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 5-2.1, part iii.
means tables=y by a.
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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 5.3-2, 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 | |
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Total | 110.000 | 31 | |
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Top of page 173, t-tests 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 | |
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Total | 110.000 | 31 | |
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|
|
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. (2-tailed) | |
---|---|---|---|---|---|---|---|
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 5.4-1, 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 5-4.1, 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 | |
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|
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Mean Difference (I-J) | 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 | 1.499E-02 | |
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 | -1.4986E-02 | 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 | |
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|
|
A | |||
---|---|---|---|---|
Contrast | 1.00 | 2.00 | 3.00 | 4.00 |
1 | 3 | -1 | -1 | -1 |
|
Contrast | Value of Contrast | Std. Error | t | df | Sig. (2-tailed) | |
---|---|---|---|---|---|---|---|
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 177-182. 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 | |
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Total | 688.000 | 32 | |
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|
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Corrected Total | 110.000 | 31 | |
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a R Squared = .445 (Adjusted R Squared = .386) |
In Table 5.7-2, 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 5.7-3 for the test of "quadratic" and "cubic" trend, and is summarized in Table 5.7-4, 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 | |
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Total | 110.000 | 31 | |
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Figure 5.7-3, page 199. skipped for now.
Table 5.7-6 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.