PART A. INTERPRETING SPSS OUTPUT Below are three tables related to an analysis of female's education. The sample consists of 1,134 adult females who took the GSS survey in 2018. The first table contains descriptive statistics for all variables in the analysis (Education, Family income at 16, respondent's age, and mother's education). The second is a model summary and the third contains the regression coefficients for three independent variables (family income, age, and mother's education) predicting the dependent variable respondent's education. Table 1. Descriptive Statistics for All Variables used in Analysis

Descriptive Statistics for All Variables in Analysis (N=1,134)
	Minimum	Maximum	Mean	Std. Deviation
Respondent Education (Highest year completed)	1	20	13.99	2.846
Family income at 16 years old	1	5	2.71	.931
Age of respondent (years)	18	89	48.18	17.693
Mothers' education (Highest year completed)	0	20	11.77	3.769

Note: *Family income is measured as a self-reported assessment at the time of survey. Reponses include (1) far below average, (2) Below Average, (3) Average, (4) Above Average, and (5) Far Above Average

Table 2. Model Summary from Multivariate Regression prediction education with family income at 16, age, and mother's education

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.414a	.172	.169	2.620

a. Predictors: (Constant), Highest year school completed, mother, Age of respondent, R's family income when 16 years old

Table 3. Regression coefficients for model predicting education

Unstandardized Coefficients	Standardized Coefficients	t	Sig.
	B	Std. Error	Beta
Family Income at 16 years old	.290	.087	.094	3.335	.001
Age of respondent (Years)	.011	.005	.068	2.466	.014
Mother's Education (Highest Year Completed)	.295	.022	.387	13.404	.000
(Constant)	9.182	.403	--	22.762	.000

a. Dependent Variable: Highest year of school completed 1. Referring to the model summary, what proportion of the data is predicted by the model? 2. What is the slope for family income at 16 years old? Is there a positive or negative relationship between family income at 16 and respondent's education? Is the relationship significant? 3. What is the slope for age? Is there a positive or negative relationship between age and education? Is the relationship significant?

4. What is the slope for mother's education? Is there a positive or negative relationship between mother's education and respondent's education? Is the relationship significant? 5. Use the unstandardized coefficients of the three independent variables and the constant to write the equation SPSS has created to predict the highest year of education attained. This should look similar to the equation in the video on multivariate regression. Use the following abbreviations for your independent variables: X1 (Family income at 16), X2 (Age), and X3 (Mother's education). PART B. UNDERSTANDING PREVIOUS RESEARCH Use the article listed below (available online) to answer the following questions. Pryce, D. 2018. "U.S. Citizens' Current Attitudes Toward Immigrants and Immigration: A Study from the General Social Survey." Social Science Quarterly 99(4): 1467-1483. In Pryce's article he tests the impact of patriotism, nationalism, xenophobia, world citizenship and three control variables (sex, age, and education) on pro-immigration attitudes. Model five in Table 3 (p. 1475) includes all seven variables as predictors of pro-immigrant attitudes. The appendix at the end of the article explains how each variable is coded. Examine Model 5 in table 3 and the appendix at the end of the article to answer the following questions. You may also find it helpful to read the results section of the article. Xenophobia 1. In model 5 of table 3, what is the coefficient for the variable Xenophobia? 2. According to table 3, is the result for xenophobia statistically significant? If so, at which level (i.e., is p<.05, p<.01, or p<.001)? hint: This is marked with asterisks next to the coefficient. 3. Judging from the coefficient you found above, as the xenophobia variable increases do pro-immigrant attitudes, increase, decrease, or not change? How do you know? Nationalism 4. In model 5 of table 3, what is the coefficient for the variable Nationalism? 5. Is the result for nationalism statistically significant? If so, at which level (i.e., is p<.05, p<.01, or p<.001)? 6. As the nationalism variable increases, do pro-immigrant attitudes increase, decrease, or not change? How do you know? Sex 7. What is the coefficient for sex in model 5 of table 3? 9. Is the result for sex statistically significant? If so, at which level (i.e., is p<.05, p<.01, or p<.001)? 10. In this analysis, sex is a nominal variable (male=0 and female =1). As the sex variable increases, do pro-immigrant attitudes increase, decrease, or not change? How do you know?

Overall Model 11 Looking at model 5 in table 3, which independent variables are significant at the .05 level (i.e., p<.05)? 12. Report the R2 for each of the 5 models presented in table 3. 13. According to the R2 values, which model explains the dependent variable the best?