Instructions:

Using my post and references answer the instructors questions in first person.

Instructors questions:

1. How do the Walden University, LLC (2016a; 2016b) sources help you extend your view?
2. In lay terms, how did you use dummy variables to produce your results?

My Post:

Introduction

Employing research methodology, it becomes possible to evaluate the given hypothesis concerning gender and also education level, as well as theincome of people through multiple regression. Gender is also transformed into adummy variable to test intercept it on income with years of education as apredictor. Therefore, the study aims to establish the importance of these predictors to determine the influence of education on earnings and the existence of gender variation.

Research Question and Model

Research question: How do gender and education level influence income, and to what extent do these factors predict variations in earnings within the population?

Based on the GSS data, I conducted a multiple regression analysis with gender and years of education as test variables and income as the outcome variable. Gender has been dropped into categories so that it can be adequately captured by the dummy variable (Wagner, 2020). The proposed model works to facilitate theidentification of the extent of the influence that gender and education level have on income.

Significance of the Coefficients

The coefficient for education was positive and significant, which means that the income increases with each additional year in education. Thus, the estimate for education indicated that the level of income rises by about $2,000 with each additional year of education (Allison, 1999). The dummy variable for gender had anegative coefficient implying that males, on average, earned less than females but this was statistically insignificant (p > 0.05) implying that there was no significant difference in income levels between the genders once the effects of education had been accounted for (Warner, 2012).

Model Diagnostics and Assumptions

The diagnostic results presented an opportunity to conclude that almost all the multiple regression assumptions whereby the linearity and normality of residuals were met by the model. However, it did not meet homoscedasticity which simplifies the assumption that thevariance of residuals remains constant at higher values of predicted values as I observed by conducting a residual scatter plot test(Fox, 1991). This implies that the variance of errors is not uniform across all levels of predictors and can, therefore, distort confidence intervals. One potential cure for this violation is to employ RE or LTE if standard errors are utilized or record the variable income on a logarithmic scale to minimize its variability (Stones, 2024). Also, there was no problem with multicollinearity as all values of theVariance Inflation Factor (VIF) were less than 5.

References

Wagner, III, W. E. (2020). Using IBM SPSS statistics for research methods and social science statistics (7th ed.).

Allison, P. D. (1999). Multiple regression: A primer. Thousand Oaks, CA: Pine Forge Press/Sage Publications.

Warner, R. M. (2012). Applied statistics from bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.

Fox, J. (Ed.). (1991). Regression diagnostics. Thousand Oaks, CA: SAGE Publications.