model - 路径分析 : CFI = 1, RMSEA = 0

Question

我正在运行路径分析模型，但似乎 模型拟合指数完美 :CFI = 1.00，RMSEA = 0.00。然而，完美的模型拟合通常表明模型饱和。不过好像我的型号是不是 因为我有额外的自由度。那么，如何解释CFI和RMSEA呢？非常感谢你的帮助!

lavaan (0.5-21) converged normally after  39 iterations

  Number of observations                           109

  Number of missing patterns                         6

  Estimator                                         ML
  Minimum Function Test Statistic                6.199
  Degrees of freedom                                11
  P-value (Chi-square)                           0.860

Model test baseline model:

  Minimum Function Test Statistic              150.084
  Degrees of freedom                                20
  P-value                                        0.000

User model versus baseline model:

  Comparative Fit Index (CFI)                    1.000
  Tucker-Lewis Index (TLI)                       1.067

Loglikelihood and Information Criteria:

  Loglikelihood user model (H0)              -1000.419
  Loglikelihood unrestricted model (H1)       -997.320

  Number of free parameters                         19
  Akaike (AIC)                                2038.838
  Bayesian (BIC)                              2089.974
  Sample-size adjusted Bayesian (BIC)         2029.936

Root Mean Square Error of Approximation:

  RMSEA                                          0.000
  90 Percent Confidence Interval          0.000  0.054
  P-value RMSEA <= 0.05                          0.941

Standardized Root Mean Square Residual:

  SRMR                                           0.052

Parameter Estimates:

  Information                                 Observed
  Standard Errors                             Standard

Regressions:
                          Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
  SelfEsteem ~                                                                 
    EnglishNam (a)          -0.382    0.184   -2.073    0.038   -0.382   -0.200
  Well_Being ~                                                                 
    SelfEsteem (b)           0.668    0.095    6.998    0.000    0.668    0.558
  EnglishName ~                                                                
    RmmbrChnsN              -0.057    0.035   -1.623    0.105   -0.057   -0.204
    PrnncChnsN              -0.064    0.032   -1.981    0.048   -0.064   -0.249
  MentalHealth ~                                                               
    SelfEsteem (c)           0.779    0.088    8.846    0.000    0.779    0.656
  GeneralPhysicalHealth ~                                                      
    SelfEsteem (d)           0.335    0.099    3.368    0.001    0.335    0.314

Covariances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
 .Well_Being ~~                                                         
   .MentalHealth      0.085    0.079    1.076    0.282    0.085    0.105
   .GnrlPhysclHlth    0.196    0.091    2.153    0.031    0.196    0.214
 .MentalHealth ~~                                                       
   .GnrlPhysclHlth    0.191    0.083    2.308    0.021    0.191    0.233

    Intercepts:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .SelfEsteem        5.605    0.126   44.424    0.000    5.605    5.880
   .Well_Being        0.860    0.525    1.638    0.101    0.860    0.754
   .EnglishName       1.014    0.132    7.701    0.000    1.014    2.031
   .MentalHealth      0.708    0.485    1.460    0.144    0.708    0.626
   .GnrlPhysclHlth    3.756    0.548    6.854    0.000    3.756    3.700

Variances:
                   Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
   .SelfEsteem        0.872    0.119    7.356    0.000    0.872    0.960
   .Well_Being        0.896    0.122    7.329    0.000    0.896    0.689
   .EnglishName       0.206    0.029    7.127    0.000    0.206    0.826
   .MentalHealth      0.728    0.101    7.201    0.000    0.728    0.569
   .GnrlPhysclHlth    0.929    0.129    7.211    0.000    0.929    0.901

Answer 1

我在网上某处读到，当卡方贡献小于模型的任何给定步骤的自由度时存在建模问题(即，用于测试配置不变性的基线拟合或将度量模型与配置模型进行比较的步骤等)我以前从未遇到过这个问题，我也不太明白。但是，总体而言，所有具有相应“完美拟合”的模型似乎都是这种情况。