R-Lavan-sem-负方差误差
我建立了这样一个模型：R-Lavan-sem-负方差误差,r,variance,r-lavaan,structural-equation-model,invariance,R,Variance,R Lavaan,Structural Equation Model,Invariance,我建立了这样一个模型： model3<-' # MEASUREMENT union =~ V24 + V25 loyality =~ V52 + V53 + V54 experience =~ V37 + V38 + V39 + V40 # STRUCTURAL union ~ loyality union ~ experience # CORRELATED RESIDUALS V37 ~~ V39 V37 ~~ V38 ' model3汇总（fit3，标准化=T，拟合度量=T，rsqu
model3<-'
# MEASUREMENT
union =~ V24 + V25
loyality =~ V52 + V53 + V54
experience =~ V37 + V38 + V39 + V40
# STRUCTURAL
union ~ loyality
union ~ experience
# CORRELATED RESIDUALS
V37 ~~ V39
V37 ~~ V38
'
model3汇总（fit3，标准化=T，拟合度量=T，rsquare=T）
Lavan（0.5-23.1097）在77次迭代后正常收敛
意见数目21972
估计DWLS稳健
最小功能测试统计330.153 394.021
自由度22
P值（卡方检验）0.000 0.000
标度校正系数0.847
移位参数4.299
用于简单的二阶校正（Mplus变体）
模型测试基线模型：
最小功能测试统计30573.174 22082.251
自由度36
P值0.000 0.000
用户模型与基线模型：
比较拟合指数（CFI）0.990 0.983
塔克-刘易斯指数（TLI）0.983 0.972
稳健比较拟合指数（CFI）NA
稳健塔克-刘易斯指数（TLI）NA
近似均方根误差：
RMSEA 0.025 0.028
90%置信区间0.023 0.028 0.025 0.030
P值RMSEA | z |）标准低压标准所有
联合=~
V24 1.000 1.061 0.928
V25 0.777 0.058 13.387 0.000 0.824 0.752
忠诚=~
V52 1.000 0.690 0.650
V53 1.167 0.023 51.579 0.000 0.806 0.845
V54 0.936 0.017 55.844 0.000 0.646 0.520
经验=~
V37 1.000 0.349 0.363
V38 2.570 0.071 36.179 0.000 0.897 0.695
V39 0.915 0.033 28.040 0 0.000 0.319 0.302
V40 2.359 0.100 23.481 0.000 0.824 0.680
回归：
估算标准误差z值P（>| z |）标准低压标准所有
联盟~
忠诚0.039 0.015 2.656 0.008 0.025 0.025
经验0.346 0.0311.169 0.000 0.114 0.114
协方差：
估算标准误差z值P（>| z |）标准低压标准所有
.V37~
.V39 0.336 0.008 44.158 0.000 0.336 0.372
.V38 0.146 0.012 11.717 0.000 0.146 0.176
忠诚~~
经验-0.0340.003-11.7990.000-0.142-0.142
截取：
估算标准误差z值P（>| z |）标准低压标准所有
.V24 2.521 0.008 326.748 0.000 2.521 2.204
.V25 2.411 0.007 326.002 0.000 2.411 2.199
.V52 2.413 0.007 336.811 0.000 2.413 2.272
.V53 2.262 0.006 351.664 0.000 2.262 2.372
.V54 3.211 0.008 382.509 0.000 3.211 2.581
.V37 2.680 0.006 413.404 0.000 2.680 2.789
.V38 3.494 0.009 401.170 0.000 3.494 2.706
.V39 2.857 0.007 400.392 0.000 2.857 2.701
.V40 3.926 0.008 480.422 0.000 3.926 3.241
.工会0.000 0.000 0.000
忠诚0.000 0.000 0.000
经验0.000 0.000 0.000
差异：
估算标准误差z值P（>| z |）标准低压标准所有
.V24 0.181 0.084 2.152 0.031 0.181 0.139
.V25 0.522 0.051 10.244 0.000 0.522 0.435
.V52 0.651 0.012 53.863 0.000 0.651 0.578
.V53 0.260 0.012 21.187 0.000 0.260 0.286
.V54 1.130 0.013 88.016 0.000 1.130 0.730
.V37 0.801 0.010 81.075 0.000 0.801 0.868
.V38 0.861 0.030 28.782 0.000 0.861 0.517
.V39 1.017 0.010 104.495 0.000 1.017 0.909
.V40 0.789 0.026 29.953 0.000 0.789 0.538
.活接头1.112 0.084 13.155 0.000 0.987 0.987
忠诚0.476 0.013 37.173 0.000 1.000 1.000
经验0.122 0.008 16.093 0.000 1.000 1.000
R-Square：
估计
V24 0.861
V25 0.565
V52 0.422
V53 0.714
V54 0.270
V37 0.132
V38 0.483
V390.091
V40 0.462
工会0.013
> measurementInvariance(model3,data=sub1,
+ group="SEX")
Measurement invariance models:
Model 1 : fit.configural
Model 2 : fit.loadings
Model 3 : fit.intercepts
Model 4 : fit.means
Chi Square Difference Test
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
fit.configural 44 556605 557116 451.90
fit.loadings 50 556754 557218 613.68 161.78 6 < 2.2e-16 ***
fit.intercepts 56 557229 557645 1100.58 486.90 6 < 2.2e-16 ***
fit.means 59 558714 559106 2591.56 1490.98 3 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Fit measures:
cfi rmsea cfi.delta rmsea.delta
fit.configural 0.990 0.029 NA NA
fit.loadings 0.987 0.032 0.004 0.003
fit.intercepts 0.976 0.041 0.011 0.009
fit.means 0.941 0.063 0.035 0.021
Warning messages:
1: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
2: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
3: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
>测量方差（模型3，数据=sub1，
+group=“SEX”）
测量不变性模型：
模型1：fit.configural
模型2：拟合载荷
模型3:fit.intercepts
模型4：拟合平均值
卡方差检验
Df AIC BIC Chisq Chisq diff Df diff Pr（>Chisq）
安装配置44 556605 557116 451.90
安装荷载50 556754 557218 613.68 161.78 6<2.2e-16***
安装截距565572295576451100.58486.906<2.2e-16***
是指59 5
> measurementInvariance(model3,data=sub1,
+ group="SEX")
Measurement invariance models:
Model 1 : fit.configural
Model 2 : fit.loadings
Model 3 : fit.intercepts
Model 4 : fit.means
Chi Square Difference Test
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
fit.configural 44 556605 557116 451.90
fit.loadings 50 556754 557218 613.68 161.78 6 < 2.2e-16 ***
fit.intercepts 56 557229 557645 1100.58 486.90 6 < 2.2e-16 ***
fit.means 59 558714 559106 2591.56 1490.98 3 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Fit measures:
cfi rmsea cfi.delta rmsea.delta
fit.configural 0.990 0.029 NA NA
fit.loadings 0.987 0.032 0.004 0.003
fit.intercepts 0.976 0.041 0.011 0.009
fit.means 0.941 0.063 0.035 0.021
Warning messages:
1: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
2: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative
3: In lav_object_post_check(object) :
lavaan WARNING: some estimated ov variances are negative