product_indicator creates interaction measurement items by scaled product indicator approach.
This function automatically generates interaction measurement items for a PLS SEM using scaled product indicator approach.
# standardized product indicator approach as per Henseler & Chin (2010): product_indicator(iv, moderator, weights)
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 |
An un-evaluated function (promise) for estimating a product-indicator interaction effect.
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.