Antisemitism {cmm} | R Documentation |

A classical data set that has been used several times in the past, but not analyzed by means
of the methods advocated in this book (Glock, 1955; Campbell & Clayton, 1961; Hagenaars, 1990,
pp. 215-233, and Hagenaars, 1990, Section 5.3). The data are from a panel study among 503
white Christians living in and around Baltimore. The study's purpose was to determine the
effect of seeing the film ‘Gentleman’s Agreement' on reducing the level of antisemitism
(Glock, 1955, p. 243). Antisemitism was measured in November 1947 (variable `A`

) prior to the
movie being locally shown and consisted of three categories : 1 = high, 2 = moderate, and
3 = low. Antisemitism was measured again in May 1948 (variable `B`

). In addition, the respondents
were asked whether or not they had (voluntary) seen the movie, which had been shown in Baltimore
theaters during the period between the two interviews (variable `X`

). The experimental group (with
`X=1`

) consisted of those respondents who saw the movie; the control group (with `X=2`

)
consisted of those who did not. The data are tabulated in Bergsma, Croon, and Hagenaars (2009, Table 5.8).

Section 5.2.2 in Bergsma, Croon, and Hagenaars (2009).

`data(GSS93)`

A data frame with 496 observations on the following three variables.

`X`

Seen the film (factor): 1 = Seen; 2 = Not seen;

`A`

Antisemitism at Time 1 (ordered): 1 = High; 2 = Moderate; 3 = Low.

`B`

Antisemitism at Time 2 (ordered): 1 = High; 2 = Moderate; 3 = Low.

Glock (1955).

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

Campell & Clayton (1961)

Glock (1955)

Hagenaars, 1990

```
data(Antisemitism)
## Sample marginal distributions
# at applied to data gives vectorized 2x2x3 table TXR (Time x Seen film or not x Response)
at <- MarginalMatrix(c("X","A","B"), list(c("X","A"), c("X","B")), c(2,3,3));
stats = SampleStatistics(
dat = Antisemitism,
coeff = at,
Labels = c("T","X","R"),
CoefficientDimensions = c(2,2,3))
## Models for table XR given T
# at1 applied to data gives vectorized conditional 2x3 table XR (XR conditional on T<-1)
at1 <- MarginalMatrix(c("X", "A", "B"), list(c("X", "A")), c(2, 3, 3));
# at2 applied to data gives vectorized conditional 2x3 table XR (XR conditional on T<-2)
at2 <- MarginalMatrix(c("X", "A", "B"), list(c("X", "B")), c(2, 3, 3));
bt1 <- ConstraintMatrix(c("X", "R"), list(c("X"), c("R")), c(2, 3));
bt2 <- ConstraintMatrix(c("X", "R"), list(c("X"), c("R")), c(2, 3));
model1 <- list(bt1, "log", at1);
model2 <- list(bt2, "log", at2);
# model1 doesn't converge, I don't know the reason and am trying to find out
# (it does converge in the Mathematica programme).
fit = MarginalModelFit(
dat = Antisemitism,
model = model2,
Labels = c("X","R"),
CoefficientDimensions = c(2,3),
MaxSteps=100,
ShowProgress=10,
MaxStepSize=.5)
```

[Package *cmm* version 1.0 Index]