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The Use Of Effect Size Estimates To Evaluate Covariate Selection, Group Separation, And Sensitivity To Hidden Bias In Propensity Score Matching.

Covariate quality has been primarily theory driven in propensity score matching with a general adversity to the interpretation of group prediction. However, effect sizes are well supported in the literature and may help to inform the method. Specifically, I index can be used as a measure of effect size in logistic regression to evaluate group prediction. As such, simulation was used to create 35 conditions of I, initial bias and sample size to examine statistical differences in (a) post-matching bias reduction and (b) treatment effect sensitivity. The results of this study suggest these conditions do not explain statistical differences in percent bias reduction of treatment likelihood after matching. However, I and sample size do explain statistical differences in treatment effect sensitivity. Treatment effect sensitivity was lower when sample sizes and I increased. However, this relationship was mitigated within smaller sample sizes as I increased above I = .50.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc103349
Date12 1900
CreatorsLane, Forrest C.
ContributorsHenson, Robin K., Hull, Darrell, Natesan, Prathiba, Hayes, DeMarquis, Beaujean, Alexander
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
FormatText
RightsPublic, Lane, Forrest C., Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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