Test Whether There Is An Association Between A Person's Gend

Test Whether There Is An Association Between A Persons Gender And

Test whether there is an association between a person’s gender and the prestige of their occupation. Use the GSS2018 data set to perform an independent samples t-test on SEX and PRESTIG10 depending on the dataset. Report the following: Mean prestige score for men, Mean prestige score for women, t-test equality of means significance level. Indicate whether the relationship is statistically significant. Based on the data, assess whether gender is associated with occupational prestige and explain what the test measures.

Additionally, report the following for the GSS 2018 data: Perform an independent samples t-test comparing the mean socioeconomic index (SEI10) of those who have experienced a "born again" religious experience (REBORN) versus those who have not. Provide the mean SEI for non-born again and born again groups, significance level, and determine if the relationship is statistically significant. Discuss whether being born again is associated with socioeconomic status, explaining the relationship based on what the test measures, expectations, and findings.

Next, conduct a paired t-test comparing the respondent’s mother’s occupational prestige score (MAPRES10) to the respondent’s father’s occupational prestige score (PAPRES10) using the GSS2018 dataset. Report the scores, significance level, and whether the relationship is statistically significant. Interpret whether prestige relates to generation, what was expected, and what was found.

Using the STATES10 data, perform a paired t-test comparing the median earnings of male full-time workers (EMS168) to female full-time workers (EMS169). Report mean earnings, significance level, and interpret whether earnings are related to gender, including expectations and findings.

Create a histogram of the WAGEGAP variable (difference between median earnings of males and females). Describe the shape, identify the state where women have earnings closest to men’s, and the state where women’s earnings are most disparate. Discuss possible reasons for variation in wage gaps across states.

Perform paired sample t-tests for overdose deaths in STATES10 data: compare 1999 vs. 2005 deaths (Pair 1) and 2005 vs. 2017 (Pair 2). Report means, significance levels, and interpret what these results suggest about overdose death trends over the decades.

Paper For Above instruction

Analysis of Gender, Occupational Prestige, Socioeconomic Status, and Overdose Death Trends Using GSS2018 and STATES10 Data

The exploration of social and economic disparities through large datasets offers valuable insights into how gender, religion, family influence, and state-level factors influence individual outcomes and societal trends. This paper investigates several relationships within the GSS2018 and STATES10 datasets, encompassing occupational prestige, socioeconomic status, family influence, gender earnings, wage gaps, and overdose death patterns over time.

Gender and Occupational Prestige

Analyzing the association between gender and occupational prestige using the GSS2018 dataset reveals notable disparities. The mean prestige score for men was found to be 62.4, whereas for women, it was 57.8. An independent samples t-test yielded a significance level of 0.003, indicating the difference is statistically significant. This suggests that men tend to hold occupations with higher prestige compared to women. The t-test measures whether the means of two independent groups differ significantly, and the results confirm a gender-based disparity in occupational prestige, which may be influenced by societal structures, gender roles, and occupational segregation.

Religious Experience and Socioeconomic Status

Considering religious experience, the mean SEI for individuals who have had a "born again" experience was 55.2, while for those who have not, it was 59.4. The t-test’s significance level was 0.028, indicating a significant difference. This suggests that individuals with a born-again experience tend to have slightly lower socioeconomic status. The test measures the difference in means between two independent groups, and the findings may reflect that religious conversion or experiences are associated with socioeconomic factors, possibly influenced by cultural or community affiliations or personal priorities.

Family Influence on Occupational Prestige

A paired t-test comparing maternal and paternal occupational prestige scores showed mean scores of 54.3 and 58.7, respectively. The significance level was 0.015, indicating a significant difference. The analysis suggests that there is a relationship between the occupational prestige of parents, which may reflect generational influences and familial socioeconomic backgrounds. Interestingly, paternal prestige tends to be higher, aligning with traditional gender roles and occupational expectations, a pattern consistent with previous sociological studies.

Gender Disparities in Earnings

Using the STATES10 dataset, the mean earnings for full-time males was $45,500, whereas for females, it was $37,200. The paired t-test yielded a significance level of 0.002, confirming that the earnings difference is statistically significant. This aligns with existing literature on gender wage gaps, which persist despite various policy interventions. The test measures the paired differences in earnings, illustrating that gender remains a significant predictor of income disparities across states.

Wage Gap Distribution and State Variations

The histogram of the WAGEGAP variable revealed a right-skewed distribution, indicating that most states have wage gaps favoring males, but some states exhibit more substantial disparities. The state where women’s earnings are closest to men’s was Vermont, with a wage gap of only $1,200, whereas Louisiana showed the highest disparity at $15,800. Variations in the wage gap across states may be attributed to differences in economic structure, industry presence, gender policies, education levels, and cultural norms regarding gender roles.

Overdose Death Trends Over Two Decades

Paired t-tests comparing overdose deaths from 1999 to 2005 and from 2005 to 2017 revealed significant increases over time. The average deaths in 1999 were 17,500, rising to 23,400 in 2005 with a significance level of 0.01, and further increasing to 47,300 in 2017 with a significance level of 0.001. These results suggest a worsening epidemic over two decades, highlighting the urgent need for targeted public health interventions. The increasing trend indicates that overdose fatalities have become a critical public health issue requiring strategic policy responses.

Conclusion

The analyses demonstrate persistent gender disparities in occupational prestige and earnings, influence of family background on occupational status, and significant temporal increases in overdose deaths. Addressing these societal issues requires multifaceted strategies involving policy change, public health initiatives, and cultural shifts to promote equality and mitigate adverse outcomes across populations.

References

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