1. Formulate four research questions about support for capital punishment—one question per research purpose: (1) exploratory, (2) descriptive, (3) explanatory, and (4) evaluative. Develop these questions so that you can answer at least two of them with variables in the GSS2016 data set you are using. Highlight these two.

2. Now, to develop some foundation from the literature, check the bibliography of this book for the following articles that drew on the GSS: Adalberto Aguirre Jr. and David Baker (1993); Steven Barkan and Steven Cohn (1994); Marian Borg (1997, 1998); Mark Warr (1995); and Robert Young (1992). How have social scientists used social theory to explain support for capital punishment? What potential inf luences on capital punishment have been tested? What inf luences could you test again with the 2016 GSS?

3. State four hypotheses in which support for capital punishment (CAPPUN) is the dependent variable and another variable in the GSS2016 data set is the independent variable. Justify each hypothesis in a sentence or two.

4. Test at least one hypothesis. Borg (1997) suggests that region might be expected to influence support for the death penalty. Test this as follows (after opening the GSS2016 file, as explained in Chapter 1, SPSS Exercise 3):

a. Click on Analyze/Descriptive Statistics/Crosstabs.
b. Highlight CAPPUN and click on the arrow so that it moves into the Rows box; highlight REGION and click on the arrow to move it into the Columns box.
c. Click on Cells, click off Counts-Observed, and click on Percentages-Column.
d. Click Continue and then OK. Inspect the table.

5. Does support for capital punishment vary by region? Scroll down to the percentage table (in which regions appear across the top) and compare the percentages in the Favor row for each region. Describe what you have found.

6. Now you can go on to test your other hypotheses in the same way, if you have the time. Because of space constraints, I can’t give you more guidance, but I will warn you that there could be some problems at this point (e.g., if your independent variable has lots of values). Proceed with caution!