Antisemitism               package:cmm               R Documentation

_C_h_a_n_g_e _i_n _a_n_t_i_s_e_m_i_t_i_s_m _a_f_t_e_r _s_e_e_i_n_g _a _m_o_v_i_e

_D_e_s_c_r_i_p_t_i_o_n:

     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).

_U_s_a_g_e:

     data(GSS93)

_F_o_r_m_a_t:

     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.


_S_o_u_r_c_e:

     Glock (1955).

_R_e_f_e_r_e_n_c_e_s:

     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

_E_x_a_m_p_l_e_s:

     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));
     SampleStatistics(Antisemitism, 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(Antisemitism, model2, Labels = c("X","R"), CoefficientDimensions = c(2,3), MaxSteps=100, MaxStepSize=.5)

