Assessing Causality

Foundations for Population-Based Health Care Managerial Decision Making
  • Thomas T. H. Wan


This chapter is written to identify and explain the fundamental principles in conducting causal analysis in health services management. Epidemiological study designs and methods are reviewed and illustrated as the orientation of health care managers is increasingly focused on population-based concerns. The application of the scientific thinking, and hence a knowl- edge management approach to health service problems, can guide the development and implementation of solution sets (i.e., interventions). The principal criteria of causality and their application in the conduct of causal analysis are central to developing scientific, evidence- based knowledge for navigating organizational changes (Keats and Hitt, 1988)and innovations (Scott and Bruce, 1994).

The causal approach advocated here does not imply that employing explicit and practical knowledge in organizational sciences can solve every managerial problem. However, causal analysis and its application can make it possible to search more efficiently for errors that may be amenable to organizational and behavioral interventions. The health care manager thinking causally will recognize that multiple pathways, intermediate factors, the measures used and the multiple levels of effect that risk factors have along the pathway affect the differences in outcomes observed.


Intimate Partner Violence Structural Equation Modeling Multilevel Analysis Motor Vehicle Accident Causal Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Al-Haider, A. S., and Wan, T. T. H., 1991, Modeling organizational determinants of hospital mortality, Health Services Res. 26:303–323.Google Scholar
  2. Bender, R., and Grouven, U., 1997, Ordinal logistic regression in medical research, J. R. College Physicians Lond. 31:546–551.Google Scholar
  3. Boles, R., and Wan, T. T. H., 1992, Longitudinal analysis of patient satisfaction among Medicare beneficiaries in different HMOs and fee-for-service care, Health Serv. Manage. Res. 5:198–206.PubMedGoogle Scholar
  4. Bollen, K. A., 1989, Structural Equations With Latent Variables, John Wiley & Sons, New York.Google Scholar
  5. Bollen, K. A., 2000, Modeling strategies; In search of the holy grail, Structural Equation Modeling 7:74–81.CrossRefGoogle Scholar
  6. Bryk, A. S., and Raudenbush, S. W., 1992, Hierarchical Linear Models, Sage Publications, Newbury Park, CA.Google Scholar
  7. Bullock, H. E., Harlow, L. L., and Mulaik, S. A., 1994, Causation issues in SEM research, Structural Equation Modeling 1:253–267.Google Scholar
  8. Campbell, R. T., Mutran, E., and Parker, R. N., 1986, Longitudinal design and longitudinal analysis, Res. Aging 8:480–502.PubMedGoogle Scholar
  9. Chalmers, T. C., 1988, Data Analysis for Clinical Medicine, International University Press, New York.Google Scholar
  10. Cunradi, C. B., Caetano, R., Clark, C., and Schafer, J., 2000, Neighborhood poverty as a predictor of intimate partner violence among white, black, and Hispanic couples in the United States: A multilevel analysis, Ann. Epidemiol. 19:297–308.Google Scholar
  11. Duncan, T. E., Alpert, A., and Duncan, S. C., 1998, Multilevel covariance structure analysis of sibling antisocial behavior, Structure Equation Modeling 5:211–228.Google Scholar
  12. Greenland, S., 1994, Invited commentary: A critical look at some popular meta-analytic methods, Am. J. Epidemiol. 140:290–296.PubMedGoogle Scholar
  13. Hays, R. D., Marchall, G. N., Yu, E., Wang, I., and Sherbourne, C. D., 1994, Four year crosslagged associations between physical and mental health in the medical outcomes study, J. Consult. Clin. Psychol. 62:441–449.PubMedGoogle Scholar
  14. Heck, R. H., and Thomas, S. L., 2000, An Introduction to Multilevel Modeling Techniques, Lawrence Erlbaum Associates Publishers, Mahwah, NJ.Google Scholar
  15. Hill, A. B., 1965, The environment and disease: Association or causation? Proc. R. Soc. Med. 58:295–300.PubMedGoogle Scholar
  16. Hoffman, D. A., 1997, An overview of the logic and rationale of hierarchical linear models, J. Management 23:723–744.Google Scholar
  17. Hosmer, D. W., Jr., and Lemeshow, S., 1989, Applied Logistic Regression, John Wiley & Sons, New York.Google Scholar
  18. Howard, G., Anderson, R. T., Russell, G., Howard, V. J., and Burke, G., 2000, Race, socioeconomic status, and causespecific mortality, Ann. Epidemiol. 10:214–223.PubMedCrossRefGoogle Scholar
  19. Jöreskog, K. G., 1977, Structural equation models in the social sciences: Specification, estimation and testing, in Applications of Statistics (P. R. Krishnaiah, ed.), pp. 265–287, North-Holland Publishing, Amsterdam.Google Scholar
  20. Jöreskog, K. G., 1993, Testing structural equation models, in Testing Structural Equation Models (K. A. Bollen and J. S. Long, eds.), pp. 294–316, Sage Publications, Newbury Park, CA.Google Scholar
  21. Jöreskog, K. G., and Sörbom, D., 1979, Advances in Factor Analysis and Structural Equation Models, ABT Books, Cambridge, MA.Google Scholar
  22. Kaplan, D., and Elliott, P. R., 1997, A didactic example of multilevel structural equation modeling applicable to the study of organizations, Structural Equation Modeling 4:1–24.Google Scholar
  23. Keats, B. W., and Hitt, M., 1988, A causal model of linkages among environmental dimensions, macro-organizational characteristics, and performance, Acad. Manage. J. 31:570–598.Google Scholar
  24. Liebowitz, J. (ed.), 1999, Knowledge Management: Handbook, CRC Press, New York.Google Scholar
  25. Long, J. S., 1983a, Confirmatory Factor Analysis, Sage Publications, Beverly Hills, CA.Google Scholar
  26. Long, J. S., 1983b, Covariance Structure Models: An Introduction to LISREL, Sage Publications, Beverly Hills, CA.Google Scholar
  27. Morgenstern, H., 1988, Ecologic studies, in Modem Epidemiology K. J. Rothman and S. Greenland, eds., pp. 459–480, Lippincott Williams and Wilkins, Chestnut Hill, MA.Google Scholar
  28. Mulaik, S. A., 1987, Toward a conception of causality applicable to experimentation and causal modeling, Child Dev. 58:18–32.CrossRefGoogle Scholar
  29. Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., and Stilwell, C. D., 1989, Evaluation of goodness-off-it indices for structural equation models, Psychol. Bull. 105:430–445.CrossRefGoogle Scholar
  30. Muthén, B. O., 1991, Multilevel factor analysis of class and student achievement components, J. Educ. Measure. 28:338–354.Google Scholar
  31. Muthén, B. O., 1994, Multilevel covariance structure analysis, Social. Res. Meth. Res. 22:376–399.Google Scholar
  32. Phillips, K. A., Morrison, K. R., Anderson, R., and Aday, L. A., 1998, Understanding the context of healthcare utilization: Assessing environmental and provider-related variables in the behavioral model of utilization, Health Ser. Res. 33:571–596.Google Scholar
  33. Rothman, K. J., and Greenland, S., 1998, Causation and causal inference, in Modern Epidemiology (K. J. Rothman and S. Greenland, eds.), pp. 7–28, Lippincott Raven, Philadelphia.Google Scholar
  34. Sahin, I., Wan, T. T. H., and Sahin, B., 1999, The determinants of CABG patients’ outcomes, Health Care Manage. Sci. 2:215–222.CrossRefGoogle Scholar
  35. SAS Institute Inc., 1995 Logistic regression examples using the SAS® system, version 6, first edition, SAS Institute, Cary, NC.Google Scholar
  36. Scott, S. G. and Bruce, R. A., 1994, Determinants of innovative behavior, Acad. Manage. J. 37:580–607.Google Scholar
  37. Short, L. M., and Hennessy, M., 1994, Using structural equations to estimate effects of behavioral interventions, Structural Equation Modeling 1:68–81.Google Scholar
  38. SPSS, Inc., 1999, SPSS Advanced Models™ 10.0, Chicago, Illinois.Google Scholar
  39. Susser, M., 1991, What is a cause and how do we know one? A grammar for pragmatic epidemiology, Am. J. Epidemiol. 133:635–648.PubMedGoogle Scholar
  40. Szeinbach, S. L., Barnes, J. H., and Summers, K. H., 1995, Comparison of a behavioral model of physicians’ drug product choice decision with pharmacists’; product choice recommendations: A study of the choice for the treatment of panic disorder, Structural Equation Modeling 2:232–245.CrossRefGoogle Scholar
  41. Takkouche, B., Cadarso-Sqarez, C., and Spiegelman, D., 1999, Evaluation of old and new tests of heterogeneity in epidemiologic meta analysis, Am. J. Epidemiol. 150:206–215.PubMedGoogle Scholar
  42. Wan, T. T. H., 1992, Hospital variations in adverse patient outcomes, Qual. Assurance Utilization Rev. 7:50–53.Google Scholar
  43. Wan, T. T. H., 1995, Analysis and Evaluation of Health Systems: An Integrated Approach to Managerial Decision Making, Health Professions Press, Baltimore, MD.Google Scholar
  44. Wan, T. T. H., Pai, C. W., and Wan, G. J., 1998, Organizational and market determinants of HMOs’ performance in preventive practice, J. Healthcare Qual. 20:14–129.Google Scholar
  45. Wong, G. Y., and Mason, W. M., 1985, The hierarchical logistic regression model for multilevel analysis, J. Am. Stat. Assoc. 80:513–524.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Thomas T. H. Wan
    • 1
  1. 1.Department of Health Administration, School of Allied Health ProfessionsVirginia Commonwealth UniversityRichmond

Personalised recommendations