A data frame containing 10 variables with 47 observations.
A data frame containing the following variables for 47 countries:
The Gini index of concentration
The percentage of landholders who collectively occupy one-half of all the agricultural land (starting with the farmers with the smallest plots of land and working toward the largest)
The percentage of the total number of farms that rent all their land. Transformation: ln (x + 1)
The 1955 gross national product per capita in U.S. dollars. Transformation: ln (x)
The percentage of the labor force employed in agriculture. Transformation: ln (x)
Instability of personnel based on the term of office of the chief executive. Transformation: exp (x - 16.3)
The total number of politically motivated violent incidents, from plots to protracted guerrilla warfare. Transformation: ln (x + 1)
The number of people killed as a result of internal group violence per 1,000,000 people. Transformation: ln (x + 1)
One if the country has a stable democracy, and zero otherwise
One if the country experiences a dictatorship, and zero otherwise
From: Henseler (2021)
The dataset was initially compiled by Russett (1964) , discussed and reprinted by Gifi (1990) , and partially transformed by Tenenhaus and Tenenhaus (2011) . It is also used in Henseler (2021) for demonstration purposes.
Gifi A (1990).
Nonlinear multivariate analysis.
Henseler J (2021). Composite-Based Structural Equation Modeling: Analyzing Latent and Emergent Variables. Guilford Press, New York.
Russett BM (1964). “Inequality and Instability: The Relation of Land Tenure to Politics.” World Politics, 16(3), 442--454. doi:10.2307/2009581 .
Tenenhaus A, Tenenhaus M (2011). “Regularized generalized canonical correlation analysis.” Psychometrika, 76(2), 257--284.
#============================================================================ # Example is taken from Henseler (2020) #============================================================================ model_Russett=" # Composite model AgrIneq <~ gini + farm + rent IndDev <~ gnpr + labo PolInst <~ inst + ecks + deat + stab + dict # Structural model PolInst ~ AgrIneq + IndDev " out <- csem(.data = Russett, .model = model_Russett, .PLS_weight_scheme_inner = 'factorial', .tolerance = 1e-06 )