Social Science Data Archive

Posted: August 26th, 2021

Social Science Data Archive

Name

Institutional Affiliation

Social Science Data Archive

Description of the Data

The data is about the survey on a population of adults living in the ACT and surrounding Local Government Areas (LGAS) of Palerang, Cooma-Monaro, Yass, and Queanbeyan. These are the research region that the study concentrated on. The objective of collecting the data was to assess the views of the community on the quality of water. The study aimed at generating data that could inform design strategies for improving the quality of water and maintaining as well as enhancing the social benefits of the waterways. The survey method targeted at collecting quantitative data that would be analyzed and meet the objectives of the research.

List of Questions That Could Be Answered Through Visual Analysis of the Data

1. What are the patterns and characteristics that the target population exhibit? (distribution statistics)
2. What are the differences and correlations amongst the study population? (Inferential analysis)
3. How is the frequency of the entire dataset values? (Frequency table)
4. What is the intensity of water pollution across the region? (Heat maps)
5. How is the trend in water pollution over time? (Trend line)
6. What is the relationship in the data variables collected across the regions? (Cross-tabulations tables).

Names of Data Visualization Analysis Tools

1. RAWGraphs 2.0, which is a visualization application for implementing graphical analysis.
2. MicroStrategy Analytics Desktop, which allows for a simple and insightful analysis of the data.
3. Tableau, which is useful in creating and distributing interactive information to assess trends, densities, and changes in the data using charts and graphs.

List of Sources and Brief Summary

Sapaty, P. (2018). Holistic Analysis and Management of Distributed Social Systems. Cham, Switzerland: Springer Nature Springer.

The book is about the application of technology in solving problems in distributed social networks.

Simoff, S., Hlen, M. & Mazeika, A. (2008). Visual data mining: theory, techniques, and tools for visual analytics. Berlin-New York: Springer.

The book discusses frameworks for building visual analytics in analyzing big data.

Stacey, M., Salvatore, J. & Jorgensen, A. (2013). Visual intelligence: Microsoft tools and techniques for visualizing data. Indianapolis, Ind: John Wiley & Sons.

The source provides a step-by-step guide to creating visual analytics, with thorough coverage in several Microsoft tools for visual analysis.

McCormick, K., Salcedo, J., Peck, J. & Wheeler, A. (2017). SPSS statistics for data analysis and visualization. Indianapolis, IN Wiley.

The book discusses the use of SPSS Statistics for analyzing data. It explores the tool and goes beyond explaining its capabilities in visualization analysis.

Khan, A. (2016). Jumpstart Tableau: a step-by-step guide to better data visualization. The United States New York, NY: Apress, Distributed to the Book trade worldwide by Springer.

The book discusses on Jumpstart Tableau tool for organizing and performing data visualization analysis.

References

Khan, A. (2016). Jumpstart Tableau: a step-by-step guide to better data visualization. The United States New York, NY: Apress, Distributed to the Book trade worldwide by Springer.

McCormick, K., Salcedo, J., Peck, J. & Wheeler, A. (2017). SPSS statistics for data analysis and visualization. Indianapolis, IN Wiley.

Sapaty, P. (2018). Holistic Analysis and Management of Distributed Social Systems. Cham, Switzerland: Springer Nature Springer.

Simoff, S., Hlen, M. & Mazeika, A. (2008). Visual data mining: theory, techniques, and tools for visual analytics. Berlin-New York: Springer.

Stacey, M., Salvatore, J. & Jorgensen, A. (2013). Visual intelligence: Microsoft tools and techniques for visualizing data. Indianapolis, Ind: John Wiley & Sons.