题名

遺漏值處理法與模型設定對結構方程模型適合度指標之影響

并列篇名

Effects of Missing Data Treatments and Model Specification on Fit Indices in Structural Equation Modeling

DOI

10.6129/CJP.2003.4504.04

作者

鄭中平(Chung-Ping Cheng)；翁儷禎(Li-Jen Weng)

关键词

遺漏值 ； 結構方程模型 ； 適合度指標 ； missing data ； structural equation modeling ； fit index

期刊名称

中華心理學刊

卷期/出版年月

45卷4期（2003 / 12 / 01）

页次

345 - 360

内容语文

繁體中文
中文摘要

遺漏值處理是社會科學研究經常面對的問題，本研究目的即探討結構方程模型不同遺漏值處理法與模型適合度指標的關係。經由模擬研究，討論不同遺漏值處理法在模型設定正確與錯誤下的過度適配情形，及不同適合度指標在具遺漏資料時之表現。結果顯示，當模型設定錯誤時，結構化最大概似法在部分指標上的確有過度適配情形，且隨遺漏值比率增加而越形嚴重；採用無結構最大概似法，再進行結構方程模型分析的兩階段方法則無過度適配情形，然模型設定正確時，其第一類型錯誤偏高。本研究結果並未發現在所有情形下表現優良之遺漏值處理法或適合度指標，使用者宜考量不同情形，選取適當的遺漏值處理法與適合度指標的組合。本研究亦發現Hu與Bentler(1998)所建議部分指標的檢定力過低，使用上宜加注意。
英文摘要

This Monte Carlo study explored effects of missing data treatment and model specification on 8 recommended fit indices in structural equation modeling. The results indicated that the structured maximum likelihood method tended to overestimate the degree of model-data fit, and the degree of overfitting increased as the percentage of missing data increased. Overfitting was not observed with unstructured maximum likelihood method, although this method tended to reject the model too often when the model was correctly specified. None of the fit index or missing data treatment was found to be superior across all conditions. The power of Gamma hat and Mc was found to be low. A careful selection of missing data treatment and fit indices was called for.
主题分类 社會科學 > 心理學
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被引用次数
  1. 鄭中平、翁儷禎(2005)。輔助變項對全訊息最大概似法表現之影響：非隨機遺漏情形之結構方程模型。調查研究：方法與應用，17，151-173。
  2. (2011)。葛特曼量表之拒答處理：簡易、多重與最鄰近插補法的比較。臺灣社會學刊，47，143-178。
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