Free PDF Longitudinal Data Analysis for the Behavioral Sciences Using R, by Jeffrey D. Long
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Longitudinal Data Analysis for the Behavioral Sciences Using R, by Jeffrey D. Long
Free PDF Longitudinal Data Analysis for the Behavioral Sciences Using R, by Jeffrey D. Long
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This book is unique in its focus on showing students in the behavioral sciences how to analyze longitudinal data using R software. The book focuses on application, making it practical and accessible to students in psychology, education, and related fields, who have a basic foundation in statistics. It provides explicit instructions in R computer programming throughout the book, showing students exactly how a specific analysis is carried out and how output is interpreted.
- Sales Rank: #571828 in eBooks
- Published on: 2011-10-31
- Released on: 2013-06-04
- Format: Kindle eBook
About the Author
Jeffrey D. Long, PhD, is Professor of Psychiatry in the Carver College of Medicine at the University of Iowa. He is also the Head Statistician for Neurobiological Predictors of Huntington's Disease (PREDICT-HD), a longitudinal NIH-funded study of early detection of Huntington's Disease. His undergraduate degree is from the University of California at Los Angeles, and his doctoral degree is from the University of Southern California in the area of quantitative psychology.
Most helpful customer reviews
7 of 7 people found the following review helpful.
Wonderful!
By 2353ak
If you are looking for an _applied_ guide to using R's lmer and the lme4 package for your repeated measures/longitudinal analyses, you are home. This book is really, really great.
I came to this text comfortable with both R and mixed models, but I have had no formal guidance using the lme4 package or its predecessor (nlme). I had a hard time finding any easily-digestible information about appropriate coding conventions for lme4. I found the free R code for this textbook online, and it was great! Then I started reading the textbook online (some portions are available for free on Google Books). At that point, I realized that I just had to have the full book in my hands. It was that useful, and very readable. I was tired of scrounging around on the R message boards. I needed one-stop shopping - and nearly everything I need is in this book. The only thing missing is a thorough discussion of assumption violations and subsequent approaches should they arise. I guess Long assumes you have thorough knowledge of how to test and correct for assumption violations in R already, which could be an issue for readers.
I am no stats or math whiz, and I don't have a thorough or fluent understanding of the inner mathy workings of mixed models or of the lme4 package, so I can't provide any critique of the text on those grounds. Hopefully someone else will provide some notes there. For what it's worth, I can say that I did some reading on the R message boards prior to purchasing this book, and nothing I've read in Long violates anything I read/skimmed by Douglas Bates or Ben Bolker (the authors of lme4). Everything in the text is also consistent with suggestions from stats-oriented professors in my department (I'm a PhD student).
Bottom line: Readable, practical guide to longitudinal analysis using lme4. Abundant R code. Understandable, clear use of sample data.
0 of 0 people found the following review helpful.
would like to understand more about how to handle model comparisons ...
By david fabienke
From the perspective of someone learning mixed models in regards with lm4. Really wanted to understand plotting results of predicted models in original units when DV and time variable are transformed. Couldn't find discussion of how to interpret coefficients from transformation. Also, would like to understand more about how to handle model comparisons when AIC and BIC don't agree. Still found book informative and pretty easy to read. Having explanations of R script very important. Note: I had to return electronic copy because script examples were too small to read and there was no way to fix. Hard copy was fine. Book website could have more resources IMO.
0 of 0 people found the following review helpful.
Excellent Book: Highly Recommended
By JoeT
Very good book with detailed explanations in handling longitudinal data analysis using R.
Pros:
1. Excellent number of worked examples.
2. The explanations for the worked examples were first rate. You could easily use this book for self study.
Cons:
I really can't think of anything that I found wrong or bad about the book.
Summary
I'd recommend this book unconditionally. Do not be put off by the fact that "behavioral science" is in the title, if you plan to use it in another discipline. The explanations for model designs are first rate and can be adapted anywhere.
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