latentmeasures: Means, Variances, and Correlations of Latent Variables

In gesca: Generalized Structured Component Analysis Structural Equation Modeling

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 gesca.run function.

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

gesca.run

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)

gesca documentation built on Oct. 22, 2024, 9:06 a.m.