SPSS FAQ
How can I do ANOVA contrasts in SPSS?

Let's use an example dataset, crf24, adapted from Kirk (1968, First Edition).

get file 'd:\crf24.sav'.

These data are from a 2x4 factorial design but the same data can also be used for one-way ANOVA examples.  The variable y is the dependent variable.  The variable a is an independent variable with two levels, while b is an independent variable with four levels.

Using the contrast command in a one-way ANOVA

glm y by b.

Between-Subjects Factors

		N
B	1	8
	2	8
	3	8
	4	8

Tests of Between-Subjects Effects
Dependent Variable: Y

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	194.500(a)	3	64.833	44.276	.000
Intercept	924.500	1	924.500	631.366	.000
B	194.500	3	64.833	44.276	.000
Error	41.000	28	1.464
Total	1160.000	32
Corrected Total	235.500	31
a R Squared = .826 (Adjusted R Squared = .807)

means tables = y by b
 / cells mean.

Case Processing Summary

	Cases
	Included		Excluded		Total
	N	Percent	N	Percent	N	Percent
Y * B	32	100.0%	0	.0%	32	100.0%

Report
Mean

B	Y
1	2.75
2	3.50
3	6.25
4	9.00
Total	5.38

It is quite clear that there is a significant overall F for the independent variable b (F(3, 28) = 44.276, p = .000).  Now, let's devise some contrasts that we can test: 
1) group 3 versus group 4 
2) the average of groups 1 and 2 versus the average of groups 3 and 4 
3) the average of groups 1, 2, and 3 versus group 4

glm y by b
 /contrast(b)=special (0 0 1 -1).

Between-Subjects Factors

		N
B	1	8
	2	8
	3	8
	4	8

Tests of Between-Subjects Effects
Dependent Variable: Y

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	194.500(a)	3	64.833	44.276	.000
Intercept	924.500	1	924.500	631.366	.000
B	194.500	3	64.833	44.276	.000
Error	41.000	28	1.464
Total	1160.000	32
Corrected Total	235.500	31
a R Squared = .826 (Adjusted R Squared = .807)

Contrast Results (K Matrix)

			Dependent Variable
B Special Contrast			Y
L1	Contrast Estimate		-2.750
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-2.750
	Std. Error		.605
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-3.989
		Upper Bound	-1.511

Test Results
Dependent Variable: Y

Source	Sum of Squares	df	Mean Square	F	Sig.
Contrast	30.250	1	30.250	20.659	.000
Error	41.000	28	1.464

This contrast is statistically significant (F(1, 28) = 20.659, p = .000).

glm y by b
 /contrast(b)=special (1 1 -1 -1).

Between-Subjects Factors

		N
B	1	8
	2	8
	3	8
	4	8

Tests of Between-Subjects Effects
Dependent Variable: Y

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	194.500(a)	3	64.833	44.276	.000
Intercept	924.500	1	924.500	631.366	.000
B	194.500	3	64.833	44.276	.000
Error	41.000	28	1.464
Total	1160.000	32
Corrected Total	235.500	31
a R Squared = .826 (Adjusted R Squared = .807)

Contrast Results (K Matrix)

			Dependent Variable
B Special Contrast			Y
L1	Contrast Estimate		-9.000
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-9.000
	Std. Error		.856
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-10.753
		Upper Bound	-7.247

Test Results
Dependent Variable: Y

Source	Sum of Squares	df	Mean Square	F	Sig.
Contrast	162.000	1	162.000	110.634	.000
Error	41.000	28	1.464

This contrast is also statistically significant (F(1, 28) = 110.634, p = .000).

glm y by b
 /contrast(b)=special (1 1 1 -3).

Between-Subjects Factors

		N
B	1	8
	2	8
	3	8
	4	8

Tests of Between-Subjects Effects
Dependent Variable: Y

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	194.500(a)	3	64.833	44.276	.000
Intercept	924.500	1	924.500	631.366	.000
B	194.500	3	64.833	44.276	.000
Error	41.000	28	1.464
Total	1160.000	32
Corrected Total	235.500	31
a R Squared = .826 (Adjusted R Squared = .807)

Contrast Results (K Matrix)

			Dependent Variable
B Special Contrast			Y
L1	Contrast Estimate		-14.500
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-14.500
	Std. Error		1.482
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-17.536
		Upper Bound	-11.464

Test Results
Dependent Variable: Y

Source	Sum of Squares	df	Mean Square	F	Sig.
Contrast	140.167	1	140.167	95.724	.000
Error	41.000	28	1.464

This contrast is also statistically significant (F(1, 28) = 95.724, p = .000).

Note that you can enter multiple contrasts in a single subcommand, as shown below.  Each contrast must be separated by a comma.  While you get the significance tests for each individual test, you do not get the t-value.  To obtain the t-value, you will have to divide the contrast estimate by the std. error in the Contrast Results (K Matrix) table.

glm y by b
 /contrast(b)=special (0 0 1 -1, 1 1 -1 -1,  1 1 1 -3). 

Between-Subjects Factors

		N
B	1	8
	2	8
	3	8
	4	8

Tests of Between-Subjects Effects
Dependent Variable: Y

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	194.500(a)	3	64.833	44.276	.000
Intercept	924.500	1	924.500	631.366	.000
B	194.500	3	64.833	44.276	.000
Error	41.000	28	1.464
Total	1160.000	32
Corrected Total	235.500	31
a R Squared = .826 (Adjusted R Squared = .807)

Contrast Results (K Matrix)

			Dependent Variable
B Special Contrast			Y
L1	Contrast Estimate		-2.750
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-2.750
	Std. Error		.605
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-3.989
		Upper Bound	-1.511
L2	Contrast Estimate		-9.000
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-9.000
	Std. Error		.856
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-10.753
		Upper Bound	-7.247
L3	Contrast Estimate		-14.500
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-14.500
	Std. Error		1.482
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-17.536
		Upper Bound	-11.464

Test Results
Dependent Variable: Y

Source	Sum of Squares	df	Mean Square	F	Sig.
Contrast	192.250	2	96.125	65.646	.000
Error	41.000	28	1.464

Using the contrast command in a two-way ANOVA

Now let's try the same contrasts on b but in a two-way ANOVA.

glm y by a b.

Between-Subjects Factors

		N
A	1	16
	2	16
B	1	8
	2	8
	3	8
	4	8

Tests of Between-Subjects Effects
Dependent Variable: Y

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	217.000(a)	7	31.000	40.216	.000
Intercept	924.500	1	924.500	1199.351	.000
A	3.125	1	3.125	4.054	.055
B	194.500	3	64.833	84.108	.000
A * B	19.375	3	6.458	8.378	.001
Error	18.500	24	.771
Total	1160.000	32
Corrected Total	235.500	31
a R Squared = .921 (Adjusted R Squared = .899)

glm y by a b
/contrast(b)=special (0 0 1 -1).

Between-Subjects Factors

		N
A	1	16
	2	16
B	1	8
	2	8
	3	8
	4	8

Tests of Between-Subjects Effects
Dependent Variable: Y

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	217.000(a)	7	31.000	40.216	.000
Intercept	924.500	1	924.500	1199.351	.000
A	3.125	1	3.125	4.054	.055
B	194.500	3	64.833	84.108	.000
A * B	19.375	3	6.458	8.378	.001
Error	18.500	24	.771
Total	1160.000	32
Corrected Total	235.500	31
a R Squared = .921 (Adjusted R Squared = .899)

Contrast Results (K Matrix)

			Dependent Variable
B Special Contrast			Y
L1	Contrast Estimate		-2.750
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-2.750
	Std. Error		.439
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-3.656
		Upper Bound	-1.844

Test Results
Dependent Variable: Y

Source	Sum of Squares	df	Mean Square	F	Sig.
Contrast	30.250	1	30.250	39.243	.000
Error	18.500	24	.771

glm y by a b
 /contrast(b)=special (1 1 -1 -1).

Between-Subjects Factors

		N
A	1	16
	2	16
B	1	8
	2	8
	3	8
	4	8

Tests of Between-Subjects Effects
Dependent Variable: Y

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	217.000(a)	7	31.000	40.216	.000
Intercept	924.500	1	924.500	1199.351	.000
A	3.125	1	3.125	4.054	.055
B	194.500	3	64.833	84.108	.000
A * B	19.375	3	6.458	8.378	.001
Error	18.500	24	.771
Total	1160.000	32
Corrected Total	235.500	31
a R Squared = .921 (Adjusted R Squared = .899)

Contrast Results (K Matrix)

			Dependent Variable
B Special Contrast			Y
L1	Contrast Estimate		-9.000
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-9.000
	Std. Error		.621
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-10.281
		Upper Bound	-7.719

Test Results
Dependent Variable: Y

Source	Sum of Squares	df	Mean Square	F	Sig.
Contrast	162.000	1	162.000	210.162	.000
Error	18.500	24	.771

glm y by a b
 /contrast(b)=special (1 1 1 -3).

Between-Subjects Factors

		N
A	1	16
	2	16
B	1	8
	2	8
	3	8
	4	8

Tests of Between-Subjects Effects
Dependent Variable: Y

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	217.000(a)	7	31.000	40.216	.000
Intercept	924.500	1	924.500	1199.351	.000
A	3.125	1	3.125	4.054	.055
B	194.500	3	64.833	84.108	.000
A * B	19.375	3	6.458	8.378	.001
Error	18.500	24	.771
Total	1160.000	32
Corrected Total	235.500	31
a R Squared = .921 (Adjusted R Squared = .899)

Contrast Results (K Matrix)

			Dependent Variable
B Special Contrast			Y
L1	Contrast Estimate		-14.500
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-14.500
	Std. Error		1.075
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-16.719
		Upper Bound	-12.281

Test Results
Dependent Variable: Y

Source	Sum of Squares	df	Mean Square	F	Sig.
Contrast	140.167	1	140.167	181.838	.000
Error	18.500	24	.771

Note that the F-ratios in these contrasts are larger than the F-ratios in the one-way ANOVA example.  This is because the two-way ANOVA has a smaller mean square residual than the one-way ANOVA.

SPSS has a number of built-in contrasts that you can use, of which special (used in the above examples) is only one.  Below is a table listing those contrasts with an explanation of the contrasts that they make and an example of how the syntax works.  The repeated contrast compares group 1 with 2, 2 with 3, and 3 with 4 as shown in the Contrast Results (K Matrix) table in the results.

Name of contrast	Comparison made
Simple	Compares each level of a variable to the last level (or whichever level is specified)
Deviation	Compares deviations from the grand mean
Difference	Compares levels of a variable with the mean of the previous levels of the variable
Helmert	Compare levels of a variable with the mean of the subsequent levels of the variable
Polynomial	Orthogonal polynomial contrasts
Repeated	Adjacent levels of a variable
Special	User-defined contrast

glm y by a b
 /contrast(b)=repeated. 

Between-Subjects Factors

		N
A	1	16
	2	16
B	1	8
	2	8
	3	8
	4	8

Tests of Between-Subjects Effects
Dependent Variable: Y

Source	Type III Sum of Squares	df	Mean Square	F	Sig.
Corrected Model	217.000(a)	7	31.000	40.216	.000
Intercept	924.500	1	924.500	1199.351	.000
A	3.125	1	3.125	4.054	.055
B	194.500	3	64.833	84.108	.000
A * B	19.375	3	6.458	8.378	.001
Error	18.500	24	.771
Total	1160.000	32
Corrected Total	235.500	31
a R Squared = .921 (Adjusted R Squared = .899)

Contrast Results (K Matrix)

			Dependent Variable
B Repeated Contrast			Y
Level 1 vs. Level 2	Contrast Estimate		-.750
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-.750
	Std. Error		.439
	Sig.		.100
	95% Confidence Interval for Difference	Lower Bound	-1.656
		Upper Bound	.156
Level 2 vs. Level 3	Contrast Estimate		-2.750
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-2.750
	Std. Error		.439
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-3.656
		Upper Bound	-1.844
Level 3 vs. Level 4	Contrast Estimate		-2.750
	Hypothesized Value		0
	Difference (Estimate - Hypothesized)		-2.750
	Std. Error		.439
	Sig.		.000
	95% Confidence Interval for Difference	Lower Bound	-3.656
		Upper Bound	-1.844

Test Results
Dependent Variable: Y

Source	Sum of Squares	df	Mean Square	F	Sig.
Contrast	194.500	3	64.833	84.108	.000
Error	18.500	24	.771

For more information on coding contrasts, please see How can I use the lmatrix subcommand to understand a three-way interaction in ANOVA? .

References

Kirk, Roger E. (1968) Experimental Design: Procedures for the Behavioral Sciences. Monterey, California: Brooks/Cole Publishing.