Pagerunning Head Week 11 Sex And Class

Pagerunning Head Week 11 Sex And Class 1week 11 Sex And Clas

This assignment involves analyzing the relationship between sex (gender) and subjective social class using data from the GSS04 dataset. The goal is to perform a Chi-Square Test of Independence to determine if there is a statistically significant association between these variables. The analysis includes verifying assumptions of the Chi-Square test, interpreting residuals, calculating effect size with Cramer's V, and summarizing findings with supporting visualizations and references.

Paper For Above instruction

The relationship between gender and social class has long been a focus of sociological research, aiming to understand societal hierarchies and disparities. In this analysis, we utilize data from the General Social Survey (GSS) to investigate whether a statistical association exists between respondents' sex and their subjective social class, categorized into lower, working, middle, and upper classes. Employing a Chi-Square Test of Independence allows us to explore this relationship with appropriate assumptions and interpret the findings within a sociological context.

First, the assumptions underlying the Chi-Square test were carefully examined. The primary assumption is the independence of observations, meaning no individual belongs to more than one category or appears in multiple cells; this condition was satisfied as each respondent could be classified uniquely with respect to sex and class. The second key assumption pertains to the expected cell counts; all expected frequencies exceeded five (minimum expected count was 23.53), ensuring the Chi-Square distribution approximates well for our sample size, which consisted of 1,496 valid cases. These fulfill the requirements for valid application of the Chi-Square test (Green & Salkind, 2014).

The cross-tabulation of sex and subjective social class revealed interesting patterns. In the lower class category, approximately 62% of respondents were female, while males comprised about 38%. Residual analysis indicated that the observed counts are close to expected, with adjusted residuals of less than 2 in magnitude, implying no significant deviation from expectation. Conversely, in the middle class segment, a notable deviation emerged: the residuals of |2.0| for males and females suggest that the number of males in middle class was higher than expected, specifically approximately 19.5 more males than anticipated, and fewer females than expected, with about 19.5 fewer. Similar patterns, although less pronounced, appeared in the upper class, where the observed counts aligned closely with expectations, and residuals underscored no significant differences.

The results of the Chi-Square test yielded a statistic of 8.628 with 3 degrees of freedom, and a p-value of 0.035. Since this p-value is less than the conventional alpha level of 0.05, the null hypothesis—that sex and subjective class are independent—is rejected. This indicates a statistically significant association between gender and social class in the dataset. Despite the significance, the effect size calculated via Cramer's V was approximately 0.076, indicating a small effect (Cohen, 1988). This suggests that while the relationship exists, it is weak and likely accompanied by considerable overlap between genders across social classes.

Visual representation, such as a clustered bar graph, further clarified the findings. Men and women were relatively evenly distributed within the middle and working classes, but disparities were evident in the lower and upper classes. The higher proportion of women in the lower class aligns with sociological theories about economic marginalization of women, whereas men appeared more prevalent in middle and upper classes, echoing patterns of gender-based socio-economic stratification (England, 2005). These findings fortify the premise that gender influences social class positioning, although the effect remains modest across the population.

The implications of this study extend to understanding social inequalities and gender dynamics. The small yet significant association signals ongoing disparities, possibly rooted in structural, cultural, or economic factors. Recognizing these patterns informs policymakers and social scientists aiming to address gendered inequities, especially in economic mobility and class stratification. Moreover, the study demonstrates the utility of Chi-Square analyses in sociological research, emphasizing the importance of rigorous assumption testing, residual analysis, and effect size interpretation for comprehensive reporting.

In conclusion, the analysis confirms a small but statistically significant relationship between sex and subjective social class among survey respondents. The findings underscore the nuanced underpinnings of social stratification and gender roles, aligning with existing sociological literature. Future research may delve deeper into causal mechanisms, longitudinal trends, and the impact of intersecting identities on social class mobility. Understanding these dynamics is essential for fostering a more equitable society where gender does not predetermine social standing.

References

• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
• England, P. (2005). Gender inequalities in labor markets. Annual Review of Sociology, 31, 91-115.
• Green, S. B., & Salkind, N. J. (2014). Using SPSS for Windows and Macintosh: Analyzing and understanding data (7th ed.). Pearson.
• Frankfort-Nachmias, C., & Nachmias, D. (2008). Research methods in the social sciences (7th ed.). Worth Publishers.
• Frankfort-Nachmias, C., Nachmias, D., & Deward, D. (2015). Social statistics (7th ed.). Worth Publishers.
• McHugh, M. L. (2013). The Chi-square test of independence. Biochemia Medica, 23(2), 143-149.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
• van der Slik, W., & Goossens, L. (2010). Statistical methods: A beginner's guide. Sage Publications.
• Yates, F. (1934). Contingency table involving small numbers and the χ² test. Supplement to Journal of the Royal Statistical Society, 1(2), 217-235.
• Zwiggelaar, R. (2011). Principles of statistical analysis. Springer Science & Business Media.