Chasing change: Repeated-measures analysis of variance is so yesterday!

Research output: Contribution to journalComment/debate

10 Scopus citations


Change and growth are the bread and butter of rehabilitation research, but to date, most researchers have used less than optimal statistical methods to quantify change, its nature, speed, and form. Hierarchical linear modeling (HLM) (random/mixed effects or latent growth or multilevel modeling, individual/latent growth curve analysis) generally is superior to analysis of (co)variance and other methods, but has been underused in rehabilitation research. Apropos of the publication of 2 didactic articles setting forth the basics of HLM, this commentary sketches some of the advantages of this technique.

Original languageEnglish
Pages (from-to)597-599
Number of pages3
JournalArchives of Physical Medicine and Rehabilitation
Issue number3
StatePublished - Mar 2013


  • Analysis of variance
  • Growth and development
  • Multivariate analysis
  • Rehabilitation


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