**Chapter Outline**

- 5.1 Simple Coding
- 5.2 Forward Difference Coding
- 5.3 Backward Difference Coding
- 5.4 Helmert Coding
- 5.5 Reverse Helmert Coding
- 5.6 Deviation Coding
- 5.7 Orthogonal Polynomial Coding
- 5.8 User-Defined Coding
- 5.9 Summary
- 5.10 For more information

**
Introduction**

Categorical variables require special attention in regression analysis because,
unlike dichotomous or continuous variables, they cannot by entered into the
regression equation just as they are. For example, if you have a
variable called **race** that is coded 1 = Hispanic, 2 = Asian 3 = Black 4 =
White,
then entering **race** in your regression will look at the linear
effect of race, which is probably not what you intended. Instead, categorical variables like this need to be
recoded into a series of variables which can then be
entered into the regression model. There are a variety of coding systems that can be used when
coding categorical
variables. Ideally, you would choose a
coding system that reflects the comparisons that you want to make. In Chapter
3 of the Regression with SPSS Web Book
we covered the use of categorical variables in regression analysis focusing on
the use of dummy variables, but that is not the only coding scheme that you can
use. For example,
you may want to compare each level to the next higher
level, in which case you would want to use “forward difference” coding, or you
might want to compare each level to the mean of the subsequent levels of the
variable, in which case you would want to use “Helmert” coding. By
deliberately choosing a coding system, you can obtain comparisons that are most
meaningful for testing your hypotheses. Regardless of the coding system you choose, the
test of the overall effect
of the categorical variable (i.e., the overall effect of **race**) will remain the same.
Below is a table listing various types of contrasts and the
comparison that they make.

Name of contrast |
Comparison made |

Simple Coding | Compares each level of a variable to the reference level |

Forward Difference Coding | Adjacent levels of a variable (each level minus the next level) |

Backward Difference Coding | Adjacent levels of a variable (each level minus the prior level) |

Helmert Coding | Compare levels of a variable with the mean of the subsequent levels of the variable |

Reverse Helmert Coding | Compares levels of a variable with the mean of the previous levels of the variable |

Deviation Coding | Compares deviations from the grand mean |

Orthogonal Polynomial Coding | Orthogonal polynomial contrasts |

User-Defined Coding | User-defined contrast |

There are a couple of notes to be made about the coding systems listed
above. The first is that they represent planned comparisons and not post
hoc comparisons. In other words, they are comparisons that you plan to do
before you begin analyzing your data, not comparisons that you think of once you have seen
the results of preliminary analyses. Also, some forms of coding
make more sense with ordinal categorical variables than with nominal categorical
variables. Below we will show examples using **race** as a categorical
variable, which is a nominal variable. Because simple effect coding compares the mean of the
dependent variable for each level of the categorical variable to the mean of the
dependent variable at for the reference level, it makes sense with a nominal
variable.
However, it may not make as much sense to use a coding scheme that tests the linear
effect of **race**. As we describe each type of coding system, we note
those coding systems with which it does not make as much sense to use a nominal
variable. Also, you may notice that we follow several rules when
creating the contrast coding schemes. For more information about these
rules, please see the section on User-Defined Coding.

This page will illustrate three ways that you can conduct analyses using
these coding schemes: 1) using the **glm** command with **/lmatrix** to
define “contrast” coefficients that specify levels of the categorical
variable that are to be
compared**, **2) using the **glm** command with **/contrast** to
specify one of the SPSS predefined coding schemes, or 3) using **regression**.
When using **regression** to do contrasts, you first need to create k-1 new variables (where k is the number of
levels of the categorical variable) and use
these new variables as predictors in your regression model. While methods 1
and 3 both involve manually specifying “contrasts”, method 1 uses a
type of coding we will call “contrast coding”; method 3 uses a type of coding
we will call “regression coding”.

There are benefits and drawbacks of each of these three methods. For example, methods 1 and 3 allow you to manually code the contrasts and give you absolute control over the coding, but the drawback is that it is relatively easy to make an error in the coding. By contrast, method 2 automates the process by letting SPSS do the coding for you, but you are limited to just the pre-defined coding schemes that SPSS has created. Method 3 can be the most difficult, but it can be used with any kind of regression procedure.

**The Example Data File**

The examples in this page will use dataset called
hsb2.sav
and we will focus on the categorical variable **race**, which has four levels (1 =
Hispanic, 2 = Asian, 3 = African American and 4 = white) and we will use **write**
as our dependent variable. Although our
example uses a variable with four levels, these coding systems work with
variables that have more or fewer categories. No matter which coding system you select, you will always have one fewer recoded variables
than levels of the original variable. In our example, our categorical
variable has four levels so we will have three new variables (a variable corresponding to the final level of the categorical
variables would be redundant and therefore unnecessary).

Before considering any analyses, let’s look at the mean of the dependent
variable, **write**, for each level of **race**. This will help in interpreting
the output from later analyses.

means tables = write by race.

Cases | ||||||
---|---|---|---|---|---|---|

Included | Excluded | Total | ||||

N | Percent | N | Percent | N | Percent | |

writing score * RACE | 200 | 100.0% | 0 | .0% | 200 | 100.0% |

RACE | Mean | N |
---|---|---|

hispanic | 46.4583 | 24 |

asian | 58.0000 | 11 |

african-amer | 48.2000 | 20 |

white | 54.0552 | 145 |

Total | 52.7750 | 200 |

The results of simple coding are very similar to dummy coding in that each level is compared to the reference level. In the example below, level 4 is the reference level and the first comparison compares level 1 to level 4, the second comparison compares level 2 to level 4, and the third comparison compares level 3 to level 4.

**Method 1: GLM with /LMATRIX**

The table below shows the simple coding making the comparisons described above. The first contrast compares level 1 to level 4, and level 1 is coded as 1 and level 4 is coded as -1. Likewise, the second contrast compares level 2 to level 4 by coding level 2 as 1 and level 4 as -1. As you can see with contrast coding, you can discern the meaning of the comparisons simply by inspecting the contrast coefficients. For example, looking at the contrast coefficients for c3, you can see that it compares level 3 to level 4.

SIMPLE contrast coding

Level of race | New variable 1 (c1) | New variable 2 (c2) | New variable 3 (c3) |

1 (Hispanic) | 1 | 0 | 0 |

2 (Asian) | 0 | 1 | 0 |

3 (African American) | 0 | 0 | 1 |

4 (white) | -1 | -1 | -1 |

Below we illustrate how to form these comparisons using the **glm** command with **/lmatrix**. As you see, a separate **/lmatrix**
subcommand is used for each contrast.

glm write by race /lmatrix "level 1 versus level 4" race 1 0 0 -1 /lmatrix "level 2 versus level 4" race 0 1 0 -1 /lmatrix "level 3 versus level 4" race 0 0 1 -1.

Each of the above **/lmatrix** subcommands produced two tables shown below,
“Contrast Results (K Matrix)” and “Test Results”. The
contrast estimate for the first contrast compares the mean of the dependent
variable, **write**, for levels 1 and 4 yielding -7.597 and
is statistically significant (p<.000). The F-value associated with this test
is given in the “Test Results” table and is 14.590. The p-value given
in the “Contrast Results (K Matrix)” table and the p-value in the
“Test Results” table are the same because they both refer
to the same test of the contrast coefficient to zero. The results of the second
contrast, comparing the mean of **write** for levels 2 and 4 is not
statistically significant (F = 1.953, p = .164), while the third contrast is
statistically significant.

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -7.597 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -7.597 | ||

Std. Error | 1.989 | ||

Sig. | .000 | ||

95% Confidence Interval for Difference | Lower Bound | -11.519 | |

Upper Bound | -3.675 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 1 versus group 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 1188.388 | 1 | 1188.388 | 14.590 | .000 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 3.945 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 3.945 | ||

Std. Error | 2.823 | ||

Sig. | .164 | ||

95% Confidence Interval for Difference | Lower Bound | -1.622 | |

Upper Bound | 9.511 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 2 versus group 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 159.108 | 1 | 159.108 | 1.953 | .164 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -5.855 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -5.855 | ||

Std. Error | 2.153 | ||

Sig. | .007 | ||

95% Confidence Interval for Difference | Lower Bound | -10.101 | |

Upper Bound | -1.610 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 3 versus group 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 602.550 | 1 | 602.550 | 7.398 | .007 |

Error | 15964.717 | 196 | 81.453 |

**
Method 2: GLM with /CONTRAST**

Instead of using the **/lmatrix** subcommand, we can achieve the same
results using the **/contrast** subcommand with the **glm** command.
Instead of specifying the numbers to be used in the contrast as we did above, we
can simply type in the name of the contrast that we wish to use, and SPSS will
do the coding for us. We will use the **/print = test(lmatrix)** subcommand to
have SPSS print out the coding scheme that it used to make the contrasts. You
will notice that the table entitled “Contrast Coefficients (L’
Matrix)” is the same as the table we used in method 1 above.

glm write by race /contrast (race)=simple /print = test(lmatrix).

As you see in the output below, the table titled “Contrast Coefficients (L’ Matrix)” shows
the coding scheme that was used for each comparison. The table entitled
“Contrast Results (K Matrix)” shows the results of the various
contrasts. In our example, the difference between level 1 of ** race** and
level 4 of ** race** is statistically significant. You will notice that the
contrast estimate is the difference between the mean for the dependent variable
for the first level minus the mean of the dependent variable for the omitted level.
In other words, the mean for level 1 minus the mean for level 4 which is 46.4583 – 54.0552 = -7.597.
The row labeled “Sig.” is .000, indicating that this difference is
significant, and this is followed by a confidence interval for the difference.
The next part of the table compares level 2 of ** race** and level 4 of
**
race** and shows that this difference is not statistically significant and
the next part of the table shows the difference between level 3 of
**
race** and level 4 of ** race** is statistically significant. You
might note that while the significance (“Sig.”) is given for each of
these tests, there is no “t” value, but you could obtain this by
dividing the “Contrast Estimate” by the “Std. Error”, i.e.,
-7.597 / 1.989.

The table entitled “Test Results” indicates that the test of the
overall effect **race** is
statistically significant. In other words, it is a test of all of the
contrasts taken together.

RACE Simple Contrast(a) | |||||||
---|---|---|---|---|---|---|---|

Parameter | Level 1 vs. Level 4 | Level 2 vs. Level 4 | Level 3 vs. Level 4 | ||||

Intercept | 0 | 0 | 0 | ||||

[RACE=1.00] | 1 | 0 | 0 | ||||

[RACE=2.00] | 0 | 1 | 0 | ||||

[RACE=3.00] | 0 | 0 | 1 | ||||

[RACE=4.00] | -1 | -1 | -1 | ||||

The default display of this matrix is the transpose of the corresponding L matrix. | a Reference category = 4 |

Dependent Variable | |||
---|---|---|---|

RACE Simple Contrast(a) | writing score | ||

Level 1 vs. Level 4 | Contrast Estimate | -7.597 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -7.597 | ||

Std. Error | 1.989 | ||

Sig. | .000 | ||

95% Confidence Interval for Difference | Lower Bound | -11.519 | |

Upper Bound | -3.675 | ||

Level 2 vs. Level 4 | Contrast Estimate | 3.945 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 3.945 | ||

Std. Error | 2.823 | ||

Sig. | .164 | ||

95% Confidence Interval for Difference | Lower Bound | -1.622 | |

Upper Bound | 9.511 | ||

Level 3 vs. Level 4 | Contrast Estimate | -5.855 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -5.855 | ||

Std. Error | 2.153 | ||

Sig. | .007 | ||

95% Confidence Interval for Difference | Lower Bound | -10.101 | |

Upper Bound | -1.610 | ||

a Reference category = 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 1914.158 | 3 | 638.053 | 7.833 | .000 |

Error | 15964.717 | 196 | 81.453 |

**Method 3: Regression**

The regression coding is a bit more complex
than contrast coding. In our example below, level 4 is the reference level
and ** x1** compares level 1 to level 4, ** x2** compares level 2 to level 4, and
** x3** compares
level 3 to level 4. For ** x1** the coding is
3/4 for level 1, and -1/4 for all other levels. Likewise, for
**
x2** the coding is 3/4 for level 2, and -1/4 for all other levels, and for
**
x3** the coding is 3/4 for level 3, and -1/4
for all other levels. It is not intuitive that this regression coding
scheme yields these comparisons; however, if you desire simple comparisons, you
can follow this general rule to obtain these comparisons.

SIMPLE regression coding

Level of race | New variable 1 (x1) | New variable 2 (x2) | New variable 3 (x3) |

1 (Hispanic) | 3/4 | -1/4 | -1/4 |

2 (Asian) | -1/4 | 3/4 | -1/4 |

3 (African American) | -1/4 | -1/4 | 3/4 |

4 (white) | -1/4 | -1/4 | -1/4 |

Below we show the more general rule for creating this kind of coding scheme using regression coding, where k is the number of levels of the categorical variable (in this instance, k=4).

SIMPLE regression coding

Level of race | New variable 1 (x1) | New variable 2 (x2) | New variable 3 (x3) |

1 (Hispanic) | (k-1) / k | -1 / k | -1 / k |

2 (Asian) | -1 / k | (k-1) / k | -1 / k |

3 (African American) | -1 / k | -1 / k | (k-1) / k |

4 (white) | -1 / k | -1 / k | -1 / k |

Below we illustrate how to create **x1**, **x2** and **x3** and enter
these new variables into the regression model using the **regression**
command.

if race = 1 x1 = 3/4. if any(race,2,3,4) x1 = -1/4. if race = 2 x2 = 3/4. if any(race,1,3,4) x2 = -1/4. if race = 3 x3 = 3/4. if any(race,1,2,4) x3 = -1/4.execute.

regression /dependent = write /method = enter x1 x2 x3.

You will notice that the regression coefficients in the table below are the same
as the contrast coefficients that we saw using the **glm** command. Both the regression coefficient for
** x1** and the contrast estimate for
c1 are the mean of ** write** for level 1 of **race** (Hispanic) minus the mean of
** write**
for level 4 (white). Likewise, the
regression coefficient for ** x2** and the contrast estimate for c2 are the mean of ** write** for level 2 (Asian) minus the mean of
** write**
for level 4 (white). The F-value shown in the **glm** output is the square of
the t-value shown in the regression table below. You also can see that the
significance levels are also the same as those from the **glm** output.

Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |||
---|---|---|---|---|---|---|

Model | B | Std. Error | Beta | |||

1 | (Constant) | 51.678 | .982 | 52.619 | .000 | |

X1 | -7.597 | 1.989 | -.261 | -3.820 | .000 | |

X2 | 3.945 | 2.823 | .095 | 1.398 | .164 | |

X3 | -5.855 | 2.153 | -.186 | -2.720 | .007 | |

a Dependent Variable: writing score |

In this coding system, the mean of the dependent variable for one level
of the categorical variable is compared to the mean of the dependent variable
for the next (adjacent) level. In our example below, the first comparison
compares the mean of ** write** for level 1 with the mean of ** write ** for level 2 of
**
race** (Hispanics minus Asians). The second comparison compares the mean of
**
write** for level 2 minus level 3, and the third comparison compares the mean of
**
write** for level 3 minus level 4. This type of
coding may be useful with either a nominal or an ordinal
variable.

**Method 1: GLM with /LMATRIX**

FORWARD DIFFERENCE contrast coding

Level of race | New variable 1 (c1) | New variable 2 (c2) | New variable 3 (c3) |

Level 1 v. Level 2 | Level 2 v. Level 3 | Level 3 v. Level 4 | |

1 (Hispanic) | 1 | 0 | 0 |

2 (Asian) | -1 | 1 | 0 |

3 (African American) | 0 | -1 | 1 |

4 (white) | 0 | 0 | -1 |

glm write by race /lmatrix "level 1 versus level 2" race 1 -1 0 0 /lmatrix "level 2 versus level 3" race 0 1 -1 0 /lmatrix "level 3 versus level 4" race 0 0 1 -1.

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -11.542 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -11.542 | ||

Std. Error | 3.286 | ||

Sig. | .001 | ||

95% Confidence Interval for Difference | Lower Bound | -18.022 | |

Upper Bound | -5.061 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 1 versus group 2 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 1004.785 | 1 | 1004.785 | 12.336 | .001 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 9.800 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 9.800 | ||

Std. Error | 3.388 | ||

Sig. | .004 | ||

95% Confidence Interval for Difference | Lower Bound | 3.119 | |

Upper Bound | 16.481 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 2 versus group 3 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 681.574 | 1 | 681.574 | 8.368 | .004 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -5.855 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -5.855 | ||

Std. Error | 2.153 | ||

Sig. | .007 | ||

95% Confidence Interval for Difference | Lower Bound | -10.101 | |

Upper Bound | -1.610 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 3 versus group 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 602.550 | 1 | 602.550 | 7.398 | .007 |

Error | 15964.717 | 196 | 81.453 |

With this coding system, adjacent levels of the categorical variable are compared. Hence, the mean of the dependent variable at level 1 is compared to the mean of the dependent variable at level 2: 46.4583 – 58 = -11.542, which is statistically significant. For the comparison between levels 2 and 3, the calculation of the contrast coefficient would be 58 – 48.2 = 9.8, which is also statistically significant. Finally, comparing levels 3 and 4, 48.2 – 54.0552 = -5.855, a statistically significant difference. One would conclude from this that each adjacent level of ** race** is statistically significantly different.

**Method 2: GLM with /CONTRAST**

As with the previous examples, we will conduct the above analysis again, this time using the** /contrast(race)=repeated **subcommand to request forward difference contrasts. You can compare the results below to those above to verify that the results are identical to those obtained using the **/lmatrix** subcommand.

glm write by race /contrast (race)=repeated /print = test(lmatrix).

RACE Repeated Contrast | |||
---|---|---|---|

Parameter | Level 1 vs. Level 2 | Level 2 vs. Level 3 | Level 3 vs. Level 4 |

Intercept | 0 | 0 | 0 |

[RACE=1.00] | 1 | 0 | 0 |

[RACE=2.00] | -1 | 1 | 0 |

[RACE=3.00] | 0 | -1 | 1 |

[RACE=4.00] | 0 | 0 | -1 |

The default display of this matrix is the transpose of the corresponding L matrix. |

Dependent Variable | |||
---|---|---|---|

RACE Repeated Contrast | writing score | ||

Level 1 vs. Level 2 | Contrast Estimate | -11.542 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -11.542 | ||

Std. Error | 3.286 | ||

Sig. | .001 | ||

95% Confidence Interval for Difference | Lower Bound | -18.022 | |

Upper Bound | -5.061 | ||

Level 2 vs. Level 3 | Contrast Estimate | 9.800 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 9.800 | ||

Std. Error | 3.388 | ||

Sig. | .004 | ||

95% Confidence Interval for Difference | Lower Bound | 3.119 | |

Upper Bound | 16.481 | ||

Level 3 vs. Level 4 | Contrast Estimate | -5.855 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -5.855 | ||

Std. Error | 2.153 | ||

Sig. | .007 | ||

95% Confidence Interval for Difference | Lower Bound | -10.101 | |

Upper Bound | -1.610 |

Again, we see that the results are the same as those obtained using the **/lmatrix** subcommand: all three comparisons are statistically significant.

**Method 3: Regression**

For the first comparison, where the first and second levels are compared, ** x1** is coded 3/4 for level 1 and the other levels are coded -1/4. For the second comparison where level 2 is compared with level 3, ** x2** is coded 1/2 1/2 -1/2 -1/2, and for the third comparison where** **level 3 is compared with level 4, ** x3** is coded 1/4 1/4 1/4 -3/4.

FORWARD DIFFERENCE regression coding

Level of race | New variable 1 (x1) | New variable 2 (x2) | New variable 3 (x3) |

Level 1 v. Level 2 | Level 2 v. Level 3 | Level 3 v. Level 4 | |

1 (Hispanic) | 3/4 | 1/2 | 1/4 |

2 (Asian) | -1/4 | 1/2 | 1/4 |

3 (African American) | -1/4 | -1/2 | 1/4 |

4 (white) | -1/4 | -1/2 | -3/4 |

The general rule for this regression coding scheme is shown below, where k is the number of levels of the categorical variable (in this case k=4).

FORWARD DIFFERENCE regression coding

Level of race | New variable 1 (x1) | New variable 2 (x2) | New variable 3 (x3) |

Level 1 v. Level 2 | Level 2 v. Level 3 | Level 3 v. Level 4 | |

1 (Hispanic) | (k-1)/k | (k-2)/k | (k-3)/k |

2 (Asian) | -1/k | (k-2)/k | (k-3)/k |

3 (African American) | -1/k | -2/k | (k-3)/k |

4 (white) | -1/k | -2/k | -3/k |

if race = 1 x1 = 3/4. if any(race,2,3,4) x1 = -1/4. if any(race,1,2) x2 = 1/2. if any(race,3,4) x2 = -1/2. if any(race,1,2,3) x3 = 1/4. if race = 4 x3 = -3/4. execute. regression /dep write /method = enter x1 x2 x3.

Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |||
---|---|---|---|---|---|---|

Model | B | Std. Error | Beta | |||

1 | (Constant) | 51.678 | .982 | 52.619 | .000 | |

X1 | -11.542 | 3.286 | -.397 | -3.512 | .001 | |

X2 | 9.800 | 3.388 | .394 | 2.893 | .004 | |

X3 | -5.855 | 2.153 | -.277 | -2.720 | .007 | |

a Dependent Variable: writing score |

You can see the regression coefficient for ** x1** is the mean of ** write** for level 1 (Hispanic) minus the mean of ** write** for level 2 (Asian). Likewise, the regression coefficient for ** x2** is the mean of ** write** for level 2 (Asian) minus the mean of ** write** for level 3 (African American), and the regression coefficient for ** x3** is the mean of ** write** for level 3 (African American) minus the mean of ** write** for level 4 (white).

**5.3 Backward Difference Coding**

In this coding system, the mean of the dependent variable for one level of the categorical variable is compared to the mean of the dependent variable for the prior adjacent level. In our example below, the first comparison compares the mean of ** write** for level 2 with the mean of ** write ** for level 1 of ** race** (Asians minus Hispanics). The second comparison compares the mean of ** write** for level 3 minus level 2, and the third comparison compares the mean of ** write** for level 4 minus level 3. This type of coding may be useful with either a nominal or an ordinal variable.

**Method 1: GLM with /LMATRIX**

BACKWARD DIFFERENCE contrast coding

Level of race | New variable 1 (c1) | New variable 2 (c2) | New variable 3 (c3) |

Level 1 v. Level 2 | Level 2 v. Level 3 | Level 3 v. Level 4 | |

1 (Hispanic) | -1 | 0 | 0 |

2 (Asian) | 1 | -1 | 0 |

3 (African American) | 0 | 1 | -1 |

4 (white) | 0 | 0 | 1 |

glm write by race /lmatrix "level 1 versus level 2" race -1 1 0 0 /lmatrix "level 2 versus level 3" race 0 -1 1 0 /lmatrix "level 3 versus level 4" race 0 0 -1 1.

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 11.542 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 11.542 | ||

Std. Error | 3.286 | ||

Sig. | .001 | ||

95% Confidence Interval for Difference | Lower Bound | 5.061 | |

Upper Bound | 18.022 | ||

a Based on the user-specified contrast coefficients (L’) matrix: level 1 versus level 2 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 1004.785 | 1 | 1004.785 | 12.336 | .001 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -9.800 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -9.800 | ||

Std. Error | 3.388 | ||

Sig. | .004 | ||

95% Confidence Interval for Difference | Lower Bound | – 16.481 | |

Upper Bound | – 3.119 | ||

a Based on the user-specified contrast coefficients (L’) matrix: level 2 versus level 3 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 681.574 | 1 | 681.574 | 8.368 | .004 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 5.855 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 5.855 | ||

Std. Error | 2.153 | ||

Sig. | .007 | ||

95% Confidence Interval for Difference | Lower Bound | 1.610 | |

Upper Bound | 10.101 | ||

a Based on the user-specified contrast coefficients (L’) matrix: level 3 versus level 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 602.550 | 1 | 602.550 | 7.398 | .007 |

Error | 15964.717 | 196 | 81.453 |

With this coding system, adjacent levels of the categorical variable are compared, with each level compared to the prior level. Hence, the mean of the dependent variable at level 2 is compared to the mean of the dependent variable at level 1: 58-46.4583 = 11.542, which is statistically significant. For the comparison between levels 3 and 2, the calculation of the contrast coefficient is 48.2 – 58 = -9.8, which is also statistically significant. Finally, comparing levels 4 and 3, 54.0552 – 48.2 = 5.855, a statistically significant difference. One would conclude from this that each adjacent level of ** race** is statistically significantly different.

**Method 2: GLM with /CONTRAST**

While SPSS has the **/contrast (race)=repeated **subcommand for comparing each level to the next level, it does not have an equivalent command for comparing each level to a prior level. This would need to be done via the **/lmatrix** subcommand, or by reversing the coding of the categorical variable.

**Method 3: Regression**

For the first comparison, where the first and second levels are compared, ** x1** is coded 3/4 for level 1 while the other levels are coded -1/4. For the second comparison where level 2 is compared with level 3, ** x2** is coded 1/2 1/2 -1/2 -1/2, and for the third comparison where** **level 3 is compared with level 4, ** x3** is coded 1/4 1/4 1/4 -3/4.

BACKWARD DIFFERENCE regression coding

Level of race | New variable 1 (x1) | New variable 2 (x2) | New variable 3 (x3) |

Level 2 v. Level 1 | Level 3 v. Level 2 | Level 4 v. Level 3 | |

1 (Hispanic) | – 3/4 | -1/2 | -1/4 |

2 (Asian) | 1/4 | -1/2 | -1/4 |

3 (African American) | 1/4 | 1/2 | -1/4 |

4 (white) | 1/4 | 1/2 | 3/4 |

The general rule for this regression coding scheme is shown below, where k is the number of levels of the categorical variable (in this case, k is 4).

BACKWARD DIFFERENCE regression coding

Level of race | New variable 1 (x1) | New variable 2 (x2) | New variable 3 (x3) |

Level 1 v. Level 2 | Level 2 v. Level 3 | Level 3 v. Level 4 | |

1 (Hispanic) | -(k-1)/k | -(k-2)/k | -(k-3)/k |

2 (Asian) | 1/k | -(k-2)/k | -(k-3)/k |

3 (African American) | 1/k | 2/k | -(k-3)/k |

4 (white) | 1/k | 2/k | 3/k |

if race = 1 x1 = -3/4. if any(race,2,3,4) x1 = 1/4. if any(race,1,2) x2 = -1/2. if any(race,3,4) x2 = 1/2. if any(race,1,2,3) x3 = -1/4. if race = 4 x3 = 3/4. execute. regression /dep write /method = enter x1 x2 x3.

Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |||
---|---|---|---|---|---|---|

Model | B | Std. Error | Beta | |||

1 | (Constant) | 51.678 | .982 | 52.619 | .000 | |

X1 | 11.542 | 3.286 | .397 | 3.512 | .001 | |

X2 | -9.800 | 3.388 | -.394 | -2.893 | .004 | |

X3 | 5.855 | 2.153 | .277 | 2.720 | .007 | |

a Dependent Variable: writing score |

In the above example, the regression coefficient for ** x1** is the mean of ** write** for level 2 minus the mean of ** write** for level 1 (58- 46.4583 = 11.542). Likewise, the regression coefficient for ** x2** is the mean of ** write** for level 3 minus the mean of ** write** for level 2, and the regression coefficient for ** x3** is the mean of ** write** for level 4 minus the mean of ** write** for level 3.

Helmert coding compares each level of a categorical variable to the mean of the subsequent levels. Hence, the first contrast compares the mean of the dependent variable for level 1 of ** race** with the mean of all of the subsequent levels of ** race** (levels 2, 3, and 4), the second contrast compares the mean of the dependent variable for level 2 of ** race** with the mean of all of the subsequent levels of ** race** (levels 3 and 4), and the third contrast compares the mean of the dependent variable for level 3 of ** race** with the mean of all of the subsequent levels of ** race** (level 4). While this type of coding system does not make much sense with a nominal variable like **race**, it is useful in situations where the levels of the categorical variable are ordered say, from lowest to highest, or smallest to largest, etc.

For Helmert coding, we see that the first comparison comparing level 1 with levels 2, 3 and 4 is coded 1, -1/3, -1/3 and -1/3, reflecting the comparison of level 1 with all other levels. The second comparison is coded 0, 1, -1/2 and -1/2, reflecting that it compares level 2 with levels 3 and 4. The third comparison is coded 0, 0, 1 and -1, reflecting that level 3 is compared to level 4.

**Method 1: GLM with /LMATRIX**

HELMERT contrast coding

Level of race | New variable 1 (c1) | New variable 2 (c2) | New variable 3 (c3) |

Level 1 v. Later | Level 2 v. Later | Level 3 v. Later | |

1 (Hispanic) | 1 | 0 | 0 |

2 (Asian) | -1/3 | 1 | 0 |

3 (African American) | -1/3 | -1/2 | 1 |

4 (white) | -1/3 | -1/2 | -1 |

Below we illustrate how to form these comparisons using the **glm** command with **/lmatrix**. Note the use of fractions on the first **/lmatrix** subcommand. You cannot use .333 instead of 1/3: SPSS will give an error message and fail to calculate the contrast coefficient. The problem is that .333 + .333 + .333 – 1 is not sufficiently close to zero. (If you wanted to use decimals, you would need to use something like .333333 so that .333333 + .333333 + .333333 – 1 would be sufficiently close to zero.)

glm write by race /lmatrix "level 1 versus levels 2 3 and 4" race 1 -1/3 -1/3 -1/3. /lmatrix "level 2 versus levels 3 and 4" race 0 1 -1/2 -1/2. /lmatrix "level 3 versus level 4" race 0 0 1 -1.

The contrast estimate for the comparison between level 1 and the remaining levels is calculated by taking the mean of the dependent variable for level 1 and subtracting the mean of the dependent variable for levels 2, 3 and 4: 46.4583 – [(58 + 48.2 + 54.0552) / 3] = -6.960, which is statistically significant. This means that the mean of ** write** for level 1 of ** race** is statistically significantly different from the mean of ** write** for levels 2 through 4. As noted above, this comparison probably is not meaningful because the variable ** race** is nominal. This type of comparison would be more meaningful if the categorical variable was ordinal.

To calculate the contrast coefficient for the comparison between level 2 and the later levels, you subtract the mean of the dependent variable for levels 3 and 4 from the mean of the dependent variable for level 2: 58 – [(48.2 + 54.0552) / 2] = 6.872, which is statistically significant. The contrast estimate for the comparison between level 3 and level 4 is the difference between the mean of the dependent variable for the two levels: 48.2 – 54.0552 = -5.855, which is also statistically significant.

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -6.960 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -6.960 | ||

Std. Error | 2.175 | ||

Sig. | .002 | ||

95% Confidence Interval for Difference | Lower Bound | -11.250 | |

Upper Bound | -2.670 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 1 versus groups 2 3 and 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 833.927 | 1 | 833.927 | 10.238 | .002 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 6.872 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 6.872 | ||

Std. Error | 2.926 | ||

Sig. | .020 | ||

95% Confidence Interval for Difference | Lower Bound | 1.101 | |

Upper Bound | 12.644 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 2 versus groups 3 and 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 449.240 | 1 | 449.240 | 5.515 | .020 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -5.855 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -5.855 | ||

Std. Error | 2.153 | ||

Sig. | .007 | ||

95% Confidence Interval for Difference | Lower Bound | -10.101 | |

Upper Bound | -1.610 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 3 versus group 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 602.550 | 1 | 602.550 | 7.398 | .007 |

Error | 15964.717 | 196 | 81.453 |

**Method 2: GLM with /CONTRAST**

As with the previous examples, we will conduct the analysis above again, this time using the **/contrast** subcommand.

glm write by race /contrast (race)=helmert /print = test(lmatrix).

This output shows the three comparisons: the mean of **write** for level 1 of **race** to the mean of **write** for the other three levels (called “later” in this output), the mean of **write** for level 2 of **race** to the mean of **write** for the other two levels, etc. Again, all three comparisons are statistically significant.

RACE Helmert Contrast | |||
---|---|---|---|

Parameter | Level 1 vs. Later | Level 2 vs. Later | Level 3 vs. Level 4 |

Intercept | .000 | .000 | .000 |

[RACE=1.00] | 1.000 | .000 | .000 |

[RACE=2.00] | -.333 | 1.000 | .000 |

[RACE=3.00] | -.333 | -.500 | 1.000 |

[RACE=4.00] | -.333 | -.500 | -1.000 |

The default display of this matrix is the transpose of the corresponding L matrix. |

Dependent Variable | |||
---|---|---|---|

RACE Helmert Contrast | writing score | ||

Level 1 vs. Later | Contrast Estimate | -6.960 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -6.960 | ||

Std. Error | 2.175 | ||

Sig. | .002 | ||

95% Confidence Interval for Difference | Lower Bound | -11.250 | |

Upper Bound | -2.670 | ||

Level 2 vs. Later | Contrast Estimate | 6.872 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 6.872 | ||

Std. Error | 2.926 | ||

Sig. | .020 | ||

95% Confidence Interval for Difference | Lower Bound | 1.101 | |

Upper Bound | 12.644 | ||

Level 3 vs. Level 4 | Contrast Estimate | -5.855 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -5.855 | ||

Std. Error | 2.153 | ||

Sig. | .007 | ||

95% Confidence Interval for Difference | Lower Bound | -10.101 | |

Upper Bound | -1.610 |

**Method 3: Regression**

Below we see an example of Helmert regression coding. For the first comparison (comparing level 1 with levels 2, 3 and 4) the codes are 3/4 and -1/4 -1/4 -1/4. The second comparison compares level 2 with levels 3 and 4 and is coded 0 2/3 -1/3 -1/3. The third comparison compares level 3 to level 4 and is coded 0 0 1/2 -1/2.

HELMERT regression coding

Level of race | New variable 1 (x1) | New variable 2 (x2) | New variable 3 (x3) |

Level 1 v. Later | Level 2 v. Later | Level 3 v. Later | |

1 (Hispanic) | 3/4 | 0 | 0 |

2 (Asian) | -1/4 | 2/3 | 0 |

3 (African American) | -1/4 | -1/3 | 1/2 |

4 (white) | -1/4 | -1/3 | -1/2 |

Below we illustrate how to create **x1**, **x2** and **x3** and enter these new variables into the regression model using the **regression** command.

if race = 1 x1 = 3/4. if any(race,2,3,4) x1 = -1/4. if race = 1 x2 = 0. if race = 2 x2 = .667. if any(race,3,4) x2 = -1/3. if any(race,1,2) x3 = 0. if race = 3 x3 = 1/2. if race = 4 x3 = -1/2. execute. regression /dep write /method = enter x1 x2 x3.

As you see below, the regression coefficient for ** x1** is the mean of ** write** for level 1 (Hispanic) versus all subsequent levels (levels 2, 3 and 4). Likewise, the regression coefficient for ** x2** is the mean of ** write** for level 2 minus the mean of ** write** for levels 3 and 4. Finally, the regression coefficient for ** x3** is the mean of ** write** for level 3 minus the mean of ** write** for level 4.

Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |||
---|---|---|---|---|---|---|

Model | B | Std. Error | Beta | |||

1 | (Constant) | 51.677 | .982 | 52.635 | .000 | |

X1 | -6.958 | 2.175 | -.239 | -3.199 | .002 | |

X2 | 6.872 | 2.926 | .177 | 2.348 | .020 | |

X3 | -5.855 | 2.153 | -.204 | -2.720 | .007 | |

a Dependent Variable: writing score |

Reverse Helmert coding (also know as difference coding) is just the opposite of Helmert coding: instead of comparing each level of categorical variable to the mean of the subsequent level(s), each is compared to the mean of the previous level(s). In our example, the first contrast codes the comparison of the mean of the dependent variable for level 2 of ** race** to the mean of the dependent variable for level 1 of **race**. The second comparison compares the mean of the dependent variable level 3 of ** race** with both levels 1 and 2 of ** race**, and the third comparison compares the mean of the dependent variable for level 4 of ** race** with levels 1, 2 and 3. Clearly, this coding system does not make much sense with our example of ** race** because it is a nominal variable. However, this system is useful when the levels of the categorical variable are ordered in a meaningful way. For example, if we had a categorical variable in which work-related stress was coded as low, medium or high, then comparing the means of the previous levels of the variable would make more sense.

For reverse Helmert coding, we see that the first comparison comparing levels 1 and 2 are coded -1 and 1 to compare these levels, and 0 otherwise. The second comparison comparing levels 1, 2 with level 3 are coded -1/2, -1/2, 1 and 0, and the last comparison comparing levels 1, 2 and 3 with level 4 are coded -1/3, -1/3, -1/3 and 1.

**Method 1: GLM with /LMATRIX**

REVERSE HELMERT contrast coding

New variable 1 (c1) | New variable 2 (c2) | New variable 3 (c3) | |

Level 2 v. Level 1 | Level 3 v. Previous | Level 4 v. Previous | |

1 (Hispanic) | -1 | -1/2 | -1/3 |

2 (Asian) | 1 | -1/2 | -1/3 |

3 (African American) | 0 | 1 | -1/3 |

4 (white) | 0 | 0 | 1 |

Below we illustrate how to form these comparisons using the **glm** command with **/lmatrix**. Note the use of fractions on the **/lmatrix** subcommand. As mentioned above, you need to use numbers that sum to zero, such as 1/3 + 1/3 + 1/3 – 1. You cannot use .333 instead of 1/3: SPSS will give an error message and fail to calculate the contrast coefficient. The problem is that .333 + .333 + .333 – 1 is not sufficiently close to zero. (If you wanted to use decimals, you would need to use something like .333333 so that .333333 + .333333 + .333333 – 1 would be sufficiently close to zero.)

glm write by race /lmatrix "level 2 versus level 1" race -1 1 0 0 /lmatrix "level 3 versus levels 1 and 2" race -1/2 -1/2 1 0 /lmatrix "level 4 versus levels 1 2 and 3" race -1/3 -1/3 -1/3 1.

The contrast estimate for the first comparison shown in this output was calculated by subtracting the mean of the dependent variable for level 2 of the categorical variable from the mean of the dependent variable for level 1: 58 – 46.4583 = 11.542. This result is statistically significant. The contrast estimate for the second comparison (between level 3 and the previous levels) was calculated by subtracting the mean of the dependent variable for levels 1 and 2 from that of level 3: 48.2 – [(46.4583 + 58) / 2] = -4.029. This result is not statistically significant, meaning that there is not a reliable difference between the mean of ** write** for level 3 of ** race** compared to the mean of ** write** for levels 1 and 2 (Hispanics and Asians). As noted above, this type of coding system does not make much sense for a nominal variable such as **race**. For the comparison of level 4 and the previous levels, you take the mean of the dependent variable for the those levels and subtract it from the mean of the dependent variable for level 4: 54.0552 – [(46.4583 + 58 + 48.2) / 3] = 3.169. This result is statistically significant.

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 11.542 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 11.542 | ||

Std. Error | 3.286 | ||

Sig. | .001 | ||

95% Confidence Interval for Difference | Lower Bound | 5.061 | |

Upper Bound | 18.022 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 2 versus group 1 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 1004.785 | 1 | 1004.785 | 12.336 | .001 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -4.029 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -4.029 | ||

Std. Error | 2.602 | ||

Sig. | .123 | ||

95% Confidence Interval for Difference | Lower Bound | -9.161 | |

Upper Bound | 1.103 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 3 versus groups 1 and 2 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 195.254 | 1 | 195.254 | 2.397 | .123 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 3.169 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 3.169 | ||

Std. Error | 1.488 | ||

Sig. | .034 | ||

95% Confidence Interval for Difference | Lower Bound | .235 | |

Upper Bound | 6.104 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 4 versus groups 1 2 and 3 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 369.460 | 1 | 369.460 | 4.536 | .034 |

Error | 15964.717 | 196 | 81.453 |

**Method 2: GLM with /CONTRAST**

As with the previous examples, we will conduct the analysis above again, this time using the** /contrast(race)=difference **subcommand to request reverse Helmert contrasts.

glm write by race /contrast(race)=difference /print = test(lmatrix).

These contrasts are interpreted in the same way as the contrasts obtained using method 1. Again, we see that the first and third contrasts are statistically significant, while the second one is not.

RACE Difference Contrast | |||
---|---|---|---|

Parameter | Level 2 vs. Level 1 | Level 3 vs. Previous | Level 4 vs. Previous |

Intercept | .000 | .000 | .000 |

[RACE=1.00] | -1.000 | -.500 | -.333 |

[RACE=2.00] | 1.000 | -.500 | -.333 |

[RACE=3.00] | .000 | 1.000 | -.333 |

[RACE=4.00] | .000 | .000 | 1.000 |

The default display of this matrix is the transpose of the corresponding L matrix. |

Dependent Variable | |||
---|---|---|---|

RACE Difference Contrast | writing score | ||

Level 2 vs. Level 1 | Contrast Estimate | 11.542 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 11.542 | ||

Std. Error | 3.286 | ||

Sig. | .001 | ||

95% Confidence Interval for Difference | Lower Bound | 5.061 | |

Upper Bound | 18.022 | ||

Level 3 vs. Previous | Contrast Estimate | -4.029 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -4.029 | ||

Std. Error | 2.602 | ||

Sig. | .123 | ||

95% Confidence Interval for Difference | Lower Bound | -9.161 | |

Upper Bound | 1.103 | ||

Level 4 vs. Previous | Contrast Estimate | 3.169 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 3.169 | ||

Std. Error | 1.488 | ||

Sig. | .034 | ||

95% Confidence Interval for Difference | Lower Bound | .235 | |

Upper Bound | 6.104 |

**Method 3: Regression**

The regression coding for reverse Helmert coding is shown below. For the first comparison, where the first and second level are compared, **x1** is coded -1/2 and 1/2 and 0 otherwise. For the second comparison, the values of **x2** are coded -1/3 -1/3 2/3 and 0. Finally, for the third comparison, the values of **x3** are coded -1/4 -1/4 -/14 and 3/4.

REVERSE HELMERT regression coding

Level of race | New variable 1 (x1) | New variable 2 (x2) | New variable 3 (x3) |

1 (Hispanic) | -1/2 | -1/3 | -1/4 |

2 (Asian) | 1/2 | -1/3 | -1/4 |

3 (African American) | 0 | 2/3 | -1/4 |

4 (white) | 0 | 0 | 3/4 |

Below we illustrate how to create **x1**, **x2** and **x3** and enter these new variables into the regression model using the **regression** command.

if race = 1 x1 = -1/2. if race = 2 x1 = 1/2. if any(race,3,4) x1 = 0. if any(race,1,2) x2 = -1/3. if race = 3 x2 = 2/3. if race = 4 x2 = 0. if any(race,1,2,3) x3 = -1/4. if race = 4 x3 = 3/4. execute. regression /dep write /method = enter x1 x2 x3.

Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |||
---|---|---|---|---|---|---|

Model | B | Std. Error | Beta | |||

1 | (Constant) | 51.679 | .982 | 52.616 | .000 | |

X1 | 11.542 | 3.286 | .252 | 3.512 | .001 | |

X2 | -4.029 | 2.602 | -.108 | -1.548 | .123 | |

X3 | 3.168 | 1.488 | .150 | 2.129 | .035 | |

a Dependent Variable: writing score |

In the above examples, both the regression coefficient for ** x1** and the contrast estimate for c1 would be the mean of ** write** for level 1 (Hispanic) minus the mean of ** write** for level 2 (Asian). Likewise, the regression coefficient for ** x2** and the contrast estimate for c2 would be the mean of ** write** for levels 1 and 2 combined minus the mean of ** write** for level 3. Finally, the regression coefficient for ** x3** and the contrast estimate for c3 would be the mean of ** write** for levels 1, 2 and 3 combined minus the mean of ** write** for level 4.

This coding system compares the mean of the dependent variable for a given level to the overall mean of the dependent variable. In our example below, the first comparison compares level 1 (Hispanics) to all levels of **race**, the second comparison compares level 2 (Asians) to all levels of **race**, and the third comparison compares level 3 (African Americans) to all levels of **race**.

As you can see, the logic of the contrast coding is fairly straightforward. The first comparison compares level 1 to levels 2, 3 and 4. A value of 3/4 is assigned to level 1 and a value of -1/4 is assigned to levels 2, 3 and 4. Likewise, the second comparison compares level 2 to levels 1, 3 and 4. A value of 3/4 is assigned to level 2 and a value of -1/4 is assigned to levels 1, 3 and 4. A similar pattern is followed for assigning values for the third comparison. Note that you could substitute 3 for 3/4 and 1 for 1/4 and you would get the same test of significance, but the contrast coefficient would be different.

**Method 1: GLM with /LMATRIX**

DEVIATION contrast coding

Level of race | New variable 1 (c1) | New variable 2 (c2) | New variable 3 (c3) |

Level 1 v. Mean | Level 2 v. Mean | Level 3 v. Mean | |

1 (Hispanic) | 3/4 | -1/4 | -1/4 |

2 (Asian) | -1/4 | 3/4 | -1/4 |

3 (African American) | -1/4 | -1/4 | 3/4 |

4 (white) | -1/4 | -1/4 | -1/4 |

Below we illustrate how to form these comparisons using the **glm** command with **/lmatrix**. As you see, a separate **/lmatrix** subcommand is used for each contrast.

glm write by race /lmatrix "level 1 versus levels 2 3 and 4" race 3/4 -1/4 -1/4 -1/4 /lmatrix "level 2 versus levels 1 3 and 4" race -1/4 3/4 -1/4 -1/4 /lmatrix "level 3 versus levels 1 2 and 4" race -1/4 -1/4 3/4 -1/4.

In the first “Contrast Results (K Matrix)” table, the contrast estimate is the mean for level 1 minus the grand mean. However, this grand mean is not the mean of the dependent variable that is listed in the output of the **means** command above. Rather it is the mean of means of the dependent variable at each level of the categorical variable: (46.4583 + 58 + 48.2 + 54.0552) / 4 = 51.678375. This contrast estimate is then 46.4583 – 51.678375 = -5.220. The difference between this value and zero (the null hypothesis that the contrast coefficient is zero) is statistically significant (p = .002), and the “Test Results” table below that shows the F-value for this test of 10.328. The results for the next two contrasts were computed in a similar manner.

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -5.220 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -5.220 | ||

Std. Error | 1.631 | ||

Sig. | .002 | ||

95% Confidence Interval for Difference | Lower Bound | -8.437 | |

Upper Bound | -2.003 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 1 versus groups 1 2 and 3 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 833.927 | 1 | 833.927 | 10.238 | .002 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 6.322 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 6.322 | ||

Std. Error | 2.160 | ||

Sig. | .004 | ||

95% Confidence Interval for Difference | Lower Bound | 2.061 | |

Upper Bound | 10.582 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 2 versus groups 1 3 and 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 697.475 | 1 | 697.475 | 8.563 | .004 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -3.478 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -3.478 | ||

Std. Error | 1.732 | ||

Sig. | .046 | ||

95% Confidence Interval for Difference | Lower Bound | -6.895 | |

Upper Bound | -6.203E-02 | ||

a Based on the user-specified contrast coefficients (L’) matrix: group 3 versus groups 1 2 and 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 328.405 | 1 | 328.405 | 4.032 | .046 |

Error | 15964.717 | 196 | 81.453 |

**Method 2: GLM with /CONTRAST**

Now let’s conduct the same analysis using the **/contrast** subcommand instead of the **/lmatrix** subcommand. Instead of providing the values for the contrasts that we want to perform, we can have SPSS provide those for us by indicating the type of coding that we wish to use, in this case, deviation coding via the **/contrast (race)=deviation** subcommand.

glm write by race /contrast (race)=deviation /print = test(lmatrix).

The contrasts estimates in the table entitled “Contrast Results (K Matrix)” are the mean of the particular level minus the grand (unweighted) mean. This grand mean is not the mean of the dependent variable that is listed in the output of the **means** command above. Rather it is the mean of means of the dependent variable at each level of the categorical variable: (46.4583 + 58 + 48.2 + 54.0552) / 4 = 51.678375. The contrast estimate for level 1 versus mean is then 46.4583 – 51.678375 = -5.220. The difference between this value and zero (the null hypothesis that the contrast coefficient is zero) is statistically significant (p = .002). The contrast coefficients for the other comparisons are calculated in the same manner.

RACE Deviation Contrast(a) | |||||||
---|---|---|---|---|---|---|---|

Parameter | Level 1 vs. Mean | Level 2 vs. Mean | Level 3 vs. Mean | ||||

Intercept | .000 | .000 | .000 | ||||

[RACE=1.00] | .750 | -.250 | -.250 | ||||

[RACE=2.00] | -.250 | .750 | -.250 | ||||

[RACE=3.00] | -.250 | -.250 | .750 | ||||

[RACE=4.00] | -.250 | -.250 | -.250 | ||||

The default display of this matrix is the transpose of the corresponding L matrix. | a Omitted category = 4 |

Dependent Variable | |||
---|---|---|---|

RACE Deviation Contrast(a) | writing score | ||

Level 1 vs. Mean | Contrast Estimate | -5.220 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -5.220 | ||

Std. Error | 1.631 | ||

Sig. | .002 | ||

95% Confidence Interval for Difference | Lower Bound | -8.437 | |

Upper Bound | -2.003 | ||

Level 2 vs. Mean | Contrast Estimate | 6.322 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 6.322 | ||

Std. Error | 2.160 | ||

Sig. | .004 | ||

95% Confidence Interval for Difference | Lower Bound | 2.061 | |

Upper Bound | 10.582 | ||

Level 3 vs. Mean | Contrast Estimate | -3.478 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -3.478 | ||

Std. Error | 1.732 | ||

Sig. | .046 | ||

95% Confidence Interval for Difference | Lower Bound | -6.895 | |

Upper Bound | -6.203E-02 | ||

a Omitted category = 4 |

**Method 3: Regression**

As you see in the example below, the regression coding is accomplished by assigning 1 to level 1 for the first comparison (because level 1 is the level to be compared to all others), a 1 to level 2 for the second comparison (because level 2 is to be compared to all others), and 1 to level 3 for the third comparison (because level 3 is to be compared to all others). Note that a -1 is assigned to level 4 for all three comparisons (because it is the level that is never compared to the other levels) and all other values are assigned a 0. This regression coding scheme yields the comparisons described above.

DEVIATION regression coding

Level of race | New variable 1 (x1) | New variable 2 (x2) | New variable 3 (x3) |

Level 1 v. Mean | Level 2 v. Mean | Level 3 v. Mean | |

1 (Hispanic) | 1 | 0 | 0 |

2 (Asian) | 0 | 1 | 0 |

3 (African American) | 0 | 0 | 1 |

4 (white) | -1 | -1 | -1 |

**x1**, **x2** and **x3** and enter these new variables into the regression model using the **regression** command.

if race = 1 x1 = 1. if any(race,2,3) x1 = 0. if race = 4 x1 = -1. if race = 2 x2 = 1. if any(race,1,3) x2 = 0. if race = 4 x2 = -1. if race = 3 x3 = 1. if any(race,1,2) x3 = 0. if race = 4 x3 = -1. execute. regression /dep write /method = enter x1 x2 x3.

In this example, both the regression coefficient for ** x1** and the contrast estimate for c1 would be the mean of ** write** for level 1 (Hispanic) minus the mean of ** write** for levels 2, 3 and 4 combined. Likewise, the regression coefficient for ** x2** and the contrast estimate for c2 would be the mean of ** write** for level 2 (Asian) minus the mean of ** write** for levels 1, 3, and 4 combined. As we saw in the previous analyses, all three contrasts are statistically significant.

Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |||
---|---|---|---|---|---|---|

Model | B | Std. Error | Beta | |||

1 | (Constant) | 51.678 | .982 | 52.619 | .000 | |

X1 | -5.220 | 1.631 | -.382 | -3.200 | .002 | |

X2 | 6.322 | 2.160 | .385 | 2.926 | .004 | |

X3 | -3.478 | 1.732 | -.242 | -2.008 | .046 | |

a Dependent Variable: writing score |

**5.7 Orthogonal Polynomial Coding**

Orthogonal polynomial coding is a form of trend analysis in that it is looking for the linear, quadratic and cubic trends in the categorical variable. This type of coding system should be used only with an ordinal variable in which the levels are equally spaced. Examples of such a variable might be income or education. The table below shows the contrast coefficients for the linear, quadratic and cubic trends for the four levels. These could be obtained from most statistics books on linear models.

POLYNOMIAL

Level of race | Linear (x1) | Quadratic (x2) | Cubic (x3) |

1 (Hispanic) | -.671 | .5 | -.224 |

2 (Asian) | -.224 | -.5 | .671 |

3 (African American) | .224 | -.5 | -.671 |

4 (white) | .671 | .5 | .224 |

**Method 1: GLM with /LMATRIX**

glm write by race /lmatrix "linear" race -.671 -.224 .224 .671 /lmatrix "quadratic" race .5 -.5 -.5 .5 /lmatrix "cubic" race -.224 .671 -.671 .224.

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 2.902 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 2.902 | ||

Std. Error | 1.535 | ||

Sig. | .060 | ||

95% Confidence Interval for Difference | Lower Bound | -.125 | |

Upper Bound | 5.930 | ||

a Based on the user-specified contrast coefficients (L’) matrix: linear |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 291.104 | 1 | 291.104 | 3.574 | .060 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -2.843 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -2.843 | ||

Std. Error | 1.964 | ||

Sig. | .149 | ||

95% Confidence Interval for Difference | Lower Bound | -6.717 | |

Upper Bound | 1.031 | ||

a Based on the user-specified contrast coefficients (L’) matrix: quadratic |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 170.665 | 1 | 170.665 | 2.095 | .149 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 8.277 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 8.277 | ||

Std. Error | 2.316 | ||

Sig. | .000 | ||

95% Confidence Interval for Difference | Lower Bound | 3.709 | |

Upper Bound | 12.846 | ||

a Based on the user-specified contrast coefficients (L’) matrix: cubic |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 1040.029 | 1 | 1040.029 | 12.769 | .000 |

Error | 15964.717 | 196 | 81.453 |

To calculate the contrast estimates for these comparisons, you need to multiply the code used in the new variable by the mean for the dependent variable for each level of the categorical variable, and then sum the values. For example, the code used in ** x1** for level 1 of ** race** is -.671 and the mean of ** write** for level 1 is 46.4583. Hence, you would multiply -.671 and 46.4583 and add that to the product of the code for level 2 of ** x1** and its mean, and so on. To obtain the contrast estimate for the linear contrast, you would do the following: -.671*46.4583 + -.224*58 + .224*48.2 + .671*54.0552 = 2.905 (with rounding error). This result is not statistically significant at the .05 alpha level, but it is close. The quadratic component is also not statistically significant, but the cubic one is. This suggests that, if the mean of the dependent variable was plotted against **race**, the line would tend to have two bends. As noted earlier, this type of coding system does not make much sense with a nominal variable such as **race**.

**Method 2: GLM with /CONTRAST**

As with the previous examples, we will conduct the above analysis again, this time using the** /contrast(race)=polynomial **subcommand to request orthogonal polynomial contrasts.

glm write by race /contrast (race)=polynomial /print = test(lmatrix).

RACE Polynomial Contrast(a) | |||||||
---|---|---|---|---|---|---|---|

Parameter | Linear | Quadratic | Cubic | ||||

Intercept | .000 | .000 | .000 | ||||

[RACE=1.00] | -.671 | .500 | -.224 | ||||

[RACE=2.00] | -.224 | -.500 | .671 | ||||

[RACE=3.00] | .224 | -.500 | -.671 | ||||

[RACE=4.00] | .671 | .500 | .224 | ||||

The default display of this matrix is the transpose of the corresponding L matrix. | a Metric = 1.000, 2.000, 3.000, 4.000 |

Dependent Variable | |||
---|---|---|---|

RACE Polynomial Contrast(a) | writing score | ||

Linear | Contrast Estimate | 2.905 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 2.905 | ||

Std. Error | 1.534 | ||

Sig. | .060 | ||

95% Confidence Interval for Difference | Lower Bound | -.121 | |

Upper Bound | 5.931 | ||

Quadratic | Contrast Estimate | -2.843 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -2.843 | ||

Std. Error | 1.964 | ||

Sig. | .149 | ||

95% Confidence Interval for Difference | Lower Bound | -6.717 | |

Upper Bound | 1.031 | ||

Cubic | Contrast Estimate | 8.273 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 8.273 | ||

Std. Error | 2.316 | ||

Sig. | .000 | ||

95% Confidence Interval for Difference | Lower Bound | 3.706 | |

Upper Bound | 12.840 | ||

a Metric = 1.000, 2.000, 3.000, 4.000 |

Again, we see that only the cubic effect is statistically significant.
In other words, if the mean of **write** was plotted
against **race**, the line would tend to have two bends.

**Method 3: Regression**

The regression coding for orthogonal polynomial coding is the same as the
contrast coding. Below you can see the SPSS code for creating **x1**, **x2**
and **x3** that correspond to the linear, quadratic and cubic trends for **race**.

if race = 1 x1 = -.671. if race = 2 x1 = -.224. if race = 3 x1 = .224. if race = 4 x1 = .671. if race = 1 x2 = .5. if race = 2 x2 = -.5. if race = 3 x2 = -.5. if race = 4 x2 = .5. if race = 1 x3 = -.224. if race = 2 x3 = .671. if race = 3 x3 = -.671. if race = 4 x3 = .224. execute. regression /dep write /method = enter x1 x2 x3.

Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |||
---|---|---|---|---|---|---|

Model | B | Std. Error | Beta | |||

1 | (Constant) | 51.678 | .982 | 52.619 | .000 | |

X1 | 2.900 | 1.534 | .142 | 1.890 | .060 | |

X2 | -2.843 | 1.964 | -.109 | -1.448 | .149 | |

X3 | 8.271 | 2.315 | .278 | 3.573 | .000 | |

a Dependent Variable: writing score |

The regression coefficients obtained from this analysis are the same as the contrast coefficients obtained using the **glm** command with either the **/lmatrix** or the **/contrast** subcommands.

SPSS allows users to define their own coding systems. For our example, we will make the following three comparisons:

1) level 1 to level 3 2) level 2 to levels 1 and 4 3) levels 1 and 2 to levels 3 and 4.

In order to compare level 1 to level 3, we use the contrast coefficients 1 0 -1 0. To compare level 2 to levels 1 and 4 we use the contrast coefficients -1/2 1 0 -1/2 . Finally, to compare levels 1 and 2 with levels 3 and 4 we use the coefficients 1/2 1/2 -1/2 -1/2. Before proceeding to the SPSS code necessary to conduct these analyses, let’s take a moment to more fully explain the logic behind the selection of these contrast coefficients.

For the first contrast, we are comparing level 1 to level 3, and the contrast coefficients are 1 0 -1 0. This means that the levels associated with the contrast coefficients with opposite signs are being compared. In fact, the mean of the dependent variable is multiplied by the contrast coefficient. Hence, levels 2 and 4 are not involved in the comparison: they are multiplied by zero and “dropped out.” You will also notice that the contrast coefficients sum to zero. This is necessary. If the contrast coefficients do not sum to zero, the contrast is not estimable and SPSS will issue an error message. Which level of the categorical variable is assigned a positive or negative value is not terribly important: 1 0 -1 0 is the same as -1 0 1 0 in that both of these codings compare the first and the third levels of the variable. However, the sign of the regression coefficient would change.

Now let’s look at the contrast coefficients for the second and third comparisons. You will notice that in both cases we use fractions that sum to one (or minus one). They do not have to sum to one (or minus one). You may wonder why we would use fractions like -1/2 1 0 -1/2 instead of whole numbers such as -1 2 0 -1. While -1/2 1 0 -1/2 and -1 2 0 -1 both compare level 2 with levels 1 and 4 and both will give you the same t-value and p-value for the regression coefficient, the contrast estimates/regression coefficients themselves would be different, as would their interpretation. The coefficient for the -1/2 1 0 -1/2 contrast is the mean of level 2 minus the mean of the means for levels 1 and 4: 58 – (46.4583 + 54.0552)/2 = 7.74325. (Alternatively, you can multiply the contrasts by the mean of the dependent variable for each level of the categorical variable: -1/2*46.4583 + 1*58.00 + 0*48.20 + -1/2*54.0552 = 7.74325. Clearly these are equivalent ways of thinking about how the contrast coefficient is calculated.) By comparison, the coefficient for the -1 2 0 -1 contrast is two times the mean for level 2 minus the means of the dependent variable for levels 1 and 4: 2*58 – (46.4583 + 54.0552) = 15.4865, which is the same as -1*46.4583 + 2*58 + 0*48.20 – 1*54.0552 = 15.4865. Note that the regression coefficient using the contrast coefficients -1 2 0 -1 is twice the regression coefficient obtained when -1/2 1 0 -1/2 is used.

**Method 1: GLM with /LMATRIX**

In order to compare level 1 to level 3, we use the contrast coefficients 1 0 -1 0. To compare level 2 to levels 1 and 4 we use the contrast coefficients -1/2 1 0 -1/2 . Finally, to compare levels 1 and 2 with levels 3 and 4, we use the coefficients 1/2 1/2 -1/2 -1/2. These coefficients are used in the **/lmatrix** subcommands below.

glm write by race /lmatrix "compare level 1 to level 3" race 1 0 -1 0 /lmatrix "compare level 2 to levels 1 and 4" race -1/2 1 0 -1/2 /lmatrix "compare levels 1 and 2 to levels 3 and 4" race 1/2 1/2 -1/2 -1/2.

The first “Contrast Results” table shows the results of comparing level 1 to level 3. The contrast estimate for this comparison is the mean of level 1 minus the mean for level 3, and the significance of this is .525, i.e., not significant. The second “Contrast Results” output shows the contrast estimate to be 7.743, which is the mean of level 2 minus the mean of level 1 and level 4, and this difference is significant, p = 0.008. The final contrast estimate is 1.1 which is the mean of levels 1 and 2 minus the mean of levels 3 and 4, and this contrast is not statistically significant, p = .576.

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | -1.742 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -1.742 | ||

Std. Error | 2.732 | ||

Sig. | .525 | ||

95% Confidence Interval for Difference | Lower Bound | -7.131 | |

Upper Bound | 3.647 | ||

a Based on the user-specified contrast coefficients (L’) matrix: compare group 1 to group 3 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 33.092 | 1 | 33.092 | .406 | .525 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 7.743 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 7.743 | ||

Std. Error | 2.897 | ||

Sig. | .008 | ||

95% Confidence Interval for Difference | Lower Bound | 2.030 | |

Upper Bound | 13.457 | ||

a Based on the user-specified contrast coefficients (L’) matrix: compare group 2 to groups 1 and 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 581.833 | 1 | 581.833 | 7.143 | .008 |

Error | 15964.717 | 196 | 81.453 |

Dependent Variable | |||
---|---|---|---|

Contrast | writing score | ||

L1 | Contrast Estimate | 1.102 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 1.102 | ||

Std. Error | 1.964 | ||

Sig. | .576 | ||

95% Confidence Interval for Difference | Lower Bound | -2.772 | |

Upper Bound | 4.975 | ||

a Based on the user-specified contrast coefficients (L’) matrix: compare groups 1 and 2 to groups 3 and 4 |

Source | Sum of Squares | df | Mean Square | F | Sig. |
---|---|---|---|---|---|

Contrast | 25.618 | 1 | 25.618 | .315 | .576 |

Error | 15964.717 | 196 | 81.453 |

**Method 2: GLM with /CONTRAST**

When using **glm** with the **/contrast** subcommand, you can specify your own contrast coefficients with the **special** keyword, followed by the contrasts you would like to test. To compare level 1 to level 3, we use the contrast coefficients 1 0 -1 0 and then to compare level 2 to levels 1 and 4 we use the contrast coefficients -.5 1 0 -.5 and finally, to compare levels 1 and 2 with levels 3 and 4 we use the coefficients .5 .5 -.5 -.5 (note that when using the keyword **special** these values cannot be expressed as fractions, i.e., 1/2).

glm write by race /contrast (race)=special(1 0 -1 0, -.5 1 0 -.5, .5 .5 -.5 -.5) /print = test(lmatrix).

As you can see, the **glm** results below correspond to the **glm** results above using method 1.

RACE Special Contrast | |||
---|---|---|---|

Parameter | L1 | L2 | L3 |

Intercept | .000 | .000 | .000 |

[RACE=1.00] | 1.000 | -.500 | .500 |

[RACE=2.00] | .000 | 1.000 | .500 |

[RACE=3.00] | -1.000 | .000 | -.500 |

[RACE=4.00] | .000 | -.500 | -.500 |

The default display of this matrix is the transpose of the corresponding L matrix. |

Dependent Variable | |||
---|---|---|---|

RACE Special Contrast | writing score | ||

L1 | Contrast Estimate | -1.742 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | -1.742 | ||

Std. Error | 2.732 | ||

Sig. | .525 | ||

95% Confidence Interval for Difference | Lower Bound | -7.131 | |

Upper Bound | 3.647 | ||

L2 | Contrast Estimate | 7.743 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 7.743 | ||

Std. Error | 2.897 | ||

Sig. | .008 | ||

95% Confidence Interval for Difference | Lower Bound | 2.030 | |

Upper Bound | 13.457 | ||

L3 | Contrast Estimate | 1.102 | |

Hypothesized Value | 0 | ||

Difference (Estimate – Hypothesized) | 1.102 | ||

Std. Error | 1.964 | ||

Sig. | .576 | ||

95% Confidence Interval for Difference | Lower Bound | -2.772 | |

Upper Bound | 4.975 |

** Method 3: Regression**

As in the prior examples, we will make the following three comparisons:

1) level 1 to level 3, 2) level 2 to levels 1 and 4 and 3) levels 1 and 2 to levels 3 and 4.

For methods 1 and 2 it was quite easy to translate the comparisons we wanted to make into contrast codings, but it is not as easy to translate the comparisons we want into a regression coding scheme. If we know the contrast coding system, then we can convert that into a regression coding system using the SPSS program shown below. As you can see, we place the three contrast codings we want into the matrix **c** and then perform a set of matrix operations on **c,** yielding the matrix **x**. We then display **x** using the **print** command.

matrix. compute c = { 1, -.5, .5 ; 0, 1, .5 ; -1, 0, -.5 ; 0, -.5, -.5 }. compute x = c*inv( t(c)*c ). print x. end matrix.

Below we see the output from this program showing the regression coding scheme we would use.

X -.500000000 -1.000000000 1.500000000 .500000000 1.000000000 -.500000000 -1.500000000 -1.000000000 1.500000000 1.500000000 1.000000000 -2.500000000

This converted the contrast coding into the regression
coding that we need for running this analysis with the **regression**
command. Below, we use **if** command to create **x1**, ** x2** and **x3**
according to the coding shown above and then enter them into the regression
analysis.

if race = 1 x1 = -.5. if race = 2 x1 = .5. if race = 3 x1 = -1.5. if race = 4 x1 = 1.5.

if race = 1 x2 = -1. if race = 2 x2 = 1. if race = 3 x2 = -1. if race = 4 x2 = 1. if race = 1 x3 = 1.5. if race = 2 x3 = -.5. if race = 3 x3 = 1.5. if race = 4 x3 =-2.5. execute.

regression /dep write /method = enter x1 x2 x3.

Here is a shortcut to save typing all of the ** compute** commands. This assumes that ** race** is coded 1 2 3 4.

get file = "d:spsshsb2.sav". sort cases by race. save outfile = "c:temprace.sav". matrix. compute c = { 1, -.5, .5 ; 0, 1, .5 ; -1, 0, -.5 ; 0, -.5, -.5 }. compute x = c*inv( t(c)*c ). save x /outfile=* /var=x1 x2 x3 end matrix. compute race = $CASENUM. execute. match files /table=* /file="c:temprace.sav" /by race. execute.

regression /dep write /method = enter x1 x2 x3.

The first comparison of the mean of the dependent variable for level 1 to level 3 of the categorical variable was not statistically significant, while the comparison of the mean of the dependent variable for level 2 to that of levels 1 and 4 was. The comparison of the mean of the dependent variable for levels 1 and 2 to that of levels 3 and 4 also was not statistically significant.

Unstandardized Coefficients | Standardized Coefficients | t | Sig. | |||
---|---|---|---|---|---|---|

Model | B | Std. Error | Beta | |||

1 | (Constant) | 51.678 | .982 | 52.619 | .000 | |

X1 | -1.742 | 2.732 | -.192 | -.637 | .525 | |

X2 | 7.743 | 2.897 | .679 | 2.673 | .008 | |

X3 | 1.102 | 1.964 | .194 | .561 | .576 | |

a Dependent Variable: writing score |

**5.9 Summary**

This page has described a number of different coding systems that you could
use for categorical data, and three different strategies you could use for
performing the analyses. You can choose a coding system that yields
comparisons that make the most sense for testing your hypotheses. Among
the three strategies (**glm **with **/lmatrix**, **glm **with **/contrast**
and **regression**), each has its strengths and weaknesses. You may
have noticed that there is a balance between ease of use and
flexibility/power. The method that is easiest to use (**glm **with **/contrast**)
is not as powerful as the other two methods. The most general method is the use
of **regression**, but it can be the trickiest. In general we would
recommend using the easiest method that accomplishes your goals.

**5.10 For more information**

Here are some additional resources.

- SPSS Textbook Examples from Design and Analysis: Chapter 6
- SPSS Textbook Examples from Design and Analysis: Chapter 7
- SPSS Textbook Examples: Applied Regression Analysis, Chapter 8
- An Overview of SPSS GLM (courtesy of SPSS)
- MANOVA and GLM (courtesy of SPSS)
- Understanding and Interpreting Parameter Estimates in Regression and ANOVA (courtesy of SPSS)
- Data Setup Comparing Means in SPSS (courtesy of SPSS)
- One-Way ANOVA Contrast Code Problems From Charles Judd and Gary McClelland
- Two-way contrast code solutions