B2. Now you can create the new dummy-coded variables. Select Transform ➞ Recode ➞ Into Different Variables. Find racethn in the variable list and move it into the slot for

“Numeric Variable Output Variable.” On the right, under Output Variable, type in a name for your first new variable

(e.g., black, afamer). In the slot for Label, you can type in a longer label, such as “African American” if you so choose.

Then click Change, which will confirm the new variable as the output variable. Next, click the “Old and New Values”

button, which will bring up a new dialog box. Under “Old Value,” enter 1, which is the code for African Americans in the original (“old”) variable racethn. Then, on the right under New Value, click “Copy old value,” and then click the Add button. Women who were coded 1 on racethn will also be coded 1 on the new African-American variable. Next, under Old Value, click Range and enter 2 and then “through” 3 (the two codes for Hispanic and White/other women on racethn).

On the right under New Value, enter 0, then click Add, which will code all non–African-American women as 0. Finally, under Old Value, click “System- or user-missing,” and then under New Value click “System missing,” then click Add.

Women who have missing data for racethn will now have a missing values code for the new variable. Click Continue to go back to the original dialog box, then click OK to run the command. If you look at the last variable in your file in Data View, you should see the new variable with values of .00 and 1.00 (which can be changed to 0 and 1 by going into Variable View and changing the number of decimals to 0). Do the analogous procedure for the next new race/ethnic group—

except remember to change the values coded 1 and 0. Now, run frequencies on your new variables, and compare the results to those from Exercise B1, making sure that your new variables accurately reflect the original racial/ethnic distribution.