Reconciling Statistical and Systems Science Approaches to Public Health

Edward H. Ip, Hazhir Rahmandad, David A. Shoham, Ross Hammond, Terry T.K. Huang, Youfa Wang, Patricia L. Mabry

Research output: Contribution to journalArticlepeer-review


Although systems science has emerged as a set of innovative approaches to study complex phenomena, many topically focused researchers including clinicians and scientists working in public health are somewhat befuddled by this methodology that at times appears to be radically different from analytic methods, such as statistical modeling, to which the researchers are accustomed. There also appears to be conflicts between complex systems approaches and traditional statistical methodologies, both in terms of their underlying strategies and the languages they use. We argue that the conflicts are resolvable, and the sooner the better for the field. In this article, we show how statistical and systems science approaches can be reconciled, and how together they can advance solutions to complex problems. We do this by comparing the methods within a theoretical framework based on the work of population biologist Richard Levins. We present different types of models as representing different tradeoffs among the four desiderata of generality, realism, fit, and precision.

Original languageEnglish (US)
Pages (from-to)123S-131S
JournalHealth Education and Behavior
Issue number1 SUPPL.
StatePublished - 2013
Externally publishedYes


  • Levins framework
  • agent-based model
  • childhood obesity
  • complex systems
  • computational model
  • social network analysis
  • statistical model
  • system dynamics model

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Public Health, Environmental and Occupational Health


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