How Long to Read Growth Curve Analysis and Visualization Using R

How Long Does it Take to Read Growth Curve Analysis and Visualization Using R?

It takes the average reader 3 hours and 13 minutes to read Growth Curve Analysis and Visualization Using R by Daniel Mirman

Assuming a reading speed of 250 words per minute. Learn more

Description

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.

How long is Growth Curve Analysis and Visualization Using R?

Growth Curve Analysis and Visualization Using R by Daniel Mirman is 192 pages long, and a total of 48,384 words.

This makes it 65% the length of the average book. It also has 59% more words than the average book.

How Long Does it Take to Read Growth Curve Analysis and Visualization Using R Aloud?

The average oral reading speed is 183 words per minute. This means it takes 4 hours and 24 minutes to read Growth Curve Analysis and Visualization Using R aloud.

What Reading Level is Growth Curve Analysis and Visualization Using R?

Growth Curve Analysis and Visualization Using R is suitable for students ages 10 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy Growth Curve Analysis and Visualization Using R?

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