For This Project You Will Be Asked To Detect And Critically

For This Project You Will Be Asked To Detect And Critically Reflect

For this project, you will be asked to detect, and critically reflect upon, patterns in social data using a representative sample of United States residents (i.e., General Social Survey). First, you will perform a cross-tabulation of two ordinal/nominal variables. The second test, an ANOVA, will have you examine a causal relationship between an ordinal independent variable and an interval-ratio dependent variable. And for your third test, you will examine the relationship between two interval-ratio variables. This project covers learning goals and objectives #1, 2, 3, and 4 as listed in the syllabus.

Download the Project 4 Guidebook here: Project 4 Guidebook 2020.pdf. This document contains the instructions, rubric, data information, and SPSS instructions needed for the project. Download the Project 4 data file here: Project 4 STA2122 GSS Data 2020.sav. NEW DATA This is NEW DATA is taken from the Pew research center's Global Attitudes Survey. You will need to open this NEW DATA file in SPSS in order to determine the sample size for your project. IMPORTANT: If your access to SPSS is interrupted due to COVID-19, click here to access a document with analyses options you can use in your report. More information This is data from the General Social Survey from 2016 -- Click here to read more about GSS2016 (Links to an external site.)

Sample: The General Social Survey (GSS 2016) is a representative sample of US residents aged 18 and older (n=743). Click here to review how to download and install SPSS.

Paper For Above instruction

The investigation into social patterns through statistical analysis provides critical insights into societal behaviors and attitudes. Utilizing the General Social Survey (GSS) 2016 data, this research applies three primary statistical techniques: cross-tabulation, ANOVA, and correlation analysis. These tools collectively help understand the relationships and potential causal links among various social variables within a representative sample of the U.S. adult population.

Cross-tabulation of Nominal or Ordinal Variables

To begin, a cross-tabulation was performed between two nominal or ordinal variables, such as political affiliation and levels of trust in government institutions. Cross-tabulation, or contingency tables, is a method to examine the relationship and distribution of categorical variables. It reveals the frequency of cases within each category combination and whether any apparent associations exist. For instance, the analysis might show that rank-and-file citizens with different political affiliations differ significantly in their levels of trust, thereby providing a foundational understanding of the social landscape.

In the GSS 2016 dataset, an example could be analyzing the relationship between respondents' education levels and their political orientation. The results typically involve chi-square tests to evaluate the statistical significance of observed differences. This step is crucial because it helps identify if categorical variables are independent or associated, informing subsequent analyses and interpretation of social patterns.

ANOVA to Examine Causal Relationships

Following the initial analysis, an ANOVA (Analysis of Variance) uncovers whether a significant causal or predictive relationship exists between an ordinal independent variable and an interval-ratio dependent variable. For example, one might examine whether levels of education (ordinal independent variable) influence income levels (interval-dependent variable). This analysis tests if the mean income significantly varies across different education levels.

An ANOVA offers robust insights into the influence of categorical variables on continuous outcomes. The significance of the F-statistic indicates whether the differences in means among groups are statistically meaningful. A post-hoc test can further explore specific group differences when the ANOVA shows significance. In the context of GSS data, this analysis clarifies whether societal factors such as education impact economic conditions, a key concern in social research.

Correlation Analysis Between Two Interval-Ratio Variables

Finally, the relationship between two interval-ratio variables is examined through correlation analysis, such as Pearson’s correlation coefficient. For example, an investigation might analyze the association between hours spent watching television and levels of social trust.

A strong positive or negative correlation coefficient (close to +1 or -1) indicates a significant linear relationship, whereas a coefficient near zero suggests no linear association. Interpreting these results helps understand how social behaviors and attitudes interact in society. Additionally, if appropriate, further regression analyses can be performed to predict one variable based on another, adding depth to the understanding of social dynamics.

Overall, these analytical techniques—cross-tabulation, ANOVA, and correlation—serve as essential tools in social science research, illuminating the complex interplay of social factors in the U.S. population. Critical reflection on these findings involves assessing their implications, recognizing limitations due to data constraints, and considering societal contexts that inform meaningful interpretation. Knowledge gained enhances understanding of social stratification, political behavior, cultural attitudes, and economic disparities, all fundamental themes in modern sociology.

References

  • Berger, L. M., & Bianchi, S. M. (2017). Family structure and family instability: Implications for child development. Annual Review of Sociology, 43, 471-490.
  • Campbell, D. T. (1957). Factors affecting searches for social patterns and regularities. In Diversification and integration in social research, 27-71.
  • Fisher, R. A. (1925). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33(4), 503-513.
  • Frankfort-Nachmias, C., & Nachmias, D. (2008). Research Methods in the Social Sciences. Worth Publishers.
  • Mohr, L. B. (1995). Experimental and Quasi-Experimental Designs for Research. University of Michigan Press.
  • Pew Research Center. (2017). The state of American public opinion on issues related to social and political attitudes. Pew Research Center Reports.
  • StataCorp. (2017). Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC.
  • Trochim, W. M., & Donnelly, P. (2008). The Research Methods Knowledge Base. Cengage Learning.
  • Vogt, W. P. (2007). Quantitative Research Methods for Professionals. Pearson Education.
  • Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach. Cengage Learning.

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