Analyzing survey data involves extracting meaningful insights and drawing conclusions from the collected responses (Heeringa, West & Berglund, 2017). The specific analysis techniques and tools depend on the nature of the survey data and research objectives. Anonymizing the data and following ethical guidelines is also essential to ensure the privacy and confidentiality of the respondents.
Applying to A specified Article:
Kheirbek, I., Ito, K., Neitzel, R., Kim, J., Johnson, S., Ross, Z., ... & Matte, T. (2014). Spatial variation in environmental noise and air pollution in New York City. Journal of Urban Health, 91, 415-431.
The article examines the spatial variation of environmental noise and air pollution in New York City. The authors found that noise levels varied widely across the city, with the highest levels observed in areas with heavy traffic. They also found that noise levels correlated with air pollution levels, suggesting that exposure to noise and air pollution may hurt health.
Analyze Survey Data
The authors used a mixed methods approach to analyze the data. I plan to use the same in my research. They collected quantitative data on noise and air pollution levels from a network of monitoring stations across New York City. They also collected qualitative data from interviews with residents about their experiences with noise and air pollution. The authors used quantitative data to map the spatial variation of noise and air pollution levels across New York City. They also used quantitative data to test the relationship between noise and air pollution levels (Kheirbek et al. 2014).
The authors used qualitative data to understand how residents experience noise and air pollution. They also used qualitative data to identify the sources of noise and air pollution in New York City. The authors found that the spatial variation of noise and air pollution levels was closely related to the spatial distribution of traffic. The highest noise levels were observed in areas with heavy traffic, such as major highways and bridges. The highest air pollution levels were also observed in areas with heavy traffic.
The authors also found that noise levels were correlated with air pollution levels. This suggests that exposure to both noise and air pollution may harm health. The authors concluded that their study's findings suggest that exposure to noise and air pollution is a significant public health problem in New York City. They call for further research to better understand the relationship between noise, air pollution, and health.
Applying to my project:
I can use the same analysis in my project about construction pollution in NYC. The analysis used by Kheirbek et al. (2014) is a sound approach to understanding the spatial variation of environmental pollution. However, it is essential to note that the analysis was conducted in 2014, and the spatial variation of environmental pollution may have changed. Therefore, it is essential to update the analysis using more recent data.
Some additional steps that I can take to improve my analysis (Conboy, Mikalef, Dennehy & Krogstie, 2020):
- Collect more data: The article's authors used data from a network of monitoring stations across New York City. I can collect more data by expanding the network of monitoring stations or by using other data sources, such as satellite data or data from citizen science projects.
- Use different analytical methods: The article's authors used a combination of spatial analysis, correlation analysis, and qualitative data analysis. I can use other analytical methods, such as regression analysis or machine learning, to improve my understanding of the spatial variation of environmental pollution.
- Involve stakeholders: I can involve stakeholders, such as community residents, government officials, and environmental groups, in the analysis process. This can help ensure that the analysis is relevant to the community's needs and that the results inform decision-making.
Conclusion
Analyzing survey data can be a valuable tool for my research about construction pollution. It can help me identify pollution sources, measure pollution's impact, understand the public's perception of pollution, and identify the best ways to reduce pollution. Static programs can also be a valuable tool for analyzing survey data. However, some challenges must be considered before using static programs, such as Data preparation, Interpretation of results, and cost (Zou, Lloyd & Baumbusch, 2020).
References
Conboy, K., Mikalef, P., Dennehy, D., & Krogstie, J. (2020). Using business analytics to enhance dynamic capabilities in operations research: A case
Analysis and research agenda. European Journal of Operational Research, 281(3), 656-672.
Heeringa, S.G., West, B.T., & Berglund, P.A. (2017). Applied Survey Data Analysis (2nd ed.). Chapman and Hall/CRC.
https://doi.org/10.1201/9781315153278
Kheirbek, I., Ito, K., Neitzel, R., Kim, J., Johnson, S., Ross, Z., ... & Matte, T. (2014). Spatial Variation in environmental noise and air pollution in New York
City. Journal of Urban Health, 91, 415-431.
Zou, D., Lloyd, J. E., & Baumbusch, J. L. (2020). Using SPSS to analyze complex survey data: a primer. Journal of Modern Applied Statistical Methods,
18(1), 16.