netdiffuseR (version 1.17.0)

diffusion-data: Diffusion Network Datasets

Description

Diffusion Network Datasets

Right censored data

By convention, non-adopting actors are coded as one plus the last observed time of adoption. Prior empirical event history approaches have used this approach (Valente, 2005; Marsden and Podolny, 1990) and studies have shown that omitting such observations leads to biased results (van den Bulte & Iyengar, 2011).

Details

The three classic network diffusion datasets included in netdiffuseR are the medical innovation data originally collected by Coleman, Katz & Menzel (1966); the Brazilian Farmers collected as part of the three country study implemented by Everett Rogers (Rogers, Ascroft, & Röling, 1970), and Korean Family Planning data collected by researchers at the Seoul National University's School of Public (Rogers & Kincaid, 1981). The table below summarizes the three datasets:

	Medical Innovation	Brazilian Farmers
Korean Family Planning	Country	USA
Brazil	Korean	# Respondents
125 Doctors	692 Farmers	1,047 Women
# Communities	4	11
25	Innovation	Tetracycline
Hybrid Corn Seed	Family Planning	Time for Diffusion
18 Months	20 Years	11 Years
Year Data Collected	1955-1956	1966
1973	Ave. Time to 50%	6
16	7	Highest Saturation
0.89	0.98	0.83
Lowest Saturation	0.81	0.29
0.44	Citation	Coleman et al (1966)
Rogers et al (1970)	Rogers & Kincaid (1981)

All datasets include a column called study which is coded as (1) Medical Innovation (2) Brazilian Farmers, (3) Korean Family Planning.

References

Burt, R. S. (1987). "Social Contagion and Innovation: Cohesion versus Structural Equivalence". American Journal of Sociology, 92(6), 1287–1335. http://doi.org/10.1086/228667

Coleman, J., Katz, E., & Menzel, H. (1966). Medical innovation: A diffusion study (2nd ed.). New York: Bobbs-Merrill

Granovetter, M., & Soong, R. (1983). Threshold models of diffusion and collective behavior. The Journal of Mathematical Sociology, 9(October 2013), 165–179. http://doi.org/10.1080/0022250X.1983.9989941

Rogers, E. M., Ascroft, J. R., & Röling, N. (1970). Diffusion of Innovation in Brazil, Nigeria, and India. Unpublished Report. Michigan State University, East Lansing.

Everett M. Rogers, & Kincaid, D. L. (1981). Communication Networks: Toward a New Paradigm for Research. (C. Macmillan, Ed.). New York; London: Free Press.

Mardsen, P., & Podolny, J. (1990). Dynamic Analysis of Network Diffusion Processes, J. Weesie, H. Flap, eds. Social Networks Through Time, 197–214.

Marsden, P. V., & Friedkin, N. E. (1993). Network Studies of Social Influence. Sociological Methods & Research, 22(1), 127–151. http://doi.org/10.1177/0049124193022001006

Van den Bulte, C., & Iyengar, R. (2011). Tricked by Truncation: Spurious Duration Dependence and Social Contagion in Hazard Models. Marketing Science, 30(2), 233–248. http://doi.org/10.1287/mksc.1100.0615

Valente, T. W. (1991). Thresholds and the critical mass: Mathematical models of the diffusion of innovations. University of Southern California.

Valente, T. W. (1995). "Network models of the diffusion of innovations" (2nd ed.). Cresskill N.J.: Hampton Press.

Valente, T. W. (2005). Network Models and Methods for Studying the Diffusion of Innovations. In Models and Methods in Social Network Analysis, Volume 28 of Structural Analysis in the Social Sciences (pp. 98–116). New York: Cambridge University Press.

See Also

Other diffusion datasets: brfarmersDiffNet, brfarmers, fakeDynEdgelist, fakeEdgelist, fakesurveyDyn, fakesurvey, kfamilyDiffNet, kfamily, medInnovationsDiffNet, medInnovations