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Exponential-family random graph models for rank-order relational data

Journal Article


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Abstract


  • Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of exponential-family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require the assumption of comparability across raters. These statistics allow estimation of effects for a variety of plausible mechanisms governing rank structure, both in a cross-sectional context and evolving over time. The authors apply this framework to model the evolution of liking judgments in an acquaintance process and to model recall of relative volume of interpersonal interaction among members of a technology education program.

Publication Date


  • 2017

Citation


  • Krivitsky, P. N. & Butts, C. T. (2017). Exponential-family random graph models for rank-order relational data. Sociological Methodology, 47 (1), 68-112.

Scopus Eid


  • 2-s2.0-85044042698

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1528&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/527

Number Of Pages


  • 44

Start Page


  • 68

End Page


  • 112

Volume


  • 47

Issue


  • 1

Place Of Publication


  • United States

Abstract


  • Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of exponential-family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require the assumption of comparability across raters. These statistics allow estimation of effects for a variety of plausible mechanisms governing rank structure, both in a cross-sectional context and evolving over time. The authors apply this framework to model the evolution of liking judgments in an acquaintance process and to model recall of relative volume of interpersonal interaction among members of a technology education program.

Publication Date


  • 2017

Citation


  • Krivitsky, P. N. & Butts, C. T. (2017). Exponential-family random graph models for rank-order relational data. Sociological Methodology, 47 (1), 68-112.

Scopus Eid


  • 2-s2.0-85044042698

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1528&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/527

Number Of Pages


  • 44

Start Page


  • 68

End Page


  • 112

Volume


  • 47

Issue


  • 1

Place Of Publication


  • United States