Book recommendation: Longitudinal data analysis using structural equation models

In the wake of our recent posts about longitudinal studies we’d like to recommend a recently published book by By John J. McArdle and John R. Nesselroade.

b2ap3_thumbnail_McArdleNesselroade2014

Longitudinal studies are on the rise, no doubt. Properly conducting longitudinal studies and then analyzing the data can be a complex undertaking. John McArdle and John Nesselroade focus on five basic questions that can be tackled with structural equation models, when analyzing longitudinal data:

  • Direct identification of intraindividual changes.
  • Direct identification of interindividual differences in intraindividual changes.
  • Examining interrelationships in intraindividual changes.
  • Analyses of causes (determinants) of intraindividual changes.
  • Analyses of causes (determinants) of interindividual differences in intraindividual changes.

I find it especially noteworthy, that the authors put an emphasis on factorial invariance over time and latent change scores. In my view, this makes this book a must read to become a longitudinal data wizard.

Need another argument? Afraid of cumbersome mathematical language? Here is what the authors say about it: „We focus on the big picture approach rather than the algebraic details.“

 

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