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angel 发表于 2011-7-2 21:44

急，请论坛内的大侠帮我解答下

<br /> Degrees of Freedom = 87<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; Minimum Fit Function Chi-Square = 333.11 (P = 0.0)<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;Normal Theory Weighted Least Squares Chi-Square = 300.59 (P = 0.0)<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; Estimated Non-centrality Parameter (NCP) = 213.59<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;90 Percent Confidence Interval for NCP = (164.60 ; 270.19)<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;Minimum Fit Function Value = http://bbs.pinggu.org/1.02<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; Population Discrepancy Function Value (F0) = 0.65<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;90 Percent Confidence Interval for F0 = (0.50 ; 0.82)<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; Root Mean Square Error of Approximation (RMSEA) = 0.087<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;90 Percent Confidence Interval for RMSEA = (0.076 ; 0.097)<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;P-Value for Test of Close Fit (RMSEA &lt; 0.05) = 0.00<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;Expected Cross-Validation Index (ECVI) = 1.12<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; 90 Percent Confidence Interval for ECVI = (0.97 ; 1.29)<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; ECVI for Saturated Model = 0.73<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;ECVI for Independence Model = 26.86<br /> &nbsp; &nbsp;&nbsp;&nbsp;Chi-Square for Independence Model with 105 Degrees of Freedom = 8780.83<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; Independence AIC = 8810.83<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;Model AIC = 366.59<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;Saturated AIC = 240.00<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;Independence CAIC = 8882.77<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; Model CAIC = 524.86<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;Saturated CAIC = 815.53<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;Normed Fit Index (NFI) = 0.96<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;Non-Normed Fit Index (NNFI) = 0.97<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;Parsimony Normed Fit Index (PNFI) = 0.80<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;Comparative Fit Index (CFI) = 0.97<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;Incremental Fit Index (IFI) = 0.97<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; Relative Fit Index (RFI) = 0.95<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;Critical N (CN) = 119.74<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; Root Mean Square Residual (RMR) = 0.24<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp;&nbsp;Standardized RMR = 0.041<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;Goodness of Fit Index (GFI) = 0.89<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp; Adjusted Goodness of Fit Index (AGFI) = 0.85<br /> &nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;&nbsp; &nbsp;Parsimony Goodness of Fit Index (PGFI) = 0.65<br /> <br /> 其中，GFI、AGFI和RMSEA的指标只能说是一般，但是，最让我头大的是RMR这个值和标准比起来太不靠谱了。哪位大侠能对我的这个模型拟合情况做个判断吗？还有就是，RMR的值可以如何修正呢？<br /> 另外，我的样本数是407个，我听一些书上介绍RMR在大样本的情况下是很敏感的，所有有人提出用SRMR来衡量，不知道是不是有这么回事？跪求大侠

潜变量分析 发表于 2011-7-2 21:49

跟你一起等大侠 看RMR怎么修正…… 不过小声说一下：我觉得这个结果已经很不错了…… 感觉不少文章都不报告RMR……

结构方程 发表于 2011-7-2 21:54

不知道，不过帮你顶一下

variable 发表于 2011-7-2 21:59

兄弟，我之前看到，有说样本量比较大的时候，RMR是比较敏感的，所以有些人会用SRMR来衡量，不知道有没有这么回事啊？<strong> 2# <i>xiaomaha</i> </strong>

freeshuju 发表于 2011-7-2 22:04

一般而言, SEM所產生的指標很多, 也很難有一個所有指標都完全很亮眼的結果. 解釋上只要作者知道為何會有這樣指標結果即可, 因為没有一個完美的指標. 所有的指標都有這樣那樣的問題.<br /> 老實說我也覺得你的結果相當好

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