全部 标题 作者
关键词 摘要


Empiricism and Theorizing in Epidemiology and Social Network Analysis

DOI: 10.1155/2011/157194

Full-Text   Cite this paper   Add to My Lib

Abstract:

The connection between theory and data is an iterative one. In principle, each is informed by the other: data provide the basis for theory that in turn generates the need for new information. This circularity is reflected in the notion of abduction, a concept that focuses on the space between induction (generating theory from data) and deduction (testing theory with data). Einstein, in the 1920s, placed scientific creativity in that space. In the field of social network analysis, some remarkable theory has been developed, accompanied by sophisticated tools to develop, extend, and test the theory. At the same time, important empirical data have been generated that provide insight into transmission dynamics. Unfortunately, the connection between them is often tenuous and the iterative loop is frayed. This circumstance may arise both from data deficiencies and from the ease with which data can be created by simulation. But for whatever reason, theory and empirical data often occupy different orbits. Fortunately, the relationship, while frayed, is not broken, to which several recent analyses merging theory and extant data will attest. Their further rapprochement in the field of social network analysis could provide the field with a more creative approach to experimentation and inference. 1. Introduction Theory and empirical data are in principle intimately interwoven. Yet in the practice of social network analysis, there appears to be a disconnect: theorizing and empiricism often seem to occupy separate orbits, and these separate discussions may be difficult to relate to each other. The root of the problem may lie in the different skill sets required by each, or perhaps in the substantial obstacles to collection of human network data. The following exploration of the distance between theory and empiricism suggests that a rapprochement would be of considerable benefit to the field. The mid-19th Century American philosopher Charles Peirce coined the term “abduction” (which he also called “retroduction”) to fill a gap he perceived in the territory occupied by induction and deduction. As distilled by Professor Burch [1], Peirce used syllogisms to explain this term, substituting Rule, Case, and Result for the more familiar Major Premise, Minor Premise, and Conclusion. But perhaps more interesting to epidemiologists and social network analysts, he related this logical process to sampling. As Professor Burch explains it, a standard valid syllogism would progress as follows.Rule: All balls in this urn are red.Case: All balls in this particular random sample are

References

[1]  R. Burch, Charles Sanders Peirce. Stanford Encyclopedia of Philosophy, 2009, http://plato.stanford.edu/entries/peirce/.
[2]  S. N. Goodman and D. R. Bellhouse, “Hypothesis tests, and likelihood: implications for epidemiology of a neglected historical debate,” American Journal of Epidemiology, vol. 137, no. 5, pp. 485–501, 1993.
[3]  K. Popper, The Logic of Scientific Discovery, Routledge, London, UK, 1959.
[4]  A. M. Adam, “Farewell to certitude: einstein's novelty on induction and deduction, fallibilism,” Journal for General Philosophy of Science, vol. 31, no. 1, pp. 19–37, 2000.
[5]  R. M. Anderson and G. P. Garnett, “Mathematical models of the transmission and control of sexually transmitted diseases,” Sexually Transmitted Diseases, vol. 27, no. 10, pp. 636–643, 2000.
[6]  M. E. J. Newman, “The structure and function of complex networks,” SIAM Review, vol. 45, no. 2, pp. 167–256, 2003.
[7]  M. S. Handcock and J. H. Jones, “Likelihood-based inference for stochastic models of sexual network formation,” Theoretical Population Biology, vol. 65, no. 4, pp. 413–422, 2004.
[8]  M. S. Handcock, “Modeling social networks with sampled or missing data,” Working Paper 75, CSSS, University of Washington, Seattle, Wash, USA, 2007.
[9]  A. C. Ghani, C. A. Donnelly, and G. P. Garnett, “Sampling biases and missing data in explorations of sexual partner networks for the spread of sexually transmitted diseases,” Statistics in Medicine, vol. 17, no. 18, pp. 2079–2097, 1998.
[10]  M. E. J. Newman, Networks: An Introduction, Oxford University Press, Oxford, UK, 2010.
[11]  N. Pearce and D. Crawford-Brown, “Critical discussion in epidemiology: problems with the Popperian approach,” Journal of Clinical Epidemiology, vol. 42, no. 3, pp. 177–184, 1989.
[12]  C. Buck, “Popper's philosophy for epidemiologists,” International Journal of Epidemiology, vol. 4, no. 3, pp. 159–168, 1975.
[13]  R. Rothenberg and S. Q. Muth, “Large-network concepts and small-network characteristics: fixed and variable factors,” Sexually Transmitted Diseases, vol. 34, no. 8, pp. 604–612, 2007.
[14]  R. B. Rothenberg, D. M. Long, C. E. Sterk et al., “The Atlanta urban networks study: a blueprint for endemic transmission,” AIDS, vol. 14, no. 14, pp. 2191–2200, 2000.
[15]  E. Costenbader and T. W. Valente, “The stability of centrality measures when networks are sampled,” Social Networks, vol. 25, no. 4, pp. 283–307, 2003.
[16]  R. B. Rothenberg, J. J. Potterat, D. E. Woodhouse, W. W. Darrow, S. Q. Muth, and A. S. Klovdahl, “Choosing a centrality measure: epidemiologic correlates in the Colorado Springs study of social networks,” Social Networks, vol. 17, no. 3-4, pp. 273–297, 1995.
[17]  A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509–512, 1999.
[18]  A. L. Barabási, R. Albert, H. Jeong, and G. Bianconi, “Power law distribution of the World Wide Web,” Science, vol. 287, p. 2115, 2000.
[19]  A.-L. Barabási, R. Albert, and H. Jeong, “Scale-free characteristics of random networks: the topology of the world-wide web,” Physica A, vol. 281, no. 1, pp. 69–77, 2000.
[20]  F. Liljeros, C. R. Edling, L. A. Nunes Amaral, H. E. Stanley, and Y. ?berg, “Social networks: the web of human sexual contacts,” Nature, vol. 411, no. 6840, pp. 907–908, 2001.
[21]  D. T. Hamilton, M. S. Handcock, and M. Morris, “Degree distributions in sexual networks: a framework for evaluating evidence,” Sexually Transmitted Diseases, vol. 35, no. 1, pp. 30–40, 2008.
[22]  C. P. Hudson, “Concurrent partnership could cause AIDS epidemics,” International Journal of STD and AIDS, vol. 4, no. 5, pp. 249–253, 1993.
[23]  C. P. Hudson, A. J. M. Hennis, P. Kataaha et al., “Risk factors for the spread of AIDS in rural Africa: evidence from a comparative seroepidemiological survey of AIDS, hepatitis B and syphilis in Southwestern Uganda,” AIDS, vol. 2, no. 4, pp. 255–260, 1988.
[24]  C. H. Watts and R. M. May, “The influence of concurrent partnerships on the dynamics of HIV/AIDS,” Mathematical Biosciences, vol. 108, no. 1, pp. 89–104, 1992.
[25]  M. Kretzschmar and M. Morris, “Measures of concurrency in networks and the spread of infectious disease,” Mathematical Biosciences, vol. 133, no. 2, pp. 165–195, 1996.
[26]  M. Morris and M. Kretzschmar, “Concurrent partnerships and transmission dynamics in networks,” Social Networks, vol. 17, no. 3-4, pp. 299–318, 1995.
[27]  M. Morris and M. Kretzschmar, “Concurrent partnerships and the spread of HIV,” AIDS, vol. 11, no. 5, pp. 641–648, 1997.
[28]  D. T. Halperin and H. Epstein, “Concurrent sexual partnerships help to explain Africa's high HIV prevalence: implications for prevention,” The Lancet, vol. 364, no. 9428, pp. 4–6, 2004.
[29]  T. L. Mah and D. T. Halperin, “Concurrent sexual partnerships and the HIV epidemics in africa: evidence to move forward,” AIDS and Behavior, vol. 14, no. 1, pp. 11–16, 2008.
[30]  M. N. Lurie and S. Rosenthal, “Concurrent partnerships as a driver of the HIV epidemic in sub-saharan Africa? The evidence is limited,” AIDS and Behavior, vol. 14, pp. 17–24, 2009.
[31]  L. Sawers and E. Stillwaggon, “Concurrent sexual partnerships do not explain the HIV epidemics in Africa: a systematic review of the evidence,” Journal of the International AIDS Society, vol. 13, article 34, 2010.
[32]  H. Epstein, “The mathematics of concurrent partnerships and HIV: a commentary on lurie and rosenthal, 2009,” AIDS and Behavior, vol. 14, no. 1, pp. 29–30, 2009.
[33]  S. Helleringer and H.-P. Kohler, “Sexual network structure and the spread of HIV in Africa: evidence from Likoma Island, Malawi,” AIDS, vol. 21, no. 17, pp. 2323–2332, 2007.
[34]  M. S. Cohen and C. D. Pilcher, “Amplified HIV transmission and new approaches to HIV prevention,” Journal of Infectious Diseases, vol. 191, no. 9, pp. 1391–1393, 2005.
[35]  M. Morris, A. E. Kurth, D. T. Hamilton, J. Moody, and S. Wakefield, “Concurrent partnerships and HIV prevalence disparities by race: linking science and public health practice,” American Journal of Public Health, vol. 99, no. 6, pp. 1023–1031, 2009.
[36]  F. A. von Hayek, The Pretence of Knowledge, 2010, http://nobelprize.org/nobel_prizes/economics/laureates/1974/hayek-lecture.html.
[37]  D. J. Watts and S. H. Strogatz, “Collective dynamics of 'small-world9 networks,” Nature, vol. 393, no. 6684, pp. 440–442, 1998.

Full-Text

comments powered by Disqus