本文選題：結(jié)構(gòu)方程模型　切入點：非正態(tài)　出處：《山西醫(yī)科大學(xué)》2007年碩士論文

【摘要】： 當(dāng)前結(jié)構(gòu)方程模型(structural equation modeling,SEM)已經(jīng)廣泛應(yīng)用于醫(yī)學(xué)研究中因果關(guān)系和先驗假設(shè)理論的檢驗,是一種多功能和方便使用的分析技術(shù)。通常使用默認的ML估計方法。多元正態(tài)分布和大樣本是應(yīng)用ML估計的兩個關(guān)鍵假定。然而,應(yīng)用實踐中收集的數(shù)據(jù)明顯地違反了正態(tài)分布假定,又沒有足夠大的樣本來使用新發(fā)展的任意分布估計方法。迫切需要在小樣本且數(shù)據(jù)分布非正態(tài)條件下,仍能夠準(zhǔn)確地檢驗?zāi)Ｐ蛥?shù)和評價模型擬合的方法。 本文介紹兩種估計非正態(tài)、較小樣本SEM的方法:S-B調(diào)整方法(Satorra-Bentler scaled,S-B)和自助抽樣方法(bootstrap resampling)。可以分別使用SEM估計的軟件包EQS和AMOS得到。采用了蒙特卡羅模擬(monte carlo simulation)研究了三個樣本大小(100,250,500)和三種多元分布(多元正態(tài),輕度和嚴(yán)重偏離正態(tài)類型)條件下,模型正確指定時候這兩種方法和常用的ML估計的性能比較。另外,將研究的兩種較穩(wěn)健的估計方法應(yīng)用到醫(yī)護人員職業(yè)緊張SEM模型估計的實例中,得到的結(jié)論與正態(tài)理論估計方法的不一致,說明了忽視數(shù)據(jù)條件而使用常規(guī)的正態(tài)理論估計方法的偏差。 模擬研究表明,對于適當(dāng)指定的模型,ML只有在多元正態(tài)條件下估計準(zhǔn)確,隨著數(shù)據(jù)偏離正態(tài),它估計的模型檢驗統(tǒng)計量偏高,參數(shù)的標(biāo)準(zhǔn)誤偏低,假設(shè)檢驗的Ⅰ型誤差率升高,對模型和參數(shù)的評價出現(xiàn)歪曲。S-B調(diào)整方法除了在最小樣本條件外較穩(wěn)健,而自助方法在各條件下估計的結(jié)果都較理想。自助抽樣的次數(shù)(B=250和500)對于模型估計沒有大的差異。 通過當(dāng)前的研究,使得結(jié)構(gòu)方程模型的應(yīng)用能在一定程度上突破樣本量和數(shù)據(jù)分布類型的約束,指導(dǎo)醫(yī)學(xué)應(yīng)用研究者正確使用結(jié)構(gòu)方程模型,并為揭示醫(yī)學(xué)中的因果機制和驗證事先架構(gòu)的理論假設(shè)提供依據(jù)。
[Abstract]:Structural equation modeling (SEM) has been widely used in the testing of causality and priori hypothesis theory in medical research. It is a multifunctional and convenient analytical technique.The default ML estimation method is usually used.Multivariate normal distribution and large samples are the two key assumptions in the application of ML estimation.However, the data collected in practice obviously violate the assumption of normal distribution, and there are not enough samples to use the newly developed arbitrary distribution estimation method.It is urgent to test the parameters of the model and evaluate the fitting method of the model accurately under the condition of small sample and non-normal data distribution.In this paper, we introduce two methods of estimating non-normal distribution, one is the small sample SEM, the other is the small sample SEM, and the other is the small sample SEM. The two methods are: Satorra-Bentler scaledS-B) and bootstrap sampling method bootstrap amplification.It can be obtained by using EQS and AMOS software packages estimated by SEM, respectively.Monte Carlo simulation of monte carlo is used to study the performance of three sample sizes (100250500) and three multivariate distributions (multivariate normality, mild or severe deviation from normal type). When the model is correctly specified, the performance of these two methods is compared with that of the commonly used ML estimators.In addition, two more robust estimation methods are applied to the SEM model estimation of occupational stress in medical and nursing personnel, and the results are inconsistent with the normal theory estimation method.In this paper, the deviation of the conventional normal theory estimation method is explained by ignoring the data condition.The simulation results show that the model ML can only be estimated accurately under the condition of multivariate normality. With the deviation of the data from the normal state, the estimated statistical quantity of the model test is higher, the standard error of the parameters is lower, and the type I error rate of the hypothesis test increases.The evaluation of the model and parameters is distorted. S-B adjustment method is more robust than the minimum sample condition, while the self-help method estimates the results well under each condition.The frequency of self-help sampling is not significantly different from that of model estimation.Through the current research, the application of structural equation model can break through the constraints of sample size and data distribution to a certain extent, and instruct medical application researchers to use the structural equation model correctly.It also provides the basis for revealing the causality mechanism in medicine and validating the theoretical hypothesis of prior structure.
【學(xué)位授予單位】：山西醫(yī)科大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2007
【分類號】：R311

【引證文獻】

相關(guān)期刊論文 前1條

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相關(guān)碩士學(xué)位論文 前2條

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2 馬瑞;非正態(tài)驗證性因子分析在基因整體效應(yīng)中的應(yīng)用[D];山西醫(yī)科大學(xué);2012年

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本文編號：1704322