This dataset consists of three objects, each based on data from King County, Washington, USA (where Seattle is located) derived from the National Survey of Family Growth (NSFG) (https://www.cdc.gov/nchs/nsfg/index.htm). The full dataset cannot be released publicly, so some aspects of these objects are simulated based on the real data. These objects may be used to illustrate that network modeling may be performed using data that are collected on egos only, i.e., without directly observing information about alters in a network except for information reported from egos. The hypothetical population reepresented by this dataset consists of only a subset of individuals, as categorized by their age, race / ethnicity / immigration status, and gender and sexual identity.
The three objects are
Mixing matrix on 'race'. Based on ego reports of the race / ethnicity / immigration status of their cohabiting partners, this matrix gives counts of ego-alter ties by the race of each individual for a hypothetical population. These counts are based on the NSFG mixing matrix. Only five categories of the 'race' variable are included here: Black, Black immigrant, Hispanic, Hispanic immigrant, and White.
Data frame of demographic characteristics together with relative counts (weights) in a hypothetical population. Individuals are classified according to five variables: age in years, race (same five categories of race / ethnicity / immigration status as above), sex (Male or Female), sexual identity (Female, Male who has sex with Females, or Male who has sex with Males or Females), and number of model-predicted persistent partnerships with non-cohabiting partners (0 or 1, where 1 means any nonzero value; the number is capped at 3), and number of partners (0 or 1).
Vector of target (expected) statistics for a 15-term ERGM applied
to a network of 50,000 nodes in which a tie represents a cohabitation relationship between
two nodes. It is assumed for the purposes of these statistics that only male-female
cohabitation relationships are allowed and that no individual may have such a relationship
with more than one person. That is, each node must have degree zero or one. The ergm formula
~ edges + nodefactor("sex.ident", levels = 3) + nodecov("age") + nodecov("agesq") + nodefactor("race", levels = -5) + nodefactor("othr.net.deg", levels = -1) + nodematch("race", diff = TRUE) + absdiff("sqrt.age.adj")
Krivitsky, P.N., Hunter, D.R., Morris, M., and Klumb, C. (2021). ergm 4.0: New Features and Improvements. arXiv
National Center for Health Statistics (NCHS). (2020). 2006-2015 National Survey of Family Growth Public-Use Data and Documentation. Hyattsville, MD: CDC National Center for Health Statistics. Retrieved from https://www.cdc.gov/nchs/nsfg/index.htm
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