1
SPSS Textbook Examples
Applied Regression Analysis by John Fox
Chapter 15: Logit and probit models
page 440 Figure 15.1 Scatterplot of voting intention (1 represents yes, 0
represents no) by a scale of support for the status quo, for a sample of Chilean
voters surveyed prior to the 1988 plebiscite. The points are jittered vertically to
minimize overlapping. The solid straight line shows the linear least-squares fit;
the solid curved line shows the fit of the logistic regression model; the broken
line represents a lowess nonparametric regression.
NOTE: SPSS will not allow the multiple regression lines to be placed on a single
graph. Also, we do not know how to do a lowess non-parametric regression in SPSS.
GET FILE='D:\chile.sav'.
if intvote = 1 voting = 1.
if intvote = 2 voting = 0.
IGRAPH
/X1 = VAR(statquo)
/Y = VAR (voting)
/FITLINE METHOD = REGRESSION LINEAR LINE = TOTAL
/SCATTER COINCIDENT = NONE.
page 452 Table 15.1 Deviances (-2 log likelihood) for several models fit to the
women's labor force participation data. The following code is used for terms in the
models: C constant; I husband's income; K presence of children; R region. The

2
column labeled K + 1 gives the number of regressors in the model, including the
constant.
GET FILE='D:\womenlf.sav'.
if workstat = 1 or workstat = 2 ws = 1.
if workstat = 0 ws = 0.
compute ik = husbinc*chilpres.
compute cons = 1.
compute rgn1 = 0.
if region = "Atlantic" rgn1 = 1.
compute rgn2 = 0.
if region = "BC" rgn2 = 1.
compute rgn3 = 0.
if region = "Ontario" rgn3 = 1.
compute rgn4 = 0.
if region = "Prairie" rgn4 = 1.
compute rgn5 = 0.
if region = "Quebec" rgn5 = 1.
execute.
model 0 with C:
NOTE: SPSS will not allow a regression without a predictor. (i.e., just the
constant). Therefore, you need to create a variable - here we created const. Then
we entered our constant with the /noconst subcommand, which, in effect, gives us a
model with just a constant.
LOGISTIC REGRESSION VAR=ws
/METHOD=ENTER cons
/noconst.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b,c)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 0 155 .0Step 0
WS
1.00 0 108 100.0

3
Overall Percentage 41.1
a No terms in the model.
b Initial Log-likelihood Function: -2 Log Likelihood = 364.595
c The cut value is .500
Variables not in the Equation
Score df Sig.
Variables CONS 8.399 1 .004
Step 0
Overall Statistics 8.399 1 .004
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 8.445 1 .004
Block 8.445 1 .004Step 1
Model 8.445 1 .004
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 356.151 .032 .042
Classification Table(a)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 155 0 100.0
WS
1.00 108 0 .0Step 1
Overall Percentage 58.9
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1(a) CONS -.361 .125 8.308 1 .004 .697
a Variable(s) entered on step 1: CONS.
model 1 with C, I, K, R, I*K:
LOGISTIC REGRESSION VAR=ws
/METHOD=ENTER husbinc chilpres rgn2 rgn3 rgn4 rgn5 ik.
Case Processing Summary

4
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 155 0 100.0
WS
1.00 108 0 .0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.361 .125 8.308 1 .004 .697
Variables not in the Equation
Score df Sig.
HUSBINC 4.928 1 .026
CHILPRES 31.599 1 .000
RGN2 1.530 1 .216
RGN3 .008 1 .929
RGN4 .244 1 .622
RGN5 .242 1 .623
Variables
IK 25.164 1 .000
Step 0
Overall Statistics 38.657 7 .000
Omnibus Tests of Model Coefficients

5
Chi-square df Sig.
Step 39.609 7 .000
Block 39.609 7 .000Step 1
Model 39.609 7 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 316.542 .140 .188
Classification Table(a)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 135 20 87.1
WS
1.00 58 50 46.3Step 1
Overall Percentage 70.3
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC -.068 .034 4.094 1 .043 .934
CHILPRES -2.139 .692 9.567 1 .002 .118
RGN2 .331 .585 .320 1 .571 1.392
RGN3 .183 .466 .154 1 .694 1.201
RGN4 .469 .557 .709 1 .400 1.599
RGN5 -.203 .502 .163 1 .686 .816
IK .036 .041 .755 1 .385 1.037
Step 1(a)
Constant 1.625 .698 5.414 1 .020 5.078
a Variable(s) entered on step 1: HUSBINC, CHILPRES, RGN2, RGN3, RGN4, RGN5, IK.
model 2 with C, I, K, R:
LOGISTIC REGRESSION VAR=ws
/METHOD=ENTER husbinc chilpres rgn2 rgn3 rgn4 rgn5.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0

6
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
WS
Observed .00
1.00 Percentage Correct
.00 155 0 100.0
WS
1.00 108 0 .0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.361 .125 8.308 1 .004 .697
Variables not in the Equation
Score df Sig.
HUSBINC 4.928 1 .026
CHILPRES 31.599 1 .000
RGN2 1.530 1 .216
RGN3 .008 1 .929
RGN4 .244 1 .622
Variables
RGN5 .242 1 .623
Step 0
Overall Statistics 37.765 6 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 38.850 6 .000
Block 38.850 6 .000Step 1
Model 38.850 6 .000
Model Summary

7
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 317.301 .137 .185
Classification Table(a)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 132 23 85.2
WS
1.00 55 53 49.1Step 1
Overall Percentage 70.3
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC -.045 .021 4.857 1 .028 .956
CHILPRES -1.604 .302 28.245 1 .000 .201
RGN2 .342 .585 .342 1 .559 1.408
RGN3 .188 .468 .161 1 .688 1.207
RGN4 .472 .557 .718 1 .397 1.603
RGN5 -.173 .500 .120 1 .729 .841
Step 1(a)
Constant 1.268 .553 5.256 1 .022 3.553
a Variable(s) entered on step 1: HUSBINC, CHILPRES, RGN2, RGN3, RGN4, RGN5.
model 3 with C, I, K, I*K:
LOGISTIC REGRESSION VAR=ws
/METHOD=ENTER husbinc chilpres ik.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1

8
Classification Table(a,b)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 155 0 100.0
WS
1.00 108 0 .0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.361 .125 8.308 1 .004 .697
Variables not in the Equation
Score df Sig.
HUSBINC 4.928 1 .026
CHILPRES 31.599 1 .000Variables
IK 25.164 1 .000
Step 0
Overall Statistics 36.471 3 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 37.027 3 .000
Block 37.027 3 .000Step 1
Model 37.027 3 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 319.124 .131 .177
Classification Table(a)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 133 22 85.8Step 1
WS
1.00 59 49 45.4

9
Overall Percentage 69.2
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC -.062 .033 3.604 1 .058 .940
CHILPRES -2.046 .677 9.134 1 .003 .129
IK .032 .041 .605 1 .437 1.032
Step 1(a)
Constant 1.640 .558 8.646 1 .003 5.153
a Variable(s) entered on step 1: HUSBINC, CHILPRES, IK.
model 4 with C, I, R:
LOGISTIC REGRESSION VAR=ws
/METHOD=ENTER husbinc rgn2 rgn3 rgn4 rgn5.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 155 0 100.0
WS
1.00 108 0 .0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)

10
Step 0 Constant -.361 .125 8.308 1 .004 .697
Variables not in the Equation
Score df Sig.
HUSBINC 4.928 1 .026
RGN2 1.530 1 .216
RGN3 .008 1 .929
RGN4 .244 1 .622
Variables
RGN5 .242 1 .623
Step 0
Overall Statistics 8.011 5 .156
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 8.302 5 .140
Block 8.302 5 .140Step 1
Model 8.302 5 .140
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 347.849 .031 .042
Classification Table(a)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 141 14 91.0
WS
1.00 87 21 19.4Step 1
Overall Percentage 61.6
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC -.045 .019 5.435 1 .020 .956
RGN2 .858 .545 2.476 1 .116 2.359
RGN3 .458 .444 1.060 1 .303 1.580
RGN4 .466 .535 .760 1 .383 1.594
RGN5 .204 .469 .190 1 .663 1.227
Step 1(a)
Constant -.093 .463 .040 1 .841 .911

11
a Variable(s) entered on step 1: HUSBINC, RGN2, RGN3, RGN4, RGN5.
model 5: with C, K, R:
LOGISTIC REGRESSION VAR=ws
/METHOD=ENTER chilpres rgn2 rgn3 rgn4 rgn5.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 155 0 100.0
WS
1.00 108 0 .0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.361 .125 8.308 1 .004 .697
Variables not in the Equation
Score df Sig.
CHILPRES 31.599 1 .000
RGN2 1.530 1 .216
RGN3 .008 1 .929
Step 0 Variables
RGN4 .244 1 .622

12
RGN5 .242 1 .623
Overall Statistics 33.493 5 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 33.724 5 .000
Block 33.724 5 .000Step 1
Model 33.724 5 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 322.427 .120 .162
Classification Table(a)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 129 26 83.2
WS
1.00 55 53 49.1Step 1
Overall Percentage 69.2
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
CHILPRES -1.603 .298 28.905 1 .000 .201
RGN2 .241 .576 .174 1 .676 1.272
RGN3 .042 .457 .008 1 .927 1.043
RGN4 .492 .550 .798 1 .372 1.635
RGN5 -.156 .493 .100 1 .752 .856
Step 1(a)
Constant .672 .476 1.988 1 .159 1.958
a Variable(s) entered on step 1: CHILPRES, RGN2, RGN3, RGN4, RGN5.
page 452 Table 15.2 Analysis of deviance table for terms in the logit model fit to
the women's labor force participation data.
NOTE: To get the G**2 terms, subtract the deviances.
Model 0 versus model 1: 356.16 - 316.54 = 39.62.
Model 2 versus model 1: 317.30 - 316.54 = .76.
Model 5 versus model 2: 322.44 - 317.30 = 5.14.
Model 4 versus model 2: 347.86 - 317.30 = 30.56.
Model 3 versus model 1: 319.12 - 316.54 = 2.58.
page 453 Figure 15.4 Fitted probability of young married women working outside the
home, as a function of husband's income and presence of children. The solid line

13
shows the logit model fit by maximum likelihood; the broken line shows the linear
least-squares fit.
NOTE: The four lines in Figure 15.4 have been done in separate graphs.
logistic regression var = ws
/method=enter chilpres husbinc
/save pre.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 155 0 100.0
WS
1.00 108 0 .0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.361 .125 8.308 1 .004 .697
Variables not in the Equation
Score df Sig.
CHILPRES 31.599 1 .000
Variables
HUSBINC 4.928 1 .026Step 0
Overall Statistics 35.714 2 .000

14
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 36.418 2 .000
Block 36.418 2 .000Step 1
Model 36.418 2 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 319.733 .129 .174
Classification Table(a)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 132 23 85.2
WS
1.00 55 53 49.1Step 1
Overall Percentage 70.3
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
CHILPRES -1.576 .292 29.065 1 .000 .207
HUSBINC -.042 .020 4.575 1 .032 .959Step 1(a)
Constant 1.336 .384 12.116 1 .000 3.803
a Variable(s) entered on step 1: CHILPRES, HUSBINC.
regression
/dep = ws
/method=enter chilpres husbinc
/save pre.
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 Husband's income, $1000, Children present(a) . Enter
a All requested variables entered.
b Dependent Variable: WS
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .369(a) .136 .129 .45996
a Predictors: (Constant), Husband's income, $1000, Children present
b Dependent Variable: WS

15
ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
Regression 8.643 2 4.322 20.427 .000(a)
Residual 55.007 260 .2121
Total 63.650 262
a Predictors: (Constant), Husband's income, $1000, Children present
b Dependent Variable: WS
Coefficients(a)
Unstandardized
Coefficients
Standardized
Coefficients
Model B Std. Error
Beta t Sig.
(Constant) .794 .077 10.350 .000
Children present -.367 .062 -.342 -5.934 .0001
Husband's income,
$1000
-8.538E-03 .004 -.125 -2.170 .031
a Dependent Variable: WS
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value .0421 .7851 .4106 .18163 263
Residual -.7510 .8981 .0000 .45820 263
Std. Predicted Value -2.029 2.062 .000 1.000 263
Std. Residual -1.633 1.953 .000 .996 263
a Dependent Variable: WS
if chilpres = 1 pw1 = pre_1.
if chilpres = 0 pw2 = pre_1.
if chilpres = 1 lw1 = pre_2.
if chilpres = 0 lw2 = pre_2.
execute.
SORT CASES BY husbinc (A).
IGRAPH
/X1 = VAR(husbinc)
/Y = VAR(pw1)
/LINE(MEAN) STYLE = LINE INTERPOLATE = STRAIGHT.

16
IGRAPH
/X1 = VAR(husbinc)
/Y = VAR(pw2)
/LINE(MEAN) STYLE = LINE INTERPOLATE = STRAIGHT.

17
IGRAPH
/X1 = VAR(husbinc)
/Y = VAR(lw1)
/LINE(MEAN) STYLE = LINE INTERPOLATE = STRAIGHT.

18
IGRAPH
/X1 = VAR(husbinc)
/Y = VAR(lw2)
/LINE(MEAN) STYLE = LINE INTERPOLATE = STRAIGHT.

19
page 459 Figure 15.5 Partial-residual plot for husband's income in the women's
labor force participation data. The broken line gives the logit fit; the solid line
shows a lowess smooth of the plot. Note the four bands due to the four combinations
of values of the dichotomous dependent variable and the dichotomous independent
variable presence of children. Because husband's income is also discrete, many
points are overplotted.
NOTE: SPSS does not do lowess smoothing in IGRAPH, so that line is not done. The
other two are done on separate graphs.
NOTE: Leverage, studentized residuals and dfbetas are being saved here so that this
regression only has to be run once.
logistic regression var=ws
/method=enter chilpres husbinc
/save pre lev sre dfbeta.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0

20
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 155 0 100.0
WS
1.00 108 0 .0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.361 .125 8.308 1 .004 .697
Variables not in the Equation
Score df Sig.
CHILPRES 31.599 1 .000
Variables
HUSBINC 4.928 1 .026Step 0
Overall Statistics 35.714 2 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 36.418 2 .000
Block 36.418 2 .000Step 1
Model 36.418 2 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 319.733 .129 .174

21
Classification Table(a)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 132 23 85.2
WS
1.00 55 53 49.1Step 1
Overall Percentage 70.3
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
CHILPRES -1.576 .292 29.065 1 .000 .207
HUSBINC -.042 .020 4.575 1 .032 .959Step 1(a)
Constant 1.336 .384 12.116 1 .000 3.803
a Variable(s) entered on step 1: CHILPRES, HUSBINC.
NOTE: pre_3 is generated here.
compute par = (ws-pre_3)/(pre_3*(1-pre_3)) - .0423*husbinc.
regression
/dep=par
/method=enter husbinc
/save pre.
Variables Entered/Removed(b)
Model Variables Entered Variables Removed Method
1 Husband's income, $1000(a) . Enter
a All requested variables entered.
b Dependent Variable: PAR
Model Summary(b)
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .100(a) .010 .006 2.25325
a Predictors: (Constant), Husband's income, $1000
b Dependent Variable: PAR
ANOVA(b)
Model Sum of Squares df Mean Square F Sig.
Regression 13.494 1 13.494 2.658 .104(a)
Residual 1325.132 261 5.0771
Total 1338.626 262
a Predictors: (Constant), Husband's income, $1000
b Dependent Variable: PAR

22
Coefficients(a)
Unstandardized
Coefficients
Standardized
Coefficients
Model B Std. Error
Beta
t Sig.
(Constant) -.140 .316 -.443 .658
1
Husband's income,
$1000
-3.141E-02 .019 -.100
-
1.630
.104
a Dependent Variable: PAR
Casewise Diagnostics(a)
Case Number Std. Residual PAR
260 3.138 5.74
261 3.138 5.74
a Dependent Variable: PAR
Residuals Statistics(a)
Minimum Maximum Mean Std. Deviation N
Predicted Value -1.5536 -.1717 -.6037 .22694 263
Residual -3.9922 7.0705 .0000 2.24895 263
Std. Predicted Value -4.186 1.904 .000 1.000 263
Std. Residual -1.772 3.138 .000 .998 263
a Dependent Variable: PAR
IGRAPH
/X1 = VAR(husbinc)
/Y = VAR(pre_4)
/LINE(MEAN) STYLE = LINE INTERPOLATE = STRAIGHT.

23
GRAPH
/SCATTERPLOT(BIVAR)=husbinc WITH par.

24
page 461 Figure 15.6 Plot of studentized residuals versus hat values for the logit
model fit to the women's labor force participation data. Vertical lines are drawn
at twice and three times the average hat value. Many points are overplotted.
logistic regression var=ws
/method=enter chilpres husbinc
/save lev sre dfbeta.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1

25
Classification Table(a,b)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 155 0 100.0
WS
1.00 108 0 .0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.361 .125 8.308 1 .004 .697
Variables not in the Equation
Score df Sig.
CHILPRES 31.599 1 .000
Variables
HUSBINC 4.928 1 .026Step 0
Overall Statistics 35.714 2 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 36.418 2 .000
Block 36.418 2 .000Step 1
Model 36.418 2 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 319.733 .129 .174
Classification Table(a)
Predicted
WS
Observed .00
1.00
Percentage Correct
.00 132 23 85.2
WS
1.00 55 53 49.1Step 1
Overall Percentage 70.3
a The cut value is .500

26
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
CHILPRES -1.576 .292 29.065 1 .000 .207
HUSBINC -.042 .020 4.575 1 .032 .959Step 1(a)
Constant 1.336 .384 12.116 1 .000 3.803
a Variable(s) entered on step 1: CHILPRES, HUSBINC.
compute pr = (ws - pre_3)/sqrt(pre_3*(1 - pre_3)).
GRAPH
/SCATTERPLOT(BIVAR)=lev_1 WITH sre_1.
page 462 Figure 15.7 Index plots of approximate influence of each observation on
the coefficients of husband's income and presence of children.
Panel (a)
GRAPH
/SCATTERPLOT(BIVAR)=obs WITH dfb2_1.

27
Panel (b)
GRAPH
/SCATTERPLOT(BIVAR)=obs WITH dfb1_1.

28
page 469 Figure 15.8 Fitted probabilities for the polytomous logit model, showing
women's labor force participation as a function of husband's income and presence of
children. The upper panel is for children present, the lower panel for children
absent.
NOTE: The scaling of the x-axis is very different than in the text.
Panel (a)
GET FILE='D:\womenlf.sav'.
compute w0 = 0.
if workstat = 0 w0 = 1.
compute w1 = 0.
if workstat = 1 w1 = 1.
compute w2 = 0.
if workstat = 2 w2 = 1.
execute.
logistic regression var=w0
/method=enter husbinc chilpres
/save pre.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0

29
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
W0
Observed .00
1.00
Percentage Correct
.00 0 108 .0
W0
1.00 0 155 100.0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant .361 .125 8.308 1 .004 1.435
Variables not in the Equation
Score df Sig.
HUSBINC 4.928 1 .026
Variables
CHILPRES 31.599 1 .000Step 0
Overall Statistics 35.714 2 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 36.418 2 .000
Block 36.418 2 .000Step 1
Model 36.418 2 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 319.733 .129 .174

30
Classification Table(a)
Predicted
W0
Observed .00
1.00
Percentage Correct
.00 53 55 49.1
W0
1.00 23 132 85.2Step 1
Overall Percentage 70.3
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC .042 .020 4.575 1 .032 1.043
CHILPRES 1.576 .292 29.065 1 .000 4.834Step 1(a)
Constant -1.336 .384 12.116 1 .000 .263
a Variable(s) entered on step 1: HUSBINC, CHILPRES.
USE ALL.
COMPUTE filter_$=(chilpres=1).
VARIABLE LABEL filter_$ 'chilpres=1 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMAT filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
Children present / not working.
graph
/scatterplot(bivar) = husbinc with pre_1.

31
Children present / part-time.
logistic regression var=w1
/method=enter husbinc chilpres
/save pre.
Case Processing Summary
Unweighted Cases(b) N Percent
Included in Analysis 184 100.0
Missing Cases 0 .0Selected Cases(a)
Total 184 100.0
Unselected Cases 0 .0
Total 184 100.0
a The variable Children present is constant for all selected cases. Since a
constant was requested in the model, it will be removed from the analysis.
b If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1

32
Classification Table(a,b)
Predicted
W1
Observed .00
1.00
Percentage Correct
.00 149 0 100.0
W1
1.00 35 0 .0Step 0
Overall Percentage 81.0
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -1.449 .188 59.473 1 .000 .235
Variables not in the Equation
Score df Sig.
Variables HUSBINC .757 1 .384
Step 0
Overall Statistics .757 1 .384
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step .732 1 .392
Block .732 1 .392Step 1
Model .732 1 .392
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 178.314 .004 .006
Classification Table(a)
Predicted
W1
Observed .00
1.00
Percentage Correct
.00 149 0 100.0
W1
1.00 35 0 .0Step 1
Overall Percentage 81.0
a The cut value is .500

33
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC .022 .025 .751 1 .386 1.022
Step 1(a)
Constant -1.783 .437 16.626 1 .000 .168
a Variable(s) entered on step 1: HUSBINC.
graph
/scatterplot(bivar) = husbinc with pre_2.
Children present / full-time.
logistic regression var=w2
/method=enter husbinc chilpres
/save pre.
Case Processing Summary
Unweighted Cases(b) N Percent
Included in Analysis 184 100.0
Missing Cases 0 .0Selected Cases(a)
Total 184 100.0
Unselected Cases 0 .0
Total 184 100.0
a The variable Children present is constant for all selected cases. Since a
constant was requested in the model, it will be removed from the analysis.

34
b If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
W2
Observed .00
1.00 Percentage Correct
.00 164 0 100.0
W2
1.00 20 0 .0Step 0
Overall Percentage 89.1
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -2.104 .237 78.923 1 .000 .122
Variables not in the Equation
Score df Sig.
Variables HUSBINC 8.720 1 .003
Step 0
Overall Statistics 8.720 1 .003
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 11.063 1 .001
Block 11.063 1 .001Step 1
Model 11.063 1 .001
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 115.448 .058 .117
Classification Table(a)
Predicted

35
W2
Observed .00
1.00
Percentage Correct
.00 164 0 100.0
W2
1.00 20 0 .0Step 1
Overall Percentage 89.1
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC -.141 .047 9.019 1 .003 .869
Step 1(a)
Constant -.309 .573 .290 1 .590 .734
a Variable(s) entered on step 1: HUSBINC.
graph
/scatterplot(bivar) = husbinc with pre_3.
Panel (b)
GET FILE='D:\womenlf.sav'.
compute w0 = 0.
if workstat = 0 w0 = 1.
compute w1 = 0.
if workstat = 1 w1 = 1.

36
compute w2 = 0.
if workstat = 2 w2 = 1.
execute.
logistic regression var=w0
/method=enter husbinc chilpres
/save pre.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
W0
Observed .00
1.00
Percentage Correct
.00 0 108 .0
W0
1.00 0 155 100.0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant .361 .125 8.308 1 .004 1.435
Variables not in the Equation
Score df Sig.
HUSBINC 4.928 1 .026
Variables
CHILPRES 31.599 1 .000Step 0
Overall Statistics 35.714 2 .000
Omnibus Tests of Model Coefficients

37
Chi-square df Sig.
Step 36.418 2 .000
Block 36.418 2 .000Step 1
Model 36.418 2 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 319.733 .129 .174
Classification Table(a)
Predicted
W0
Observed .00
1.00
Percentage Correct
.00 53 55 49.1
W0
1.00 23 132 85.2Step 1
Overall Percentage 70.3
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC .042 .020 4.575 1 .032 1.043
CHILPRES 1.576 .292 29.065 1 .000 4.834Step 1(a)
Constant -1.336 .384 12.116 1 .000 .263
a Variable(s) entered on step 1: HUSBINC, CHILPRES.
USE ALL.
COMPUTE filter_$=(chilpres=0).
VARIABLE LABEL filter_$ 'chilpres=1 (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMAT filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
Children absent / not working.
graph
/scatterplot(bivar) = husbinc with pre_1.

38
Children absent / part-time.
logistic regression var=w1
/method=enter husbinc chilpres
/save pre.
Case Processing Summary
Unweighted Cases(b) N Percent
Included in Analysis 79 100.0
Missing Cases 0 .0Selected Cases(a)
Total 79 100.0
Unselected Cases 0 .0
Total 79 100.0
a The variable Children present is constant for all selected cases. Since a
constant was requested in the model, it will be removed from the analysis.
b If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1

39
Classification Table(a,b)
Predicted
W1
Observed .00
1.00
Percentage Correct
.00 72 0 100.0
W1
1.00 7 0 .0Step 0
Overall Percentage 91.1
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -2.331 .396 34.657 1 .000 .097
Variables not in the Equation
Score df Sig.
Variables HUSBINC .576 1 .448
Step 0
Overall Statistics .576 1 .448
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step .543 1 .461
Block .543 1 .461Step 1
Model .543 1 .461
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 46.747 .007 .015
Classification Table(a)
Predicted
W1
Observed .00
1.00
Percentage Correct
.00 72 0 100.0
W1
1.00 7 0 .0Step 1
Overall Percentage 91.1
a The cut value is .500

40
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC .037 .049 .568 1 .451 1.038
Step 1(a)
Constant -2.894 .886 10.661 1 .001 .055
a Variable(s) entered on step 1: HUSBINC.
graph
/scatterplot(bivar) = husbinc with pre_2.
Children absent / full-time.
logistic regression var=w2
/method=enter husbinc chilpres
/save pre.
Case Processing Summary
Unweighted Cases(b) N Percent
Included in Analysis 79 100.0
Missing Cases 0 .0Selected Cases(a)
Total 79 100.0
Unselected Cases 0 .0
Total 79 100.0
a The variable Children present is constant for all selected cases. Since a
constant was requested in the model, it will be removed from the analysis.
b If weight is in effect, see classification table for the total number of cases.

41
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
W2
Observed .00
1.00
Percentage Correct
.00 0 33 .0
W2
1.00 0 46 100.0Step 0
Overall Percentage 58.2
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant .332 .228 2.120 1 .145 1.394
Variables not in the Equation
Score df Sig.
Variables HUSBINC 5.299 1 .021
Step 0
Overall Statistics 5.299 1 .021
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 5.396 1 .020
Block 5.396 1 .020Step 1
Model 5.396 1 .020
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 101.973 .066 .089
Classification Table(a)
Predicted
W2 Percentage Correct

42
Observed .00
1.00
.00 9 24 27.3
W2
1.00 6 40 87.0Step 1
Overall Percentage 62.0
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC -.074 .033 4.877 1 .027 .929
Step 1(a)
Constant 1.406 .542 6.734 1 .009 4.079
a Variable(s) entered on step 1: HUSBINC.
graph
/scatterplot(bivar) = husbinc with pre_3.
page 473 calculations in the middle of page 473 and the top of 474.
NOTE: The R-squared values given by SPSS are different from those in the text.
GET FILE='D:\womenlf.sav'.
compute nwk = 1.
if workstat = 0 nwk = 0.
execute.

43
logistic regression var=nwk
/method=enter husbinc chilpres.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 263 100.0
Missing Cases 0 .0Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
NWK
Observed .00
1.00
Percentage Correct
.00 155 0 100.0
NWK
1.00 108 0 .0Step 0
Overall Percentage 58.9
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -.361 .125 8.308 1 .004 .697
Variables not in the Equation
Score df Sig.
HUSBINC 4.928 1 .026
Variables
CHILPRES 31.599 1 .000Step 0
Overall Statistics 35.714 2 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 36.418 2 .000

44
Block 36.418 2 .000
Model 36.418 2 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 319.733 .129 .174
Classification Table(a)
Predicted
NWK
Observed .00
1.00 Percentage Correct
.00 132 23 85.2
NWK
1.00 55 53 49.1Step 1
Overall Percentage 70.3
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC -.042 .020 4.575 1 .032 .959
CHILPRES -1.576 .292 29.065 1 .000 .207Step 1(a)
Constant 1.336 .384 12.116 1 .000 3.803
a Variable(s) entered on step 1: HUSBINC, CHILPRES.
if workstat = 1 ptime = 0.
if workstat = 2 ptime = 1.
execute.
logistic regression var=ptime
/method=enter husbinc chilpres.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 108 41.1
Missing Cases 155 58.9Selected Cases
Total 263 100.0
Unselected Cases 0 .0
Total 263 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1

45
Classification Table(a,b)
Predicted
PTIME
Observed .00
1.00
Percentage Correct
.00 0 42 .0
PTIME
1.00 0 66 100.0Step 0
Overall Percentage 61.1
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant .452 .197 5.243 1 .022 1.571
Variables not in the Equation
Score df Sig.
HUSBINC 7.602 1 .006
Variables
CHILPRES 28.882 1 .000Step 0
Overall Statistics 35.149 2 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 39.847 2 .000
Block 39.847 2 .000Step 1
Model 39.847 2 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 104.495 .309 .419
Classification Table(a)
Predicted
PTIME
Observed .00
1.00
Percentage Correct
.00 33 9 78.6
PTIME
1.00 11 55 83.3Step 1
Overall Percentage 81.5
a The cut value is .500

46
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
HUSBINC -.107 .039 7.506 1 .006 .898
CHILPRES -2.651 .541 24.013 1 .000 .071Step 1(a)
Constant 3.478 .767 20.554 1 .000 32.387
a Variable(s) entered on step 1: HUSBINC, CHILPRES.
page 480 Figure 15.13 Empirical logits for voter turnout by intensity of partisan
preference and perceived closeness of the election, for the . 1956 U.S.
presidential election.
data list list / logv1 logvc inten.
begin data.
.847 .9 0
.904 1.318 1
.981 2.084 2
end data.
execute.
One-sided
IGRAPH
/X1 = VAR(inten)
/Y = VAR(logv1)
/LINE(MEAN) STYLE = DOTLINE INTERPOLATE = STRAIGHT.

47
Close.
IGRAPH
/X1 = VAR(inten)
/Y = VAR(logvc)
/LINE(MEAN) STYLE = DOTLINE INTERPOLATE = STRAIGHT.

48
page 482 Table 15.4 Deviances for models fit to the American voter data. Terms:
alpha - perceived closeness; beta - intensity of preference; gamma - closeness by
preference interaction. The column labeled k + 1 gives the number of parameters in
the model, including the constant mu.
data list list / perclose inten1 inten2 voted wv.
begin data.
0 0 0 1 91
0 0 0 0 39
0 1 0 1 121
0 1 0 0 49
0 0 1 1 64
0 0 1 0 24
1 0 0 1 214
1 0 0 0 87
1 1 0 1 284
1 1 0 0 76
1 0 1 1 201
1 0 1 0 25
end data.
execute.
weight by wv.
compute clspref1 = perclose*inten1.
compute clspref2 = perclose*inten2.
execute.

49
Model 1:
logistic regression var=voted
/method=enter perclose inten1 inten2 clspref1 clsp ref2.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 12 100.0
Missing Cases 0 .0Selected Cases
Total 12 100.0
Unselected Cases 0 .0
Total 12 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 0
Overall Percentage 76.5
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant 1.179 .066 318.704 1 .000 3.250
Variables not in the Equation
Score df Sig.
PERCLOSE 8.828 1 .003
INTEN1 .002 1 .969
INTEN2 14.539 1 .000
CLSPREF1 1.631 1 .202
Variables
CLSPREF2 23.730 1 .000
Step 0
Overall Statistics 31.884 5 .000

50
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 34.832 5 .000
Block 34.832 5 .000Step 1
Model 34.832 5 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 1356.434 .027 .041
Classification Table(a)
Predicted
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 1
Overall Percentage 76.5
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
PERCLOSE .053 .230 .053 1 .818 1.054
INTEN1 .057 .256 .049 1 .824 1.058
INTEN2 .134 .306 .190 1 .663 1.143
CLSPREF1 .362 .313 1.331 1 .249 1.435
CLSPREF2 1.051 .394 7.121 1 .008 2.860
Step 1(a)
Constant .847 .191 19.599 1 .000 2.333
a Variable(s) entered on step 1: PERCLOSE, INTEN1, INTEN2, CLSPREF1, CLSPREF2.
Model 2:
logistic regression var=voted
/method=enter perclose inten1 inten2.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 12 100.0
Missing Cases 0 .0Selected Cases
Total 12 100.0
Unselected Cases 0 .0
Total 12 100.0
a If weight is in effect, see classification table for the total number of cases.

51
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 0
Overall Percentage 76.5
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant 1.179 .066 318.704 1 .000 3.250
Variables not in the Equation
Score df Sig.
PERCLOSE 8.828 1 .003
INTEN1 .002 1 .969Variables
INTEN2 14.539 1 .000
Step 0
Overall Statistics 27.142 3 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 27.713 3 .000
Block 27.713 3 .000Step 1
Model 27.713 3 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 1363.553 .022 .032
Classification Table(a)

52
Predicted
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 1
Overall Percentage 76.5
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
PERCLOSE .407 .140 8.427 1 .004 1.502
INTEN1 .302 .148 4.165 1 .041 1.352
INTEN2 .800 .189 17.958 1 .000 2.224
Step 1(a)
Constant .607 .141 18.457 1 .000 1.835
a Variable(s) entered on step 1: PERCLOSE, INTEN1, INTEN2.
Model 3:
logistic regression var=voted
/method=enter perclose clspref1 clspref2.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 12 100.0
Missing Cases 0 .0Selected Cases
Total 12 100.0
Unselected Cases 0 .0
Total 12 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 0
Overall Percentage 76.5

53
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant 1.179 .066 318.704 1 .000 3.250
Variables not in the Equation
Score df Sig.
PERCLOSE 8.828 1 .003
CLSPREF1 1.631 1 .202Variables
CLSPREF2 23.730 1 .000
Step 0
Overall Statistics 31.667 3 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 34.641 3 .000
Block 34.641 3 .000Step 1
Model 34.641 3 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 1356.625 .027 .040
Classification Table(a)
Predicted
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 1
Overall Percentage 76.5
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
PERCLOSE -.002 .169 .000 1 .991 .998
CLSPREF1 .418 .181 5.324 1 .021 1.519
Step 1(a)
CLSPREF2 1.184 .247 22.942 1 .000 3.269

54
Constant .902 .112 64.806 1 .000 2.464
a Variable(s) entered on step 1: PERCLOSE, CLSPREF1, CLSPREF2.
Model 4:
logistic regression var=voted
/method=enter inten1 inten2 clspref1 clspref2.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 12 100.0
Missing Cases 0 .0Selected Cases
Total 12 100.0
Unselected Cases 0 .0
Total 12 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 0
Overall Percentage 76.5
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant 1.179 .066 318.704 1 .000 3.250
Variables not in the Equation
Score df Sig.
INTEN1 .002 1 .969
INTEN2 14.539 1 .000
CLSPREF1 1.631 1 .202
Step 0
Variables
CLSPREF2 23.730 1 .000

55
Overall Statistics 31.823 4 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 34.779 4 .000
Block 34.779 4 .000Step 1
Model 34.779 4 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 1356.487 .027 .041
Classification Table(a)
Predicted
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 1
Overall Percentage 76.5
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
INTEN1 .020 .200 .010 1 .920 1.020
INTEN2 .097 .262 .137 1 .712 1.102
CLSPREF1 .414 .213 3.784 1 .052 1.513
CLSPREF2 1.104 .320 11.909 1 .001 3.015
Step 1(a)
Constant .884 .106 69.683 1 .000 2.421
a Variable(s) entered on step 1: INTEN1, INTEN2, CLSPREF1, CLSPREF2.
Model 5:
logistic regression var=voted
/method=enter perclose.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 12 100.0
Missing Cases 0 .0Selected Cases
Total 12 100.0
Unselected Cases 0 .0
Total 12 100.0

56
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
VOTED
Observed .00
1.00 Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 0
Overall Percentage 76.5
a Constant is included in the model.
b The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant 1.179 .066 318.704 1 .000 3.250
Variables not in the Equation
Score df Sig.
Variables PERCLOSE 8.828 1 .003
Step 0
Overall Statistics 8.828 1 .003
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 8.608 1 .003
Block 8.608 1 .003Step 1
Model 8.608 1 .003
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 1382.658 .007 .010
Classification Table(a)
Predicted

57
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 1
Overall Percentage 76.5
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
PERCLOSE .411 .139 8.764 1 .003 1.509
Step 1(a)
Constant .902 .112 64.806 1 .000 2.464
a Variable(s) entered on step 1: PERCLOSE.
Model 6:
logistic regression var=voted
/method=enter inten1 inten2.
Case Processing Summary
Unweighted Cases(a) N Percent
Included in Analysis 12 100.0
Missing Cases 0 .0Selected Cases
Total 12 100.0
Unselected Cases 0 .0
Total 12 100.0
a If weight is in effect, see classification table for the total number of cases.
Dependent Variable Encoding
Original Value Internal Value
.00 0
1.00 1
Classification Table(a,b)
Predicted
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 0
Overall Percentage 76.5
a Constant is included in the model.
b The cut value is .500

58
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant 1.179 .066 318.704 1 .000 3.250
Variables not in the Equation
Score df Sig.
INTEN1 .002 1 .969
Variables
INTEN2 14.539 1 .000Step 0
Overall Statistics 18.756 2 .000
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 19.428 2 .000
Block 19.428 2 .000Step 1
Model 19.428 2 .000
Model Summary
Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 1371.838 .015 .023
Classification Table(a)
Predicted
VOTED
Observed .00
1.00
Percentage Correct
.00 0 300 .0
VOTED
1.00 0 975 100.0Step 1
Overall Percentage 76.5
a The cut value is .500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
INTEN1 .292 .147 3.920 1 .048 1.338
INTEN2 .804 .188 18.246 1 .000 2.234Step 1(a)
Constant .884 .106 69.683 1 .000 2.421
a Variable(s) entered on step 1: INTEN1, INTEN2.
page 482 Table 15.5 Analysis of deviance table for the American voter data, showing
alternative likelihood ratio tests for the main effects of perceived closeness of
the election and intensity of partisan preference.

59
NOTE: To get the G**2 terms, subtract the deviances.
Model 6 versus model 2: 1371.838 - 1363.552 = 8.286.
Model 4 versus model 1: 1368.554 - 1356.434 = 12.120.
Model 5 versus model 2: 1382.658 - 1363.552 = 19.106.
Model 3 versus model 1: 1368.042 - 1356.434 = 11.608.
Model 2 versus model 1: 1363.552 - 1356.434 = 7.118.