Immigration Logistic Regression Example using WebStats/SPSS Syntax

      Total number of cases:      1402 (Unweighted)
      Number of selected cases:   1402
      Number of unselected cases: 0

      Number of selected cases:                 1402
      Number rejected because of missing data:  243
      Number of cases included in the analysis: 1159



Dependent Variable Encoding:

Original       Internal
Value          Value
 0             0
 1             1

Dependent Variable..   Q6         6. Do you think Canada should accept more, few
er, or about the same number of immigrants as we accept now?

Beginning Block Number  0.  Initial Log Likelihood Function

-2 Log Likelihood   1570.9754

* Constant is included in the model.


Beginning Block Number  1.  Method: Enter

Variable(s) Entered on Step Number
1..       Q3        3. Are you... worried about you or a member of your family f
inding or keeping a stable, full-time job?
          Q38       38. Over the next decade, do you think that it is .. likely.
.. reduce prejudice against ethnic and racial minorities?
          Q42       42. What is the highest level of education you have reached?
          Q45       45. Total annual income, before taxes and deductions

Estimation terminated at iteration number 3 because
parameter estimates changed by less than .001

 -2 Log Likelihood     1514.532
 Goodness of Fit       1156.755

                     Chi-Square    df Significance

 Model Chi-Square        56.444     4        .0000
 Improvement             56.444     4        .0000

Classification Table for Q6
                            Predicted
                        Fewer    More/Same    Percent Correct
                           F    |     M
Observed             +----------+----------+
   Fewer         F   |    147   |    331   |   30.75%
                     +----------+----------+
   More/Same     M   |    132   |    549   |   80.62%
                     +----------+----------+
                                      Overall  60.05%

---------------------- Variables in the Equation -----------------------

Variable           B      S.E.     Wald    df      Sig       R   Exp(B)

Q3            -.1430     .0587   5.9373     1    .0148  -.0501    .8667
Q38            .2568     .0758  11.4604     1    .0007   .0776   1.2927
Q42            .4161     .0784  28.1860     1    .0000   .1291   1.5160
Q45            .0177     .0377    .2201     1    .6390   .0000   1.0179
Constant      -.8656     .2527  11.7350     1    .0006



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