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Linear Models with plssem

This vignette demonstrates how to estimate a traditional linear PLS-SEM using continuous indicators.

Theory of Planned Behavior (Continuous Indicators)

tpb <- ' 
# Outer Model (Based on Hagger et al., 2007)
  ATT =~ att1 + att2 + att3 + att4 + att5
  SN =~ sn1 + sn2
  PBC =~ pbc1 + pbc2 + pbc3
  INT =~ int1 + int2 + int3
  BEH =~ b1 + b2

# Inner Model (Based on Steinmetz et al., 2011)
  INT ~ ATT + SN + PBC
  BEH ~ INT + PBC 
'
fit_tpb <- pls(
  tpb,
  data      = modsem::TPB,
  bootstrap = TRUE,
  sample    = 50
)
summary(fit_tpb)
#> plssem (0.1.0) ended normally after 3 iterations
#> 
#>   Estimator                                       PLSc
#>   Link                                          LINEAR
#>                                                       
#>   Number of observations                          2000
#>   Number of iterations                               3
#>   Number of latent variables                         5
#>   Number of observed variables                      15
#> 
#> R-squared (indicators):
#>   att1                                           0.847
#>   att2                                           0.825
#>   att3                                           0.805
#>   att4                                           0.745
#>   att5                                           0.845
#>   sn1                                            0.817
#>   sn2                                            0.863
#>   pbc1                                           0.856
#>   pbc2                                           0.859
#>   pbc3                                           0.787
#>   int1                                           0.816
#>   int2                                           0.827
#>   int3                                           0.742
#>   b1                                             0.762
#>   b2                                             0.821
#> 
#> R-squared (latents):
#>   INT                                            0.367
#>   BEH                                            0.210
#> 
#> Latent Variables:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>   ATT =~        
#>     att1            0.921      0.013   72.312    0.000
#>     att2            0.908      0.015   61.880    0.000
#>     att3            0.897      0.015   58.854    0.000
#>     att4            0.863      0.017   51.331    0.000
#>     att5            0.919      0.015   62.925    0.000
#>   SN =~         
#>     sn1             0.904      0.010   89.350    0.000
#>     sn2             0.929      0.011   84.809    0.000
#>   PBC =~        
#>     pbc1            0.925      0.012   79.987    0.000
#>     pbc2            0.927      0.012   75.373    0.000
#>     pbc3            0.887      0.012   71.665    0.000
#>   INT =~        
#>     int1            0.903      0.011   85.575    0.000
#>     int2            0.909      0.012   74.604    0.000
#>     int3            0.861      0.013   66.596    0.000
#>   BEH =~        
#>     b1              0.873      0.016   53.012    0.000
#>     b2              0.906      0.018   50.421    0.000
#> 
#> Regressions:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>   INT ~         
#>     ATT             0.243      0.028    8.747    0.000
#>     SN              0.201      0.031    6.491    0.000
#>     PBC             0.240      0.030    7.870    0.000
#>   BEH ~         
#>     PBC             0.308      0.027   11.548    0.000
#>     INT             0.210      0.028    7.597    0.000
#> 
#> Covariances:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>   ATT ~~        
#>     SN              0.633      0.014   43.787    0.000
#>     PBC             0.692      0.012   56.523    0.000
#>   SN ~~         
#>     PBC             0.696      0.014   50.524    0.000
#> 
#> Variances:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>     ATT             1.000                             
#>     SN              1.000                             
#>     PBC             1.000                             
#>    .INT             0.633      0.021   30.509    0.000
#>    .BEH             0.790      0.022   35.866    0.000
#>    .att1            0.153      0.023    6.521    0.000
#>    .att2            0.175      0.027    6.584    0.000
#>    .att3            0.195      0.027    7.154    0.000
#>    .att4            0.255      0.029    8.789    0.000
#>    .att5            0.155      0.027    5.741    0.000
#>    .sn1             0.183      0.018    9.987    0.000
#>    .sn2             0.137      0.020    6.727    0.000
#>    .pbc1            0.144      0.021    6.696    0.000
#>    .pbc2            0.141      0.023    6.202    0.000
#>    .pbc3            0.213      0.022    9.686    0.000
#>    .int1            0.184      0.019    9.658    0.000
#>    .int2            0.173      0.022    7.852    0.000
#>    .int3            0.258      0.022   11.561    0.000
#>    .b1              0.238      0.029    8.239    0.000
#>    .b2              0.179      0.033    5.492    0.000

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.