EVS {cmm}

European Values Study (EVS): attitude towards women's role in society

Description

European Values Study 1999/2000, Views on Women's Roles.

The data are tabulated in Bergsma, Croon, and Hagenaars (2009, Table 5.1a). Section 5.1.2 in Bergsma, Croon and Hagenaars (2009).

Usage

data(EVS)

Format

A data frame with 960 observations on the following variables.

S

Sex (factor): 1 = Male; 2 = Female.

A

Date of Birth (ordered): 1 = Before 1945; 2 = 1945-1963; 3 = After 1963.

E

Level of education (ordered): 1 = Lower; 2 = Intermediate; 3 = Higher.

R

Religion (ordered): 1 = Religious person; 2 = Not a religious person; 3 = Convinced atheist.

W

Attitude women's role in society (factor): 1 = Traditional; 2 = Nontraditional.

Source

European Values Study 1999/2000

References

Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009). Marginal models for dependent, clustered, and longitudinal categorical data. New York: Springer.

Examples

data(EVS)

# Table SAERW
var = c("S", "A", "E", "R", "W");
dim = c(2, 3, 3, 3, 2);

# matrices for table SA
at1 <- MarginalMatrix(var, c("S", "A"), dim);
bt1 <- ConstraintMatrix(c("S", "A"), list(c("S"), c("A")), c(2, 3));

# matrices for table SAER
at2 <- MarginalMatrix(var, c("S", "A", "E", "R"), dim);
bt2 <- ConstraintMatrix(var = c("S", "A", "E", "R"), 
                       suffconfigs = list(c("S", "A", "E"), c("S", "R"), c("A", "R")), 
                       dim = c(2, 3, 3, 3));

# matrices for table SAERW: constraints
at3 <- MarginalMatrix(var, c("S", "A", "E", "R", "W"), dim);
bt3 <- ConstraintMatrix(var = c("S", "A", "E", "R", "W"), 
 suffconfigs = list(c("S", "A", "E", "R"), c("S", "W"), c("A", "W"), c("E", "W"), c("R", "W")), 
 dim = c(2, 3, 3, 3, 2))

# matrix for table SAERW: design matrix
x <- DesignMatrix(var = c("S", "A", "E", "R", "W"), 
 suffconfigs = list(c("S", "A", "E", "R"), c("S", "W"), c("A", "W"), c("E", "W"), c("R", "W")), 
 dim = c(2, 3, 3, 3, 2));

# model1: specification using only constraints
at <- rbind(at1, at2, at3);
bt <- DirectSum(bt1, bt2);
bt <- DirectSum(bt, bt3);
model1 <- list(bt, "log", at);

# model2: same as model1 but using both constraints and a design matrix 
# to specify a loglinear model for the joint distribution
at <- rbind(at1, at2);
bt <- DirectSum(bt1, bt2);
model2 <- list(list(bt, "log", at), x);

nkps3 <- MarginalModelFit(EVS, model2, MaxError = 10.^-25,
    MaxSteps = 1000,
    ShowProgress = 10,
    MaxStepSize = .3 );