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題名 A Penalized Likelihood Method for Structural Equation Modeling
作者 黃柏僩
Huang, Po-Hsien
Chen, Hung
Weng, Li-Jen
貢獻者 心理系
日期 2017-04
上傳時間 10-Aug-2021 16:43:51 (UTC+8)
摘要 A penalized likelihood (PL) method for structural equation modeling (SEM) was proposed as a methodology for exploring the underlying relations among both observed and latent variables. Compared to the usual likelihood method, PL includes a penalty term to control the complexity of the hypothesized model. When the penalty level is appropriately chosen, the PL can yield an SEM model that balances the model goodness-of-fit and model complexity. In addition, the PL results in a sparse estimate that enhances the interpretability of the final model. The proposed method is especially useful when limited substantive knowledge is available for model specifications. The PL method can be also understood as a methodology that links the traditional SEM to the exploratory SEM (Asparouhov & Muthén in Struct Equ Model Multidiscipl J 16:397–438, 2009). An expectation-conditional maximization algorithm was developed to maximize the PL criterion. The asymptotic properties of the proposed PL were also derived. The performance of PL was evaluated through a numerical experiment, and two real data illustrations were presented to demonstrate its utility in psychological research.
關聯 Psychometrika, Vol.82, No.2, pp.329-354
資料類型 article
DOI https://doi.org/10.1007/s11336-017-9566-9
dc.contributor 心理系
dc.creator (作者) 黃柏僩
dc.creator (作者) Huang, Po-Hsien
dc.creator (作者) Chen, Hung
dc.creator (作者) Weng, Li-Jen
dc.date (日期) 2017-04
dc.date.accessioned 10-Aug-2021 16:43:51 (UTC+8)-
dc.date.available 10-Aug-2021 16:43:51 (UTC+8)-
dc.date.issued (上傳時間) 10-Aug-2021 16:43:51 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136771-
dc.description.abstract (摘要) A penalized likelihood (PL) method for structural equation modeling (SEM) was proposed as a methodology for exploring the underlying relations among both observed and latent variables. Compared to the usual likelihood method, PL includes a penalty term to control the complexity of the hypothesized model. When the penalty level is appropriately chosen, the PL can yield an SEM model that balances the model goodness-of-fit and model complexity. In addition, the PL results in a sparse estimate that enhances the interpretability of the final model. The proposed method is especially useful when limited substantive knowledge is available for model specifications. The PL method can be also understood as a methodology that links the traditional SEM to the exploratory SEM (Asparouhov & Muthén in Struct Equ Model Multidiscipl J 16:397–438, 2009). An expectation-conditional maximization algorithm was developed to maximize the PL criterion. The asymptotic properties of the proposed PL were also derived. The performance of PL was evaluated through a numerical experiment, and two real data illustrations were presented to demonstrate its utility in psychological research.
dc.format.extent 757327 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Psychometrika, Vol.82, No.2, pp.329-354
dc.title (題名) A Penalized Likelihood Method for Structural Equation Modeling
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1007/s11336-017-9566-9
dc.doi.uri (DOI) https://doi.org/10.1007/s11336-017-9566-9