library(lavaan)
#https://lavaan.ugent.be/tutorial/growth.htmldta<-read.csv("D://GSdata_20211113.csv", head=T, fileEncoding = "UTF-8-BOM")
summary(dta) Placeofliving Age Gender Weight
Min. :1.000 Min. :17.0 Min. :1.000 Min. : 40.00
1st Qu.:1.000 1st Qu.:20.0 1st Qu.:1.000 1st Qu.: 52.00
Median :1.000 Median :21.0 Median :2.000 Median : 58.00
Mean :1.241 Mean :21.6 Mean :1.612 Mean : 61.74
3rd Qu.:1.000 3rd Qu.:23.0 3rd Qu.:2.000 3rd Qu.: 70.00
Max. :3.000 Max. :30.0 Max. :2.000 Max. :135.00
NA's :105
Height BMI Major TFEQ1r
Min. :145.0 Min. :15.35 Min. :1.000 Min. :1.000
1st Qu.:159.0 1st Qu.:19.53 1st Qu.:1.000 1st Qu.:2.000
Median :165.0 Median :21.60 Median :2.000 Median :2.000
Mean :165.5 Mean :22.39 Mean :1.738 Mean :2.415
3rd Qu.:171.0 3rd Qu.:24.30 3rd Qu.:2.000 3rd Qu.:3.000
Max. :185.0 Max. :47.86 Max. :2.000 Max. :4.000
TFEQ2r TFEQ3r TFEQ4r TFEQ5r
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :2.000 Median :2.000 Median :2.000 Median :2.000
Mean :2.228 Mean :2.372 Mean :2.258 Mean :2.378
3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
TFEQ6r TFEQ7r TFEQ8r TFEQ9r
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
Median :2.000 Median :2.000 Median :2.000 Median :2.000
Mean :2.215 Mean :2.468 Mean :2.089 Mean :1.994
3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
TFEQ10r TFEQ11r TFEQ12r TFEQ13r
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
Median :2.000 Median :2.000 Median :2.000 Median :2.000
Mean :2.203 Mean :2.274 Mean :1.914 Mean :2.031
3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000
Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
TFEQ14 TFEQ15 TFEQ16 TFEQ17
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :2.000 Median :2.000 Median :3.000 Median :2.000
Mean :2.258 Mean :2.292 Mean :2.637 Mean :2.012
3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000
Max. :4.000 Max. :4.000 Max. :4.000 Max. :4.000
TFEQ18c IPAQ0 IPAQ1 IPAQ2 IPAQ3
Min. :1.000 Min. :1.000 Min. :0.000 Min. : 0 Min. :0.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:0.000 1st Qu.: 0 1st Qu.:0.000
Median :2.000 Median :3.000 Median :1.000 Median : 15 Median :2.000
Mean :2.135 Mean :2.409 Mean :1.563 Mean : 35 Mean :1.991
3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.: 60 3rd Qu.:3.000
Max. :4.000 Max. :3.000 Max. :7.000 Max. :300 Max. :7.000
IPAQ4 IPAQ5 IPAQ6 IPAQ7
Min. : 0.00 Min. :0.000 Min. : 0.00 Min. : 0.00
1st Qu.: 0.00 1st Qu.:4.000 1st Qu.: 20.00 1st Qu.: 6.00
Median : 24.00 Median :6.000 Median : 30.00 Median : 8.00
Mean : 34.78 Mean :5.348 Mean : 61.74 Mean : 19.85
3rd Qu.: 50.00 3rd Qu.:7.000 3rd Qu.: 60.00 3rd Qu.: 12.00
Max. :300.00 Max. :7.000 Max. :2046.00 Max. :360.00
IPAQ7c IPAQtotalMET IPAQexerciselevel Attitude1a
Min. : 0.000 Min. : 0 Min. :1.000 Min. :2.000
1st Qu.: 5.000 1st Qu.: 560 1st Qu.:1.000 1st Qu.:4.000
Median : 8.000 Median : 1386 Median :2.000 Median :5.000
Mean : 8.138 Mean : 2008 Mean :2.022 Mean :5.117
3rd Qu.:10.000 3rd Qu.: 2613 3rd Qu.:3.000 3rd Qu.:6.000
Max. :24.000 Max. :17598 Max. :3.000 Max. :7.000
Attitude1b Attitude1c Attitude1d Attitude1e Attitude1f
Min. :3.000 Min. :1.0 Min. :3.000 Min. :2.000 Min. :1.000
1st Qu.:6.000 1st Qu.:6.0 1st Qu.:5.000 1st Qu.:6.000 1st Qu.:4.000
Median :7.000 Median :7.0 Median :6.000 Median :7.000 Median :5.000
Mean :6.354 Mean :6.4 Mean :6.083 Mean :6.298 Mean :4.818
3rd Qu.:7.000 3rd Qu.:7.0 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:6.000
Max. :7.000 Max. :7.0 Max. :7.000 Max. :7.000 Max. :7.000
Attitude1g Attitude1h Attitude2a Attitude2b
Min. :1.000 Min. :3.000 Min. :2.000 Min. :3.000
1st Qu.:4.000 1st Qu.:6.000 1st Qu.:5.000 1st Qu.:6.000
Median :5.000 Median :7.000 Median :6.000 Median :7.000
Mean :4.935 Mean :6.274 Mean :5.471 Mean :6.289
3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
Attitude2c Attitude2d Attitude2e Attitude2f Attitude2g
Min. :3.000 Min. :3.00 Min. :4.000 Min. :1.000 Min. :1.000
1st Qu.:6.000 1st Qu.:5.00 1st Qu.:6.000 1st Qu.:4.000 1st Qu.:5.000
Median :7.000 Median :6.00 Median :7.000 Median :5.000 Median :6.000
Mean :6.363 Mean :6.12 Mean :6.292 Mean :5.298 Mean :5.557
3rd Qu.:7.000 3rd Qu.:7.00 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
Max. :7.000 Max. :7.00 Max. :7.000 Max. :7.000 Max. :7.000
Attitude2h Subjectivenorm1 Subjectivenorm2 Subjectivenorm3
Min. :3.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:6.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:2.000
Median :7.000 Median :5.000 Median :5.000 Median :4.000
Mean :6.302 Mean :4.477 Mean :4.603 Mean :3.751
3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:5.000
Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
Subjectivenorm4 Subjectivenorm5 Subjectivenorm6 PBC1
Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.0 1st Qu.:3.000 1st Qu.:4.000
Median :3.000 Median :4.0 Median :4.000 Median :5.000
Mean :3.477 Mean :3.8 Mean :4.234 Mean :4.868
3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.:6.000 3rd Qu.:6.000
Max. :7.000 Max. :7.0 Max. :7.000 Max. :7.000
PBC2 PBC3 PBC4 PBC5
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:5.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000
Median :6.000 Median :4.000 Median :5.000 Median :5.000
Mean :5.825 Mean :3.945 Mean :4.686 Mean :4.935
3rd Qu.:7.000 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:6.000
Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
PBC6 PBC7 PBC8 PBC9
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
Median :5.000 Median :5.000 Median :4.000 Median :4.000
Mean :4.582 Mean :4.655 Mean :4.188 Mean :4.385
3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000
Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
PBC10 Behavioralintention1 Behavioralintention2 Behavioralintention3
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
Median :5.000 Median :5.000 Median :5.000 Median :5.000
Mean :4.606 Mean :4.495 Mean :4.714 Mean :4.726
3rd Qu.:6.000 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:6.000
Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
Behavioralintention4 Behavioralintention5 Behavioralintention6 WBIS1
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000
Median :5.000 Median :5.000 Median :5.000 Median :4.000
Mean :4.631 Mean :4.757 Mean :4.809 Mean :3.443
3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:4.000
Max. :7.000 Max. :7.000 Max. :7.000 Max. :5.000
WBIS2 WBIS3 WBIS4 WBIS5
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :3.000 Median :3.000 Median :4.000 Median :3.000
Mean :3.009 Mean :3.163 Mean :3.372 Mean :2.858
3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
WBIS6 WBIS7 WBIS8 WBIS9
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000
Median :3.000 Median :2.000 Median :2.000 Median :3.000
Mean :2.982 Mean :1.895 Mean :1.997 Mean :3.262
3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
WBIS10 WBIS11 WSSQ1 WSSQ2
Min. :1.000 Min. :1.000 Min. :0.000 Min. :0.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.000 1st Qu.:1.000
Median :2.000 Median :2.000 Median :2.000 Median :3.000
Mean :2.182 Mean :2.169 Mean :1.803 Mean :2.557
3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
WSSQ3 WSSQ4 WSSQ5 WSSQ6
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:1.000 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:1.000
Median :2.000 Median :1.000 Median :2.000 Median :3.000
Mean :2.212 Mean :1.498 Mean :2.068 Mean :2.437
3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:4.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
WSSQ7 WSSQ8 WSSQ9 WSSQ10
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:0.000
Median :3.000 Median :1.000 Median :1.000 Median :2.000
Mean :2.628 Mean :1.658 Mean :1.542 Mean :1.914
3rd Qu.:4.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
WSSQ11 WSSQ12 Weight_group WBIS1_r WBIS9_r
Min. :0.000 Min. :0.000 Min. :1.00 Min. :1.000 Min. :1.000
1st Qu.:0.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:2.000 1st Qu.:2.000
Median :1.000 Median :1.000 Median :1.00 Median :2.000 Median :3.000
Mean :1.529 Mean :1.535 Mean :1.32 Mean :2.557 Mean :2.738
3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.00 3rd Qu.:3.000 3rd Qu.:4.000
Max. :5.000 Max. :5.000 Max. :2.00 Max. :5.000 Max. :5.000
WBIS_Total_Score WSSQ_Q1to6_Total WSSQ_Q7to12_Total WSSQ_Total_Score
Min. :11.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:22.00 1st Qu.: 7.00 1st Qu.: 6.00 1st Qu.:12.00
Median :30.00 Median :14.00 Median :12.00 Median :25.00
Mean :28.92 Mean :12.58 Mean :10.81 Mean :23.38
3rd Qu.:36.00 3rd Qu.:18.00 3rd Qu.:16.00 3rd Qu.:33.00
Max. :55.00 Max. :28.00 Max. :28.00 Max. :53.00
TFEQ_Total Attitude_Eat Attitude_PA SubNorm_Eat
Min. :12.0 Min. : 47.92 Min. : 41.67 Min. : 0.00
1st Qu.:22.0 1st Qu.: 70.83 1st Qu.: 72.92 1st Qu.: 38.89
Median :26.0 Median : 81.25 Median : 83.33 Median : 55.56
Mean :26.7 Mean : 79.75 Mean : 82.69 Mean : 54.62
3rd Qu.:31.0 3rd Qu.: 89.58 3rd Qu.: 95.83 3rd Qu.: 72.22
Max. :45.0 Max. :100.00 Max. :100.00 Max. :100.00
SubNorm_PA PBC_Eat PBC_PA Int_Eat
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 27.78 1st Qu.: 58.33 1st Qu.: 37.50 1st Qu.: 50.00
Median : 50.00 Median : 66.67 Median : 58.33 Median : 66.67
Mean : 47.28 Mean : 67.97 Mean : 59.28 Mean : 60.75
3rd Qu.: 66.67 3rd Qu.: 79.17 3rd Qu.: 83.33 3rd Qu.: 77.78
Max. :100.00 Max. :100.00 Max. :100.00 Max. :100.00
Int_PA IPAQtotalMET2
Min. : 0.00 Min. : 0.00
1st Qu.: 50.00 1st Qu.: 5.60
Median : 66.67 Median : 13.86
Mean : 62.21 Mean : 20.08
3rd Qu.: 83.33 3rd Qu.: 26.13
Max. :100.00 Max. :175.98
#factor1 =~ PBC1+PBC2+PBC3+PBC4+PBC5
#factor2 =~ PBC6+PBC7+PBC8+PBC9+PBC10
#做出第一個模型 PCB.model
PBC.model <-
'
factor1 =~ PBC1+PBC2+PBC3+PBC4+PBC5
factor2 =~ PBC6+PBC7+PBC8+PBC9+PBC10
'
fit<-cfa(PBC.model, data=dta)
summary(fit,fit.measures=T, standardized=T)lavaan 0.6-9 ended normally after 38 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 21
Number of observations 325
Model Test User Model:
Test statistic 165.095
Degrees of freedom 34
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2216.449
Degrees of freedom 45
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.940
Tucker-Lewis Index (TLI) 0.920
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4940.755
Loglikelihood unrestricted model (H1) -4858.208
Akaike (AIC) 9923.510
Bayesian (BIC) 10002.971
Sample-size adjusted Bayesian (BIC) 9936.360
Root Mean Square Error of Approximation:
RMSEA 0.109
90 Percent confidence interval - lower 0.093
90 Percent confidence interval - upper 0.126
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.068
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 =~
PBC1 1.000 0.989 0.683
PBC2 0.301 0.064 4.674 0.000 0.298 0.273
PBC3 -0.423 0.091 -4.649 0.000 -0.418 -0.271
PBC4 1.253 0.088 14.241 0.000 1.239 0.901
PBC5 1.210 0.085 14.292 0.000 1.196 0.913
factor2 =~
PBC6 1.000 1.592 0.913
PBC7 0.916 0.037 24.907 0.000 1.458 0.880
PBC8 -0.228 0.058 -3.942 0.000 -0.363 -0.219
PBC9 1.037 0.034 30.232 0.000 1.650 0.944
PBC10 1.015 0.037 27.562 0.000 1.616 0.914
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 ~~
factor2 0.799 0.116 6.915 0.000 0.508 0.508
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 1.119 0.096 11.671 0.000 1.119 0.534
.PBC2 1.102 0.087 12.652 0.000 1.102 0.926
.PBC3 2.198 0.174 12.653 0.000 2.198 0.926
.PBC4 0.354 0.058 6.084 0.000 0.354 0.188
.PBC5 0.285 0.053 5.411 0.000 0.285 0.166
.PBC6 0.503 0.052 9.676 0.000 0.503 0.166
.PBC7 0.617 0.058 10.685 0.000 0.617 0.225
.PBC8 2.605 0.205 12.718 0.000 2.605 0.952
.PBC9 0.332 0.043 7.757 0.000 0.332 0.109
.PBC10 0.513 0.053 9.642 0.000 0.513 0.164
factor1 0.977 0.144 6.773 0.000 1.000 1.000
factor2 2.534 0.237 10.675 0.000 1.000 1.000#factor1 =~ PBC1+PBC2+PBC6+PBC7
#factor2 =~ PBC3+PBC4+PBC5+PBC8+PBC9+PBC10
#做出第2個模型 PCB2.model
PBC2.model <-
'
factor1=~ PBC1+PBC2+PBC6+PBC7
factor2=~ PBC3+PBC4+PBC5+PBC8+PBC9+PBC10
'
fit2<-cfa(PBC2.model, data=dta)
summary(fit2,fit.measures=T, standardized=T)lavaan 0.6-9 ended normally after 340 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 21
Number of observations 325
Model Test User Model:
Test statistic 622.136
Degrees of freedom 34
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 2216.449
Degrees of freedom 45
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.729
Tucker-Lewis Index (TLI) 0.642
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -5169.276
Loglikelihood unrestricted model (H1) -4858.208
Akaike (AIC) 10380.552
Bayesian (BIC) 10460.012
Sample-size adjusted Bayesian (BIC) 10393.402
Root Mean Square Error of Approximation:
RMSEA 0.231
90 Percent confidence interval - lower 0.215
90 Percent confidence interval - upper 0.247
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.143
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 =~
PBC1 1.000 0.488 0.337
PBC2 0.565 0.152 3.714 0.000 0.276 0.253
PBC6 3.263 0.525 6.215 0.000 1.593 0.914
PBC7 3.015 0.487 6.190 0.000 1.473 0.889
factor2 =~
PBC3 1.000 0.026 0.017
PBC4 -25.413 83.909 -0.303 0.762 -0.673 -0.490
PBC5 -24.853 82.058 -0.303 0.762 -0.658 -0.502
PBC8 13.320 44.096 0.302 0.763 0.353 0.213
PBC9 -62.526 206.354 -0.303 0.762 -1.655 -0.947
PBC10 -60.855 200.843 -0.303 0.762 -1.611 -0.911
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 ~~
factor2 -0.013 0.042 -0.303 0.762 -0.988 -0.988
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 1.858 0.147 12.663 0.000 1.858 0.886
.PBC2 1.115 0.088 12.703 0.000 1.115 0.936
.PBC6 0.498 0.058 8.595 0.000 0.498 0.164
.PBC7 0.574 0.058 9.870 0.000 0.574 0.209
.PBC3 2.372 0.186 12.747 0.000 2.372 1.000
.PBC4 1.436 0.114 12.561 0.000 1.436 0.760
.PBC5 1.283 0.102 12.547 0.000 1.283 0.748
.PBC8 2.613 0.205 12.719 0.000 2.613 0.955
.PBC9 0.314 0.045 7.049 0.000 0.314 0.103
.PBC10 0.529 0.055 9.614 0.000 0.529 0.169
factor1 0.239 0.078 3.057 0.002 1.000 1.000
factor2 0.001 0.005 0.151 0.880 1.000 1.000fitmeasures(fit) npar fmin chisq df
21.000 0.254 165.095 34.000
pvalue baseline.chisq baseline.df baseline.pvalue
0.000 2216.449 45.000 0.000
cfi tli nnfi rfi
0.940 0.920 0.920 0.901
nfi pnfi ifi rni
0.926 0.699 0.940 0.940
logl unrestricted.logl aic bic
-4940.755 -4858.208 9923.510 10002.971
ntotal bic2 rmsea rmsea.ci.lower
325.000 9936.360 0.109 0.093
rmsea.ci.upper rmsea.pvalue rmr rmr_nomean
0.126 0.000 0.164 0.164
srmr srmr_bentler srmr_bentler_nomean crmr
0.068 0.068 0.068 0.075
crmr_nomean srmr_mplus srmr_mplus_nomean cn_05
0.075 0.068 0.068 96.677
cn_01 gfi agfi pgfi
111.360 0.913 0.860 0.565
mfi ecvi
0.817 0.637 fitmeasures(fit2) npar fmin chisq df
21.000 0.957 622.136 34.000
pvalue baseline.chisq baseline.df baseline.pvalue
0.000 2216.449 45.000 0.000
cfi tli nnfi rfi
0.729 0.642 0.642 0.628
nfi pnfi ifi rni
0.719 0.543 0.731 0.729
logl unrestricted.logl aic bic
-5169.276 -4858.208 10380.552 10460.012
ntotal bic2 rmsea rmsea.ci.lower
325.000 10393.402 0.231 0.215
rmsea.ci.upper rmsea.pvalue rmr rmr_nomean
0.247 0.000 0.286 0.286
srmr srmr_bentler srmr_bentler_nomean crmr
0.143 0.143 0.143 0.158
crmr_nomean srmr_mplus srmr_mplus_nomean cn_05
0.158 0.143 0.143 26.390
cn_01 gfi agfi pgfi
30.286 0.722 0.551 0.447
mfi ecvi
0.405 2.043 #AIC越小越好、BIC越小越好使用卡方差異檢定，看△CFI △TLI △RSMEA △SRMR full invariance： partial invariance：抓出不恆等的題目，在兩個族群當中某些面向的題目不恆等，建議使用在兩個不同族群時
full invariance作法 做3個模型 1.Model-0:configual model自由估計 2.Model-1:factor loading+configual model 3.Model-2:factor loading+item intercept+configual model
比較Modle-0和Model-1→如果兩個沒差異，表示可以接受model-1 筆Model-1和Model-2→如果沒差異，表示可以接受model-2
#factor1 =~ PBC1+PBC2+PBC3+PBC4+PBC5
#factor2 =~ PBC6+PBC7+PBC8+PBC9+PBC10
#以group做分組，做出第一個模型 PCB.model
PBC.model <-
'
factor1 =~ PBC1+PBC2+PBC3+PBC4+PBC5
factor2 =~ PBC6+PBC7+PBC8+PBC9+PBC10
'
fit3<-cfa(PBC.model,
data=dta,
group="Gender")
summary(fit3,fit.measures=T, standardized=T)lavaan 0.6-9 ended normally after 54 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 62
Number of observations per group:
2 199
1 126
Model Test User Model:
Test statistic 194.743
Degrees of freedom 68
P-value (Chi-square) 0.000
Test statistic for each group:
2 104.254
1 90.489
Model Test Baseline Model:
Test statistic 2133.931
Degrees of freedom 90
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.938
Tucker-Lewis Index (TLI) 0.918
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4896.279
Loglikelihood unrestricted model (H1) -4798.908
Akaike (AIC) 9916.559
Bayesian (BIC) 10151.156
Sample-size adjusted Bayesian (BIC) 9954.497
Root Mean Square Error of Approximation:
RMSEA 0.107
90 Percent confidence interval - lower 0.090
90 Percent confidence interval - upper 0.125
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.063
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Group 1 [2]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 =~
PBC1 1.000 0.942 0.651
PBC2 0.316 0.091 3.467 0.001 0.297 0.262
PBC3 -0.471 0.118 -4.004 0.000 -0.444 -0.304
PBC4 1.254 0.123 10.207 0.000 1.181 0.892
PBC5 1.211 0.118 10.231 0.000 1.140 0.906
factor2 =~
PBC6 1.000 1.568 0.913
PBC7 0.895 0.050 17.802 0.000 1.404 0.851
PBC8 -0.281 0.067 -4.197 0.000 -0.441 -0.295
PBC9 1.009 0.045 22.471 0.000 1.582 0.933
PBC10 0.994 0.048 20.767 0.000 1.559 0.906
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 ~~
factor2 0.665 0.136 4.887 0.000 0.450 0.450
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 4.734 0.103 46.136 0.000 4.734 3.271
.PBC2 5.714 0.081 70.969 0.000 5.714 5.031
.PBC3 3.869 0.104 37.368 0.000 3.869 2.649
.PBC4 4.422 0.094 47.136 0.000 4.422 3.341
.PBC5 4.688 0.089 52.595 0.000 4.688 3.728
.PBC6 4.166 0.122 34.201 0.000 4.166 2.424
.PBC7 4.271 0.117 36.533 0.000 4.271 2.590
.PBC8 4.176 0.106 39.312 0.000 4.176 2.787
.PBC9 3.899 0.120 32.446 0.000 3.899 2.300
.PBC10 4.176 0.122 34.245 0.000 4.176 2.428
factor1 0.000 0.000 0.000
factor2 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 1.208 0.131 9.191 0.000 1.208 0.577
.PBC2 1.201 0.121 9.899 0.000 1.201 0.931
.PBC3 1.937 0.196 9.870 0.000 1.937 0.908
.PBC4 0.357 0.076 4.665 0.000 0.357 0.204
.PBC5 0.282 0.069 4.094 0.000 0.282 0.178
.PBC6 0.493 0.068 7.235 0.000 0.493 0.167
.PBC7 0.748 0.087 8.569 0.000 0.748 0.275
.PBC8 2.051 0.207 9.926 0.000 2.051 0.913
.PBC9 0.370 0.059 6.273 0.000 0.370 0.129
.PBC10 0.530 0.071 7.467 0.000 0.530 0.179
factor1 0.887 0.179 4.953 0.000 1.000 1.000
factor2 2.460 0.296 8.321 0.000 1.000 1.000
Group 2 [1]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 =~
PBC1 1.000 1.042 0.732
PBC2 0.218 0.089 2.464 0.014 0.227 0.229
PBC3 -0.469 0.147 -3.186 0.001 -0.488 -0.296
PBC4 1.165 0.119 9.826 0.000 1.214 0.899
PBC5 1.130 0.115 9.869 0.000 1.177 0.908
factor2 =~
PBC6 1.000 1.405 0.895
PBC7 0.944 0.061 15.461 0.000 1.326 0.899
PBC8 -0.222 0.120 -1.845 0.065 -0.312 -0.167
PBC9 1.028 0.060 17.093 0.000 1.445 0.936
PBC10 1.050 0.066 15.897 0.000 1.475 0.909
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 ~~
factor2 0.698 0.166 4.194 0.000 0.477 0.477
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 5.079 0.127 40.062 0.000 5.079 3.569
.PBC2 6.000 0.088 67.891 0.000 6.000 6.048
.PBC3 4.063 0.147 27.626 0.000 4.063 2.461
.PBC4 5.103 0.120 42.430 0.000 5.103 3.780
.PBC5 5.325 0.115 46.121 0.000 5.325 4.109
.PBC6 5.238 0.140 37.434 0.000 5.238 3.335
.PBC7 5.262 0.131 40.027 0.000 5.262 3.566
.PBC8 4.206 0.167 25.191 0.000 4.206 2.244
.PBC9 5.151 0.137 37.462 0.000 5.151 3.337
.PBC10 5.286 0.145 36.567 0.000 5.286 3.258
factor1 0.000 0.000 0.000
factor2 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 0.940 0.134 7.027 0.000 0.940 0.464
.PBC2 0.932 0.118 7.895 0.000 0.932 0.947
.PBC3 2.488 0.316 7.865 0.000 2.488 0.913
.PBC4 0.349 0.088 3.985 0.000 0.349 0.191
.PBC5 0.293 0.080 3.660 0.000 0.293 0.175
.PBC6 0.492 0.078 6.303 0.000 0.492 0.200
.PBC7 0.418 0.067 6.220 0.000 0.418 0.192
.PBC8 3.415 0.431 7.926 0.000 3.415 0.972
.PBC9 0.294 0.059 5.028 0.000 0.294 0.124
.PBC10 0.457 0.076 5.980 0.000 0.457 0.173
factor1 1.085 0.234 4.630 0.000 1.000 1.000
factor2 1.975 0.308 6.407 0.000 1.000 1.000結論： 會分兩組，各自估計，做出兩個卡方值，自由度也是兩組都加起來df=68
fit4<-cfa(PBC.model,
data=dta,
group="Gender",
group.equal=c("loadings"))
summary(fit4,fit.measures=T)lavaan 0.6-9 ended normally after 48 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 62
Number of equality constraints 8
Number of observations per group:
2 199
1 126
Model Test User Model:
Test statistic 196.398
Degrees of freedom 76
P-value (Chi-square) 0.000
Test statistic for each group:
2 104.919
1 91.479
Model Test Baseline Model:
Test statistic 2133.931
Degrees of freedom 90
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.941
Tucker-Lewis Index (TLI) 0.930
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4897.107
Loglikelihood unrestricted model (H1) -4798.908
Akaike (AIC) 9902.214
Bayesian (BIC) 10106.540
Sample-size adjusted Bayesian (BIC) 9935.257
Root Mean Square Error of Approximation:
RMSEA 0.099
90 Percent confidence interval - lower 0.082
90 Percent confidence interval - upper 0.116
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.064
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Group 1 [2]:
Latent Variables:
Estimate Std.Err z-value P(>|z|)
factor1 =~
PBC1 1.000
PBC2 (.p2.) 0.269 0.064 4.226 0.000
PBC3 (.p3.) -0.468 0.091 -5.136 0.000
PBC4 (.p4.) 1.211 0.086 14.163 0.000
PBC5 (.p5.) 1.174 0.083 14.217 0.000
factor2 =~
PBC6 1.000
PBC7 (.p7.) 0.915 0.038 23.774 0.000
PBC8 (.p8.) -0.268 0.059 -4.560 0.000
PBC9 (.p9.) 1.015 0.036 28.269 0.000
PBC10 (.10.) 1.014 0.039 26.164 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
factor1 ~~
factor2 0.679 0.132 5.126 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.PBC1 4.734 0.104 45.589 0.000
.PBC2 5.714 0.080 71.447 0.000
.PBC3 3.869 0.104 37.303 0.000
.PBC4 4.422 0.094 47.251 0.000
.PBC5 4.688 0.089 52.632 0.000
.PBC6 4.166 0.121 34.461 0.000
.PBC7 4.271 0.118 36.233 0.000
.PBC8 4.176 0.106 39.485 0.000
.PBC9 3.899 0.120 32.551 0.000
.PBC10 4.176 0.123 33.980 0.000
factor1 0.000
factor2 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.PBC1 1.203 0.131 9.175 0.000
.PBC2 1.205 0.121 9.919 0.000
.PBC3 1.935 0.196 9.868 0.000
.PBC4 0.361 0.070 5.135 0.000
.PBC5 0.280 0.063 4.440 0.000
.PBC6 0.497 0.067 7.371 0.000
.PBC7 0.746 0.087 8.566 0.000
.PBC8 2.053 0.207 9.932 0.000
.PBC9 0.370 0.058 6.384 0.000
.PBC10 0.527 0.071 7.460 0.000
factor1 0.942 0.154 6.115 0.000
factor2 2.411 0.278 8.674 0.000
Group 2 [1]:
Latent Variables:
Estimate Std.Err z-value P(>|z|)
factor1 =~
PBC1 1.000
PBC2 (.p2.) 0.269 0.064 4.226 0.000
PBC3 (.p3.) -0.468 0.091 -5.136 0.000
PBC4 (.p4.) 1.211 0.086 14.163 0.000
PBC5 (.p5.) 1.174 0.083 14.217 0.000
factor2 =~
PBC6 1.000
PBC7 (.p7.) 0.915 0.038 23.774 0.000
PBC8 (.p8.) -0.268 0.059 -4.560 0.000
PBC9 (.p9.) 1.015 0.036 28.269 0.000
PBC10 (.10.) 1.014 0.039 26.164 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
factor1 ~~
factor2 0.688 0.157 4.384 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.PBC1 5.079 0.125 40.702 0.000
.PBC2 6.000 0.089 67.178 0.000
.PBC3 4.063 0.147 27.703 0.000
.PBC4 5.103 0.121 42.278 0.000
.PBC5 5.325 0.116 46.072 0.000
.PBC6 5.238 0.142 36.924 0.000
.PBC7 5.262 0.130 40.402 0.000
.PBC8 4.206 0.168 25.008 0.000
.PBC9 5.151 0.138 37.283 0.000
.PBC10 5.286 0.143 37.023 0.000
factor1 0.000
factor2 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.PBC1 0.950 0.133 7.147 0.000
.PBC2 0.932 0.118 7.881 0.000
.PBC3 2.489 0.316 7.874 0.000
.PBC4 0.352 0.082 4.277 0.000
.PBC5 0.288 0.074 3.874 0.000
.PBC6 0.487 0.078 6.273 0.000
.PBC7 0.421 0.067 6.322 0.000
.PBC8 3.418 0.431 7.921 0.000
.PBC9 0.293 0.058 5.071 0.000
.PBC10 0.463 0.075 6.130 0.000
factor1 1.012 0.184 5.510 0.000
factor2 2.049 0.289 7.085 0.000anova(fit3, fit4)Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
fit3 68 9916.6 10151 194.74
fit4 76 9902.2 10106 196.40 1.6549 8 0.9898自由估計的時候，factor1裡面有5個變項，其中一個是Anchor，自由度是4，所以
fit5<-cfa(PBC.model,
data=dta,
group="Gender",
group.equal=c("loadings", "intercepts"))
summary(fit5,fit.measures=T, standardized=T)lavaan 0.6-9 ended normally after 78 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 64
Number of equality constraints 18
Number of observations per group:
2 199
1 126
Model Test User Model:
Test statistic 212.283
Degrees of freedom 84
P-value (Chi-square) 0.000
Test statistic for each group:
2 110.639
1 101.644
Model Test Baseline Model:
Test statistic 2133.931
Degrees of freedom 90
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.937
Tucker-Lewis Index (TLI) 0.933
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4905.050
Loglikelihood unrestricted model (H1) -4798.908
Akaike (AIC) 9902.099
Bayesian (BIC) 10076.155
Sample-size adjusted Bayesian (BIC) 9930.247
Root Mean Square Error of Approximation:
RMSEA 0.097
90 Percent confidence interval - lower 0.081
90 Percent confidence interval - upper 0.113
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.066
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Group 1 [2]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 =~
PBC1 1.000 0.945 0.651
PBC2 (.p2.) 0.297 0.064 4.667 0.000 0.280 0.247
PBC3 (.p3.) -0.422 0.090 -4.671 0.000 -0.399 -0.274
PBC4 (.p4.) 1.251 0.087 14.370 0.000 1.182 0.893
PBC5 (.p5.) 1.208 0.084 14.423 0.000 1.142 0.907
factor2 =~
PBC6 1.000 1.541 0.908
PBC7 (.p7.) 0.917 0.036 25.287 0.000 1.413 0.853
PBC8 (.p8.) -0.239 0.056 -4.245 0.000 -0.368 -0.248
PBC9 (.p9.) 1.035 0.034 30.296 0.000 1.595 0.935
PBC10 (.10.) 1.017 0.037 27.766 0.000 1.567 0.907
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 ~~
factor2 0.659 0.128 5.151 0.000 0.453 0.453
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 (.24.) 4.659 0.091 51.097 0.000 4.659 3.209
.PBC2 (.25.) 5.774 0.063 91.819 0.000 5.774 5.094
.PBC3 (.26.) 4.003 0.088 45.674 0.000 4.003 2.749
.PBC4 (.27.) 4.437 0.092 48.279 0.000 4.437 3.351
.PBC5 (.28.) 4.694 0.088 53.447 0.000 4.694 3.728
.PBC6 (.29.) 4.145 0.117 35.288 0.000 4.145 2.442
.PBC7 (.30.) 4.249 0.111 38.225 0.000 4.249 2.567
.PBC8 (.31.) 4.258 0.092 46.133 0.000 4.258 2.870
.PBC9 (.32.) 3.937 0.119 33.062 0.000 3.937 2.308
.PBC10 (.33.) 4.161 0.119 34.844 0.000 4.161 2.408
factor1 0.000 0.000 0.000
factor2 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 1.215 0.132 9.231 0.000 1.215 0.576
.PBC2 1.206 0.122 9.911 0.000 1.206 0.939
.PBC3 1.961 0.198 9.894 0.000 1.961 0.925
.PBC4 0.355 0.070 5.045 0.000 0.355 0.203
.PBC5 0.282 0.063 4.465 0.000 0.282 0.178
.PBC6 0.506 0.068 7.463 0.000 0.506 0.176
.PBC7 0.746 0.087 8.585 0.000 0.746 0.272
.PBC8 2.066 0.208 9.942 0.000 2.066 0.939
.PBC9 0.364 0.058 6.248 0.000 0.364 0.125
.PBC10 0.531 0.071 7.508 0.000 0.531 0.178
factor1 0.893 0.146 6.125 0.000 1.000 1.000
factor2 2.374 0.272 8.741 0.000 1.000 1.000
Group 2 [1]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 =~
PBC1 1.000 0.979 0.705
PBC2 (.p2.) 0.297 0.064 4.667 0.000 0.290 0.287
PBC3 (.p3.) -0.422 0.090 -4.671 0.000 -0.413 -0.249
PBC4 (.p4.) 1.251 0.087 14.370 0.000 1.225 0.900
PBC5 (.p5.) 1.208 0.084 14.423 0.000 1.182 0.911
factor2 =~
PBC6 1.000 1.420 0.897
PBC7 (.p7.) 0.917 0.036 25.287 0.000 1.302 0.894
PBC8 (.p8.) -0.239 0.056 -4.245 0.000 -0.339 -0.179
PBC9 (.p9.) 1.035 0.034 30.296 0.000 1.470 0.938
PBC10 (.10.) 1.017 0.037 27.766 0.000 1.444 0.904
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 ~~
factor2 0.666 0.151 4.393 0.000 0.479 0.479
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 (.24.) 4.659 0.091 51.097 0.000 4.659 3.356
.PBC2 (.25.) 5.774 0.063 91.819 0.000 5.774 5.709
.PBC3 (.26.) 4.003 0.088 45.674 0.000 4.003 2.415
.PBC4 (.27.) 4.437 0.092 48.279 0.000 4.437 3.262
.PBC5 (.28.) 4.694 0.088 53.447 0.000 4.694 3.616
.PBC6 (.29.) 4.145 0.117 35.288 0.000 4.145 2.618
.PBC7 (.30.) 4.249 0.111 38.225 0.000 4.249 2.919
.PBC8 (.31.) 4.258 0.092 46.133 0.000 4.258 2.251
.PBC9 (.32.) 3.937 0.119 33.062 0.000 3.937 2.513
.PBC10 (.33.) 4.161 0.119 34.844 0.000 4.161 2.606
factor1 0.515 0.119 4.313 0.000 0.526 0.526
factor2 1.126 0.173 6.499 0.000 0.793 0.793
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 0.970 0.135 7.208 0.000 0.970 0.503
.PBC2 0.939 0.119 7.873 0.000 0.939 0.918
.PBC3 2.577 0.327 7.890 0.000 2.577 0.938
.PBC4 0.351 0.083 4.225 0.000 0.351 0.190
.PBC5 0.287 0.075 3.845 0.000 0.287 0.170
.PBC6 0.491 0.078 6.311 0.000 0.491 0.196
.PBC7 0.424 0.067 6.350 0.000 0.424 0.200
.PBC8 3.463 0.437 7.925 0.000 3.463 0.968
.PBC9 0.293 0.059 4.999 0.000 0.293 0.119
.PBC10 0.464 0.076 6.151 0.000 0.464 0.182
factor1 0.958 0.174 5.511 0.000 1.000 1.000
factor2 2.016 0.283 7.129 0.000 1.000 1.000anova(fit4,fit5)Chi-Squared Difference Test
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
fit4 76 9902.2 10106 196.40
fit5 84 9902.1 10076 212.28 15.885 8 0.04405 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1後減前(△CFI>-.0.01、△TLI>-0.01、△△RMSEA<0.01SRMR<0.01) 必須全部都符合，
fit6<-cfa(PBC.model,
data=dta,
group="Gender",
group.equal=c("loadings", "intercepts"),
group.partial=c("factor2=~PBC7", "PBC2~1"))
#定義某些變項的item intercept自由估計，或者某些變項的factor loading自由估計
#factor2的PBC7 factor loading自由估計
#PBC的intercept自由估計
summary(fit6,fit.measures=T, standardized=T)lavaan 0.6-9 ended normally after 77 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 64
Number of equality constraints 16
Number of observations per group:
2 199
1 126
Model Test User Model:
Test statistic 210.807
Degrees of freedom 82
P-value (Chi-square) 0.000
Test statistic for each group:
2 110.213
1 100.594
Model Test Baseline Model:
Test statistic 2133.931
Degrees of freedom 90
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.937
Tucker-Lewis Index (TLI) 0.931
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4904.312
Loglikelihood unrestricted model (H1) -4798.908
Akaike (AIC) 9904.623
Bayesian (BIC) 10086.247
Sample-size adjusted Bayesian (BIC) 9933.995
Root Mean Square Error of Approximation:
RMSEA 0.098
90 Percent confidence interval - lower 0.082
90 Percent confidence interval - upper 0.115
P-value RMSEA <= 0.05 0.000
Standardized Root Mean Square Residual:
SRMR 0.066
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Group 1 [2]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 =~
PBC1 1.000 0.945 0.651
PBC2 (.p2.) 0.276 0.065 4.227 0.000 0.261 0.231
PBC3 (.p3.) -0.423 0.090 -4.678 0.000 -0.400 -0.275
PBC4 (.p4.) 1.251 0.087 14.372 0.000 1.183 0.893
PBC5 (.p5.) 1.208 0.084 14.424 0.000 1.142 0.907
factor2 =~
PBC6 1.000 1.542 0.908
PBC7 0.911 0.049 18.774 0.000 1.405 0.852
PBC8 (.p8.) -0.239 0.056 -4.247 0.000 -0.368 -0.248
PBC9 (.p9.) 1.035 0.034 30.289 0.000 1.597 0.935
PBC10 (.10.) 1.017 0.037 27.774 0.000 1.568 0.907
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 ~~
factor2 0.659 0.128 5.147 0.000 0.452 0.452
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 (.24.) 4.661 0.091 51.107 0.000 4.661 3.209
.PBC2 5.714 0.080 71.449 0.000 5.714 5.065
.PBC3 (.26.) 4.003 0.088 45.673 0.000 4.003 2.749
.PBC4 (.27.) 4.439 0.092 48.277 0.000 4.439 3.351
.PBC5 (.28.) 4.696 0.088 53.453 0.000 4.696 3.729
.PBC6 (.29.) 4.146 0.117 35.286 0.000 4.146 2.441
.PBC7 (.30.) 4.248 0.112 37.962 0.000 4.248 2.575
.PBC8 (.31.) 4.258 0.092 46.132 0.000 4.258 2.869
.PBC9 (.32.) 3.939 0.119 33.062 0.000 3.939 2.307
.PBC10 (.33.) 4.162 0.119 34.844 0.000 4.162 2.407
factor1 0.000 0.000 0.000
factor2 0.000 0.000 0.000
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 1.215 0.132 9.231 0.000 1.215 0.576
.PBC2 1.205 0.121 9.919 0.000 1.205 0.947
.PBC3 1.960 0.198 9.894 0.000 1.960 0.925
.PBC4 0.356 0.071 5.039 0.000 0.356 0.203
.PBC5 0.281 0.063 4.446 0.000 0.281 0.177
.PBC6 0.505 0.068 7.453 0.000 0.505 0.175
.PBC7 0.747 0.087 8.564 0.000 0.747 0.275
.PBC8 2.066 0.208 9.942 0.000 2.066 0.939
.PBC9 0.364 0.058 6.235 0.000 0.364 0.125
.PBC10 0.530 0.071 7.496 0.000 0.530 0.177
factor1 0.894 0.146 6.126 0.000 1.000 1.000
factor2 2.379 0.273 8.712 0.000 1.000 1.000
Group 2 [1]:
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 =~
PBC1 1.000 0.980 0.705
PBC2 (.p2.) 0.276 0.065 4.227 0.000 0.270 0.270
PBC3 (.p3.) -0.423 0.090 -4.678 0.000 -0.414 -0.250
PBC4 (.p4.) 1.251 0.087 14.372 0.000 1.225 0.901
PBC5 (.p5.) 1.208 0.084 14.424 0.000 1.183 0.911
factor2 =~
PBC6 1.000 1.418 0.897
PBC7 0.922 0.046 20.006 0.000 1.308 0.895
PBC8 (.p8.) -0.239 0.056 -4.247 0.000 -0.338 -0.179
PBC9 (.p9.) 1.035 0.034 30.289 0.000 1.468 0.938
PBC10 (.10.) 1.017 0.037 27.774 0.000 1.442 0.904
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
factor1 ~~
factor2 0.665 0.152 4.391 0.000 0.479 0.479
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 (.24.) 4.661 0.091 51.107 0.000 4.661 3.356
.PBC2 5.859 0.094 62.185 0.000 5.859 5.844
.PBC3 (.26.) 4.003 0.088 45.673 0.000 4.003 2.414
.PBC4 (.27.) 4.439 0.092 48.277 0.000 4.439 3.262
.PBC5 (.28.) 4.696 0.088 53.453 0.000 4.696 3.615
.PBC6 (.29.) 4.146 0.117 35.286 0.000 4.146 2.621
.PBC7 (.30.) 4.248 0.112 37.962 0.000 4.248 2.909
.PBC8 (.31.) 4.258 0.092 46.132 0.000 4.258 2.251
.PBC9 (.32.) 3.939 0.119 33.062 0.000 3.939 2.517
.PBC10 (.33.) 4.162 0.119 34.844 0.000 4.162 2.609
factor1 0.511 0.119 4.280 0.000 0.522 0.522
factor2 1.124 0.173 6.489 0.000 0.793 0.793
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.PBC1 0.969 0.134 7.207 0.000 0.969 0.502
.PBC2 0.932 0.118 7.881 0.000 0.932 0.927
.PBC3 2.577 0.327 7.890 0.000 2.577 0.938
.PBC4 0.350 0.083 4.208 0.000 0.350 0.189
.PBC5 0.288 0.075 3.845 0.000 0.288 0.170
.PBC6 0.491 0.078 6.312 0.000 0.491 0.196
.PBC7 0.423 0.067 6.299 0.000 0.423 0.198
.PBC8 3.463 0.437 7.925 0.000 3.463 0.968
.PBC9 0.293 0.059 5.006 0.000 0.293 0.120
.PBC10 0.465 0.076 6.156 0.000 0.465 0.183
factor1 0.960 0.174 5.512 0.000 1.000 1.000
factor2 2.012 0.283 7.098 0.000 1.000 1.000通常會Conbarch alpha→EFA→CFA的路線，老師比較不常用Racsh
原始初衷是想要知道問卷的答案適不適合拿來加總運算? 現在而是來驗證題目是不是好的 把量詞用機率的概念做轉換，是Item response theory,IRT的P1，是連續的量詞是等距的 > Item response theory P1,P2,P3 (一參模型:難度，二參模型:難度+鑑別度，三參模型：難度+鑑別度+猜測度) > Partial credit model:問卷有10個題目，每個都用五點量表，但假設每一點的差距在每一題都不同，估每一題答1~5的機率都不同 > Rating scale model：限制五點量表每一題的差距都一致的 適配度PCM>RSM,Racsh<ITM P2)
Racsh infit/outfit:看有沒有outlier
MnSq:看題目跟概念是否一致0.5~1.5可留，值越小表示越多餘可刪除，超過1表示離概念越遠
person separation reliability/person separation index:考慮樣本的能力，以樣本分出來的一致性 item separation reliability/person separation index:考慮題目難易度 (跟Conbarch alpha概念類似) 通常person算出來都會比item差 index像是分布，index越大越好，可以測到的範圍更廣 reliability>0.7才可接受 separation>2才可接受
Differential item functioning,DIF:男生算出item 1的難度-女生算出item稱之DIF contrast 1的難度，期望兩個族群差異小，表示兩個族群想到的概念是一致的，通常希望<0.5
disordered category:針對likert scale做order的檢查(有人也沒做這個檢查) local dependence:題目沒有估計到的之間沒有關聯性
library(eRm)#WBIS1-11(75~85)，刪掉WBIS2, WBIS9當成全部都是同一個factor
a<-dta[75]
b<-dta[,77:82]
c<-dta[,84:85]
wbis<-cbind(a,b,c)
head(wbis) WBIS1 WBIS3 WBIS4 WBIS5 WBIS6 WBIS7 WBIS8 WBIS10 WBIS11
1 2 5 4 3 4 2 4 4 4
2 3 4 5 3 4 2 2 2 1
3 4 4 5 3 5 5 5 5 5
4 4 4 4 3 3 2 2 2 2
5 4 4 4 3 4 3 1 1 1
6 2 4 4 3 3 1 1 3 3summary(wbis) WBIS1 WBIS3 WBIS4 WBIS5
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :4.000 Median :3.000 Median :4.000 Median :3.000
Mean :3.443 Mean :3.163 Mean :3.372 Mean :2.858
3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
WBIS6 WBIS7 WBIS8 WBIS10
Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :3.000 Median :2.000 Median :2.000 Median :2.000
Mean :2.982 Mean :1.895 Mean :1.997 Mean :2.182
3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
WBIS11
Min. :1.000
1st Qu.:1.000
Median :2.000
Mean :2.169
3rd Qu.:3.000
Max. :5.000 pcm.wbis<-PCM(wbis)
pres.pcm.wbis<-person.parameter(pcm.wbis)
itemfit(pres.pcm.wbis)
Itemfit Statistics:
Chisq df p-value Outfit MSQ Infit MSQ Outfit t Infit t Discrim
WBIS1 1127.804 320 0.000 3.513 2.826 19.926 16.822 -0.320
WBIS3 273.326 320 0.972 0.851 0.859 -1.898 -1.951 0.620
WBIS4 228.926 320 1.000 0.713 0.705 -3.772 -4.289 0.705
WBIS5 171.241 320 1.000 0.533 0.510 -7.227 -8.237 0.840
WBIS6 199.653 320 1.000 0.622 0.621 -5.600 -5.966 0.779
WBIS7 215.254 320 1.000 0.671 0.701 -3.569 -4.036 0.726
WBIS8 184.459 320 1.000 0.575 0.635 -5.305 -5.059 0.785
WBIS10 202.407 320 1.000 0.631 0.665 -4.940 -4.812 0.769
WBIS11 205.966 320 1.000 0.642 0.629 -4.519 -5.534 0.779outfit MSQ應該要在0.5~1.5之間，結果看起來第一題最差 p<0.05表示題目比較不好 p>0.05表示題目跟概念比較吻合 Discrim鑑別度，僅供參考，因為在計算infit/outfit時，Discrim是假設一致的 可以把第一題刪掉，再跑一次看看
#算person separation reliability
SepRel(pres.pcm.wbis)Separation Reliability: 0.8364#0.8364結果很不錯thresholds(pcm.wbis)
Design Matrix Block 1:
Location Threshold 1 Threshold 2 Threshold 3 Threshold 4
WBIS1 -0.61786 -2.23572 -0.62043 -0.42743 0.81213
WBIS3 -0.11431 -1.15059 -0.28797 -0.71633 1.69766
WBIS4 -0.37478 -0.89075 -0.80611 -0.51965 0.71739
WBIS5 0.16603 -0.75301 -0.31379 0.07349 1.65742
WBIS6 0.03918 -0.69366 -0.63544 0.04527 1.44056
WBIS7 1.38962 0.39989 0.68063 1.36941 3.10853
WBIS8 1.15655 -0.02419 0.97955 1.04547 2.62536
WBIS10 0.89827 -0.30046 0.58485 1.11650 2.19219
WBIS11 0.99848 -0.09129 0.59349 0.71834 2.77340#看題目的答案是否有ranking
#thresholds應該要有方向性，逐漸變大或逐漸變小
#如果忽大忽小，表示disordered category不好
#WBIS1就有逐漸變大的趨勢
#WBIS3在Threshold2、Threshold3 ranking比較不按照順序，建議合併後重新收案pcm.wbis
Results of PCM estimation:
Call: PCM(X = wbis)
Conditional log-likelihood: -2740.322
Number of iterations: 37
Number of parameters: 35
Item (Category) Difficulty Parameters (eta):
WBIS1.c2 WBIS1.c3 WBIS1.c4 WBIS3.c1 WBIS3.c2 WBIS3.c3
Estimate -2.8561570 -3.2835853 -2.4714581 -1.1505871 -1.4385564 -2.1548849
Std.Err 0.2798626 0.2753554 0.2865144 0.2017425 0.2160686 0.2067263
WBIS3.c4 WBIS4.c1 WBIS4.c2 WBIS4.c3 WBIS4.c4 WBIS5.c1
Estimate -0.4572243 -0.8907466 -1.6968558 -2.2165080 -1.4991150 -0.7530095
Std.Err 0.2576536 0.2182097 0.2152803 0.2145778 0.2346465 0.1777323
WBIS5.c2 WBIS5.c3 WBIS5.c4 WBIS6.c1 WBIS6.c2 WBIS6.c3
Estimate -1.0668017 -0.9933123 0.6641069 -0.6936586 -1.3290943 -1.2838253
Std.Err 0.1870984 0.1984225 0.2618894 0.1895772 0.1889014 0.2005348
WBIS6.c4 WBIS7.c1 WBIS7.c2 WBIS7.c3 WBIS7.c4 WBIS8.c1 WBIS8.c2
Estimate 0.1567338 0.3998899 1.080520 2.4499304 5.5584610 -0.02419198 0.9553570
Std.Err 0.2509717 0.1410898 0.179407 0.2499671 0.5445029 0.13579334 0.1875039
WBIS8.c3 WBIS8.c4 WBIS10.c1 WBIS10.c2 WBIS10.c3 WBIS10.c4
Estimate 2.0008257 4.6261885 -0.3004601 0.2843908 1.4008904 3.5930769
Std.Err 0.2387218 0.4387293 0.1413998 0.1755700 0.2285092 0.3690647
WBIS11.c1 WBIS11.c2 WBIS11.c3 WBIS11.c4
Estimate -0.09128808 0.5021998 1.220536 3.9939358
Std.Err 0.14294025 0.1795317 0.212816 0.4068658