Study Questions

The American Community Survey provides information on, among other topics, 2007 per capita income.[1] 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 9, 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 9, 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.