Measurement Error and Attenuation Bias in Exponential Random Graph Models

Kim Yeaji, Antenangeli Leonardo and Kirkland Justin ()
Additional contact information
Kim Yeaji: University of Houston – Political Science, Houston, TX, USA
Antenangeli Leonardo: University of Houston – Political Science, Houston, TX, USA
Kirkland Justin: University of Houston – Political Science, 3551 Cullen Boulevard Room 447, Houston, TX 77204, USA

Statistics, Politics and Policy, 2016, vol. 7, issue 1-2, 29-54

Abstract: Exponential Random Graph Models (ERGMs) are becoming increasingly popular tools for estimating the properties of social networks across the social sciences. While the asymptotic properties of ERGMs are well understood, much less is known about how ERGMs perform in the face of violations of the assumptions that drive those asymptotic properties. Given that empirical social networks rarely meet the strenuous assumptions of the ERGM perfectly, practical researchers are often in the position of knowing their coefficients are imperfect, but not knowing precisely how wrong those coefficients may be. In this research, we examine one violation of the asymptotic assumptions of ERGMs – perfectly measured social networks. Using several Monte Carlo simulations, we demonstrate that even randomly distributed measurement errors in networks under study can cause considerable attenuation in coefficients from ERGMs, and do real harm to subsequent hypothesis tests.

Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/spp-2016-0001 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:statpp:v:7:y:2016:i:1-2:p:29-54:n:1

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/spp/html

DOI: 10.1515/spp-2016-0001

Access Statistics for this article

Statistics, Politics and Policy is currently edited by Joel A. Middleton

More articles in Statistics, Politics and Policy from De Gruyter
Bibliographic data for series maintained by Peter Golla ().