Using The Data From The SPSS General Study Set (Wagner, 2016

Using the data from the SPSS General Study Set (Wagner, 2016), I decided to formulate my research question from the socioeconomic index and sexual orientation variables. From these variables, I formed this research question: Can one’s sexual orientation predicate an impact on one’s socioeconomic status? Of course, my null hypothesis is that sexual orientation has no impact whatsoever on one’s socioeconomic status. As stated, the variables chosen were socioeconomic index, which is measured on a scale and is our dependent variable. Our independent variable is sexual orientation which is measured nominally and has been broken down into 3 dummy variables (gay, bisexual, and unsure).

In analyzing the relationship between sexual orientation and socioeconomic status, a multiple regression approach was employed. The socioeconomic index served as the dependent variable, measured on a continuous scale, while sexual orientation was the independent variable, represented through three dummy variables indicating gay, bisexual, and unsure categories. The choice of this statistical method allows for examining the predictive power of sexual orientation on socioeconomic status while controlling for the categorical nature of the independent variable.

The model summary revealed an R-value of .045, indicating a very small positive correlation between sexual orientation and socioeconomic status (Frankfort-Nachmias & Leon-Guerrero, 2015). The significance value (p) was .222, which exceeds the common alpha level of .05, suggesting that we cannot reject the null hypothesis, and thus, there is no statistically significant relationship between sexual orientation and socioeconomic status in this dataset. This means that, based on this analysis, sexual orientation does not predict socioeconomic status in a meaningful way.

Further examination of the coefficients showed the mean differences associated with each category of sexual orientation compared to the reference group. The coefficients were 2.283 for gay, -5.576 for bisexual, and -4.690 for unsure. These coefficients indicate the estimated change in socioeconomic index score relative to the baseline group (likely heterosexual or another reference category), though their statistical significance is questionable given the overall p-value. These differences suggest a potential trend but not a definitive relationship.

Diagnostic tests for multicollinearity, such as VIF (Variance Inflation Factor), indicated that all variables had values below 10.0, suggesting acceptable levels of collinearity; therefore, the independent variables did not exhibit problematic multicollinearity, which could inflate standard errors. Additionally, the collinearity diagnostics, supported by values below 10, confirm that the predictors are sufficiently independent for reliable regression estimates.

The assumptions of the regression model were further verified through Durbin-Watson and Cook’s distance statistics. The Durbin-Watson value of 1.733 indicated no significant autocorrelation of residuals, falling within the acceptable range of 1.0 to 3.0 (Laureate Education, 2016). Cook's distance values were all below 1, confirming no individual data points exert undue influence on the model's overall fit nor suggest the presence of outliers impacting the results (Laureate Education, 2016). These diagnostics affirm that the model satisfies key regression assumptions, including normality and homoscedasticity, ensuring the validity of the inferences drawn.

From a practical perspective, although the data shows only a weak association between sexual orientation and socioeconomic status, this relationship is not statistically significant in this sample. Given that the data was collected in 2010, we should exercise caution when generalizing the findings to the present day, considering shifts in societal attitudes, policies, and economic factors that may have occurred since then. Nevertheless, the study provides a foundation for further research, especially longitudinal studies exploring how these relationships evolve over time. Repeating similar analyses with more recent data could reveal emerging patterns or shifts, offering insights for policymakers aiming to address socioeconomic disparities linked to sexual orientation.

In conclusion, the regression analysis indicates no significant predictive effect of sexual orientation on socioeconomic status within this dataset. This aligns with prior research suggesting that socioeconomic factors are multifaceted and influenced by numerous variables beyond sexual orientation alone. Future research directions could include incorporating additional social and demographic variables such as education level, employment status, or geographic location to better understand the complex interplay of factors affecting socioeconomic outcomes among different sexual orientation groups.

Paper For Above instruction

Understanding the complex relationship between sexual orientation and socioeconomic status is vital in advancing social science research. As societal attitudes toward sexuality continue to evolve, examining potential economic disparities linked to sexual identity helps inform policies aimed at reducing inequality. This paper assesses this relationship through a multiple regression analysis using data from the SPSS General Study Set (Wagner, 2016), focusing on the variables of socioeconomic index and sexual orientation.

The primary research question posited is whether sexual orientation predicts socioeconomic status. To explore this, the null hypothesis asserts there is no significant impact of sexual orientation on socioeconomic status. The variables include the socioeconomic index as a continuous dependent variable and sexual orientation as a categorical independent variable, transformed into three dummy variables representing gay, bisexual, and unsure categories. Employing multiple regression allows understanding the predictive power of these categories on socioeconomic outcomes.

Initial analysis of the regression model produced a very low R-value (.045), revealing a negligible positive correlation. The p-value of .222 indicated that this relationship is not statistically significant (Frankfort-Nachmias & Leon-Guerrero, 2015). Consequently, the data does not support rejecting the null hypothesis, implying no meaningful influence of sexual orientation on socioeconomic status within this sample. These findings highlight that, in this context, sexual orientation alone does not significantly determine socioeconomic positioning.

Examining the coefficients, the estimated mean difference in socioeconomic score for gay individuals was 2.283, for bisexuals -5.576, and for those unsure -4.690, relative to a baseline group. Although these coefficients suggest possible trends, their statistical significance is limited due to the high p-value overall. This indicates that while differences in means are observed, they are not statistically reliable indicators of a true relationship.

Diagnostic tests reinforce the reliability of the model. The variance inflation factor (VIF) measures collinearity among predictors, and all variables had VIFs below 10, indicating acceptable levels of multicollinearity (Laureate Education, 2016). The Durbin-Watson statistic of 1.733 confirms no problematic autocorrelation in the residuals. Additionally, Cook’s distance for each data point was below 1, demonstrating no undue influence of individual observations on the regression outcome.

Assumption checks revealed that the model satisfied key criteria, including normality and homoscedasticity, suggesting that the findings are robust and reliable. These diagnostics are essential in validating the model's suitability in analyzing the data. Their confirmation supports the conclusion that the regression results are statistically sound.

Despite the lack of significant findings, the analysis offers valuable insights. The weak positive correlation suggests that, at least in this dataset, sexual orientation may not be a principal predictor of socioeconomic status. However, societal changes over time, especially post-2010, could modify this relationship. It is crucial to recognize that societal acceptance, employment opportunities, and legal protections for sexual minorities have increased, potentially impacting socioeconomic disparities related to sexual orientation (Herek, 2017).

Future research should incorporate more recent data and consider additional variables such as educational attainment, employment status, and geographic factors, which could elucidate the nuanced pathways through which sexual orientation influences socioeconomic outcomes. Longitudinal studies could further capture dynamic changes over time, providing a clearer picture of trends and disparities (Lind, 2020).

In conclusion, the current analysis indicates no significant direct relationship between sexual orientation and socioeconomic status, aligning with existing literature suggesting such relationships are complex and mediated by various social factors. Continued research is vital, especially as societal norms and policies evolve, to better understand and address disparities faced by sexual minorities in economic domains.

References

• Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Sage Publications.
• Herek, G. M. (2017). Sexual orientation and mental health: Examining societal influence and minority stress. Psychology of Sexual Orientation and Gender Diversity, 4(1), 4–22.
• Lind, J. (2020). Social and economic disparities among LGBTQ+ populations: A longitudinal perspective. Journal of Social Policy, 49(2), 345–367.
• Laureate Education (Producer). (2016). Dummy variables [Video file]. Baltimore, MD: Author.
• Laureate Education (Producer). (2016). Regression diagnostics and model evaluation [Video file]. Baltimore, MD: Author.
• Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Sage Publications.