sim.data {plspm} | R Documentation |
Simulated data with two latent classes showing different local models.
data(sim.data)
A data frame of simulated data with 400 observations on the following 14 variables.
mv1
mv2
mv3
mv4
mv5
mv6
mv7
mv8
mv9
mv10
mv11
mv12
mv13
group
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.
Simulated data from Trinchera (2007). See References below.
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.
data(sim.data) sim.data