是用AMOS处理的，但是结果看起来好像拟合的不好，这样的结果不知道能不能接受？

但是按照下面的要求又好像可以接受：

“卡方/df<3
RMAEA:是比较理想的指数。
Steiger[30 ]认为,RMSEA 低于0. 1 表示好的拟合;低于0. 05表示非常好的拟合;低于0. 01 表示非常出色的拟合,这种情形应用上几乎碰不到。"

大家帮我想想办法看怎么样来改进。谢谢啦！

Model Fit Summary（样本量：140多）

CMIN

Model	NPAR	CMIN	DF	P	CMIN/DF
Default model	138	1818.651	807	.000	2.254
Saturated model	945	.000	0
Independence model	42	5547.358	903	.000	6.143

Baseline Comparisons

Model	NFI Delta1	RFI rho1	IFI Delta2	TLI rho2	CFI
Default model	.672	.633	.787	.756	.782
Saturated model	1.000		1.000		1.000
Independence model	.000	.000	.000	.000	.000

Parsimony-Adjusted Measures

Model	PRATIO	PNFI	PCFI
Default model	.894	.601	.699
Saturated model	.000	.000	.000
Independence model	1.000	.000	.000

NCP

Model	NCP	LO 90	HI 90
Default model	1011.651	891.707	1139.288
Saturated model	.000	.000	.000
Independence model	4644.358	4412.855	4882.504

FMIN

Model	FMIN	F0	LO 90	HI 90
Default model	12.990	7.226	6.369	8.138
Saturated model	.000	.000	.000	.000
Independence model	39.624	33.174	31.520	34.875

RMSEA

Model	RMSEA	LO 90	HI 90	PCLOSE
Default model	.095	.089	.100	.000
Independence model	.192	.187	.197	.000

AIC

Model	AIC	BCC	BIC	CAIC
Default model	2094.651	2217.002
Saturated model	1890.000	2727.835
Independence model	5631.358	5668.595

ECVI

Model	ECVI	LO 90	HI 90	MECVI
Default model	14.962	14.105	15.873	15.836
Saturated model	13.500	13.500	13.500	19.485
Independence model	40.224	38.570	41.925	40.490