SPSS Exercises

[GSS10SSDS] 

1. Use the GSS10SSDS data file to study the relationship between the number of siblings a respondent has (SIBS) and his or her number of children (CHILDS).

  1. Construct a scatterplot of these two variables in SPSS, and place the best-fit linear regression line on the scatterplot. Describe the relationship between the number of siblings a respondent has (SIBS) and the number of his or her children (CHILDS).
  2. Have SPSS calculate the regression equation predicting CHILDS with SIBS. What are the intercept and the slope? What are the coefficient of determination and the correlation coefficient?
  3. What is the predicted number of children for someone with three siblings?
  4. What is the predicted number of children for someone without any siblings?
  5. Can you find a way for SPSS to calculate the error of prediction and predicted value for each respondent and save them as new variables?

2. Use the same variables as in Exercise 1, but do the analysis separately for men and women. Begin by locating the variable SEX. Click Data, Split File, and then select Organize Output by Groups. Insert SEX into the box and click OK. Now, SPSS will split your results by sex.

  1. Have SPSS calculate the regression equation for men and women. (Note: You will need to scroll down through your output to find the results for men and women.) How similar are they?
  2. What is the predicted number of children for a man with two siblings? Six siblings? For a woman with the same number of siblings? Which is greater?

3. Use the same variables as in Exercise 1, but do the analysis separately for whites and blacks. Click Data, Split File, and then select Organize Output by Groups. Insert RACECEN1 into the box and click OK. (Note: Be sure to remove SEX from the box if it is still there from the previous exercise.) Now, SPSS will split your results by RACECEN1.

  1. Is there any difference between the regression equations for whites and blacks?
  2. What is the predicted number for whites and blacks with the same number of siblings: one sibling, four siblings, and seven siblings?

4. Use the same variables as in Exercise 1, but do the analysis separately for married and divorced re­spondents. Begin by locating the variable MARITAL. Click Data, Split File, and then select Organize Output by Groups. Insert MARITAL into the box and click OK. (Note: Be sure to remove SEX and/or RACECEN1 from the box if they are still there from the previous exercises.) Now, SPSS will split your results by marital status.

  1. Is there any difference between the regression equations for married and divorced respondents?
  2. What is the predicted number of children for married and divorced respondents with the following number of siblings: one sibling, four siblings, and seven siblings?
  3. What differences, if any, do you find? Is the number of siblings a better predictor of number of children for married respondents or for women?

5. Use the 2010 GSS file (GSS10SSDS) to investigate the relationship between the respondent’s education (EDUC) and the education received by his or her father and mother (PAEDUC and MAEDUC, respec­tively).

  1. Use SPSS to find the correlation coefficient, the coefficient of determination, and the regres­sion equation predicting the respondent’s education with father’s education only. Interpret your results.
  2. Use SPSS to find the multiple correlation coefficient, the multiple coefficient of determination, and the regression equation predicting the respondent’s education with father’s and mother’s educa­tion. Interpret your results.
  3. Did taking into account the respondent’s mother’s education improve our prediction? Discuss this on the basis of the results from 5b.
  4. Using the regression equation from 5a, calculate the predicted number of years of education for a person with a father with 12 years of education. Then, repeat this procedure, adding in a mother’s 12 years of education and using the regression equation from 5b.

6. In Problem 3, we looked at the linear relationship between SIBS and CHILDS for whites and blacks. In this problem, we continue with this comparison except that now we want to look at ANOVA and the F statistic. What is the F statistic? What is its p level? Are there differences between whites and blacks? Are we able to reject the null hypothesis that r2 = 0? Compare these hypotheses between whites and blacks.