Sample Data: A Path Model
data_pa.RdA 10-variable dataset with 200 cases.
Format
A data frame with 200 rows and 10 variables:
- m11
Mediator. Numeric.
- m12
Mediator. Numeric.
- m21
Mediator. Numeric.
- m22
Mediator. Numeric.
- y1
Outcome variable. Numeric.
- y2
Outcome variable. Numeric.
- x1
Predictor. Numeric.
- x2
Predictor. Numeric.
- c1
Control variable. Numeric.
- c2
Control variable. Numeric.
Examples
library(lavaan)
#> This is lavaan 0.6-15
#> lavaan is FREE software! Please report any bugs.
data(data_pa)
mod <-
"
m11 ~ a11*x1 + a21*x2 + c1 + c2
m12 ~ b12*m11 + c1 + c2
m21 ~ a12*x1 + a22*x2 + c1 + c2
m22 ~ b22*m21 + c1 + c2
y1 ~ b12y1*m12 + c1 + c2
y2 ~ b22y2*m22 + c1 + c2
ind_1 := a11*b12*b12y1
ind_2 := a21*b22*b22y2
"
fit <- sem(mod, data_pa)
summary(fit, fit.measures = TRUE)
#> lavaan 0.6.15 ended normally after 7 iterations
#>
#> Estimator ML
#> Optimization method NLMINB
#> Number of model parameters 27
#>
#> Number of observations 200
#>
#> Model Test User Model:
#>
#> Test statistic 76.709
#> Degrees of freedom 18
#> P-value (Chi-square) 0.000
#>
#> Model Test Baseline Model:
#>
#> Test statistic 377.440
#> Degrees of freedom 39
#> P-value 0.000
#>
#> User Model versus Baseline Model:
#>
#> Comparative Fit Index (CFI) 0.827
#> Tucker-Lewis Index (TLI) 0.624
#>
#> Loglikelihood and Information Criteria:
#>
#> Loglikelihood user model (H0) -1709.158
#> Loglikelihood unrestricted model (H1) -1670.803
#>
#> Akaike (AIC) 3472.315
#> Bayesian (BIC) 3561.370
#> Sample-size adjusted Bayesian (SABIC) 3475.831
#>
#> Root Mean Square Error of Approximation:
#>
#> RMSEA 0.128
#> 90 Percent confidence interval - lower 0.099
#> 90 Percent confidence interval - upper 0.158
#> P-value H_0: RMSEA <= 0.050 0.000
#> P-value H_0: RMSEA >= 0.080 0.996
#>
#> Standardized Root Mean Square Residual:
#>
#> SRMR 0.090
#>
#> Parameter Estimates:
#>
#> Standard errors Standard
#> Information Expected
#> Information saturated (h1) model Structured
#>
#> Regressions:
#> Estimate Std.Err z-value P(>|z|)
#> m11 ~
#> x1 (a11) 0.543 0.066 8.202 0.000
#> x2 (a21) 0.572 0.069 8.345 0.000
#> c1 0.258 0.070 3.701 0.000
#> c2 0.460 0.074 6.213 0.000
#> m12 ~
#> m11 (b12) 0.290 0.055 5.246 0.000
#> c1 0.165 0.073 2.269 0.023
#> c2 0.185 0.079 2.322 0.020
#> m21 ~
#> x1 (a12) -0.068 0.066 -1.027 0.304
#> x2 (a22) 0.184 0.069 2.678 0.007
#> c1 -0.011 0.070 -0.158 0.874
#> c2 -0.138 0.074 -1.861 0.063
#> m22 ~
#> m21 (b22) 0.249 0.076 3.274 0.001
#> c1 0.031 0.076 0.405 0.685
#> c2 -0.023 0.082 -0.280 0.780
#> y1 ~
#> m12 (b121) 0.442 0.070 6.267 0.000
#> c1 -0.041 0.078 -0.525 0.599
#> c2 -0.053 0.084 -0.636 0.525
#> y2 ~
#> m22 (b222) 0.511 0.065 7.822 0.000
#> c1 0.210 0.072 2.895 0.004
#> c2 -0.027 0.077 -0.350 0.727
#>
#> Covariances:
#> Estimate Std.Err z-value P(>|z|)
#> .y1 ~~
#> .y2 0.028 0.075 0.377 0.706
#>
#> Variances:
#> Estimate Std.Err z-value P(>|z|)
#> .m11 0.928 0.093 10.000 0.000
#> .m12 0.987 0.099 10.000 0.000
#> .m21 0.930 0.093 10.000 0.000
#> .m22 1.118 0.112 10.000 0.000
#> .y1 1.115 0.112 10.000 0.000
#> .y2 1.005 0.101 10.000 0.000
#>
#> Defined Parameters:
#> Estimate Std.Err z-value P(>|z|)
#> ind_1 0.070 0.019 3.612 0.000
#> ind_2 0.073 0.026 2.840 0.005
#>