AMOS做CFA结果不理想怎么修正

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

用AMOS做验证性因子分析，出来的结果不理想，卡方值、RMR、NFI、NNFI、RFI均未达到适配标准，其他index达标。
请问可能是什么原因？在不删减item的情况下，怎么修正？

初学amos菜鸟一枚，请多指教，多谢！

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：amos CFA AMO 验证性因子分析 是什么原因 理想 修正

路过帮顶~

请高手指教啊

根据 MI 值来修正

板凳正解。

lihoujian 发表于 2013-3-3 19:05
根据 MI 值来修正

您好，做CFA模型的时候，根据因子载荷值剔除题项修正模型，还能依据MI值来修正吗？

做CFA的时候是不能根据MI来修正的，

How to improve model fit

      Given the complexity of structural equation modelling, it is not uncommon to find that the fit of a proposed model  is  poor.  Allowing  modification  indices  to  drive  the  process  is not always a good practice however,  some modifications can be made locally that can substantially improve results. It is good practice to assess the fit of  each  construct  and  its  items  individually  to  determine  whether  there  are  any  items  that  are  particularly
weak. Items with low multiple r2 (less than .20) should be removed from the analysis as this is an indication
of very high levels of error. Following this, each construct should be modelled in conjunction with every other
construct  in  the  model  to  determine  whether  discriminant  validity  has  been  achieved.  The  Phi  (φ)  value
between two constructs is akin to their covariance, therefore a Phi of 1.0 indicates that the two constructs are
measuring  the  same  thing.  One  test  which  is  useful  to  determine  whether  constructs  are  significantly
different is Bagozzi et al’s (1991) discriminant validity test. The formula for this is: parameter estimate (phi
value) ±1.96 * standard error. If the value is greater than 1.0 discriminant validity has not been achieved and
further inspections of item cross-loadings need to be made. Items with high Lambda-Y modification indices
are possible candidates for deletion and are likely to be causing the discriminant validity problem. By deleting
indiscriminant  items  fit  is  likely  to  improve  and  is  advantageous  in  that  it  is  unlikely  to  have  any  major
theoretical repercussions.  
   
     A further way in which fit can be improved is through the correlation of error terms. This practice is generally
means that there is some other issue that is not specified within the model that is causing the covariation. If a researcher decides to correlate error terms there needs to be strong theoretical justification behind such a move. Correlating within-factor error is easier to justify than across latent variable correlations, however it is essential that the statistical and substantive impact are clearly discussed. If a researcher feels they can substantiate this decision, correlated error terms is acceptable, however it is a step that should be taken with caution.

lihoujian 发表于 2013-4-14 15:37
做CFA的时候是不能根据MI来修正的，

是可以进行修正的吧，吴明隆的书上说，CFA测量模型允许测量变量的误差项与其他测量变量的误差项间存在共变关系（邱皓政，2005）