the book Endocrine Surgery by Daniel Mirman
Cover of the book Thermodynamics and Ecological Modelling by Daniel Mirman
Cover of the book Pharmacological Regulation of Gene Expression in the CNS Towards an Understanding of Basal Ganglial Functions (1996) by Daniel Mirman
Cover of the book Real Estate and Urban Development in South America by Daniel Mirman
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy