Assignment Detail

SPSS Political Analysis Assignment – Transforming Variables Exercises

For the exercises, you will use the GSS dataset (file name: gss.dta).

1. (Dataset: GSS. Variables: polviews, wtss.) The GSS dataset contains polviews, which measures political ideology-the extent to which individuals “think of themselves as liberal or conservative.” Here is how polviews is coded (the labels are fully written out):

A. Apply the Analyze – Descriptive Statistics – Frequencies procedure to polviews, making sure to weight the data using the wtss variable. Eyeball the percent column and make some rough-and-ready estimates.

The percentage of respondents who are either “extremely liberal,” “liberal,” or “slightly liberal” is (circle one)

about 18 percent.

about 28 percent.

about 38 percent.

The percentage of respondents who are either “slightly conservative,” “conservative,” or “extremely conservative” is (circle one)

about 10 percent.

about 20 percent.

about 30 percent.

B. Use polviews and the Transform – Recode into Different Variables procedure to create a new variable named polview3. Give polview3 this label: “Ideology: 3 categories.” Collapse the three liberal codes into one category (coded 1 on polview3), put the moderates into their own category (coded 2 on polview3), and collapse the three conservative codes into one category (coded 3 on polview3). Don’t forget to recode missing values on polviews into missing values on polview3. Run Frequencies on polview3.

The percentage of respondents who are coded 1 on polview3 is (fill in the blank) _____ percent.

The percentage of respondents who are coded 2 on polview3 is (fill in the blank) ______ percent.

Make sure that the two percentages you wrote down in part B match the percentages you recorded in part A. The numbers may be slightly different and may still be considered a match. If the two sets of numbers match, proceed to part C. If they do not match, you performed the recode incorrectly. Review this chapter’s discussion of the Recode procedure and try the recode again.

C. In the Variable View of the Data Editor, change Decimals to o, and then click in the Values cell and supply the appropriate labels for the numeric codes of the new polview3 variable: “Liberal” for code 1, “Moderate” for code 2, and “Conservative” for code 3. Run the Analyze – Descriptive Statistics – Frequencies procedure on polview3. Use your results to fill in the table below.

*Round weighted frequencies to two decimal places.

2. (Dataset: GSS. Variables: colmslm, libmslm, spkmslm, wtss.) The GSS dataset contains three variables that gauge tolerance toward “anti-American Muslim clergymen”-whether they should be allowed to teach in college (colmslm), whether their books should be removed from the library (libms1m), and whether they should be allowed to preach hatred of the United States (spkmslm). For each variable, respondents are given two choices: a less tolerant response and a more tolerant response. For this problem, you’ll create and analyze an additive index of tolerance of anti-American Muslim clergymen.

A. Create indicator variables for colmslm, libmslm, and spkmslm by completing the coding protocol below. Be sure to give the three variables you create new, descriptive names so you don’t overwrite the existing variables in the dataset. (Don’t forget to recode missing values on the colmslm, libmslm, and spkmslm variables into missing values on your new indicator variables.)

B. Imagine creating an additive index from the three variables you created in part A. The additive index would have scores that range between _________ and _________.

C. Suppose a respondent takes the more tolerant position on two questions and the less tolerant position on the third question. This respondent would have a score of _________.

D. Use the Transform – Compute Variable procedure to create an additive index from the three variables you created in part A. Name the new variable muslim_tol. Run Analyze – Descriptive Statistics – Frequencies on muslim_tol. Referring to your output, fill in the table that follows.

*Rounded frequencies.

3. (Dataset: GSS. Variables: rincom16, wtss.) In this chapter you learned to use Visual Binning to simplify a measure of respondents’ income in the NES dataset into three roughly equal ordinal categories. In this exercise, you will use Visual Binning to collapse a very similar variable from the GSS dataset (rincom16) into rincom16_q3, a three-category ordinal measure of respondents’ incomes.

Refer to this chapter’s visual binning guided example and retrace the steps to create the new rincom16_q3 variable. Here is new information you will need:

Run Analyze – Descriptive Statistics – Frequencies on your newly created variable rincom16_q3. Refer to your output to fill in the table that follows.

4. (Dataset: GSS. Variables: pornlaw, wtss.) In this chapter you learned to use the Transform – Recode into Different Variables procedure to create indicator variables. In this exercise, you will create indicator variables from pornlaw, which measures individuals’ opinions about pornography. Respondents who think pornography should be “Illegal to all” are coded 1, those saying “Illegal under 18” are coded 2, and respondents who say “Legal (to all)” are coded 3. You will create an indicator variable coded 1 for individuals saying “Illegal to all,” and coded o for any other response.

A. Run a Frequencies analysis on pornlaw. Make sure you’re weighting the sample with wtss. Use your results to fill in the frequency distribution table below.

B. Use the Transform – Recode into Different Variables procedure to create a new indicator variable that identifies respondents who think pornography should be illegal for everyone. Name this variable “porn_ban.” Run a Frequencies analysis on porn_ban and use your results to fill in the frequency distribution table below.

By performing the exercises in this chapter, you have added four variables to the GSS dataset: polview3, muslim_tol, rincom16_q3, and porn_ban. Be sure to save the dataset.