路径分析:CFI = 1，RMSEA = 0

Question

我正在运行一个路径分析模型，但似乎模型拟合指数是完美的：CFI = 1.00，RMSEA = 0.00。然而，完美的模型拟合通常表示饱和模型。但似乎我的模型不是这样，因为我有额外的自由度。那么，如何解读CFI和RMSEA？非常感谢您的帮助！

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任何给定步长的自由度时，就存在建模问题(即，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，我真的不明白。然而，总的来说，所有具有相应"完美契合"的模型似乎都是这种情况。