ESTIMATING MODELS USING DUMMY VARIABLES

Estimating Models Using Dummy Variables Student Names Institution Course Number: Course Name Instructor Name Due Date Estimating Models Using Dummy Variables What is your research question? In this case, my research question was “Is labor force participation impacted by race and highest educational attainment?” I selected my variables as; * Dependent Variable: Labor Force Status (wrkstat) * Independent Variable: Race of Respondent (race) and Highest Year of School Completed (educ) * Dummy Variable: White Null hypothesis H01: Labor force participation is not affected by the individual’s race (White vs Non-White) and level of education. Interpret the coefficients for the model, specifically commenting on the dummy variable. Table 1 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .166a .028 .027 2.323 .028 35.896 2 2532 .000 a. Predictors: (Constant), White, HIGHEST YEAR OF SCHOOL COMPLETED Table 2 Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 (Constant) 4.633 .218 21.292 .000 HIGHEST YEAR OF SCHOOL COMPLETED -.128 .015 -.167 -8.461 .000 .984 1.016 White .160 .107 .030 1.502 .133 .984 1.016 a. Dependent Variable: LABOR FORCE STATUS My dummy variable was “White,” which, together with “the highest year of school completed,” was included to predict labor force participation. The coefficient for the dummy variable White is 0.160, meaning that, on average, White respondents are associated with a 0.16 unit higher labor force status compared to other races. However, this difference is not statistically significant because the p-value is 0.133 (See Table 2), which is greater than the common alpha level of 0.05. This suggests that after controlling for educ...