seminr (version 2.3.4)

product_indicator: product_indicator creates interaction measurement items by scaled product indicator approach.

Description

This function automatically generates interaction measurement items for a PLS SEM using scaled product indicator approach.

Usage

# standardized product indicator approach as per Henseler & Chin (2010):
 product_indicator(iv, moderator, weights)

Value

An un-evaluated function (promise) for estimating a product-indicator interaction effect.

Arguments

iv

The independent variable that is subject to moderation.

moderator

The moderator variable.

weights

is the relationship between the items and the interaction terms. This can be specified as correlation_weights or mode_A for correlation weights (Mode A) or as regression_weights or mode_B for regression weights (Mode B). Default is correlation weights.

References

Henseler & Chin (2010), A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17(1),82-109.

Examples

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
 composite("Image",        multi_items("IMAG", 1:5),weights = mode_A),
 composite("Expectation",  multi_items("CUEX", 1:3),weights = mode_A),
 composite("Value",        multi_items("PERV", 1:2),weights = mode_A),
 composite("Satisfaction", multi_items("CUSA", 1:3),weights = mode_A),
 interaction_term(iv = "Image",
                  moderator = "Expectation",
                  method = product_indicator,
                  weights = mode_A),
 interaction_term(iv = "Image",
                  moderator = "Value",
                  method = product_indicator,
                  weights = mode_A)
)

# structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                "Image*Expectation", "Image*Value"))
)

# Load data, assemble model, and estimate using semPLS
mobi <- mobi
seminr_model <- estimate_pls(mobi, mobi_mm, mobi_sm, inner_weights = path_factorial)