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  • 學位論文

結構方程模型誤設之模擬研究:忽略非線性作用和方法變異

A simulation study of model misspecification omitting nonlinear effects and method variances in structural equation modeling

指導教授 : 黃麗嬌

摘要


過去有關模型誤設的研究偏重於線性作用,對於非線性作用誤設的影響所知甚少,或在探討非線性作用時,未能考慮方法變異可能造成的偏差估計,然而,此兩種模型誤設可能同時影響模型估計,因此,本研究旨在探討非線性作用和方法變異誤設對於預測效度、特質因素相關及其標準誤的估計偏差影響,和適配指數的偵測能力。採用蒙地卡羅模擬法,架構七組誤設模型,並且操弄特質因素相關、方法變異相關和方法變異占觀察指標的解釋量。研究結果顯示,適配指數對於模型誤設的偵測能力隨著設計因子而變動,以χ2、SRMR、GAMMA、TLI、CFI和RNI較能夠偵測模型誤設,RMSEA的表現最差,無法偵測模型誤設。在參數估計的影響上,方法變異誤設低估線性作用之結構參數,偏差程度隨設計因子的變化而異,對於非線性作用的估計影響較小,非線性作用誤設會高估二次方作用或交互作用之預測效度,對於線性作用之結構參數的影響較小,當假設模型同時誤設非線性作用和方法變異時,影響線性作用和非線性作用之結構參數的估計,然而,不會膨脹非線性作用的估計偏差程度。此外,模型誤設會低估結構模型參數之標準誤,影響假設考驗,可能造成偏差的研究結論。透過本研究可以釐清此兩種誤設對於參數估計的共同影響,並且提供研究者在評估模型時,哪些適配指數較能夠偵測模型誤設。

並列摘要


In the past, the research of model misspecification focused on linear effect, as the result, we knew very little about the impact of nonlinear effect misspecification. When we studied nonlinear effect, we didn’t tack into consideration of possible bias effect caused by method variance. However, these two model misspecifications may affect model estimation simultaneously. The main goal of this study is to discuss how nonlinear effect and method variance misspecification impact the estimation of predictive validity, trait correlation, and its standard error, in addition to the power of fit indices. I used the Monte Carlo simulation method to establish seven types of models and manipulate design factors such as the intercorrelations among traits, covariances among method variances, and the amount of variance explained by method variances. The results demonstrated that power of fit indices could be change with design factors and I found that χ2, SRMR, GAMMA, TLI, CFI, and RNI would detect model misspecification successfully while RMSEA had the worst performance and couldn’t detect it. Regarding the impact of parameter estimation, misspecification of method variance underestimated structural parameters of linear effect and the bias changed with different design factors with a smaller influenced on nonlinear effect. Misspecification of nonlinear effect overestimated in predict validity of quadratic effect or interaction effect with a smaller influenced on linear effect. When hypothesized model misspecified method variance and nonlinear effect simultaneously, it could change the estimation of structural parameters of linear effect and nonlinear effect. Howerever, it didn’t aggravate the bias of nonlinear effect. Furthermore, model misspecification could underestimate the standard error of structural parameters and impact the hypothesis test which caused a bias conclusion. Through my research, it can clarify the common impact of these two model misspecification of parameter estimation, and provides researchers with which fit indices that can detect model misspecification more effectively.

參考文獻


Corten, I. W., Saris, W. E., Coenders, G., Veld, W., Aalberts, C. E., & Kornelis, C. (2002). Fit of different models for multitrait-multimethod experiments. Structural Equation Modeling, 9, 213-232.
Algina, J., & Moulder, B. C. (2001). A note on estimating the Jöreskog-Yang model for latent variable interaction using LISREL 8.3. Structural Equation Modeling, 8, 40-52.
Anderson, J. C., & Gerbing, D. W. (1982). Some methods for respecifying measurement models to obtain unidimensional construct measurement. Journal of Marketing Research, 19, 453-460.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411-423.
Anderson, J. C., & Gerbing, D. W. (1992). Assumptions and comparative strengths of the two-step approach. Sociological Methods and Research, 20, 321-333.

被引用紀錄


林子新(2006)。檳榔島:敘事、品味與儀式性的分析〔碩士論文,國立清華大學〕。華藝線上圖書館。https://doi.org/10.6843%2fNTHU.2006.00209

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