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Multivariate Applications Series
Longitudinal Analysis
Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter. Written for non-mathematically-oriented readers, this text The book opens with the building blocks of longitudinal analysis―general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. Conditional longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data. Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book’s accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.
654 pages, Paperback
First published November 30, 2014
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Displaying 1 of 1 review
February 22, 2022
This is a comprehensive book on longitudinal models for within person fluctuation and change, with an applied emphasis. That doesn’t mean the book shies away from theory; it’s just that you wont see matrix algebra on every other page. In fact, nothing other than an elementary understanding of matrices, algebra and some high school math is required to grasp the necessary theoretical background for the topics covered. The book also provides syntax and code for SAS, STATA, SPSS and Mplus, and example datasets (real and simulated) on a companion website
Content summary: the book starts off with an overview (big picture) of all the topics to be discussed.
The next chapter provides a comprehensive review of the general linear model (between person analysis).
Then you get a prelude to longitudinal models by introducing within person analysis (univariate and multivariate ANOVAs), and techniques for making model comparisons.
After this, within person fluctuation is discussed with alternative covariance structures.
Random effects for time and model estimation are introduced next as we start getting into within person change.
The next chapter delves deeper into exploring within person change by modeling non linear trends using polynomial, exponential, logistic models and piecewise models.
After this, Time invariant predictors are discussed with an appendix on evaluating assumptions about model residuals (very useful).
There’s also an appendix in chapter 6 which goes over a complete checklist for building unconditional models of within person change, which is very important. To set up before including time varying and between person predictors. Many chapter have such useful appendices.
This is as far as i have read (used only lectures and notes for further topics), but for completeness: The next 2 chapters discuss time varying predictors for fluctuation and change, followed by a further section with 4 chapters on advanced applications.
What i liked about this book in a nutshell:
1) Each topic is introduced with thorough explanation and justification along with the underlying theory. There is a whole section on model estimation in one chapter.
2) Concepts have a flow and connectedness to them; each chapter builds on the previous ones
3) (Important) the author mentions caveats, drawbacks and alternatives for the methods introduced throughout the book, with due concern for assumptions of these methods. This is something i really liked about the book and is present throughout
4) Examples are clear and the author runs through the analysis output for them in every chapter with discussion of practical concerns
5) Tables and easy to understand visualizations are used appropriately (There’s a really nice chart for ACS models that shows how they’re related)
6) The book takes an integrated approach where the applications of longitudinal models are discussed as particular instances of a more general framework
It’s not an easy task to write a readable and comprehensive book on such a topic without bombarding the reader with matrix algebra and mathematical statistics on every other page, so expect the book to be wordy (it is unavoidable). I liked it and will be using it in the future for the topics i haven’t covered yet.
Readers should also note that the author has recordings of her class lectures on her YouTube channel, which follow the book closely with plenty of extra nuggets of wisdom. Check it out.
Content summary: the book starts off with an overview (big picture) of all the topics to be discussed.
The next chapter provides a comprehensive review of the general linear model (between person analysis).
Then you get a prelude to longitudinal models by introducing within person analysis (univariate and multivariate ANOVAs), and techniques for making model comparisons.
After this, within person fluctuation is discussed with alternative covariance structures.
Random effects for time and model estimation are introduced next as we start getting into within person change.
The next chapter delves deeper into exploring within person change by modeling non linear trends using polynomial, exponential, logistic models and piecewise models.
After this, Time invariant predictors are discussed with an appendix on evaluating assumptions about model residuals (very useful).
There’s also an appendix in chapter 6 which goes over a complete checklist for building unconditional models of within person change, which is very important. To set up before including time varying and between person predictors. Many chapter have such useful appendices.
This is as far as i have read (used only lectures and notes for further topics), but for completeness: The next 2 chapters discuss time varying predictors for fluctuation and change, followed by a further section with 4 chapters on advanced applications.
What i liked about this book in a nutshell:
1) Each topic is introduced with thorough explanation and justification along with the underlying theory. There is a whole section on model estimation in one chapter.
2) Concepts have a flow and connectedness to them; each chapter builds on the previous ones
3) (Important) the author mentions caveats, drawbacks and alternatives for the methods introduced throughout the book, with due concern for assumptions of these methods. This is something i really liked about the book and is present throughout
4) Examples are clear and the author runs through the analysis output for them in every chapter with discussion of practical concerns
5) Tables and easy to understand visualizations are used appropriately (There’s a really nice chart for ACS models that shows how they’re related)
6) The book takes an integrated approach where the applications of longitudinal models are discussed as particular instances of a more general framework
It’s not an easy task to write a readable and comprehensive book on such a topic without bombarding the reader with matrix algebra and mathematical statistics on every other page, so expect the book to be wordy (it is unavoidable). I liked it and will be using it in the future for the topics i haven’t covered yet.
Readers should also note that the author has recordings of her class lectures on her YouTube channel, which follow the book closely with plenty of extra nuggets of wisdom. Check it out.
Displaying 1 of 1 review