Study Questions

Chapter 10

The American Community Survey provides information on, among other topics, 2007 per capita income¹. These data are provided below for 30 U.S. states.

 Alabama 22,366 Hawaii 28,287 Massachusetts 32,822 Alaska 28,891 Idaho 23,105 Michigan 24,816 Arizona 24,811 Illinois 27,965 Minnesota 29,027 Arkansas 20,708 Indiana 23,805 Mississippi 19,365 California 28,678 Iowa 24,566 Missouri 23,915 Colorado 29,133 Kansas 25,197 Montana 22,937 Connecticut 35,904 Kentucky 21,951 Nebraska 24,174 Delaware 27,853 Louisiana 21,934 Nevada 27,729 Florida 26,696 Maine 24,977 New Hampshire 30,517 Georgia 24,928 Maryland 33,743 New Jersey 33,832
1. In order to construct a bivariate table, we need to reclassify these data into more broadly defined categories. Develop a coding scheme which permits you to classify each of the above states into one of four categories: West, Midwest, Northeast, and South. How many states fall into each of these categories?

2. Now that you have grouped states by their geographic location, do the same for per capita income. Within each of the four geographic clusters, assign each state into one of two categories based on the level of per capita income: below $25,000 or above$25,000. The end result should be a bivariate table with 4 columns and 2 rows (assuming that per capita income is the row variable). Display this table.

3. Next, we need to percentage the table presented in Question #2. Following the conventions established in Chapter 10, percentage the table within each column.

4. Considering your answer to Question #3, make the appropriate comparisons of the percentages to determine if there appears to be a weak, moderate, or strong relationship between geographic location and per capita income.

5. Is it possible to determine the direction of the relationship between geographic location and per capita income? Why or why not?

6. Could the relationship between geographic location and per capita income be spurious? Why or why not?

7. Is it possible that one or more intervening variables could affect your conclusion from Question #4? Why or why not?

8. Upon reexamining your results, you decide to further collapse some of the geographic categories you developed earlier. Collapse West and Midwest into one category and Northeast and South in a second category. Display the bivariate table for these data.

9. Next, we need to percentage the table presented in Question #2. Following the conventions established in Chapter 10, percentage the table within each column.

10. Discuss your results from Question #9. Why did these differ so drastically from those in Question #3?

11. In general, do the conclusions that we are able to draw from bivariate tables depend in part on the specification of the categories for each of the variables? Why or why not?

12. Assume that a person’s income is directly dependent on the state they live in. What is the independent variable in this relationship?

13. Define: Cross-tabulation.