New statistical methods: their role in health promotion research and evaluation practice – Part II

Roberto Forero, Jack Chen, Chris Rissel, Adrian Bauman

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Issue addressed: This second paper continues to explore advanced statistical methods and their application in health promotion research and evaluation of intervention programs; in particular, the application of multiple linear regression; Cox regression models; logistic regression (binary, multinomial and ordinal regression) and structural equation modelling is reviewed. Discussion: These statistical techniques are tools that can advance the field of health promotion research. They should be considered in planning program evaluations and applied (or via a consultant statistician) in the analysis of program or population survey data. Health promotion practitioners need to understand when traditional analytic methods are inadequate, and when and how advanced statistical methods should be used. So what?: As the health promotion workforce develops, it is important for practitioners to appreciate the availability of and place for advanced statistical methods. Practitioners should understand the principles underpinning such techniques in order to make better use of statisticians and other methodological experts.

Original languageEnglish
Pages (from-to)242-246
Number of pages5
JournalHealth Promotion Journal of Australia
Issue number3
Publication statusPublished - Jan 2002
Externally publishedYes


  • advanced statistical models
  • Evaluation
  • statistical methods
  • structural equation modelling


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