library(lavaan) ## Warning: package 'lavaan' was built under R version 4.2.2## This is lavaan 0.6-13
## lavaan is FREE software! Please report any bugs.withdraw_data <- read.csv("estress.csv")
#Print the first 6 rows of the dataset
head(withdraw_data)## tenure estress affect withdraw sex age ese
## 1 1.67 6.0 2.60 3.00 1 51 5.33
## 2 0.58 5.0 1.00 1.00 0 45 6.05
## 3 0.58 5.5 2.40 3.66 1 42 5.26
## 4 2.00 3.0 1.16 4.66 1 50 4.35
## 5 5.00 4.5 1.00 4.33 1 48 4.86
## 6 9.00 6.0 1.50 3.00 1 48 5.05#Report means, SDs and correlation matrix for all variables in the model.
#means:
mean(withdraw_data$estress)## [1] 4.620229mean(withdraw_data$affect)## [1] 1.598092mean(withdraw_data$withdraw)## [1] 2.321145mean(withdraw_data$sex)## [1] 0.6183206mean(withdraw_data$age)## [1] 43.79389#SDs
sd(withdraw_data$estress)## [1] 1.423614sd(withdraw_data$affect)## [1] 0.7237171sd(withdraw_data$withdraw)## [1] 1.24687sd(withdraw_data$sex)## [1] 0.4867283sd(withdraw_data$age)## [1] 10.3596#correlation matrix for all variables in the model
(cor(withdraw_data[, c("withdraw","estress", "affect","sex", "age")]))## withdraw estress affect sex age
## withdraw 1.00000000 0.06407186 0.41658602 0.05002922 -0.03526418
## estress 0.06407186 1.00000000 0.34006259 0.13283301 0.06598494
## affect 0.41658602 0.34006259 1.00000000 0.04621758 -0.01779056
## sex 0.05002922 0.13283301 0.04621758 1.00000000 0.08311985
## age -0.03526418 0.06598494 -0.01779056 0.08311985 1.00000000#Compute the covariance matrices
(cov(withdraw_data[, c("estress", "affect", "withdraw", "sex", "age")]))## estress affect withdraw sex age
## estress 2.02667734 0.35036442 0.11373154 0.09204177 0.9731508
## affect 0.35036442 0.52376646 0.37591943 0.01628031 -0.1333833
## withdraw 0.11373154 0.37591943 1.55468527 0.03036208 -0.4555102
## sex 0.09204177 0.01628031 0.03036208 0.23690445 0.4191161
## age 0.97315083 -0.13338335 -0.45551022 0.41911614 107.3213419################
Model<-'
withdraw~affect
affect~estress'
fit<-sem(Model,withdraw_data)
fit## lavaan 0.6.13 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 4
##
## Number of observations 262
##
## Model Test User Model:
##
## Test statistic 2.167
## Degrees of freedom 1
## P-value (Chi-square) 0.141######
summary(fit, header = FALSE, standardize = TRUE, ci = TRUE)##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## withdraw ~
## affect 0.718 0.097 7.417 0.000 0.528 0.907
## affect ~
## estress 0.173 0.030 5.853 0.000 0.115 0.231
## Std.lv Std.all
##
## 0.718 0.417
##
## 0.173 0.340
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .withdraw 1.280 0.112 11.446 0.000 1.061 1.499
## .affect 0.461 0.040 11.446 0.000 0.382 0.540
## Std.lv Std.all
## 1.280 0.826
## 0.461 0.884parameterEstimates(fit)## lhs op rhs est se z pvalue ci.lower ci.upper
## 1 withdraw ~ affect 0.718 0.097 7.417 0 0.528 0.907
## 2 affect ~ estress 0.173 0.030 5.853 0 0.115 0.231
## 3 withdraw ~~ withdraw 1.280 0.112 11.446 0 1.061 1.499
## 4 affect ~~ affect 0.461 0.040 11.446 0 0.382 0.540
## 5 estress ~~ estress 2.019 0.000 NA NA 2.019 2.019######labeled
Model<-'
withdraw ~ b * affect + age + sex + estress
affect ~ a * estress + age + sex
ind := a * b'
fit <- sem(Model,withdraw_data)
fit## lavaan 0.6.13 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 262
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0summary(fit, header = FALSE, standardize = TRUE, ci = TRUE)##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## withdraw ~
## affect (b) 0.767 0.102 7.490 0.000 0.566 0.968
## age -0.003 0.007 -0.444 0.657 -0.016 0.010
## sex 0.112 0.145 0.773 0.440 -0.172 0.396
## estress -0.080 0.053 -1.525 0.127 -0.183 0.023
## affect ~
## estress (a) 0.174 0.030 5.834 0.000 0.116 0.232
## age -0.003 0.004 -0.698 0.485 -0.011 0.005
## sex 0.006 0.087 0.071 0.944 -0.165 0.177
## Std.lv Std.all
##
## 0.767 0.445
## -0.003 -0.025
## 0.112 0.044
## -0.080 -0.091
##
## 0.174 0.342
## -0.003 -0.041
## 0.006 0.004
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .withdraw 1.266 0.111 11.446 0.000 1.049 1.483
## .affect 0.461 0.040 11.446 0.000 0.382 0.539
## Std.lv Std.all
## 1.266 0.817
## 0.461 0.883
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ind 0.133 0.029 4.603 0.000 0.077 0.190
## Std.lv Std.all
## 0.133 0.152#parameterEstimates(fit)
############
# library("semPlot")
#
# semPaths(fit, what = "est", fade = FALSE)
#
# library(lavaanPlot)
#
# lavaanPlot(model = fit)
#
# #
# withdraw_pa <- sem(model= withdraw_model, sample.cov = cov_mat, sample.nobs = 262)
# withdraw_pa
#
# summary(withdraw_pa, header = FALSE, standardize = TRUE, ci = TRUE)
library(lavaanPlot) # for plotting SEMs## Warning: package 'lavaanPlot' was built under R version 4.2.2# lavaanPlot(
# model = withdraw_pa, coefs = TRUE, covs = TRUE,
# stars = "regress")
lavaanPlot(
model = fit, coefs = TRUE, covs = TRUE,
stars = "regress", stand = TRUE)fit_mod <- sem(
model = Model, data =withdraw_data,
se = "bootstrap", bootstrap = 5000)
summary(fit_mod, standardize = TRUE, rsquare = TRUE, ci = TRUE)## lavaan 0.6.13 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 262
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 5000
## Number of successful bootstrap draws 5000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## withdraw ~
## affect (b) 0.767 0.142 5.404 0.000 0.495 1.038
## age -0.003 0.007 -0.444 0.657 -0.016 0.010
## sex 0.112 0.150 0.746 0.456 -0.184 0.396
## estress -0.080 0.055 -1.447 0.148 -0.183 0.034
## affect ~
## estress (a) 0.174 0.041 4.254 0.000 0.091 0.250
## age -0.003 0.005 -0.610 0.542 -0.012 0.006
## sex 0.006 0.093 0.066 0.947 -0.182 0.185
## Std.lv Std.all
##
## 0.767 0.445
## -0.003 -0.025
## 0.112 0.044
## -0.080 -0.091
##
## 0.174 0.342
## -0.003 -0.041
## 0.006 0.004
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .withdraw 1.266 0.094 13.435 0.000 1.062 1.428
## .affect 0.461 0.081 5.688 0.000 0.310 0.624
## Std.lv Std.all
## 1.266 0.817
## 0.461 0.883
##
## R-Square:
## Estimate
## withdraw 0.183
## affect 0.117
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ind 0.133 0.034 3.977 0.000 0.072 0.204
## Std.lv Std.all
## 0.133 0.152#The interpretation of results
# We used path analysis to test the withdrawal intentions path model based on data from 262 entrepreneurs. We used lavaan version 0.6.14 (Rosseel, 2012), with maximum likelihood estimation.
#Entrepreneurs who reported experiencing greater economic stress reported greater depressed affect (b = 0.174, se = 0.04, p = .000), and this greater depressed affect in turn was related to an increased intention to withdraw from entrepreneurial activity (b = 0.767, se = 0.142, p = .000).
#Depressed affect is the proposed mediator of economic stress on withdrawal intentions. The results demonstrated that the indirect effects from economic stress (b = 0.133, se = 0.029, p = .000)to quantitative withdrawal intentions via Depressed affect were statistically significant.
#There were no prediction of significant effect of gender or age on the withdrawal intentions P>0.05
citation("lavaan") ##
## To cite lavaan in publications use:
##
## Yves Rosseel (2012). lavaan: An R Package for Structural Equation
## Modeling. Journal of Statistical Software, 48(2), 1-36.
## https://doi.org/10.18637/jss.v048.i02
##
## A BibTeX entry for LaTeX users is
##
## @Article{,
## title = {{lavaan}: An {R} Package for Structural Equation Modeling},
## author = {Yves Rosseel},
## journal = {Journal of Statistical Software},
## year = {2012},
## volume = {48},
## number = {2},
## pages = {1--36},
## doi = {10.18637/jss.v048.i02},
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