A categorical variable of K categories is usually entered in a regression analysis as a sequence of K-1 variables, e.g. as a sequence of K-1 dummy variables. Subsequently, the regression coefficients of these K -1 variables correspond to a set of linear hypotheses on the cell means. When coding categorical variables, there are a variety of coding systems we can choose for testing different set of linear hypotheses. On this page, we will cover some of the coding schemes for categorical variables. We will show how these coding schemes are constructed and interpreted.

In R there are four built-in contrasts (dummy, deviation, helmert, orthogonal polynomial) which we will demonstrate. We will also show how to create the coding schemes using a little bit of matrix manipulation. This page is done using R 2.11 and is updated in January, 2011.

**Coding schemes covered**

Coding Scheme |
Comparisons made |

Dummy Coding | Compares each level to the reference level, intercept being the cell mean of the reference group |

Simple Coding | Compares each level to the reference level, intercept being the grand mean |

Deviation Coding | Compares each level to the grand mean |

Orthogonal Polynomial Coding | Orthogonal polynomial contrasts |

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 |

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

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

User-Defined Coding | User-defined contrast |

**The Example Data File**

The examples in this page will use data frame called **hsb2**
and we will focus on the categorical variable **race**, which has four levels (1 =
Hispanic, 2 = Asian, 3 = African American and 4 = Caucasian) 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
a contrast matrix with one less column than levels of the original variable. In our example, our categorical
variable has four levels so we will have contrast matrices with three columns and four rows.

First, we read in the data frame over the internet and then we create a factor variable, **race.f**, based on
**race**.

hsb2 = read.table('https://stats.idre.ucla.edu/stat/data/hsb2.csv', header=T, sep=",")#creating the factor variable race.fhsb2$race.f = factor(hsb2$race, labels=c("Hispanic", "Asian", "African-Am", "Caucasian"))

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.

tapply(hsb2$write, hsb2$race.f, mean)Hispanic Asian African-Am Caucasian 46.45833 58 48.2 54.05517

**1. Dummy Coding**

Dummy coding is probably the most commonly used coding scheme. It
compares each level of the categorical variable to a fixed reference level.
For example, we can choose race = 1 as the reference group and compare the
mean of variable **write** for each level of race 2, 3 and 4 to the
reference level of 1. This is the default for unordered
factors in R.

Dummy Coding

Level of race | race.f1 (1 vs. 2) | race.f2 (1 vs. 3) | race.f3 (1 vs. 4) |

1 (Hispanic) | 0 | 0 | 0 |

2 (Asian) | 1 | 0 | 0 |

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

4 (Caucasian) | 0 | 0 | 1 |

#the contrast matrix for categorical variable with four levelscontr.treatment(4)2 3 4 1 0 0 0 2 1 0 0 3 0 1 0 4 0 0 1 #assigning the treatment contrasts to race.fcontrasts(hsb2$race.f) = contr.treatment(4)#the regressionsummary(lm(write ~ race.f, hsb2))Residuals: Min 1Q Median 3Q Max -23.06 -5.458 0.9724 7 18.8 Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 46.4583 1.8422 25.2184 0.0000 race.f2 11.5417 3.2861 3.5122 0.0006 race.f3 1.7417 2.7325 0.6374 0.5246 race.f4 7.5968 1.9889 3.8197 0.0002 Residual standard error: 9.025 on 196 degrees of freedom Multiple R-Squared: 0.1071 F-statistic: 7.833 on 3 and 196 degrees of freedom, the p-value is 0.00005785

The parameter estimate for the first contrast compares the mean of the dependent
variable, **write**, for levels 1 and 2 yielding 11.5417 and
is statistically significant (p<.000). The t-value associated with this test
is 3.5122. The results of the second
contrast, comparing the mean of **write** for levels 1 and 3. The expected
difference in variable **write** between group 1 and 3 is 1.7417 and is not
statistically significant (t = 0.6374, p = .5246), while the third contrast is
statistically significant. Notice that the intercept corresponds to the cell
mean for **race** = Hispanic group.

**2 Simple Coding**

The results of simple coding are very similar to dummy coding in that each level is compared to the reference level. The difference in the regression output between dummy coding and simple coding scheme is in the intercepts. In the dummy coding scheme, the intercept corresponds to the cell mean of the reference group, while in the simple coding scheme, the intercept corresponds to the mean of cell means.

In our example below, level 1 is the reference level
and **race.f1** compares level 1 to level 2, **race.f2** compares level 1 to level
3, and **race.f3** compares level 1 to level 4. For **race.f1** the coding is
3/4 for level 2, and -1/4 for all other levels. Likewise, for **race.f2** the coding is 3/4 for level
3, and -1/4 for all other levels, and for **race.f3** the coding is 3/4 for level 4, and -1/4
for all other levels. The general rule is that the reference group is never coded anything but -1/4 and for
each contrast the level that is being contrasted is coded 3/4. Thus, for the first contrast
it is level 2 which is coded 3/4 and all other level are -1/4. Since there are four groups and the values
have to add to one there must be three levels coded as -1/4 and one level as 3/4.

SIMPLE Coding

Level of race | race.f1 (1 vs 2) | race.f2 (1 vs.3) | race.f3 (1 vs. 4) |

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

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

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

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

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 the case of the variable **race.f** k = 4).

SIMPLE Coding

Level of race | level 1 vs. 2 | level 1 vs. 3 | level 1 vs. 4 |

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

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

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

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

Let’s create the contrast matrix manually using the scheme shown above. Essentially, the difference between the dummy coding scheme and the simple coding scheme is a constant matrix whose each element is 1/k if our categorical variable has k levels.

#creating the contrast matrix manually by modifying the dummy coding scheme c<-contr.treatment(4) my.coding<-matrix(rep(1/4, 12), ncol=3) my.simple<-c-my.coding my.simple

2 3 4 1 -0.25 -0.25 -0.25 2 0.75 -0.25 -0.25 3 -0.25 0.75 -0.25 4 -0.25 -0.25 0.75contrasts(hsb2$race.f)<-my.simple summary(lm(write~race.f, hsb2))Residuals: Min 1Q Median 3Q Max -23.06 -5.458 0.9724 7 18.8 Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 51.6784 0.9821 52.6191 0.0000 race.f1 11.5417 3.2861 3.5122 0.0006 race.f2 1.7417 2.7325 0.6374 0.5246 race.f3 7.5968 1.9889 3.8197 0.0002 Residual standard error: 9.025 on 196 degrees of freedom Multiple R-Squared: 0.1071 F-statistic: 7.833 on 3 and 196 degrees of freedom, the p-value is 0.00005785

The interpretation of this output is almost the same as for the case of
dummy coding. The difference lies in the intercept.

The intercept in this case is 51.6784 = (46.45833 + 58 + 48.2 +
54.05517)/4, which is the mean of cell means, sometimes referred as grand
mean.

**3 Deviation Coding**

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 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.

We will not create the contrast matrix manually because the **contr.sum** function creates it for us.

DEVIATION Coding

Level of race | 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 (Caucasian) | -1 | -1 | -1 |

#the contrast matrix for categorical variable with four levelscontr.sum(4)[,1] [,2] [,3] 1 1 0 0 2 0 1 0 3 0 0 1 4 -1 -1 -1 #assigning the deviation contrasts to race.fcontrasts(hsb2$race.f) = contr.sum(4)#the regressionsummary(lm(write ~ race.f, hsb2))Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 51.6784 0.9821 52.6191 0.0000 race.f1 -5.2200 1.6314 -3.1997 0.0016 race.f2 6.3216 2.1603 2.9263 0.0038 race.f3 -3.4784 1.7323 -2.0079 0.0460

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 = .0016), and the
t-value for this test of -3.20. The results for the next two contrasts were computed in a similar
manner.

**4 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. In R it is not necessary to compute these
values since this contrast can be obtained for any categorical variable by using the
**contr.poly** function. This is also the default contrast used for ordered
factor variables.

For the purpose of illustration, let’s create an ordered categorical
variable based on the variable **read**. Notice that this is the only
example on this page that does not use **race** as the categorical
variable, since it is not ordered.

hsb2$readcat<-cut(hsb2$read, 4, ordered = TRUE) table(readcat)readcat (28,40] (40,52] (52,64] (64,76] 22 93 55 30

tapply(hsb2$write, hsb2$readcat, mean)(28,40] (40,52] (52,64] (64,76] 42.77273 49.97849 56.56364 61.83333

POLYNOMIAL coding

Level of readcat | Linear (readcat.L) | Quadratic (readcat.Q) | Cubic (readcat.C) |

1 (28,40] | -.671 | .5 | -.224 |

2 (40,52] | -.224 | -.5 | .671 |

3 (52,64] | .224 | -.5 | -.671 |

4 (64,76] | .671 | .5 | .224 |

#the contrast matrix for categorical variable with four levelscontr.poly(4).L .Q .C 1 -0.6708204 0.5 -0.2236068 2 -0.2236068 -0.5 0.6708204 3 0.2236068 -0.5 -0.6708204 4 0.6708204 0.5 0.2236068 #assigning the orthogonal polynomial contrasts to readcatcontrasts(hsb2$readcat) = contr.poly(4) summary(lm(write ~ readcat, hsb2))Call: lm(formula = write ~ readcat, data = hsb2) Residuals: Min 1Q Median 3Q Max -18.978 -5.824 1.227 5.436 17.022 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 52.7870 0.6339 83.268 <2e-16 *** readcat.L 14.2587 1.4841 9.607 <2e-16 *** readcat.Q -0.9680 1.2679 -0.764 0.446 readcat.C -0.1554 1.0062 -0.154 0.877 --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 7.726 on 196 degrees of freedom Multiple R-squared: 0.3456, Adjusted R-squared: 0.3356 F-statistic: 34.51 on 3 and 196 DF, p-value: < 2.2e-16

The regression results indicate a strong linear effect of **
readcat** on the outcome variable **write**. There is not a significant
quadratic effect nor a cubic effect of **readcat** on the outcome
variable **write**.

**5 Helmert Coding**

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).

In R there is a built-in function, **contr.helmert**, which,
up to a scale, corresponds to the reverse Helmert coding discussed in
next section. We will create the contrast matrix for Helmert coding
manually below.

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 Coding

race.f1 | race.f2 | race.f3 | |

Level of Race | 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 (Caucasian) | -1/4 | -1/3 | -1/2 |

#helmert for factor variable with 4 levelsmy.helmert = matrix(c(3/4, -1/4, -1/4, -1/4, 0, 2/3, -1/3, -1/3, 0, 0, 1/ 2, -1/2), ncol = 3) my.helmert[,1] [,2] [,3] [1,] 0.75 0.0000000 0.0 [2,] -0.25 0.6666667 0.0 [3,] -0.25 -0.3333333 0.5 [4,] -0.25 -0.3333333 -0.5 #assigning the new helmert coding to race.fcontrasts(hsb2$race.f) = my.helmert#the regressionsummary(lm(write ~ race.f, hsb2))Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 51.6784 0.9821 52.6191 0.0000 race.f1 -6.9601 2.1752 -3.1997 0.0016 race.f2 6.8724 2.9263 2.3485 0.0198 race.f3 -5.8552 2.1528 -2.7198 0.0071

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. 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.

**6 Reverse Helmert Coding**

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.

The regression coding for reverse Helmert coding is shown below. For the first comparison, where the
first and second level are compared, **race.f1** is coded -1/2 and 1/2 and 0
otherwise. For the second comparison, the values of **race.f2**
are coded -1/3 -1/3 2/3 and 0.
Finally, for the third comparison, the values of **race.f3** are coded -1/4 -1/4
-/14 and 3/4. The built-in Helmert coding in R is equivalent to this coding
scheme up to a constant in each column. So both give the same significance
test. We will show both ways below

Reverse HELMERT Coding

race.f1 | race.f2 | race.f3 | |

Level of Race | Level 2 v. 1 | Level 3 v. 1 and 2 | Level 4 v. 1, 2 and 3 |

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 (Caucasian) | 0 | 0 | 3/4 |

# manually reverse helmert for factor variable with 4 levesmy.rev.helmert = matrix(c(-1/2, 1/2, 0, 0, -1/3, -1/3, 2/3, 0, -1/4, -1/4, -1/4, 3/4), ncol = 3) my.rev.helmert[,1] [,2] [,3] [1,] -0.5 -0.3333333 -0.25 [2,] 0.5 -0.3333333 -0.25 [3,] 0.0 0.6666667 -0.25 [4,] 0.0 0.0000000 0.75 #assigning the reverse helmert coding to race.fcontrasts(hsb2$race.f) = my.rev.helmert#the regressionsummary(lm(write ~ race.f, hsb2))Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 51.6784 0.9821 52.6191 0.0000 race.f1 11.5417 3.2861 3.5122 0.0006 race.f2 -4.0292 2.6024 -1.5483 0.1232 race.f3 3.1691 1.4880 2.1298 0.0344

# using built-in reverse Helmert codingcontrasts(hsb2$race.f) = contr.helmert(4) summary(lm(write ~ race.f, hsb2))

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 51.6784 0.9821 52.619 < 2e-16 *** race.f1 5.7708 1.6431 3.512 0.000552 *** race.f2 -1.3431 0.8675 -1.548 0.123170 race.f3 0.7923 0.3720 2.130 0.034439 *

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.

**7 Forward 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 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.

For the first comparison, where the first and second levels are compared, **race.f1** 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, **race.f2** is coded 1/2 1/2 -1/2 -1/2, and for the
third comparison where level 3 is compared with level 4, **race.f3** is
coded 1/4 1/4 1/4 -3/4.

FORWARD DIFFERENCE coding

race.f1 | race.f2 | race.f3 | |

Level of race | 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 (Caucasian) | -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

contrast 1 | contrast 2 | contrast 3 | |

Level of race | 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 (Caucasian) | -1/k | -2/k | -3/k |

#forward difference for factor variable with 4 levesmy.forward.diff = matrix(c(3/4, -1/4, -1/4, -1/4, 1/2, 1/2, -1/2, -1/2, 1/ 4, 1/4, 1/4, -3/4), ncol = 3) my.forward.diff[,1] [,2] [,3] [1,] 0.75 0.5 0.25 [2,] -0.25 0.5 0.25 [3,] -0.25 -0.5 0.25 [4,] -0.25 -0.5 -0.75 #assigning the forward difference coding to race.fcontrasts(hsb2$race.f) = my.forward.diff#the regressionsummary(lm(write ~ race.f, hsb2))Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 51.6784 0.9821 52.6191 0.0000 race.f1 -11.5417 3.2861 -3.5122 0.0006 race.f2 9.8000 3.3878 2.8927 0.0043 race.f3 -5.8552 2.1528 -2.7198 0.0071

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.

**8 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** (Hispanics minus Asians). 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.

For the first comparison, where the first and second levels are compared, **race.f1** 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, **race.f2** is coded -1/2 -1/2 1/2 1/2, and for the
third comparison where level 3 is compared with level 4, **race.f3** is
coded -1/4 -1/4 -1/4 3/4.

BACKWARD DIFFERENCE Coding

race.f1 | race.f2 | race.f3 | |

Level of race | 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 (Caucasian) | 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).

BACKWARD DIFFERENCE Coding

contrast 1 | contrast 2 | contrast 3 | |

Level of race | 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 (Caucasian) | 1/k | 2/k | 3/k |

#backward difference for factor variable with 4 levesmy.backward.diff = matrix(c(-3/4, 1/4, 1/4, 1/4, -1/2, -1/2, 1/2, 1/2, -1/4, -1/4, -1/4, 3/4), ncol = 3) my.backward.diff[,1] [,2] [,3] [1,] -0.75 -0.5 -0.25 [2,] 0.25 -0.5 -0.25 [3,] 0.25 0.5 -0.25 [4,] 0.25 0.5 0.75 #assigning the backward difference coding to race.fcontrasts(hsb2$race.f) = my.backward.diff#the regressionsummary(lm(write ~ race.f, hsb2))Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 51.6784 0.9821 52.6191 0.0000 race.f1 11.5417 3.2861 -3.5122 0.0006 race.f2 -9.8000 3.3878 -2.8927 0.0043 race.f3 5.8552 2.1528 2.7198 0.0071

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.

**9 User Defined Coding**

In R it is possible to use any general kind of coding scheme. For
our example, we would like to 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. All these have been
expressed in terms of cell means. For convenience of the calculation, we
will assume that the intercept corresponds to the mean of cell means. So our
initial contrast matrix looks like the following, defined as **mat**. The
final contrast matrix (or coding scheme) turns out to be the inverse of **
mat** transposed.

#initial contrast matrix including the constant termmat = matrix(c(1/4, 1/4, 1/4, 1/4, 1, 0, -1, 0, -1/2, 1, 0, -1/2, -1/2, -1/2, 1/2, 1/2), ncol = 4) mat[,1] [,2] [,3] [,4] [1,] 0.25 1 -0.5 -0.5 [2,] 0.25 0 1.0 -0.5 [3,] 0.25 -1 0.0 0.5 [4,] 0.25 0 -0.5 0.5mymat = solve(t(mat)) mymat[,1] [,2] [,3] [,4] [1,] 1 -0.5 -1 -1.5 [2,] 1 0.5 1 0.5 [3,] 1 -1.5 -1 -1.5 [4,] 1 1.5 1 2.5#remove the intercept (constant) term my.contrasts<-mymat[,2:4] contrasts(hsb2$race.f) = my.contrasts summary(lm(write ~ race.f, hsb2))Call: lm(formula = write ~ race.f, data = hsb2) Residuals: Min 1Q Median 3Q Max -23.0552 -5.4583 0.9724 7.0000 18.8000 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 51.6784 0.9821 52.619 <2e-16 *** race.f1 1.7417 2.7325 0.637 0.5246 race.f2 6.8724 2.9263 2.348 0.0198 * race.f3 -2.8432 1.9642 -1.448 0.1494 --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 9.025 on 196 degrees of freedom Multiple R-squared: 0.1071, Adjusted R-squared: 0.0934 F-statistic: 7.833 on 3 and 196 DF, p-value: 5.785e-05

The intercept corresponds to the mean of the cell means as shown
earlier. The coefficient for **race.f1 **corresponds to the difference in
the mean of the variable **write** between group 3 and group 1.
Similarly, the coefficient for **race.f2 **corresponds to the difference
between group 2 and the mean of group 1 and 4, and so on.