This shows how to get the results of Chapter 5 using SPSS. Below is how to read the first data file used in this chapter.
data list free / y a order. begin data. 4 1 1 6 1 2 3 1 3 3 1 4 1 1 5 3 1 6 2 1 7 2 1 8 4 2 1 5 2 2 4 2 3 3 2 4 2 2 5 3 2 6 4 2 7 3 2 8 5 3 1 6 3 2 5 3 3 4 3 4 3 3 5 4 3 6 3 3 7 4 3 8 3 4 1 5 4 2 6 4 3 5 4 4 6 4 5 7 4 6 8 4 7 10 4 8 end data.Table 52.1, part iii.
means tables=y by a.

Cases  

Included  Excluded  Total  
N  Percent  N  Percent  N  Percent  
Y A  32  100.0%  0  .0%  32  100.0% 
A  Mean  N  Std. Deviation 

1.00  3.0000  8  1.5119 
2.00  3.5000  8  .9258 
3.00  4.2500  8  1.0351 
4.00  6.2500  8  2.1213 
Total  4.2500  32  1.8837 
Table 5.32, ANOVA table.
ONEWAY y BY a.

Sum of Squares  df  Mean Square  F  Sig. 

Between Groups  49.000  3  16.333  7.497  .001 
Within Groups  61.000  28  2.179  

Total  110.000  31  


Top of page 173, ttests for three contrasts.
ONEWAY y BY a /CONTRAST 1 1 0 0 /CONTRAST 0 0 1 1 /CONTRAST 1 1 1 1.

Sum of Squares  df  Mean Square  F  Sig. 

Between Groups  49.000  3  16.333  7.497  .001 
Within Groups  61.000  28  2.179  

Total  110.000  31  



A  

Contrast  1.00  2.00  3.00  4.00 
1  1  1  0  0 
2  0  0  1  1 
3  1  1  1  1 

Contrast  Value of Contrast  Std. Error  t  df  Sig. (2tailed)  

Y  Assume equal variances  1  .5000  .7380  .678  28  .504 
2  2.0000  .7380  2.710  28  .011  
3  4.0000  1.0437  3.833  28  .001  
Does not assume equal variances  1  .5000  .6268  .798  11.603  .441  
2  2.0000  .8345  2.397  10.155  .037  
3  4.0000  1.0437  3.833  19.431  .001 
Table 5.41, Kirk illustrates all pairwise comparisons using fisher hayter test. SPSS does not have this test, but the most similar test is a "Tukey" test, which you can get with the commands below. This does produce a table like 54.1, but you would need to compute the "critical difference" by hand using the formula on page 174.
ONEWAY y BY a /POSTHOC = TUKEY ALPHA(.05).

Sum of Squares  df  Mean Square  F  Sig. 

Between Groups  49.000  3  16.333  7.497  .001 
Within Groups  61.000  28  2.179  

Total  110.000  31  



Mean Difference (IJ)  Std. Error  Sig.  95% Confidence Interval  

(I) A  (J) A  Lower Bound  Upper Bound  
1.00  2.00  .5000  .7380  .905  2.5150  1.5150 
3.00  1.2500  .7380  .346  3.2650  .7650  
4.00  3.2500(*)  .7380  .001  5.2650  1.2350  
2.00  1.00  .5000  .7380  .905  1.5150  2.5150 
3.00  .7500  .7380  .741  2.7650  1.2650  
4.00  2.7500(*)  .7380  .005  4.7650  .7350  
3.00  1.00  1.2500  .7380  .346  .7650  3.2650 
2.00  .7500  .7380  .741  1.2650  2.7650  
4.00  2.0000  .7380  .052  4.0150  1.499E02  
4.00  1.00  3.2500(*)  .7380  .001  1.2350  5.2650 
2.00  2.7500(*)  .7380  .005  .7350  4.7650  
3.00  2.0000  .7380  .052  1.4986E02  4.0150  
The mean difference is significant at the .05 level. 

N  Subset for alpha = .05  

A  1  2  
1.00  8  3.0000  
2.00  8  3.5000  
3.00  8  4.2500  4.2500 
4.00  8  
6.2500 
Sig.  
.346  .052 
Means for groups in homogeneous subsets are displayed.  
a Uses Harmonic Mean Sample Size = 8.000. 
On the middle of page 175, Kirk illustrates how to do a Scheffe test. We can compute FS as illustrated by Kirk as shown below. This gives a "t" value of 2.766, and we can square that value to get the "FS" value of 7.651 You would need to compute the critical value of "FS" as shown on page 175 of Kirk by hand.
ONEWAY y BY a /CONTRAST= 3 1 1 1.

Sum of Squares  df  Mean Square  F  Sig. 

Between Groups  49.000  3  16.333  7.497  .001 
Within Groups  61.000  28  2.179  

Total  110.000  31  



A  

Contrast  1.00  2.00  3.00  4.00 
1  3  1  1  1 

Contrast  Value of Contrast  Std. Error  t  df  Sig. (2tailed)  

Y  Assume equal variances  1  5.0000  1.8077  2.766  28  .010 
Does not assume equal variances  1  5.0000  1.8371  2.722  11.459  .019 
Section 5.5, pages 177182. The only measure of strength of effect that SPSS automatically computes is eta squared (page 180) as illustrated below. For other measures of strength of effect or effect size, you will need to compute these as described by Kirk.
UNIANOVA y BY a /PRINT = ETASQ.

N  

A  1.00  8 
2.00  8  
3.00  8  
4.00  8 
Source  Type III Sum of Squares  df  Mean Square  F  Sig.  Eta Squared 

Corrected Model  49.000(a)  3  16.333  7.497  .001  .445 
Intercept  578.000  1  578.000  265.311  .000  .905 
A  49.000  3  16.333  7.497  .001  .445 
Error  61.000  28  2.179  


Total  688.000  32  



Corrected Total  110.000  31  



a R Squared = .445 (Adjusted R Squared = .386) 
In Table 5.72, page 194 Kirk shows how to test for linear trend, and departure for linear trend. This can be done in SPSS as shown below. The test of linear trend is shown by "Linear Term, Contrast" and the departure from linearity is shown right below labeled "Deviation". This also shows the tests shown in table 5.73 for the test of "quadratic" and "cubic" trend, and is summarized in Table 5.74, page 196.
ONEWAY y BY a /POLYNOMIAL= 3.

Sum of Squares  df  Mean Square  F  Sig.  

Between Groups  (Combined)  49.000  3  16.333  7.497  .001  
Linear Term  Contrast  44.100  1  44.100  20.243  .000  
Deviation  4.900  2  2.450  1.125  .339  
Quadratic Term  Contrast  4.500  1  4.500  2.066  .162  
Deviation  .400  1  .400  .184  .672  
Cubic Term  Contrast  .400  1  .400  .184  .672  
Within Groups  61.000  28  2.179  


Total  110.000  31  


Figure 5.73, page 199. skipped for now.
Table 5.76 shows how to test for departure from linearity. This can be done using the previous table, inspecting the "Linear Term, Deviation" to get F=1.125.