newcomb: Newcomb's Fraternity Networks

In ergm.rank: Fit, Simulate and Diagnose Exponential-Family Models for Rank-Order Relational Data

Newcomb's Fraternity Networks

Description

These 14 networks record weekly sociometric preference rankings from 17 men attending the University of Michigan in the fall of 1956; Data were collected longitudinally over 15 weeks, although data from week 9 are missing.

Format

A list of 15 networks.

Details

The men were recruited to live in off-campus (fraternity) housing, rented for them as part of the Michigan Group Study Project supervised by Theodore Newcomb from 1953 to 1956. All were incoming transfer students with no prior acquaintance of one another.

The data set, derived from one in the unreleased netdata package, contains a network list newcomb with 14 networks. Each network is complete and contains two edge attributes:

list("rank")

the preference of the ith man for the jth man from 1 through 16, with 1 being the highest preference.

list("descrank")

the same, but 1 indicates lowest preference.

Licenses and Citation

If the source of the data set does not specified otherwise, this data set is protected by the Creative Commons License https://creativecommons.org/licenses/by-nc-nd/2.5/.

When publishing results obtained using this data set the original authors should be cited. In addition this should be cited as:

Vladimir Batagelj and Andrej Mrvar (2006): Pajek datasets
http://vlado.fmf.uni-lj.si/pub/networks/data/

Source

http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm#newfrat

References

See the link above. Newcomb T. (1961). The acquaintance process. New York: Holt, Reinhard and Winston.

Nordlie P. (1958). A longitudinal study of interpersonal attraction in a natural group setting. Unpublished doctoral dissertation, University of Michigan.

White H., Boorman S. and Breiger R. (1977). Social structure from multiple networks, I. Blockmodels of roles and positions. American Journal of Sociology, 81, 730-780.

Examples

# Note: This takes a long time.
data(newcomb)

# Fit a model for the transition between initial (time 0) ranking and
# ranking after one week (time 1). Note that MCMC interval has been
# decreased to save time.
newcomb.1.2.fit <- ergm(newcomb[[2]]~
                        rank.inconsistency(newcomb[[1]],"descrank")+
                        rank.deference+rank.nonconformity("all")+
                        rank.nonconformity("localAND"),
                        response="descrank", reference=~CompleteOrder,
                        control=control.ergm(MCMC.interval=10))
# Check MCMC diagnostics (barely adequate).
mcmc.diagnostics(newcomb.1.2.fit)
summary(newcomb.1.2.fit)

ergm.rank documentation built on June 2, 2022, 1:06 a.m.