diffusion-data: Diffusion Network Datasets

In srdyal/diffusiontest: Analysis of Diffusion and Contagion Processes on Networks

Diffusion Network Datasets

Description

Diffusion Network Datasets

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.

Value

No return value (this manual entry only provides information).

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).

Author(s)

Thomas W. Valente

References

Burt, R. S. (1987). "Social Contagion and Innovation: Cohesion versus Structural Equivalence". American Journal of Sociology, 92(6), 1287–1335. \Sexpr[results=rd]{tools:::Rd_expr_doi("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. \Sexpr[results=rd]{tools:::Rd_expr_doi("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 Unpublished Report.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. \Sexpr[results=rd]{tools:::Rd_expr_doi("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. \Sexpr[results=rd]{tools:::Rd_expr_doi("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

srdyal/diffusiontest documentation built on Sept. 2, 2023, 2:49 p.m.