Means, Variances, and Correlations of Latent Variables

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

if (FALSE) {
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=50)
latentmeasures(GSCA.group)
}