# Political Orientation and Religion in the United States in 1993 (General Social Survey, 1993) Self-reported Political Orientation ($P$), Religion ($R$), and Opinion of Teenage Birth-control ($B$) of a sample of 911 US citizens in 1993. The data stem from the General Social Survey. The data are tabulated in Bergsma, Croon, and Hagenaars (2009, Table 2.1, Table 2.3). See Section~2.1 in Bergsma, Croon, and Hagenaars (2009). Several worked examples involving this data set are listed below but more can be found at http://stats.lse.ac.uk/bergsma/cmm/R files/GSS93.R data ```r data(GSS93) ``` ## Format A data frame with 911 observations on the following three variables. - **`P`**: Political orientation (ordered): 1 = Extremely liberal; 2 = Liberal; 3 = Slightly liberal; 4 = Moderate; 5 = Slightly conservative; 6 = Conservative; 6 = Extremely conservative. - **`R`**: Religion (factor): 1 = Protestant; 2 = Catholic; 3 = Other. - **`B`**: Opinion of birth control (ordered): 1 = Strongly agree; 2 = Agree; 3 = Disagree; 4 = Strongly disagree; ## Source General Social Survey (1993) ## References Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009). **Marginal models for dependent, clustered, and longitudinal categorical data.** New York: Springer General Social Survey (1993). ## Examples ```r data(GSS93) ## Table 2.1 of Bergsma, Croon, & Hagenaars (2009) addmargins(table(GSS93[,1:2])) ## Table 2.2 of Bergsma, Croon, & Hagenaars (2009) # Specify coefficients coeff <- list("log",diag(21)) SampleStatistics(dat = GSS93[, 1 : 2], coeff = coeff, CoefficientDimensions = c(7, 3), Labels = c("P","R"), ShowParameters = TRUE, ShowCoefficients = FALSE) ## Table 2.3 of Bergsma, Croon, & Hagenaars (2009) ftable(B + R ~ P , data = GSS93) ######################################################## ## Models for table PR #independence of P and R bt1 <- ConstraintMatrix(c("P", "R"), list(c("P"), c("R")), c(7,3)); #linear by nominal model bt2 <- ConstraintMatrix(var = c("P", "R"), suffconfigs = list(c("P", "R")), dim = c(7, 3), SubsetCoding = list(c("P", "R"), c("Linear", "Nominal"))) #linear by nominal model with equality of first two nominal parameters bt3 <- ConstraintMatrix(var = c("P", "R"), suffconfigs = list(c("P", "R")), dim = c(7, 3), SubsetCoding = list(c("P", "R"), list("Linear", rbind(c(1, 1, 0), c(0, 0, 1))))) m <- MarginalModelFit(dat = GSS93[,1:2], model = list(bt2,"log"), ShowCoefficients = FALSE, ShowProgress = 1, ShowParameters = TRUE, CoefficientDimensions = c(7, 3), Labels = c("P", "R"), ParameterCoding = list("Polynomial", "Effect")) ######################################################## ## Models for table PRB #various loglinear models bt1 <- ConstraintMatrix(c("P", "R","B"), list(c("P","R"),c("B")), c(7,3,4)) bt2 <- ConstraintMatrix(c("P", "R","B"), list(c("P","R"),c("R","B")), c(7,3,4)) bt3 <- ConstraintMatrix(c("P", "R","B"), list(c("P","R"),c("P","B")), c(7,3,4)) bt4 <- ConstraintMatrix(c("P", "R","B"), list(c("P","R"),c("P","B"),c("R","B")), c(7,3,4)) bt5 <- ConstraintMatrix(c("P", "R","B"), list(c("P","R"),c("P","B"),c("R","B")), c(7,3,4), SubsetCoding = list(list(c("P", "B"), c("Linear", "Linear")), list(c("R", "B"), c("Nominal", "Linear")))) m <- MarginalModelFit(dat = GSS93, model = list(bt2,"log"), ShowCoefficients = FALSE, ShowProgress = 1, ShowParameters = TRUE, CoefficientDimensions =c(7, 3, 4), Labels = c("P", "R", "B"), ParameterCoding = list("Polynomial", "Polynomial", "Effect")) ```

GSS93 dataset