Russett {cSEM}

Data: Russett

Description

A data frame containing 10 variables with 47 observations.

Usage

Russett

Format

A data frame containing the following variables for 47 countries:

gini

The Gini index of concentration

farm

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)

rent

The percentage of the total number of farms that rent all their land. Transformation: ln (x + 1)

gnpr

The 1955 gross national product per capita in U.S. dollars. Transformation: ln (x)

labo

The percentage of the labor force employed in agriculture. Transformation: ln (x)

inst

Instability of personnel based on the term of office of the chief executive. Transformation: exp (x - 16.3)

ecks

The total number of politically motivated violent incidents, from plots to protracted guerrilla warfare. Transformation: ln (x + 1)

deat

The number of people killed as a result of internal group violence per 1,000,000 people. Transformation: ln (x + 1)

stab

One if the country has a stable democracy, and zero otherwise

dict

One if the country experiences a dictatorship, and zero otherwise

Details

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.

Source

From: Henseler (2021)

References

Gifi A (1990). Nonlinear multivariate analysis. Wiley.

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.

Examples

#============================================================================
# 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
)