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Multivariate Applications Series
Modeling Intraindividual Variability With Repeated Measures Data: Methods and Applications
'.... provides all of the keys to analyzing the flow of material, information, and services within a business...'- from the jacket.
296 pages, Hardcover
First published October 1, 2001
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August 30, 2009
Deb Moskowitz and Scott Hershberger have edited a collection of essays on new techniques (or at least what were new techniques in 2002) for analyzing intra-individual data. The chapter in this book on dynamic factor analysis started me on a long, convoluted path where I do lots of crazy statistics on psychological data, so I cannot help but love this book. In particular, I applied dynamic factor analysis (DFA) to psychotherapy data and I do not believe I could have done my first DFA without this book.
Moskowitz was Reuben Barron's student -- if you're a psychologist, you are undoubtedly familiar with Barron and Dave Kenny's paper on the mediator-moderator distinction in psychology, but most people are familiar with Kenny rather than Barron. Hershberger is a quantitative psychologist who has done twins research and published with the twin researcher for whom I have been doing statistics. They have assembled articles from several individuals researching psychological constructs and using cutting edge statistical techniques. Some of the famous researchers include Dave Kenny, Steve Raudenbush, Patrick Curran, Terry Duncan, Jack McArdle, John Nesselroade, and Judy Singer. There is a good deal of information on growth curve modeling, both from a multilevel modeling perspective and from a structural equation modeling perspective. Furthermore, there are numerous sections with sample code for various programs (e.g., SAS, LISREL, etc.).
I give the DFA chapter to my students -- it is quantitatively rigorous but not so rigorous that it is inaccessible to those with a firm grasp in univariate statistics and a basic grasp of multivariate statistics. Furthermore, the code aspect is nice in that it translates more abstract statistical matters into concrete, applicable examples. In some cases, I wish more mathematical detail was provided, but the level is generally good. Also, there are a few errors present in chapters, but not so many to deter from the great impact this book might have.
The graphics in this book are of pretty low quality. In fact, I am unclear what program was used to make some of the graphics, and they seem especially poor. Still, that was not the main purpose of the book. I wish that someone would write a book on producing graphics for longitudinal and intra-individual data.
Since the release of this book, Bength Muthen has done much work on growth mixture modeling, and these techniques are notably absent from the text. Furthermore, methods of survival analysis, including frailty and event history models, are also absent from this text. Finally, Daniel Nagin's work on trajectory modeling is also omitted. These techniques are newer and there are other places a researcher might go to find them, but the text still does a good job of describing recent developments in the field.
Moskowitz was Reuben Barron's student -- if you're a psychologist, you are undoubtedly familiar with Barron and Dave Kenny's paper on the mediator-moderator distinction in psychology, but most people are familiar with Kenny rather than Barron. Hershberger is a quantitative psychologist who has done twins research and published with the twin researcher for whom I have been doing statistics. They have assembled articles from several individuals researching psychological constructs and using cutting edge statistical techniques. Some of the famous researchers include Dave Kenny, Steve Raudenbush, Patrick Curran, Terry Duncan, Jack McArdle, John Nesselroade, and Judy Singer. There is a good deal of information on growth curve modeling, both from a multilevel modeling perspective and from a structural equation modeling perspective. Furthermore, there are numerous sections with sample code for various programs (e.g., SAS, LISREL, etc.).
I give the DFA chapter to my students -- it is quantitatively rigorous but not so rigorous that it is inaccessible to those with a firm grasp in univariate statistics and a basic grasp of multivariate statistics. Furthermore, the code aspect is nice in that it translates more abstract statistical matters into concrete, applicable examples. In some cases, I wish more mathematical detail was provided, but the level is generally good. Also, there are a few errors present in chapters, but not so many to deter from the great impact this book might have.
The graphics in this book are of pretty low quality. In fact, I am unclear what program was used to make some of the graphics, and they seem especially poor. Still, that was not the main purpose of the book. I wish that someone would write a book on producing graphics for longitudinal and intra-individual data.
Since the release of this book, Bength Muthen has done much work on growth mixture modeling, and these techniques are notably absent from the text. Furthermore, methods of survival analysis, including frailty and event history models, are also absent from this text. Finally, Daniel Nagin's work on trajectory modeling is also omitted. These techniques are newer and there are other places a researcher might go to find them, but the text still does a good job of describing recent developments in the field.
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