library(lavaan)## This is lavaan 0.6-17
## lavaan is FREE software! Please report any bugs.#library(semPlot)
library(seminr)
data("PoliticalDemocracy")
str(PoliticalDemocracy)## 'data.frame': 75 obs. of 11 variables:
## $ y1: num 2.5 1.25 7.5 8.9 10 7.5 7.5 7.5 2.5 10 ...
## $ y2: num 0 0 8.8 8.8 3.33 ...
## $ y3: num 3.33 3.33 10 10 10 ...
## $ y4: num 0 0 9.2 9.2 6.67 ...
## $ y5: num 1.25 6.25 8.75 8.91 7.5 ...
## $ y6: num 0 1.1 8.09 8.13 3.33 ...
## $ y7: num 3.73 6.67 10 10 10 ...
## $ y8: num 3.333 0.737 8.212 4.615 6.667 ...
## $ x1: num 4.44 5.38 5.96 6.29 5.86 ...
## $ x2: num 3.64 5.06 6.26 7.57 6.82 ...
## $ x3: num 2.56 3.57 5.22 6.27 4.57 ...head(PoliticalDemocracy)model
simple_mm<-constructs(composite("Dm1965",multi_items("x",1:3)),composite("Ind1966",multi_items("y",1:2)),composite("Ind1967",multi_items("y",3:4)),composite("Ind1968",multi_items("y",5:6)),composite("Ind1969",multi_items("y",7:8)))
#structural model
simple_sm<-relationships(paths(from="Ind1967",to=c("Ind1966","Ind1968")),paths(from="Ind1967",to="Dm1965"),paths(from=c("Ind1966","Ind1968"),to="Dm1965"))
#estimate
plot(simple_sm)library(seminr)
#loading the data
corp_rep_data<-corp_rep_data
#measurement model
mm<-constructs(composite("QUAL",multi_items("qual_",1:8),weights=mode_B),composite("PERF",multi_items("perf_",1:5),weights=mode_B),composite("CSOR",multi_items("csor_",1:5),weights=mode_B),composite("ATTR",multi_items("attr_",1:3),weights=mode_B),composite("COMP",multi_items("comp_",1:3)),composite("LIKE",multi_items("like_",1:3)),composite("CUSA",single_item("cusa")),composite("CUSL",multi_items("cusl_",1:3)))
#structural model
sm<-relationships(paths(from=c("QUAL","PERF","CSOR","ATTR"),to=c("COMP","LIKE")),paths(from=c("COMP","LIKE"),to=c("CUSA","CUSL")),paths(from=c("CUSA"),to=c("CUSL")))
#estimate
estimate<-estimate_pls(data=corp_rep_data,measurement_model=mm,structural_model=sm,missing=mean_replacement,missing_value="-99")## Generating the seminr model## All 344 observations are valid.#summary
summary<-summary(estimate)
summary##
## Results from package seminr (2.3.2)
##
## Path Coefficients:
## COMP LIKE CUSA CUSL
## R^2 0.631 0.558 0.292 0.562
## AdjR^2 0.627 0.552 0.288 0.558
## QUAL 0.430 0.380 . .
## PERF 0.295 0.117 . .
## CSOR 0.059 0.178 . .
## ATTR 0.086 0.167 . .
## COMP . . 0.146 0.006
## LIKE . . 0.436 0.344
## CUSA . . . 0.505
##
## Reliability:
## alpha rhoC AVE rhoA
## QUAL 0.878 0.894 0.518 1.000
## PERF 0.747 0.824 0.488 1.000
## CSOR 0.816 0.854 0.545 1.000
## ATTR 0.600 0.770 0.540 1.000
## COMP 0.776 0.869 0.688 0.786
## LIKE 0.831 0.899 0.747 0.836
## CUSA 1.000 1.000 1.000 1.000
## CUSL 0.831 0.899 0.748 0.839
##
## Alpha, rhoC, and rhoA should exceed 0.7 while AVE should exceed 0.5plot(estimate)#to find if the paths are significant we are going to do bootstrapping
#bootstrap
boot<-bootstrap_model(seminr_model=estimate,nboot=1000,cores=parallel::detectCores(),seed=123)## Bootstrapping model using seminr...## SEMinR Model successfully bootstrapped#summary
boot_summary<-summary(boot,alpha=0.05)
#inspect total indirect effects
boot_summary$total_indirect_effects## NULL#inspect indirect effects
specific_effect_significance(boot,from="COMP",through="CUSA",to="CUSL",alpha=0.05)## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.07350093 0.07443445 0.03651814 2.01272396 0.00296709
## 97.5% CI
## 0.14620781specific_effect_significance(boot,from="LIKE",through="CUSA",to="CUSL",alpha=0.05)## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.22001302 0.21993527 0.03687955 5.96571802 0.14917504
## 97.5% CI
## 0.29318977#inspect the direct effects
summary$paths## COMP LIKE CUSA CUSL
## R^2 0.631 0.558 0.292 0.562
## AdjR^2 0.627 0.552 0.288 0.558
## QUAL 0.430 0.380 . .
## PERF 0.295 0.117 . .
## CSOR 0.059 0.178 . .
## ATTR 0.086 0.167 . .
## COMP . . 0.146 0.006
## LIKE . . 0.436 0.344
## CUSA . . . 0.505#inspect the confidence intervals for direct effects
boot_summary$bootstrapped_paths## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## QUAL -> COMP 0.430 0.431 0.065 6.603 0.303
## QUAL -> LIKE 0.380 0.384 0.067 5.699 0.253
## PERF -> COMP 0.295 0.301 0.064 4.611 0.173
## PERF -> LIKE 0.117 0.123 0.073 1.613 -0.011
## CSOR -> COMP 0.059 0.059 0.054 1.084 -0.044
## CSOR -> LIKE 0.178 0.177 0.056 3.205 0.065
## ATTR -> COMP 0.086 0.084 0.055 1.565 -0.018
## ATTR -> LIKE 0.167 0.165 0.065 2.573 0.034
## COMP -> CUSA 0.146 0.147 0.071 2.047 0.007
## COMP -> CUSL 0.006 0.006 0.055 0.104 -0.104
## LIKE -> CUSA 0.436 0.435 0.062 7.069 0.312
## LIKE -> CUSL 0.344 0.343 0.056 6.175 0.231
## CUSA -> CUSL 0.505 0.505 0.042 12.074 0.420
## 97.5% CI
## QUAL -> COMP 0.552
## QUAL -> LIKE 0.514
## PERF -> COMP 0.422
## PERF -> LIKE 0.261
## CSOR -> COMP 0.165
## CSOR -> LIKE 0.282
## ATTR -> COMP 0.194
## ATTR -> LIKE 0.291
## COMP -> CUSA 0.281
## COMP -> CUSL 0.115
## LIKE -> CUSA 0.555
## LIKE -> CUSL 0.449
## CUSA -> CUSL 0.586#all the paths are statistically significant,they all exceeded 1.96 mark
#calculate the sign of p1*p2*p3
summary$paths["LIKE","CUSL"]*summary$paths["LIKE","CUSA"]*summary$paths["CUSA","CUSL"]## [1] 0.07569007summary$paths["COMP","CUSL"]*summary$paths["COMP","CUSA"]*summary$paths["CUSA","CUSL"]## [1] 0.0004163559