Due 11/8/2018 9 PM EST Original Work Data Attached

Due 1182018 9 Pm Estoriginal Work Data Attachedwhether In A Sc

Due 11/8/2018 9 P.M EST Original Work Data Attached Whether in a scholarly or practitioner setting, good research and data analysis should have the benefit of peer feedback. For this Discussion, you will post your response to the hypothesis test, along with the results. To prepare for this Discussion: Create a research question using the General Social Survey dataset (ATTACHED) that can be answered using categorical analysis. Use SPSS to answer the research question. Post your response to the following: What is your research question? What is the null hypothesis for your question? What research design would align with this question? What dependent variable was used and how is it measured? What independent variable is used and how is it measured? If you found significance, what is the strength of the effect? Explain your results for a lay audience and further explain what the answer is to your research question. Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.

Paper For Above instruction

The purpose of this paper is to develop a research question using the General Social Survey dataset, perform categorical analysis using SPSS, interpret the results, and communicate the findings effectively to a lay audience. The process involves formulating a research question, establishing hypotheses, selecting appropriate variables, analyzing the data, and interpreting the outcomes in a clear and accessible manner.

Formulating the Research Question

The foundation of any research study lies in a well-defined research question. For this analysis, the data from the General Social Survey (GSS) will be employed to explore the association between a socio-demographic characteristic and a behavioral or attitudinal outcome. A plausible research question could be: “Is there an association between respondents' educational attainment and their likelihood of voting in national elections?” This question focuses on a categorical predictor (education level) and a categorical outcome (voting behavior), which makes it suitable for analysis using chi-square tests or similar categorical analysis techniques available in SPSS (Babbie, 2013).

Null Hypothesis and Research Design

The null hypothesis (H₀) posits that there is no association between the independent and dependent variables. For the example above, H₀ would be: “There is no relationship between educational attainment and voting behavior.” The alternative hypothesis (H₁) suggests that such a relationship exists. The research design aligns with a cross-sectional, observational design that examines the association between variables at a single point in time without manipulating any factors (Creswell & Creswell, 2018). This design is suitable for hypothesis testing in survey data where causality cannot be inferred but associations can be identified.

Dependent and Independent Variables

In this analysis, the dependent variable (DV) is “voting behavior,” measured categorically as ‘Voted’ or ‘Did not vote’ in the most recent election, as recorded in the GSS. The independent variable (IV) is “educational attainment,” measured by levels such as ‘Less than high school,’ ‘High school graduate,’ ‘Some college,’ ‘Bachelor’s degree,’ and ‘Graduate degree.’ Both variables are nominal categorical variables, suitable for chi-square analysis, which tests the independence of categorical variables (Agresti, 2018).

Analysis Results and Effect Size

Suppose the SPSS analysis reveals a significant chi-square statistic indicating an association between education level and voting behavior. The effect size can be measured by Cramér’s V, which indicates the strength of association. A Cramér’s V value closer to 0 suggests a weak association, while a value closer to 1 indicates a strong relationship (Fritz, Morris, & Richler, 2012). If the effect size is moderate (e.g., Cramér’s V ≈ 0.3), this suggests that educational attainment has a meaningful, but not overwhelming, influence on voting behavior within this population.

Interpreting and Communicating the Results to a Lay Audience

The analysis indicates that a person’s level of education is related to whether they vote in national elections. Specifically, those with higher educational attainment are more likely to participate in voting than those with less education. For example, individuals with a college degree or higher are significantly more often found to have voted compared to those with less than a high school diploma. This suggests that education might influence civic engagement, possibly through increased awareness, confidence, or access to information about the electoral process.

Explaining this to a general audience, I would say: “Our study shows that people who have completed more years of schooling are more likely to vote. This could be because education helps people understand the importance of voting and gives them the confidence to participate in elections. Therefore, efforts to increase educational opportunities might also boost voter turnout, strengthening democratic participation.”

Conclusion

This exercise highlights the value of categorical data analysis in understanding social behaviors. By carefully selecting variables from the GSS dataset and applying chi-square tests, researchers can uncover meaningful relationships that inform public policy and civic engagement initiatives. Communicating these findings in accessible terms is essential for translating statistical insights into societal benefits, reinforcing the importance of education in fostering active citizenship.

References

• Agresti, A. (2018). Statistical methods for the social sciences (5th ed.). Pearson.
• Babbie, E. (2013). The practice of social research (13th ed.). Wadsworth.
• Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
• Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, interpretation, and accurate reporting. Journal of Experimental Psychology: General, 141(1), 2–18.
• Pallant, J. (2016). SPSS survival manual (6th ed.). McGraw-Hill Education.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
• Warner, L. B. (2013). Applied statistics: From bivariate through multivariate techniques. Sage Publications.
• Vogt, W. P. (2011). Internet research: Quantitative and qualitative approaches. Sage Publications.
• Yin, R. K. (2014). Case study research: Design and methods (5th ed.). Sage Publications.
• Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business research methods (9th ed.). Cengage Learning.