latentmeasures {gesca} | R Documentation |
Means, Variances, and Correlations of Latent Variables
Description
The means and variances of latent variables and the correlations among the latent variables. In gesca 1.0, the individual scores of latent variables are calculated based on Fornell's (1992) approach.
Usage
latentmeasures(object)
Arguments
object |
An object of class. This can be created via the |
Value
Numeric vectors of means and variances, and correlation matrices.
References
Fornell, C. (1992). A national customer satisfaction barometer, the Swedish experience. Journal of Marketing, 56, 6-21.
Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A Component-Based Approach to Structural Equation Modeling (p.26). Boca Raton, FL: Chapman & Hall/CRC Press.
See Also
Examples
library(gesca)
data(gesca.rick2) # Organizational identification example of Bagozzi
# Model specification
myModel <- "
# Measurement model
OP =~ cei1 + cei2 + cei3
OI =~ ma1 + ma2 + ma3
AC_J =~ orgcmt1 + orgcmt2 + orgcmt3
AC_L =~ orgcmt5 + orgcmt6 + orgcmt8
# Structural model
OI ~ OP
AC_J ~ OI
AC_L ~ OI
"
# Run a multiple-group GSCA with the grouping variable gender:
GSCA.group <- gesca.run(myModel, gesca.rick2, group.name = "gender", nbt=10)
# Note: bootstrap size is set to 10 for quick execution.
# For your actual analysis, make sure to use an adequate bootstrap sample size
# (e.g., n = 100 or 500) to obtain reliable results.
latentmeasures(GSCA.group)
[Package gesca version 1.0.5 Index]