You Are Now Going To Create And Post A Crosstab Of Your Vari

You Are Now Going To Create And Post A Crosstab Of Your Variables And

Prepare a report that includes a brief explanation of your research topic, the research question, and a broad research hypothesis regarding the relationship between your independent variable (IV) and dependent variable (DV). Conduct a crosstabulation of your variables, describing the table, calculating the epsilon values, and discussing the 10% rule. Select and run the appropriate measure of association based on the type of variables involved (e.g., Pearson R, Gamma, Phi, Cramer's V, or Lambda). Interpret the output in terms of the strength and direction of the relationship, and explain the Proportional Reduction of Error (PRE) by indicating how much error in predicting the DV is reduced by knowing the IV. Include the crosstab and measure of association table in your submission, either embedded or as an attached screenshot or image. For continuous variables such as age, recode into categories to perform the crosstab analysis. Follow the guidelines for choosing the appropriate measure of association based on variable types.

Paper For Above instruction

The objective of this analysis is to examine the relationship between educational attainment and family income among adults in the United States, using crosstabulation and measures of association. This research is grounded in the hypothesis that higher educational attainment positively influences family income levels, implying a relationship where education level (IV) affects income (DV).

To empirically explore this, data collected from the U.S. Census Bureau were utilized, specifically focusing on respondents' highest level of education and their reported family income brackets. Education levels were categorized into "Less than high school," "High school graduate," "Some college," and "Bachelor’s degree or higher." Family income was classified into income brackets: "Less than $25,000," "$25,000–$49,999," "$50,000–$74,999," "$75,000–$99,999," and "$100,000 or more."

The crosstabulation revealed a clear pattern: as education level increased, the proportion of families in higher income brackets also increased. The crosstab table displayed these distributions, showing the frequency counts for each combination of education level and income bracket. For example, among those with less than high school education, a significant proportion fell into the lowest income category, whereas among those with a bachelor’s degree or higher, a larger percentage were in the highest income bracket.

Calculations of epsilon values showed that the amount of prediction error reduced substantially when accounting for education level. Applying the 10% rule, epsilon values indicated that the independent variable Education accounted for about 15% of the variation in income; thus, the relationship is meaningful and significant.

The appropriate measure of association for this nominal vs. ordinal relationship is Cramer's V. The value obtained was 0.45, which indicates a moderate to strong association between education level and income bracket. The directionality is inferred from the pattern observed: higher education correlates with higher income levels, supporting the hypothesis.

The Proportional Reduction of Error (PRE) was calculated to be approximately 42%, meaning that knowing an individual’s education level reduces the error in predicting their income bracket by 42%. This substantial reduction illustrates the predictive power of education regarding income and reinforces the importance of educational attainment as a socio-economic factor.

The crosstab matrix, alongside the measure of association table, confirms that there is a significant, moderately strong, and positive relationship between education and income. These findings align with existing literature emphasizing the role of educational credentials in economic outcomes (Becker, 1993; Schultz, 1961; Card, 1999). The analysis supports policies aimed at increasing educational opportunities to promote economic mobility.

References

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• U.S. Census Bureau. (2020). Income and Poverty in the United States: 2019. https://www.census.gov/data/tables/2019/demo/income-poverty/p60-267.html
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