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.

¹http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml