simdata: Simulated data for REBUS with two groups

In gastonstat/plspm: Tools for Partial Least Squares Path Modeling (PLS-PM)

Simulated data for REBUS with two groups

Description

Simulated data with two latent classes showing different local models.

Usage

data(simdata)

Format

A data frame of simulated data with 400 observations on the following 14 variables.

mv1

first manifest variable of the block Price Fairness

mv2

second manifest variable of the block Price Fairness

mv3

third manifest variable of the block Price Fairness

mv4

fourth manifest variable of the block Price Fairness

mv5

fifth manifest variable of the block Price Fairness

mv6

first manifest variable of the block Quality

mv7

second manifest variable of the block Quality

mv8

third manifest variable of the block Quality

mv9

fourth manifest variable of the block Quality

mv10

fifth manifest variable of the block Quality

mv11

first manifest variable of the block Customer Satisfaction

mv12

second manifest variable of the block Customer Satisfaction

mv13

third manifest variable of the block Customer Satisfaction

group

a numeric vector

Details

The postulated model overlaps the one used by Jedidi et al. (1997) and by Esposito Vinzi et al. (2007) for their numerical examples. It is composed of one latent endogenous variable, Customer Satisfaction, and two latent exogenous variables, Price Fairness and Quality. Each latent exogenous variable (Price Fairness and Quality) has five manifest variables (reflective mode), and the latent endogenous variable (Customer Satisfaction) is measured by three indicators (reflective mode).

Two latent classes showing different local models are supposed to exist. Each one is composed of 200 units. Thus, the data on the aggregate level for each one of the numerical examples includes 400 units.

The simulation scheme involves working with local models that are different at both the measurement and the structural model levels. In particular, the experimental sets of data consist of two latent classes with the following characteristics:
(a) Class 1 - price fairness seeking customers - characterized by a strong relationship between Price Fairness and Customer Satisfaction (close to 0.9) and a weak relationship between Quality and Customer Satisfaction (close to 0.1), as well as by a weak correlation between the 3rd manifest variable of the Price Fairness block (mv3) and the corresponding latent variable;
(b) Class 2 - quality oriented customers - characterized by a strong relationship between Quality and Customer Satisfaction (close to 0.1) and a weak relationship between Price Fairness and Customer Satisfaction (close to 0.9), as well as by a weak correlation between the 3rd manifest variable (mv8) of the Quality block and the corresponding latent variable.

Source

Simulated data from Trinchera (2007). See References below.

References

Esposito Vinzi, V., Ringle, C., Squillacciotti, S. and Trinchera, L. (2007) Capturing and treating unobserved heterogeneity by response based segmentation in PLS path modeling. A comparison of alternative methods by computational experiments. Working paper, ESSEC Business School.

Jedidi, K., Jagpal, S. and De Sarbo, W. (1997) STEMM: A general finite mixture structural equation model. Journal of Classification 14, pp. 23-50.

Trinchera, L. (2007) Unobserved Heterogeneity in Structural Equation Models: a new approach to latent class detection in PLS Path Modeling. Ph.D. Thesis, University of Naples "Federico II", Naples, Italy.

Examples

data(simdata)
simdata

gastonstat/plspm documentation built on April 8, 2022, 5:59 a.m.