<?xml version='1.0' encoding='UTF-8'?><codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5"><docDscr><citation><titlStmt><titl>Replication Data for: Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model</titl><IDNo agency="DOI">doi:10.7910/DVN/K3D1M2</IDNo></titlStmt><distStmt><distrbtr source="archive">Harvard Dataverse</distrbtr><distDate>2017-04-21</distDate></distStmt><verStmt source="archive"><version date="2017-04-21" type="RELEASED">1</version></verStmt><biblCit>Morgan, Jason; Box-Steffensmeier, Janet M.; Christenson, Dino P., 2017, "Replication Data for: Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model", https://doi.org/10.7910/DVN/K3D1M2, Harvard Dataverse, V1</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>Replication Data for: Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model</titl><IDNo agency="DOI">doi:10.7910/DVN/K3D1M2</IDNo></titlStmt><rspStmt><AuthEnty affiliation="The Ohio State University">Morgan, Jason</AuthEnty><AuthEnty affiliation="The Ohio State University">Box-Steffensmeier, Janet M.</AuthEnty><AuthEnty affiliation="Boston University">Christenson, Dino P.</AuthEnty></rspStmt><prodStmt><producer>Political Analysis</producer></prodStmt><distStmt><distrbtr source="archive">Harvard Dataverse</distrbtr><contact affiliation="The Ohio State University" email="jason.w.morgan@gmail.com">Morgan, Jason</contact><depositr>Morgan, Jason</depositr><depDate>2017-02-28</depDate></distStmt><serStmt><serName>Volume #, Issue #</serName></serStmt><holdings URI="https://doi.org/10.7910/DVN/K3D1M2"/></citation><stdyInfo><subject><keyword xml:lang="en">Social Sciences</keyword></subject><abstract date="2016-02-28">In the study of social processes, the presence of unobserved heterogeneity is a regular&#xd;
concern. It should be particularly worrisome for the statistical analysis of networks,&#xd;
given the complex dependencies that shape network formation combined with the re-&#xd;
strictive assumptions of related models. In this paper, we demonstrate the importance&#xd;
of explicitly accounting for unobserved heterogeneity in exponential random graph&#xd;
models (ERGM) with a Monte Carlo analysis and two applications that have played&#xd;
an important role in the networks literature. Overall, these analyses show that failing&#xd;
to account for unobserved heterogeneity can have a significant impact on inferences&#xd;
about network formation. The proposed frailty extension to the ERGM (FERGM)&#xd;
generally outperforms the ERGM in these cases, and does so by relatively large mar-&#xd;
gins. Moreover, our novel multilevel estimation strategy has the advantage of avoiding&#xd;
the problem of degeneration that plagues the standard MCMC-MLE approach.</abstract><sumDscr/></stdyInfo><method><dataColl><sources/></dataColl><anlyInfo/></method><dataAccs><setAvail/><useStmt/><notes type="DVN:TOU" level="dv">&lt;a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0&lt;/a></notes></dataAccs><othrStdyMat><relPubl><citation><titlStmt><titl>Forthcoming, Political Analysis </titl></titlStmt><biblCit>Forthcoming, Political Analysis </biblCit></citation></relPubl></othrStdyMat></stdyDscr><otherMat ID="f3012347" URI="https://dataverse.harvard.edu/api/access/datafile/3012347" level="datafile"><labl>replication-materials-20170414.zip</labl><txt>Replication materials, stored in a file hierarchy. See the included README files for instructions on how to replicate the study. (Updated April 2017.)</txt><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">application/zip</notes></otherMat></codeBook>