logobanner1
research label grad course label undergrad course label other labelbook linkarticles linkworking paper linkAdvanced Quantitative Political Methodology
	        linkmax likelihood linkvisualizing data linkpanel data linkPolitical Science as Social Science
	       LinkIntro to Soc Stat linkCase-Based Stat linkPolitical Economy Seminar linkSoftware linkData
	        link

Full CV  


Short CV  


Brief Bio  


google scholar



cadolph at uw dot edu



← Reports and unpublished papers

Small Sample Properties of Partially-Observed Rank Data Estimators  

6 October 2011

Christopher Adolph

Several estimators from the social science toolkit might be used to model the relationship between imprecisely-observed ranks in a hierarchy, and covariates explaining those ranks. But application of standard methods – such as linear regression, ordered probit, or censored regression – is complicated by the interdependence of rank observations. Monte Carlo evidence shows that estimators which either delete partially observed ranks and/or inappropriately assume ranks are iid perform poorly, yielding inefficient and sometimes biased estimates, and wildly inaccurate confidence intervals. In contrast, a Bayesian partial rank model – designed to impute missing ranks within known bounds, and account for interdependence across ranks – performs well, even when most or all ranks are observed imprecisely.




University of Washington link

CSSS Center for Statistics and the Social Sciences link

Designed by
Chris Adolph & Erika Steiskal

Copyright 2011–2024
Privacy · Terms of Use

Jefferson (2007-2011)