Study Questions

The American Community Survey provides information on, among other topics, 2018 per capita income. These data are provided below for 30 U.S. states.

Alabama

42,334

 

Hawaii

54,565

 

Massachusetts

70,073

Alaska

59,687

 

Idaho

43,155

 

Michigan

47,582

Arizona

43,650

 

Illinois

56,933

 

Minnesota

56,374

Arkansas

42,566

 

Indiana

46,646

 

Mississippi

37,994

California

62,586

 

Iowa

48,823

 

Missouri

46,635

Colorado

56,846

 

Kansas

50,155

 

Montana

47,120

Connecticut

74,561

 

Kentucky

41,779

 

Nebraska

52,110

Delaware

51,449

 

Louisiana

45,542

 

Nevada

48,225

Florida

49,417

 

Maine

48,241

 

New Hampshire

61,405

Georgia

45,745

 

Maryland

62,914

 

New Jersey

67,609

 
  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 four columns and two 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 happiness is directly dependent on marital status. What is the independent variable in this relationship?
  13. Define Cross-tabulation.