Question 1) Composite Path Models using PLS-PM
1-a) Create a PLS path model using SEMinR, with all the following
characteristics:
1-a-i) Measurement model – all constructs are measured as
composites:
sec_mm <- constructs(
composite("TRUST", multi_items("TRST", 1:4)),
composite("SEC", multi_items("PSEC", 1:4)),
composite("REP", multi_items("PREP", 1:4)),
composite("INV", multi_items("PINV", 1:3)),
composite("POL", multi_items("PPSS", 1:3)),
composite("FAML", single_item("FAML1")),
interaction_term(iv="REP", moderator="POL", method=orthogonal)
)
1-a-ii) Structural Model – paths between constructs as shown in this
causal model:
sec_sm <- relationships(
paths(from = c("FAML","POL","REP","INV","REP*POL"), to = "SEC"),
paths(from = "SEC", to = "TRUST")
)
1-b) Show us the following results in table or figure formats:
1-b-ii) Weights and loadings of composites
sec_report <- summary(sec_pls)
sec_report$weights
## FAML POL REP INV REP*POL SEC TRUST
## TRST1 0.000 0.000 0.000 0.000 0.000 0.000 0.282
## TRST2 0.000 0.000 0.000 0.000 0.000 0.000 0.280
## TRST3 0.000 0.000 0.000 0.000 0.000 0.000 0.286
## TRST4 0.000 0.000 0.000 0.000 0.000 0.000 0.278
## PSEC1 0.000 0.000 0.000 0.000 0.000 0.277 0.000
## PSEC2 0.000 0.000 0.000 0.000 0.000 0.315 0.000
## PSEC3 0.000 0.000 0.000 0.000 0.000 0.307 0.000
## PSEC4 0.000 0.000 0.000 0.000 0.000 0.292 0.000
## PREP1 0.000 0.000 0.215 0.000 0.000 0.000 0.000
## PREP2 0.000 0.000 0.334 0.000 0.000 0.000 0.000
## PREP3 0.000 0.000 0.349 0.000 0.000 0.000 0.000
## PREP4 0.000 0.000 0.287 0.000 0.000 0.000 0.000
## PINV1 0.000 0.000 0.000 0.363 0.000 0.000 0.000
## PINV2 0.000 0.000 0.000 0.395 0.000 0.000 0.000
## PINV3 0.000 0.000 0.000 0.358 0.000 0.000 0.000
## PPSS1 0.000 0.360 0.000 0.000 0.000 0.000 0.000
## PPSS2 0.000 0.395 0.000 0.000 0.000 0.000 0.000
## PPSS3 0.000 0.367 0.000 0.000 0.000 0.000 0.000
## FAML1 1.000 0.000 0.000 0.000 0.000 0.000 0.000
## PREP1*PPSS1 0.000 0.000 0.000 0.000 0.239 0.000 0.000
## PREP1*PPSS2 0.000 0.000 0.000 0.000 0.031 0.000 0.000
## PREP1*PPSS3 0.000 0.000 0.000 0.000 0.021 0.000 0.000
## PREP2*PPSS1 0.000 0.000 0.000 0.000 0.046 0.000 0.000
## PREP2*PPSS2 0.000 0.000 0.000 0.000 -0.104 0.000 0.000
## PREP2*PPSS3 0.000 0.000 0.000 0.000 -0.228 0.000 0.000
## PREP3*PPSS1 0.000 0.000 0.000 0.000 -0.341 0.000 0.000
## PREP3*PPSS2 0.000 0.000 0.000 0.000 0.095 0.000 0.000
## PREP3*PPSS3 0.000 0.000 0.000 0.000 0.108 0.000 0.000
## PREP4*PPSS1 0.000 0.000 0.000 0.000 0.443 0.000 0.000
## PREP4*PPSS2 0.000 0.000 0.000 0.000 0.382 0.000 0.000
## PREP4*PPSS3 0.000 0.000 0.000 0.000 0.271 0.000 0.000
sec_report$loadings
## FAML POL REP INV REP*POL SEC TRUST
## TRST1 0.000 0.000 0.000 0.000 -0.000 0.000 0.900
## TRST2 0.000 0.000 0.000 0.000 -0.000 0.000 0.909
## TRST3 0.000 0.000 0.000 0.000 -0.000 0.000 0.905
## TRST4 0.000 0.000 0.000 0.000 -0.000 0.000 0.838
## PSEC1 0.000 0.000 0.000 0.000 -0.000 0.813 0.000
## PSEC2 0.000 0.000 0.000 0.000 -0.000 0.865 0.000
## PSEC3 0.000 0.000 0.000 0.000 -0.000 0.868 0.000
## PSEC4 0.000 0.000 0.000 0.000 -0.000 0.807 0.000
## PREP1 0.000 0.000 0.800 0.000 0.000 0.000 0.000
## PREP2 0.000 0.000 0.913 0.000 0.000 0.000 0.000
## PREP3 0.000 0.000 0.908 0.000 0.000 0.000 0.000
## PREP4 0.000 0.000 0.718 0.000 0.000 0.000 0.000
## PINV1 0.000 0.000 0.000 0.903 -0.000 0.000 0.000
## PINV2 0.000 0.000 0.000 0.925 -0.000 0.000 0.000
## PINV3 0.000 0.000 0.000 0.855 -0.000 0.000 0.000
## PPSS1 0.000 0.868 0.000 0.000 0.000 0.000 0.000
## PPSS2 0.000 0.893 0.000 0.000 0.000 0.000 0.000
## PPSS3 0.000 0.911 0.000 0.000 0.000 0.000 0.000
## FAML1 1.000 0.000 0.000 0.000 -0.000 0.000 0.000
## PREP1*PPSS1 -0.000 -0.000 -0.000 -0.000 0.581 -0.000 -0.000
## PREP1*PPSS2 -0.000 0.000 -0.000 -0.000 0.510 -0.000 -0.000
## PREP1*PPSS3 -0.000 -0.000 -0.000 -0.000 0.506 -0.000 -0.000
## PREP2*PPSS1 -0.000 -0.000 -0.000 -0.000 0.509 -0.000 -0.000
## PREP2*PPSS2 -0.000 0.000 -0.000 -0.000 0.421 0.000 0.000
## PREP2*PPSS3 0.000 -0.000 -0.000 -0.000 0.336 0.000 0.000
## PREP3*PPSS1 0.000 -0.000 -0.000 -0.000 0.236 0.000 0.000
## PREP3*PPSS2 -0.000 0.000 -0.000 -0.000 0.555 -0.000 -0.000
## PREP3*PPSS3 0.000 -0.000 -0.000 -0.000 0.466 -0.000 -0.000
## PREP4*PPSS1 0.000 0.000 0.000 -0.000 0.900 -0.000 -0.000
## PREP4*PPSS2 -0.000 -0.000 -0.000 -0.000 0.836 -0.000 0.000
## PREP4*PPSS3 0.000 0.000 0.000 -0.000 0.859 -0.000 0.000
1-b-iii) Regression coefficients of paths between factors
sec_report$paths
## SEC TRUST
## R^2 0.420 0.367
## AdjR^2 0.412 0.365
## FAML 0.011 .
## POL 0.339 .
## REP 0.247 .
## INV 0.181 .
## REP*POL -0.105 .
## SEC . 0.606
1-b-iv) Bootstrapped path coefficients: t-values, 95% CI
boot_pls <- bootstrap_model(sec_pls, nboot = 1000)
## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrapped
summary(boot_pls)
##
## Results from Bootstrap resamples: 1000
##
## Bootstrapped Structural Paths:
## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## FAML -> SEC 0.011 0.015 0.061 0.173 -0.106
## POL -> SEC 0.339 0.342 0.057 5.967 0.229
## REP -> SEC 0.247 0.243 0.060 4.088 0.121
## INV -> SEC 0.181 0.184 0.059 3.053 0.071
## REP*POL -> SEC -0.105 -0.019 0.123 -0.851 -0.193
## SEC -> TRUST 0.606 0.609 0.035 17.483 0.538
## 97.5% CI
## FAML -> SEC 0.133
## POL -> SEC 0.452
## REP -> SEC 0.365
## INV -> SEC 0.298
## REP*POL -> SEC 0.190
## SEC -> TRUST 0.671
##
## Bootstrapped Weights:
## Original Est. Bootstrap Mean Bootstrap SD T Stat.
## TRST1 -> TRUST 0.282 0.281 0.014 19.727
## TRST2 -> TRUST 0.280 0.280 0.016 17.863
## TRST3 -> TRUST 0.286 0.285 0.017 17.147
## TRST4 -> TRUST 0.278 0.278 0.020 13.637
## PSEC1 -> SEC 0.277 0.278 0.016 17.395
## PSEC2 -> SEC 0.315 0.315 0.017 18.942
## PSEC3 -> SEC 0.307 0.307 0.016 19.824
## PSEC4 -> SEC 0.292 0.290 0.018 16.466
## PREP1 -> REP 0.215 0.213 0.026 8.406
## PREP2 -> REP 0.334 0.334 0.018 18.347
## PREP3 -> REP 0.349 0.350 0.022 15.814
## PREP4 -> REP 0.287 0.286 0.025 11.445
## PINV1 -> INV 0.363 0.363 0.026 13.937
## PINV2 -> INV 0.395 0.394 0.027 14.854
## PINV3 -> INV 0.358 0.359 0.028 12.852
## PPSS1 -> POL 0.360 0.360 0.022 16.029
## PPSS2 -> POL 0.395 0.395 0.022 17.667
## PPSS3 -> POL 0.367 0.367 0.018 20.221
## FAML1 -> FAML 1.000 1.000 0.000 .
## PREP1*PPSS1 -> REP*POL 0.239 0.092 0.153 1.558
## PREP1*PPSS2 -> REP*POL 0.031 0.062 0.093 0.337
## PREP1*PPSS3 -> REP*POL 0.021 0.064 0.111 0.190
## PREP2*PPSS1 -> REP*POL 0.046 0.074 0.110 0.417
## PREP2*PPSS2 -> REP*POL -0.104 0.053 0.153 -0.680
## PREP2*PPSS3 -> REP*POL -0.228 0.042 0.235 -0.972
## PREP3*PPSS1 -> REP*POL -0.341 0.012 0.308 -1.107
## PREP3*PPSS2 -> REP*POL 0.095 0.093 0.134 0.706
## PREP3*PPSS3 -> REP*POL 0.108 0.094 0.138 0.787
## PREP4*PPSS1 -> REP*POL 0.443 0.124 0.279 1.587
## PREP4*PPSS2 -> REP*POL 0.382 0.105 0.263 1.456
## PREP4*PPSS3 -> REP*POL 0.271 0.104 0.183 1.482
## 2.5% CI 97.5% CI
## TRST1 -> TRUST 0.253 0.309
## TRST2 -> TRUST 0.249 0.310
## TRST3 -> TRUST 0.253 0.321
## TRST4 -> TRUST 0.239 0.318
## PSEC1 -> SEC 0.249 0.310
## PSEC2 -> SEC 0.283 0.347
## PSEC3 -> SEC 0.277 0.339
## PSEC4 -> SEC 0.256 0.325
## PREP1 -> REP 0.156 0.257
## PREP2 -> REP 0.301 0.373
## PREP3 -> REP 0.310 0.398
## PREP4 -> REP 0.241 0.338
## PINV1 -> INV 0.312 0.414
## PINV2 -> INV 0.340 0.449
## PINV3 -> INV 0.306 0.417
## PPSS1 -> POL 0.314 0.404
## PPSS2 -> POL 0.355 0.441
## PPSS3 -> POL 0.331 0.402
## FAML1 -> FAML 1.000 1.000
## PREP1*PPSS1 -> REP*POL -0.263 0.378
## PREP1*PPSS2 -> REP*POL -0.150 0.223
## PREP1*PPSS3 -> REP*POL -0.192 0.265
## PREP2*PPSS1 -> REP*POL -0.192 0.274
## PREP2*PPSS2 -> REP*POL -0.257 0.347
## PREP2*PPSS3 -> REP*POL -0.401 0.455
## PREP3*PPSS1 -> REP*POL -0.586 0.663
## PREP3*PPSS2 -> REP*POL -0.227 0.331
## PREP3*PPSS3 -> REP*POL -0.251 0.342
## PREP4*PPSS1 -> REP*POL -0.439 0.541
## PREP4*PPSS2 -> REP*POL -0.423 0.571
## PREP4*PPSS3 -> REP*POL -0.271 0.411
##
## Bootstrapped Loadings:
## Original Est. Bootstrap Mean Bootstrap SD T Stat.
## TRST1 -> TRUST 0.900 0.901 0.016 57.543
## TRST2 -> TRUST 0.909 0.910 0.020 45.290
## TRST3 -> TRUST 0.905 0.906 0.021 43.564
## TRST4 -> TRUST 0.838 0.839 0.031 26.619
## PSEC1 -> SEC 0.813 0.814 0.025 32.188
## PSEC2 -> SEC 0.865 0.867 0.024 35.699
## PSEC3 -> SEC 0.868 0.869 0.021 41.554
## PSEC4 -> SEC 0.807 0.807 0.024 33.040
## PREP1 -> REP 0.800 0.796 0.040 19.864
## PREP2 -> REP 0.913 0.914 0.016 58.377
## PREP3 -> REP 0.908 0.910 0.020 46.233
## PREP4 -> REP 0.718 0.718 0.032 22.259
## PINV1 -> INV 0.903 0.904 0.024 37.686
## PINV2 -> INV 0.925 0.925 0.022 41.316
## PINV3 -> INV 0.855 0.855 0.026 32.604
## PPSS1 -> POL 0.868 0.868 0.023 37.012
## PPSS2 -> POL 0.893 0.894 0.014 63.747
## PPSS3 -> POL 0.911 0.911 0.016 55.740
## FAML1 -> FAML 1.000 1.000 0.000 .
## PREP1*PPSS1 -> REP*POL 0.581 0.584 0.268 2.167
## PREP1*PPSS2 -> REP*POL 0.510 0.569 0.249 2.049
## PREP1*PPSS3 -> REP*POL 0.506 0.583 0.264 1.916
## PREP2*PPSS1 -> REP*POL 0.509 0.619 0.281 1.810
## PREP2*PPSS2 -> REP*POL 0.421 0.586 0.291 1.444
## PREP2*PPSS3 -> REP*POL 0.336 0.592 0.337 0.996
## PREP3*PPSS1 -> REP*POL 0.236 0.505 0.347 0.679
## PREP3*PPSS2 -> REP*POL 0.555 0.618 0.281 1.971
## PREP3*PPSS3 -> REP*POL 0.466 0.601 0.298 1.565
## PREP4*PPSS1 -> REP*POL 0.900 0.595 0.356 2.529
## PREP4*PPSS2 -> REP*POL 0.836 0.514 0.351 2.381
## PREP4*PPSS3 -> REP*POL 0.859 0.571 0.327 2.626
## 2.5% CI 97.5% CI
## TRST1 -> TRUST 0.867 0.929
## TRST2 -> TRUST 0.861 0.941
## TRST3 -> TRUST 0.857 0.939
## TRST4 -> TRUST 0.771 0.893
## PSEC1 -> SEC 0.762 0.858
## PSEC2 -> SEC 0.814 0.908
## PSEC3 -> SEC 0.823 0.906
## PSEC4 -> SEC 0.757 0.852
## PREP1 -> REP 0.714 0.861
## PREP2 -> REP 0.878 0.939
## PREP3 -> REP 0.867 0.940
## PREP4 -> REP 0.653 0.776
## PINV1 -> INV 0.849 0.942
## PINV2 -> INV 0.871 0.959
## PINV3 -> INV 0.800 0.900
## PPSS1 -> POL 0.815 0.908
## PPSS2 -> POL 0.863 0.919
## PPSS3 -> POL 0.874 0.939
## FAML1 -> FAML 1.000 1.000
## PREP1*PPSS1 -> REP*POL -0.070 0.915
## PREP1*PPSS2 -> REP*POL -0.050 0.883
## PREP1*PPSS3 -> REP*POL -0.080 0.907
## PREP2*PPSS1 -> REP*POL -0.083 0.962
## PREP2*PPSS2 -> REP*POL -0.179 0.931
## PREP2*PPSS3 -> REP*POL -0.277 0.984
## PREP3*PPSS1 -> REP*POL -0.304 0.944
## PREP3*PPSS2 -> REP*POL -0.116 0.950
## PREP3*PPSS3 -> REP*POL -0.178 0.953
## PREP4*PPSS1 -> REP*POL -0.267 0.986
## PREP4*PPSS2 -> REP*POL -0.321 0.915
## PREP4*PPSS3 -> REP*POL -0.228 0.953
##
## Bootstrapped HTMT:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## FAML -> POL 0.596 0.592 0.051 0.486 0.695
## FAML -> REP 0.599 0.599 0.056 0.483 0.707
## FAML -> INV 0.494 0.493 0.056 0.382 0.606
## FAML -> REP*POL 0.046 0.066 0.023 0.030 0.122
## FAML -> SEC 0.455 0.457 0.054 0.354 0.562
## FAML -> TRUST 0.471 0.473 0.054 0.368 0.577
## POL -> REP 0.543 0.544 0.058 0.427 0.654
## POL -> INV 0.498 0.500 0.058 0.383 0.621
## POL -> REP*POL 0.000 0.000 0.000 0.000 0.000
## POL -> SEC 0.622 0.624 0.053 0.515 0.723
## POL -> TRUST 0.458 0.460 0.060 0.332 0.572
## REP -> INV 0.705 0.704 0.051 0.596 0.797
## REP -> REP*POL 0.000 0.000 0.000 0.000 0.000
## REP -> SEC 0.595 0.595 0.045 0.501 0.679
## REP -> TRUST 0.682 0.683 0.044 0.588 0.760
## INV -> REP*POL 0.085 0.103 0.032 0.056 0.176
## INV -> SEC 0.568 0.568 0.050 0.466 0.665
## INV -> TRUST 0.563 0.562 0.051 0.456 0.659
## REP*POL -> SEC 0.059 0.082 0.020 0.048 0.128
## REP*POL -> TRUST 0.044 0.071 0.017 0.043 0.115
## SEC -> TRUST 0.685 0.685 0.037 0.611 0.752
##
## Bootstrapped Total Paths:
## Original Est. Bootstrap Mean Bootstrap SD 2.5% CI 97.5% CI
## FAML -> SEC 0.011 0.015 0.061 -0.106 0.133
## FAML -> TRUST 0.006 0.009 0.037 -0.065 0.084
## POL -> SEC 0.339 0.342 0.057 0.229 0.452
## POL -> TRUST 0.205 0.208 0.037 0.134 0.280
## REP -> SEC 0.247 0.243 0.060 0.121 0.365
## REP -> TRUST 0.150 0.148 0.039 0.072 0.229
## INV -> SEC 0.181 0.184 0.059 0.071 0.298
## INV -> TRUST 0.109 0.112 0.037 0.044 0.182
## REP*POL -> SEC -0.105 -0.019 0.123 -0.193 0.190
## REP*POL -> TRUST -0.063 -0.012 0.075 -0.117 0.115
## SEC -> TRUST 0.606 0.609 0.035 0.538 0.671
Question 2) Common-Factor Models using CB-SEM
2-a) Create a common factor model using SEMinR, with the following
characteristics:
2-a-i) Either respecify all the constructs as being reflective(), or
use the as.reflective() function to convert your earlier measurement
model to being entirely reflective.
sec_cf_mm <- as.reflective(sec_mm)
2-a-ii) Use the same structural model as before (you can just reuse
it again!)
sec_cf_pls <- estimate_cbsem(
data = secdata,
measurement_model = sec_cf_mm,
structural_model = sec_sm
)
## Generating the seminr model for CBSEM
2-b) Show us the following results in table or figure formats
2-b-ii) Loadings of composites
sec_cf_report <- summary(sec_cf_pls)
sec_cf_report$loadings
## $coefficients
## TRUST SEC REP INV POL FAML
## TRST1 0.8800240 NA NA NA NA NA
## TRST2 0.8886342 NA NA NA NA NA
## TRST3 0.8690644 NA NA NA NA NA
## TRST4 0.7575988 NA NA NA NA NA
## PSEC1 NA 0.7308766 NA NA NA NA
## PSEC2 NA 0.8173481 NA NA NA NA
## PSEC3 NA 0.8151708 NA NA NA NA
## PSEC4 NA 0.7260444 NA NA NA NA
## PREP1 NA NA 0.7551328 NA NA NA
## PREP2 NA NA 0.9199208 NA NA NA
## PREP3 NA NA 0.8871362 NA NA NA
## PREP4 NA NA 0.5650059 NA NA NA
## PINV1 NA NA NA 0.8520004 NA NA
## PINV2 NA NA NA 0.9257476 NA NA
## PINV3 NA NA NA 0.7388750 NA NA
## PPSS1 NA NA NA NA 0.8051533 NA
## PPSS2 NA NA NA NA 0.8272576 NA
## PPSS3 NA NA NA NA 0.8674335 NA
## FAML1 NA NA NA NA NA 1
##
## $significance
## Std Estimate SE t-Value 2.5% CI
## TRUST -> TRST1 0.8800240 0.02272091 0.000000e+00 0.8354919
## TRUST -> TRST2 0.8886342 0.03330783 0.000000e+00 0.8233521
## TRUST -> TRST3 0.8690644 0.03749444 0.000000e+00 0.7955767
## TRUST -> TRST4 0.7575988 0.04846748 0.000000e+00 0.6626042
## SEC -> PSEC1 0.7308766 0.03679205 0.000000e+00 0.6587655
## SEC -> PSEC2 0.8173481 0.04480183 0.000000e+00 0.7295381
## SEC -> PSEC3 0.8151708 0.03728082 0.000000e+00 0.7421017
## SEC -> PSEC4 0.7260444 0.03811841 0.000000e+00 0.6513337
## REP -> PREP1 0.7551328 0.04464916 0.000000e+00 0.6676220
## REP -> PREP2 0.9199208 0.02635333 0.000000e+00 0.8682692
## REP -> PREP3 0.8871362 0.04015103 0.000000e+00 0.8084416
## REP -> PREP4 0.5650059 0.04585583 0.000000e+00 0.4751302
## INV -> PINV1 0.8520004 0.04489927 0.000000e+00 0.7639994
## INV -> PINV2 0.9257476 0.04556425 0.000000e+00 0.8364433
## INV -> PINV3 0.7388750 0.04511601 0.000000e+00 0.6504492
## POL -> PPSS1 0.8051533 0.04355300 0.000000e+00 0.7197910
## POL -> PPSS2 0.8272576 0.02807169 0.000000e+00 0.7722381
## POL -> PPSS3 0.8674335 0.03273664 0.000000e+00 0.8032708
## FAML -> FAML1 1.0000000 0.00000000 NA 1.0000000
## REP_x_POL -> PREP1_x_PPSS1 0.7781584 0.05799871 0.000000e+00 0.6644831
## REP_x_POL -> PREP1_x_PPSS2 0.7597768 0.05931838 0.000000e+00 0.6435149
## REP_x_POL -> PREP1_x_PPSS3 0.7879106 0.05013554 0.000000e+00 0.6896467
## REP_x_POL -> PREP2_x_PPSS1 0.8447368 0.03649041 0.000000e+00 0.7732169
## REP_x_POL -> PREP2_x_PPSS2 0.8034561 0.03639411 0.000000e+00 0.7321250
## REP_x_POL -> PREP2_x_PPSS3 0.8342444 0.03536430 0.000000e+00 0.7649317
## REP_x_POL -> PREP3_x_PPSS1 0.6736451 0.12948898 1.967997e-07 0.4198514
## REP_x_POL -> PREP3_x_PPSS2 0.8011944 0.03780427 0.000000e+00 0.7270994
## REP_x_POL -> PREP3_x_PPSS3 0.7902063 0.06416741 0.000000e+00 0.6644405
## REP_x_POL -> PREP4_x_PPSS1 0.6854770 0.06906812 0.000000e+00 0.5501059
## REP_x_POL -> PREP4_x_PPSS2 0.5531922 0.06212434 0.000000e+00 0.4314307
## REP_x_POL -> PREP4_x_PPSS3 0.6405843 0.05794028 0.000000e+00 0.5270235
## 97.5% CI
## TRUST -> TRST1 0.9245562
## TRUST -> TRST2 0.9539164
## TRUST -> TRST3 0.9425522
## TRUST -> TRST4 0.8525933
## SEC -> PSEC1 0.8029877
## SEC -> PSEC2 0.9051581
## SEC -> PSEC3 0.8882399
## SEC -> PSEC4 0.8007551
## REP -> PREP1 0.8426435
## REP -> PREP2 0.9715724
## REP -> PREP3 0.9658307
## REP -> PREP4 0.6548817
## INV -> PINV1 0.9400013
## INV -> PINV2 1.0150518
## INV -> PINV3 0.8273007
## POL -> PPSS1 0.8905156
## POL -> PPSS2 0.8822771
## POL -> PPSS3 0.9315961
## FAML -> FAML1 1.0000000
## REP_x_POL -> PREP1_x_PPSS1 0.8918338
## REP_x_POL -> PREP1_x_PPSS2 0.8760387
## REP_x_POL -> PREP1_x_PPSS3 0.8861744
## REP_x_POL -> PREP2_x_PPSS1 0.9162567
## REP_x_POL -> PREP2_x_PPSS2 0.8747873
## REP_x_POL -> PREP2_x_PPSS3 0.9035572
## REP_x_POL -> PREP3_x_PPSS1 0.9274389
## REP_x_POL -> PREP3_x_PPSS2 0.8752894
## REP_x_POL -> PREP3_x_PPSS3 0.9159721
## REP_x_POL -> PREP4_x_PPSS1 0.8208480
## REP_x_POL -> PREP4_x_PPSS2 0.6749536
## REP_x_POL -> PREP4_x_PPSS3 0.7541452
2-b-iii) Regression coefficients of paths between factors, and their
p-values
sec_cf_report$paths
## $coefficients
## SEC TRUST
## R^2 0.540381651 0.4951084
## FAML -0.008837653 NA
## POL 0.376401499 NA
## REP 0.299536782 NA
## INV 0.214253245 NA
## REP_x_POL 0.008355287 NA
## SEC NA 0.7036394
##
## $pvalues
## SEC TRUST
## FAML 8.996836e-01 NA
## POL 4.380974e-09 NA
## REP 3.817181e-05 NA
## INV 3.534482e-03 NA
## REP_x_POL 8.516847e-01 NA
## SEC NA 0
##
## $significance
## Std Estimate SE t-Value 2.5% CI 97.5% CI
## SEC -> FAML -0.008837653 0.07010617 8.996836e-01 -0.14624321 0.12856791
## SEC -> POL 0.376401499 0.06413246 4.380974e-09 0.25070419 0.50209881
## SEC -> REP 0.299536782 0.07273355 3.817181e-05 0.15698165 0.44209191
## SEC -> INV 0.214253245 0.07345058 3.534482e-03 0.07029275 0.35821374
## SEC -> REP_x_POL 0.008355287 0.04468802 8.516847e-01 -0.07923162 0.09594219
## TRUST -> SEC 0.703639369 0.03721630 0.000000e+00 0.63069677 0.77658197
