Quality & Quantity

, Volume 47, Issue 1, pp 567–576 | Cite as

Increasing the capacity of conceptual diagrams to embrace contextual complexity

  • David W. Britt
  • Yung-Chou Chen
Research Note


Conceptual models or diagrams have been used for many years to visually convey information, and they represent a subset of graphic presentation more generally. Historically, they have been used to describe, interpret and explain relationships among concepts deemed relevant to the understanding of some phenomena. This paper makes specific recommendations for increasing the capacity of models to clearly convey a particular form of complexity, that involving multiple contexts or levels of analysis. In particular, the inclusion of negotiated or constructed contexts as contexts, the use of small multiples to create a dialog with orienting models around specification decisions, and the representation of alternate scenarios are recommended.


Multi-level analysis Conceptual modeling Context 


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  1. Bickel R.: Multilevel Analysis for Applied Research. Guilford Press, New York (2007)Google Scholar
  2. Blalock H.M.: Correlation and causation. Soc. Forces 39, 246–251 (1961)CrossRefGoogle Scholar
  3. Blalock H.M.: Causal Inference in Non-experimental Design. University of North Carolina Press, Chapel Hill (1964)Google Scholar
  4. Blakely T., Subramanian S.V.: Multilevel studies. In: Oakes, J.M., Kaufman, J.S. (eds) Methods in Social Epidemiology, pp. 316–340. Jossey-Bass, San Fransisco (2006)Google Scholar
  5. Britt, D.W.: An Introduction to Conceptual Modeling. Lawrence Earlbaum, Manwah. Available on-line at (1997)
  6. Britt D.W.: Beyond elaborating the obvious: Context-dependent parent-involvement scenarios in a preschool program. Appl. Behav. Sci. Rev. 6, 179–197 (1998)CrossRefGoogle Scholar
  7. Britt D.W., Campbell E.Q.: Assessing the linkage of norms, environments and deviance. Soc. Forces 56, 532–549 (1977)Google Scholar
  8. Britt D.W., Risinger S.T., Mans M., Evans M.: Devastation and relief: conflicting meanings in discovering fetal anomalies. Ultrasound Obstet. Gynecol. 20, 1–5 (2002)CrossRefGoogle Scholar
  9. Diez-Roux A.V.: Bringing context back into epidemiology: variables and fallacies in multi-level analysis. Am. J. Public Health 88, 216–222 (1998)CrossRefGoogle Scholar
  10. Dohrenwend B., Dohrenwend B.S.: Socio-environmental factors, stress, and psychopathology. Am. J. Community Psychol. 9, 12–159 (1961)Google Scholar
  11. Earp J.A., Ennett S.T.: Conceptual models for health education research and practice. Health Educ. Res. 6, 163–171 (1991)CrossRefGoogle Scholar
  12. Glymour M.M.: Using causal diagrams to understand common problems in social epidemiology. In: Oakes, J.M., Kaufman, J.S. (eds) Methods in Social Epidemiology, pp. 393–428. Jossey-Bass, San Fransisco (2006)Google Scholar
  13. Greenland S., Pearl J., Robins J.M.: Causal diagrams for epidemiologic research. Epidemiology 10, 37–48 (1999)CrossRefGoogle Scholar
  14. Krieger N.: Epidemiology and the web of causation: has anyone seen the spider?. Soc. Sci. Med. 39, 887–903 (1994)CrossRefGoogle Scholar
  15. Merlo J., Chaix B., Yang M., Lynch J., Râstam.: A brief conceptual tutorial on multilevel analysis in social epidemiology: interpreting neighborhood differences and the effect of neighborhood characteristics on individual health. J. Epidemiol. Community Health 59, 1022–1029 (2005)CrossRefGoogle Scholar
  16. Otten R., Wanner B., Vitaro F., Engels R.C.M.E.: Disruptiveness, per experiences and adolescent smoking: a long-term longitudinal approach. Addiction 104, 641–650 (2009)CrossRefGoogle Scholar
  17. Paradies I., Stevens W.: Conceptual diagrams in public health research. J. Epidemiol. Community Health 59, 1012–1013 (2009)CrossRefGoogle Scholar
  18. Pearl J.: Causality. Cambridge University Press, Cambridge (2009)Google Scholar
  19. Ragin C.: The Comparative Method. University of California Press, Berkeley (1987)Google Scholar
  20. Richiardi L., Barone-Adesi F., Merletti F., Pearce N.: Using directed acyclic graphs to consider adjustment for socioeconomic status in occupational cancer studies. J. Epidemiol. Community Health 62, e14–e20 (2008)CrossRefGoogle Scholar
  21. Rothman K.J.: Causes. Am. J. Epidemiol. 104, 587–592 (1976)Google Scholar
  22. Rothman K.J., Greenland S.: Causation and causal inference in epidemiology. Am. J. Public Health 95(Suppl 1), S144 (2005)CrossRefGoogle Scholar
  23. Simon, H.A.: Causal ordering and identifiability. In: Hood, W.C., Koopmans, T.C. Studies in Econometric Method, pp. 49–74. John Wiley, New York (1953)Google Scholar
  24. Starfield B.: Equity in health. J. Epidemiol. Community Health 56, 483–484 (2002)CrossRefGoogle Scholar
  25. Tufte E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire (1982)Google Scholar
  26. Tufte E.R.: Envisioning Information. Graphics Press, Cheshire (1990)Google Scholar
  27. Turner L., Mermelstein R., Flay B.: Individual and contextual influences on adolescent smoking. Ann. N Y Acad. Sci. 1021, 175–197 (2004)CrossRefGoogle Scholar
  28. Wilcox P.: An ecological approach to understanding youth smoking rajectories: problems and prospects. Addiction 98(Suppl1), 57–77 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.University of LouisvilleLouisvilleUSA
  2. 2.Sports Affairs CouncilExecutive YuanTaiwan

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