Abstract
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 language | English (US) |
|---|---|
| Journal | Health Education and Behavior |
| Volume | 40 |
| Issue number | 1 SUPPL. |
| DOIs | |
| State | Published - 2013 |
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Keywords
- agent-based model
- childhood obesity
- complex systems
- computational model
- Levins framework
- social network analysis
- statistical model
- system dynamics model
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health
- Arts and Humanities (miscellaneous)
Cite this
Reconciling Statistical and Systems Science Approaches to Public Health. / Ip, Edward H.; Rahmandad, Hazhir; Shoham, David A.; Hammond, Ross; Huang, Terry T K; Wang, Youfa; Mabry, Patricia L.
In: Health Education and Behavior, Vol. 40, No. 1 SUPPL., 2013.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Reconciling Statistical and Systems Science Approaches to Public Health
AU - Ip, Edward H.
AU - Rahmandad, Hazhir
AU - Shoham, David A.
AU - Hammond, Ross
AU - Huang, Terry T K
AU - Wang, Youfa
AU - Mabry, Patricia L.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - agent-based model
KW - childhood obesity
KW - complex systems
KW - computational model
KW - Levins framework
KW - social network analysis
KW - statistical model
KW - system dynamics model
UR - http://www.scopus.com/inward/record.url?scp=84890925298&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890925298&partnerID=8YFLogxK
U2 - 10.1177/1090198113493911
DO - 10.1177/1090198113493911
M3 - Article
C2 - 24084395
AN - SCOPUS:84890925298
VL - 40
JO - Health Education and Behavior
JF - Health Education and Behavior
SN - 1090-1981
IS - 1 SUPPL.
ER -