Longitudinal Data Analysis for the Behavioral Sciences Using R
- Jeffrey D. Long - University of Iowa, USA
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.
"This text excels in the explanation of models with the side-by-side use of R, so the audience can see the models in action. There is a gentle coverage of the mathematics driving the models, which does not seem intimidating to a non technical audience."—William Anderson, Cornell University
This is a great book for longitudinal analysis with R. Especially appreciated the detailed discussion about data preparation (which is usually ignored) and the discussion of model selection. Would have wanted to see additional methods such as survival analysis or sequence analysis. Also, I wish there were examples from different, more realistic datasets.
Overall a great applied book for longitudinal analysis with R.
This did not fit my requirements
If Maximum Likelihood Estimation is part of your Syllabus, Chapter 6 of this book should be one of your recommended readings. It is the most clear explanation of ML I ever seen! Practical examples using R are an extraordinary pedagogical tool to facilitate student's comprehension of the process involved in this estimation procedure.
Chapter 4, "Graphing Longitudinal Data" is highly recommended too!
This books has very powerful pedagogical tools for a complex topic.
Unfortunately, SPSS ist the statistical software of choice at the department, so this book is too advanced to introduce R and the longitudinal analysis at the same time.
This textbook is one of the only textbooks on longitudinal data analysis that incorporates R, which is a bonus. However, if one is using it as a textbook for a course, there are no end of chapter exercises in the textbook. Additionally, the authors use the same data set for the entire book. More data sets that could be used both in examples in the book and on homework exercises would be beneficial.
I would definitely recommend this book as part of the longitudinal session during my Advanced Survey Methods module.
This book is excellent, but the selection of methods presented was not broad enough to be used in the course I had planned. I might use chapters of it as the text is extremely well written, but as a general introduction to longitudinal analysis in epidemiology it is not was I was looking for: The chapters on estimation and testing would be a bit tangential for my course, and I lacked something more on time to even data.
I recommend this as supplemental reading for postgraduate students. It is readable text on relatively complex statistics. The R focus is especially useful
On the recommendation list for the upcoming semester.
This is an excellant text and features within our course- many of our students have purchsed this.
Sample Materials & Chapters
Chapter 2: Brief Introduction to R