論文

査読有り 筆頭著者 最終著者 責任著者
2007年12月

Doubly robust-type estimation for covariate adjustment in latent variable modeling

PSYCHOMETRIKA
  • Takahiro Hoshino

72
4
開始ページ
535
終了ページ
549
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1007/s11336-007-9007-2
出版者・発行元
SPRINGER

Due to the difficulty in achieving a random assignment, a quasi-experimental or observational study design is frequently used in the behavioral and social sciences. If a nonrandom assignment depends on the covariates, multiple group structural equation modeling, that includes the regression function of the dependent variables on the covariates that determine the assignment, can provide reasonable estimates under the condition of correct specification of the regression function. However, it is usually difficult to specify the correct regression function because the dimensions of the dependent variables and covariates are typically large. Therefore, the propensity score adjustment methods have been proposed, since they do not require the specification of the regression function and have been applied to several applied studies. However, these methods produce biased estimates if the assignment mechanism is incorrectly specified. In order to make a more robust inference, it would be more useful to develop an estimation method that integrates the regression approach with the propensity score methodology. In this study we propose a doubly robust-type estimation method for marginal multiple group structural equation modeling. This method provides a consistent estimator if either the regression function or the assignment mechanism is correctly specified. A simulation study indicates that the proposed estimation method is more robust than the existing methods.

Web of Science ® 被引用回数 : 4

リンク情報
DOI
https://doi.org/10.1007/s11336-007-9007-2
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000252977600005&DestApp=WOS_CPL
ID情報
  • DOI : 10.1007/s11336-007-9007-2
  • ISSN : 0033-3123
  • Web of Science ID : WOS:000252977600005

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