xgao28
commented
3 weeks ago Thank you for your comment! We added sketches, plots, citations and cross-references. Regarding the model, its simplicity could lead to a better explanation in our analysis, as our attention lies on the reproducible workflow, survey design, and "telling a story". For future work, we could apply algorithms like random forest besides linear or generalized linear for a more accurate prediction.
Summary: This survey aims to investigate other factors that may affect Donald Trump's support rating in the 2024 US election. Research has found that the timing and location of public opinion polls can also have an impact on support rates, and there may be differences in survey results across different time periods and regions. By analyzing and comparing various data, we found significant differences in support rates among different states.
Strong positive points: A comprehensive and systematic analysis was conducted in this paper on how variables in public opinion polls interact to influence voter sentiment. The use of linear regression models is appropriate, and clear results indicate important factors.
Critical improvements needed: Oversimplified Treatment of Non-linearities: The current linear regression model may fail to account for non-linear relationships between predictors and Trump's support, resulting in a less comprehensive analysis.
Suggestions for improvement:
Consider adding demographic and economic variables to capture a fuller picture of Trump’s support. Consider adding more visualizations to better illustrate the findings Add in-text citations and cross-references to figures and tables.
Please consider adding/changing/removing:
add sketch Remove unused file Evaluation: R is appropriately cited: 1/1 Data are appropriately cited: 1/1 Class paper: 1/1 LLM usage is documented: 1/1 Title: 1/2 Author, date, and repo: 2/2 Abstract: 1/4 Introduction: 3/4 Estimand: 1/1 Data: 8/10 Measurement: 2/4 Model: 7/10 Results: 6/10 Discussion: 6/10 Prose: 4/6 Cross-references: 1/1 Captions: 1/2 Graphs/tables/etc: 3/4 Idealized methodology: 7/10 Idealized survey: 3/4 Pollster methodology overview and evaluation: 6/10 Referencing: 3/4 Commits: 1/2 Sketches: 0/2 Simulation: 1/4 Tests-simulation: 3/4 Tests-actual: 3/4 Parquest: 1/1 Reproducible workflow: 1/4 Miscellaneous: 2/3
Estimated overall mark: 82 of 126
Reason for the mark: However, issues such as data overlap and lack of demographic details have weakened its conclusions. Improving methods, expanding limitations, and better addressing complex models will make research more precise.