activate the Package

in this case, we will use the package of “seminr” and “readxl”. if the package is not install at your computer, firstly, we must instal the package with the following script –> install.packages(“seminR”) and install.packages(“readxl”)

library(seminr) #to generate SEM PLS
## Warning: package 'seminr' was built under R version 4.1.3
library(readxl) #to import the data from excel format
## Warning: package 'readxl' was built under R version 4.1.3

calling the dataset

namafile <- read_excel("C:/Users/almo/Dropbox/portofolio/SEM PLS/data dasar.xlsx",
                       sheet = "dasar")

arrange the item question from each variables

namafile_mm <- constructs(composite("X1",
                                    multi_items("O",
                                                c(1:9))),
                          composite("X2",
                                    multi_items("S",
                                                c(1:10))),
                          composite("Y",
                                    multi_items("WB",
                                                c(1:16))),
                          composite("X3",
                                    multi_items("Y",
                                                c(1:5))))
namafile_mm <-as.reflective(namafile_mm)

Create the PLS Models

namafile_sm <- relationships(paths(from="X1",
                                   to=c("X2","X3","Y")),
                             paths(from="X2",
                                   to="Y"),
                             paths(from="X3",
                                   to="Y"))

show the basic models

plot(namafile_sm)

SEM PLS analysis

namafile_PLS <- estimate_pls(data = namafile,
                             measurement_model = namafile_mm,
                             structural_model = namafile_sm)
## Generating the seminr model
## All 442 observations are valid.
model_summary <- summary(namafile_PLS)
model_summary
## 
## Results from  package seminr (2.3.2)
## 
## Path Coefficients:
##           X2    X3      Y
## R^2    0.697 0.372  1.585
## AdjR^2 0.697 0.371  1.589
## X1     0.835 0.610 -0.362
## X2         .     .  0.029
## X3         .     .  1.435
## 
## Reliability:
##    alpha  rhoC   AVE  rhoA
## X1 0.814 0.818 0.337 0.826
## X2 0.775 0.778 0.275 0.805
## X3 0.626 0.636 0.267 0.656
## Y  0.802 0.775 0.201 0.829
## 
## Alpha, rhoC, and rhoA should exceed 0.7 while AVE should exceed 0.5

show the finale models of SEM PLS

plot(namafile_PLS)

Convergent Validity test

test will be valid if the factor loading value > 0.5

model_summary$loadings
##         X1    X2     X3     Y
## O1   0.436 0.000  0.000 0.000
## O2   0.501 0.000  0.000 0.000
## O3   0.680 0.000  0.000 0.000
## O4   0.657 0.000  0.000 0.000
## O5   0.550 0.000  0.000 0.000
## O6   0.497 0.000  0.000 0.000
## O7   0.594 0.000  0.000 0.000
## O8   0.656 0.000  0.000 0.000
## O9   0.607 0.000  0.000 0.000
## S1   0.000 0.654  0.000 0.000
## S2   0.000 0.610  0.000 0.000
## S3   0.000 0.333  0.000 0.000
## S4   0.000 0.471  0.000 0.000
## S5   0.000 0.584  0.000 0.000
## S6   0.000 0.667  0.000 0.000
## S7   0.000 0.497  0.000 0.000
## S8   0.000 0.490  0.000 0.000
## S9   0.000 0.170 -0.000 0.000
## S10  0.000 0.561  0.000 0.000
## WB1  0.000 0.000  0.000 0.360
## WB2  0.000 0.000  0.000 0.348
## WB3  0.000 0.000  0.000 0.331
## WB4  0.000 0.000  0.000 0.307
## WB5  0.000 0.000  0.000 0.307
## WB6  0.000 0.000  0.000 0.386
## WB7  0.000 0.000  0.000 0.227
## WB8  0.000 0.000  0.000 0.148
## WB9  0.000 0.000  0.000 0.443
## WB10 0.000 0.000  0.000 0.721
## WB11 0.000 0.000  0.000 0.456
## WB12 0.000 0.000  0.000 0.734
## WB13 0.000 0.000  0.000 0.519
## WB14 0.000 0.000  0.000 0.252
## WB15 0.000 0.000  0.000 0.374
## WB16 0.000 0.000  0.000 0.713
## Y1   0.000 0.000  0.326 0.000
## Y2   0.000 0.000  0.594 0.000
## Y3   0.000 0.000  0.578 0.000
## Y4   0.000 0.000  0.571 0.000
## Y5   0.000 0.000  0.463 0.000

composite reliability test

the composite reliability test indicate based on 2 values: 1. AVE value > 0.5 2. rhoA value > 0.7

model_summary$reliability
##    alpha  rhoC   AVE  rhoA
## X1 0.814 0.818 0.337 0.826
## X2 0.775 0.778 0.275 0.805
## X3 0.626 0.636 0.267 0.656
## Y  0.802 0.775 0.201 0.829
## 
## Alpha, rhoC, and rhoA should exceed 0.7 while AVE should exceed 0.5

Discriminant Validity test

the value will show the greatest value in each own variables

model_summary$validity$cross_loadings
##         X1    X2     X3     Y
## O1   0.460 0.246  0.245 0.308
## O2   0.503 0.329  0.291 0.273
## O3   0.718 0.450  0.381 0.378
## O4   0.698 0.442  0.349 0.374
## O5   0.600 0.501  0.218 0.187
## O6   0.611 0.453  0.190 0.177
## O7   0.715 0.503  0.264 0.233
## O8   0.736 0.499  0.322 0.312
## O9   0.660 0.462  0.291 0.294
## S1   0.446 0.706  0.393 0.391
## S2   0.445 0.689  0.325 0.318
## S3   0.283 0.423  0.085 0.110
## S4   0.336 0.604  0.237 0.258
## S5   0.462 0.679  0.243 0.248
## S6   0.495 0.709  0.314 0.335
## S7   0.352 0.442  0.223 0.277
## S8   0.389 0.523  0.181 0.206
## S9   0.177 0.244 -0.027 0.004
## S10  0.493 0.660  0.161 0.158
## WB1  0.089 0.135  0.362 0.577
## WB2  0.195 0.221  0.342 0.542
## WB3  0.130 0.131  0.332 0.536
## WB4  0.183 0.199  0.302 0.466
## WB5  0.267 0.255  0.296 0.476
## WB6  0.293 0.316  0.373 0.515
## WB7  0.208 0.239  0.215 0.301
## WB8  0.174 0.141  0.141 0.268
## WB9  0.121 0.078  0.452 0.395
## WB10 0.391 0.411  0.712 0.635
## WB11 0.138 0.112  0.463 0.575
## WB12 0.249 0.222  0.742 0.674
## WB13 0.292 0.248  0.516 0.501
## WB14 0.283 0.370  0.230 0.259
## WB15 0.174 0.118  0.377 0.505
## WB16 0.330 0.260  0.717 0.643
## Y1   0.121 0.078  0.452 0.395
## Y2   0.391 0.411  0.712 0.635
## Y3   0.330 0.260  0.717 0.643
## Y4   0.249 0.222  0.742 0.674
## Y5   0.292 0.248  0.516 0.501

HTMT Value test

model_summary$validity$htmt
##       X1    X2    X3 Y
## X1     .     .     . .
## X2 0.847     .     . .
## X3 0.610 0.546     . .
## Y  0.546 0.547 1.153 .

Collinierity test

VIF value must be greater than 0.2 and less than 5

model_summary$vif_antecedents
## X2 :
## X1 
##  . 
## 
## X3 :
## X1 
##  . 
## 
## Y :
##    X1    X2    X3 
## 2.001 1.906 1.280

R Square test

model_summary$path
##           X2    X3      Y
## R^2    0.697 0.372  1.585
## AdjR^2 0.697 0.371  1.589
## X1     0.835 0.610 -0.362
## X2         .     .  0.029
## X3         .     .  1.435

F square test

model_summary$fSquare
##       X1    X2    X3      Y
## X1 0.000 2.305 0.592 -0.061
## X2 0.000 0.000 0.000  0.006
## X3 0.000 0.000 0.000 -2.026
## Y  0.000 0.000 0.000  0.000

Boostrapping Model

boot_seminr_model <- bootstrap_model(seminr_model = namafile_PLS,
                                     nboot = 1000,cores = 2,seed = NULL)
## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrapped
hasil_akhir <- summary(boot_seminr_model)
hasil_akhir
## 
## Results from Bootstrap resamples:  1000
## 
## Bootstrapped Structural Paths:
##            Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## X1  ->  X2         0.835          0.840        0.031  27.046   0.782    0.899
## X1  ->  X3         0.610          0.614        0.060  10.182   0.498    0.731
## X1  ->  Y         -0.362         -0.385        0.144  -2.513  -0.721   -0.139
## X2  ->  Y          0.029          0.031        0.117   0.249  -0.195    0.259
## X3  ->  Y          1.435          1.461        0.110  13.059   1.301    1.726
## 
## Bootstrapped Weights:
##             Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## O1  ->  X1          0.130          0.129        0.021   6.202   0.086    0.168
## O2  ->  X1          0.150          0.150        0.019   7.870   0.111    0.186
## O3  ->  X1          0.204          0.202        0.014  14.057   0.173    0.230
## O4  ->  X1          0.197          0.195        0.015  13.283   0.166    0.225
## O5  ->  X1          0.164          0.166        0.022   7.485   0.122    0.207
## O6  ->  X1          0.149          0.149        0.017   8.797   0.113    0.180
## O7  ->  X1          0.178          0.178        0.014  12.663   0.151    0.205
## O8  ->  X1          0.196          0.196        0.012  15.969   0.174    0.222
## O9  ->  X1          0.182          0.180        0.014  12.892   0.154    0.208
## S1  ->  X2          0.213          0.213        0.013  16.100   0.189    0.241
## S2  ->  X2          0.199          0.198        0.014  14.196   0.172    0.227
## S3  ->  X2          0.109          0.108        0.019   5.608   0.070    0.146
## S4  ->  X2          0.154          0.153        0.019   8.057   0.114    0.188
## S5  ->  X2          0.191          0.190        0.014  13.523   0.164    0.218
## S6  ->  X2          0.218          0.217        0.014  15.068   0.191    0.247
## S7  ->  X2          0.162          0.161        0.019   8.523   0.124    0.197
## S8  ->  X2          0.160          0.158        0.016   9.753   0.125    0.191
## S9  ->  X2          0.055          0.054        0.023   2.466   0.011    0.099
## S10  ->  X2         0.183          0.183        0.017  10.699   0.151    0.218
## WB1  ->  Y          0.102          0.101        0.010  10.413   0.082    0.120
## WB2  ->  Y          0.099          0.097        0.011   9.007   0.075    0.117
## WB3  ->  Y          0.094          0.093        0.014   6.843   0.062    0.118
## WB4  ->  Y          0.087          0.086        0.014   6.084   0.053    0.111
## WB5  ->  Y          0.087          0.086        0.012   6.944   0.059    0.109
## WB6  ->  Y          0.109          0.109        0.012   8.878   0.085    0.132
## WB7  ->  Y          0.064          0.064        0.013   4.842   0.035    0.089
## WB8  ->  Y          0.042          0.041        0.015   2.763   0.009    0.069
## WB9  ->  Y          0.125          0.125        0.024   5.325   0.077    0.169
## WB10  ->  Y         0.204          0.203        0.017  11.842   0.172    0.237
## WB11  ->  Y         0.129          0.129        0.012  10.700   0.106    0.153
## WB12  ->  Y         0.208          0.207        0.018  11.703   0.176    0.247
## WB13  ->  Y         0.147          0.146        0.016   9.160   0.114    0.175
## WB14  ->  Y         0.071          0.072        0.016   4.401   0.043    0.106
## WB15  ->  Y         0.106          0.105        0.011   9.409   0.083    0.128
## WB16  ->  Y         0.202          0.202        0.017  11.658   0.171    0.238
## Y1  ->  X3          0.198          0.197        0.036   5.567   0.125    0.260
## Y2  ->  X3          0.360          0.358        0.021  17.373   0.320    0.397
## Y3  ->  X3          0.351          0.351        0.020  17.150   0.311    0.393
## Y4  ->  X3          0.347          0.347        0.021  16.880   0.309    0.392
## Y5  ->  X3          0.281          0.279        0.026  10.803   0.225    0.331
## 
## Bootstrapped Loadings:
##             Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## O1  ->  X1          0.436          0.430        0.071   6.098   0.289    0.566
## O2  ->  X1          0.501          0.501        0.068   7.408   0.364    0.625
## O3  ->  X1          0.680          0.674        0.046  14.903   0.581    0.759
## O4  ->  X1          0.657          0.652        0.048  13.706   0.555    0.743
## O5  ->  X1          0.550          0.552        0.074   7.422   0.397    0.687
## O6  ->  X1          0.497          0.498        0.063   7.845   0.361    0.614
## O7  ->  X1          0.594          0.594        0.046  12.980   0.495    0.675
## O8  ->  X1          0.656          0.654        0.039  16.910   0.575    0.728
## O9  ->  X1          0.607          0.602        0.048  12.773   0.504    0.686
## S1  ->  X2          0.654          0.651        0.038  17.326   0.574    0.724
## S2  ->  X2          0.610          0.608        0.042  14.375   0.527    0.688
## S3  ->  X2          0.333          0.331        0.062   5.348   0.210    0.452
## S4  ->  X2          0.471          0.470        0.066   7.167   0.333    0.592
## S5  ->  X2          0.584          0.583        0.045  13.068   0.494    0.668
## S6  ->  X2          0.667          0.664        0.038  17.446   0.585    0.738
## S7  ->  X2          0.497          0.494        0.057   8.689   0.377    0.604
## S8  ->  X2          0.490          0.485        0.053   9.199   0.372    0.581
## S9  ->  X2          0.170          0.166        0.070   2.420   0.031    0.303
## S10  ->  X2         0.561          0.560        0.054  10.343   0.450    0.660
## WB1  ->  Y          0.360          0.359        0.050   7.271   0.256    0.454
## WB2  ->  Y          0.348          0.345        0.055   6.272   0.242    0.446
## WB3  ->  Y          0.331          0.332        0.066   5.033   0.201    0.465
## WB4  ->  Y          0.307          0.306        0.064   4.806   0.176    0.430
## WB5  ->  Y          0.307          0.308        0.058   5.283   0.189    0.421
## WB6  ->  Y          0.386          0.386        0.056   6.865   0.274    0.488
## WB7  ->  Y          0.227          0.227        0.054   4.166   0.122    0.333
## WB8  ->  Y          0.148          0.149        0.061   2.432   0.028    0.266
## WB9  ->  Y          0.443          0.440        0.079   5.627   0.273    0.575
## WB10  ->  Y         0.721          0.716        0.037  19.414   0.641    0.781
## WB11  ->  Y         0.456          0.455        0.046   9.862   0.359    0.542
## WB12  ->  Y         0.734          0.732        0.036  20.593   0.659    0.796
## WB13  ->  Y         0.519          0.516        0.061   8.500   0.378    0.619
## WB14  ->  Y         0.252          0.254        0.046   5.466   0.166    0.342
## WB15  ->  Y         0.374          0.372        0.053   7.069   0.270    0.475
## WB16  ->  Y         0.713          0.711        0.037  19.131   0.635    0.777
## Y1  ->  X3          0.326          0.327        0.066   4.950   0.195    0.447
## Y2  ->  X3          0.594          0.591        0.040  14.689   0.509    0.669
## Y3  ->  X3          0.578          0.580        0.039  14.647   0.500    0.656
## Y4  ->  X3          0.571          0.572        0.039  14.531   0.490    0.643
## Y5  ->  X3          0.463          0.462        0.051   9.000   0.352    0.553
## 
## Bootstrapped HTMT:
##            Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## X1  ->  X2         0.847          0.850        0.031   0.789    0.908
## X1  ->  X3         0.610          0.617        0.056   0.515    0.726
## X1  ->  Y          0.546          0.555        0.048   0.461    0.647
## X2  ->  X3         0.546          0.570        0.052   0.474    0.674
## X2  ->  Y          0.547          0.570        0.040   0.494    0.655
## X3  ->  Y          1.153          1.157        0.036   1.095    1.229
## 
## Bootstrapped Total Paths:
##            Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## X1  ->  X2         0.835          0.840        0.031   0.782    0.899
## X1  ->  X3         0.610          0.614        0.060   0.498    0.731
## X1  ->  Y          0.538          0.542        0.058   0.422    0.651
## X2  ->  Y          0.029          0.031        0.117  -0.195    0.259
## X3  ->  Y          1.435          1.461        0.110   1.301    1.726

plotting model after boostrapping

plot(boot_seminr_model)

the result of direct effect in each variables

specific_effect_significance(boot_seminr_model=boot_seminr_model,
                             from="X1",to="X2",alpha=0.05)
##  Original Est. Bootstrap Mean   Bootstrap SD        T Stat.        2.5% CI 
##      0.8351141      0.8404714      0.0308780     27.0456041      0.7822190 
##       97.5% CI 
##      0.8990292
specific_effect_significance(boot_seminr_model=boot_seminr_model,
                             from="X1",to="X3",alpha=0.05)
##  Original Est. Bootstrap Mean   Bootstrap SD        T Stat.        2.5% CI 
##      0.6099043      0.6143091      0.0598987     10.1822617      0.4980558 
##       97.5% CI 
##      0.7306332
specific_effect_significance(boot_seminr_model=boot_seminr_model,
                             from="X1",to="Y",alpha=0.05)
##  Original Est. Bootstrap Mean   Bootstrap SD        T Stat.        2.5% CI 
##     -0.3619054     -0.3849562      0.1440009     -2.5132170     -0.7211649 
##       97.5% CI 
##     -0.1386294
specific_effect_significance(boot_seminr_model=boot_seminr_model,
                             from="X2",to="Y",alpha=0.05)
##  Original Est. Bootstrap Mean   Bootstrap SD        T Stat.        2.5% CI 
##     0.02927162     0.03111142     0.11739573     0.24934146    -0.19495529 
##       97.5% CI 
##     0.25899488
specific_effect_significance(boot_seminr_model=boot_seminr_model,
                             from="X3",to="Y",alpha=0.05)
##  Original Est. Bootstrap Mean   Bootstrap SD        T Stat.        2.5% CI 
##      1.4346170      1.4607647      0.1098548     13.0592127      1.3010321 
##       97.5% CI 
##      1.7259090

result Mediated Path

specific_effect_significance(boot_seminr_model=boot_seminr_model,
                             from="X1",through="X2",
                             to="Y",alpha=0.05)
##  Original Est. Bootstrap Mean   Bootstrap SD        T Stat.        2.5% CI 
##     0.02444515     0.02630738     0.09996142     0.24454581    -0.16551964 
##       97.5% CI 
##     0.22367879
specific_effect_significance(boot_seminr_model=boot_seminr_model,
                             from="X1",through="X3",
                             to="Y",alpha=0.05)
##  Original Est. Bootstrap Mean   Bootstrap SD        T Stat.        2.5% CI 
##      0.8749790      0.9006687      0.1380564      6.3378353      0.6759174 
##       97.5% CI 
##      1.2149333