Exercise Using SPSS to Explore Measurement and Relationships Among Variables with Regard to Tolerance
Ed Nelson and Elizabeth Nelson, Department of Sociology
California State University, Fresno
© The Authors, 1998; Last Modified 05 September 2001
Note to the instructor: The data set used in this exercise is RELG9800 which is a combination of the 1998 and 2000 General Social Surveys. (Some of the variables in the GSS have been recoded to make them easier to use and some new variables have been created.) This exercise uses COMPUTE in SPSS to create new variables and CROSSTABS to explore the relationships among variables. In CROSSTABS, students are asked to use percentages, Chi Square, and an appropriate measure of association. A good reference on using SPSS is SPSS for Windows Version 9.0 A Basic Tutorial by Richard Shaffer, Edward Nelson, Nan Chico, John Korey, Elizabeth Nelson and Jim Ross. To order this book, call McGraw-Hill at 1-800-338-3987. The ISBN is 0-07-241445-6 . There is an online version of the book at SPSS Text. You have permission to use this exercise and to revise it to fit your needs. Please send a copy of any revision to the authors.
Authors:
Ed Nelson and Elizabeth Nelson
Department of Sociology
California State University, Fresno
Fresno, CA 93740
Phone: 559-278-2275
Email: ednelson@csufresno.edu
elizn@csufresno.edu
Please contact the authors for additional information.
Goals of Exercise
The goal of this exercise is to create measures of tolerance. Once we have created these measures, we will see how they are related to some other variables.
Part I
We’re going to use the General Social Survey (GSS) for this exercise. The GSS is a national probability sample of adults in the United States conducted by the National Opinion Research Center. For this exercise we’re going to use a data set that combines the 1998 and 2000 surveys. Your instructor will tell you how to access this data set.
Tolerance is the willingness to accept people with ideas that are often unacceptable to society. Several questions on the GSS are possible indicants of tolerance. Respondents were asked if they would allow various types of individuals to teach in a public college. These included a person who was opposed to religion, a communist, a homosexual, a militarist, and a racist. These variables are called COLATH, COLCOM, COLHOMO, COLMIL, and COLRAC. Another set of questions asked respondents if they would allow books written by these individuals in the public library. These variables are called LIBATH, LIBCOM, LIBHOMO, LIBMIL, and LIBRAC. The third set of questions asked respondents if they would allow these individuals to give a public speech in their community. These variables are called SPKATH, SPKCOM, SPKHOMO, SPKMIL, and SPKRAC. It’s easy to understand the abbreviations if you remember that ath means atheist, com means communist, homo means homosexual, mil means militarist, rac means racist, col means college, lib means library, and spk means speak. For each variable, 1 means they would allow the person to do something and 2 means they would not allow it.
We want to create five variables--one for each type of tolerance. We’ll call these variables ATH, COM, HOMO, MIL, and RAC. To create the variable ATH, use COMPUTE in SPSS to add up the following three variables--COLATH, LIBATH, and SPKATH. Since each variable is coded 1 or 2, the sum of these three variables will be 3, 4, 5, or 6. The value 3 means that the respondent would allow an atheist to do all three things. The value 6 means that respondent would not allow a person opposed to religion to teach in a college, a book written by an atheist in the public library, and an atheist to give a public speech. The values 4 and 5 would be intermediate values and indicate that the respondent would allow some things, but not others. If the respondent has a missing value for any of these variables, the variable would be assigned a system missing value.
Create these new variables by summing up the following variables.
1. ATH is the sum of COLATH, LIBATH, and SPKATH.
2. COM is the sum of COLCOM, LIBCOM, and SPKCOM.
3. HOMO is the sum of COLHOMO, LIBHOMO, and SPKHOMO.
4. MIL is the sum of COLMIL, LIBMIL, and SPKMIL.
5. RAC is the sum of COLRAC, LIBRAC, and SPKRAC.
Part II
Now that you have created these variables, run FREQUENCIES in SPSS to get a frequency distribution for these five variables. There are five variables in the RELG9800 data set (TOLATH, TOLCOM, TOLHOMO, TOLMIL, TOLRAC) which should be identical to the variables you created. Run FREQUENCIES to get the distributions for these variables and compare the two sets of distributions to see if they are identical. If they are not, you made a mistake and will have to exit SPSS (or close your file) being sure NOT to save it. Then get back into SPSS and open the RELG9800 file again. The reason for this is that you want to recreate these variables and it’s easier to start over than to delete the variables you just created. If you saved the data file, then you would have written over the original copy. So be careful.
Once you are sure that the variables have been recoded properly, add value labels for the recoded variables so the output will be easier to read.
When you are convinced that you did not make a mistake, use FREQUENCIES in SPSS to get the percentage distribution for each variable. Is there more tolerance for one type of individual? Is there less tolerance for one type?
Part III
How are these variables related to each other? Use CROSSTABS in SPSS to see how each variable is related to each of the other variables. This means you will have to run ten tables--ATH and COM, ATH and HOMO, ATH and MIL, ATH and RAC, COM and HOMO, COM and MIL, COM and RAC, HOMO and MIL, HOMO and RAC, MIL and RAC. It’s not going to matter which variable you choose as the independent and dependent variables since there is no causal connections among these variables. Remember to ask for percentages, Chi Square, and an appropriate measure of association.
Do respondents who are tolerant toward one type of individual tend to be tolerant toward other types? For example, do respondents who are tolerant toward people who are opposed to religion tend to be tolerant toward militarists? Or is there some other type of relationship among these five measures of tolerance?
Part IV
How are these measures of tolerance related to other variables? For example, we would expect that respondents who are tolerant toward homosexuals would be more likely to feel that homosexual relationships among people are not wrong. The variable HOMOSEX in the GSS records respondents answers to the question, "What about sexual relations between two adults of the same sex–do you think it is always wrong, almost always wrong, wrong only sometimes, or not wrong at all?" The value 1 means they feel it is always wrong, 2 means almost always wrong, 3 means sometimes wrong, and 4 means not wrong at all.
Use CROSSTABS to explore the relationship between HOMO and HOMOSEX. This time it does matter which is the independent and dependent variables. Be sure to ask for the correct percentages, Chi Square, and an appropriate measure of association. Write a paragraph using all this information to describe the relationship.
Now choose one other variable from this subset of the GSS that you think should be related to one of the measures of tolerance. It’s going to be up to you to choose the variable you want to use. Select one other variable that you think ought to be related to one of the measures of tolerance and complete the following steps:
1. Write a hypothesis stating how you expect the measure of tolerance to be related to this variable. Be sure to specify which measure of tolerance you are using.
2. Write a paragraph or two that indicate why you think your hypothesis is true. In other words, write an argument in which your hypothesis is the conclusion.
3. Use SPSS to run the crosstabulation of these two variables. Think about which is the independent and dependent variable. Remember to get the correct percentages. Use Chi Square and an appropriate measure of association.
4. Write a paragraph interpreting the table that SPSS gave you and indicate whether the data support your hypothesis. Use Chi Square and the measure of association to help you interpret the table.
