Summary
Justin Klip and Dhruv Gupta's paper explores polling trends for Kamala Harris and Joe Biden in the 2024 U.S. Presidential Election using both linear and Bayesian logistic regression models. It leverages demographic and methodological predictors to analyze variations in support across different pollsters, regions, and timeframes, while effectively addressing prediction uncertainty.
Strong Points
The paper delivers a thorough analysis of polling trends using both linear and Bayesian models, effectively illustrating variations in candidate support.
Visualizations, like Figures 1 and 2, are clear and effectively communicate trends across pollsters and states.
The use of Bayesian models, with confidence intervals, provides a transparent representation of uncertainty in polling predictions.
Critical Improvements Needed
Clarify Scope and Goals: Clearly articulate that the paper aims to compare polling trends, rather than predict future outcomes, to avoid potential confusion.
Simplify Figure Captions: Revise captions, especially for Figure 1, to enhance clarity and readability.
Correct Typos: Address minor spelling and grammatical errors throughout the text.
Suggestions for Improvement
Title and Abstract: Add a descriptive title and an abstract of about four sentences summarizing the research. Include a GitHub link for easier access to code and data.
Introduction: Expand the introduction to 3–4 paragraphs to explain the importance of election polling, reference past studies or events, and provide context for the research problem.
Data Visualization and Explanation: Include tables or graphs visualizing the raw data and offer explanations for variable selection in the logistic regression models.
Model Validation and Assumptions: Include model diagnostics, such as residual plots or checks for influential data points, and discuss the assumptions behind the logistic regression models.
Results and Discussion: Add sections to interpret final model outcomes, highlight significant predictors, and discuss their implications for the election forecast.
Appendix and References: Improve appendix formatting, separate references onto a distinct page, and remove any unnecessary files in the paper folder for better organization.
Evaluation
R is Appropriately Cited: 1/1
Data Are Appropriately Cited: 1/1
Class Paper: 1/1
LLM Usage is Documented: 1/1
Title: 2/2
Author, Date, and Repo: 2/2
Abstract: 3/4 – Could be more concise for a broader audience.
Introduction: 3/4 – Needs clearer motivation and streamlined content.
Estimand: 1/1 – Clearly stated.
Data: 6/10 – Requires more details on data processing and variable selection.
Measurement: 3/4 – Generally complete, but could be expanded.
Model: 6/10 – Further explanation of model selection and assumptions is needed.
Results: 6/10 – Needs a deeper discussion of findings.
Discussion: 4/10 – Expand comparisons with other studies.
Prose: 4/6 – Could be more concise and reduce redundancy.
Cross-references: 1/1 – Correctly referenced.
Captions: 1/2 – Simplify for better clarity.
Graphs/Tables: 3/4 – Effective, but room for improvement.
Methodology: 7/10 – Expand descriptions of sampling and data collection methods.
Survey Design: 3/4 – Needs more detail for better clarity.
Pollster Methodology: 6/10 – Improve explanations of each pollster's methods.
Referencing: 4/4 – Well done.
Commits: 2/2 – Sufficient commit history.
Sketches: 2/2 – Clear and relevant.
Simulation: 4/4 – Well-documented.
Tests - Simulation: 3/4 – Could be more comprehensive.
Tests - Actual: 3/4 – Could be improved.
Parquet: 1/1 – Correctly used.
Reproducible Workflow: 3/4 – Improve reproducibility documentation.
Miscellaneous: 2/3 – Solid, but room for minor refinements.
Total: 87/126
Overall Feedback
The paper is well-structured and informative, offering a comprehensive analysis of polling trends. It could be further enhanced by expanding the discussion section, clarifying data details, and improving descriptions of the methodology, making it more complete and easier to understand.
Summary Justin Klip and Dhruv Gupta's paper explores polling trends for Kamala Harris and Joe Biden in the 2024 U.S. Presidential Election using both linear and Bayesian logistic regression models. It leverages demographic and methodological predictors to analyze variations in support across different pollsters, regions, and timeframes, while effectively addressing prediction uncertainty.
Strong Points The paper delivers a thorough analysis of polling trends using both linear and Bayesian models, effectively illustrating variations in candidate support. Visualizations, like Figures 1 and 2, are clear and effectively communicate trends across pollsters and states. The use of Bayesian models, with confidence intervals, provides a transparent representation of uncertainty in polling predictions. Critical Improvements Needed Clarify Scope and Goals: Clearly articulate that the paper aims to compare polling trends, rather than predict future outcomes, to avoid potential confusion. Simplify Figure Captions: Revise captions, especially for Figure 1, to enhance clarity and readability. Correct Typos: Address minor spelling and grammatical errors throughout the text. Suggestions for Improvement Title and Abstract: Add a descriptive title and an abstract of about four sentences summarizing the research. Include a GitHub link for easier access to code and data. Introduction: Expand the introduction to 3–4 paragraphs to explain the importance of election polling, reference past studies or events, and provide context for the research problem. Data Visualization and Explanation: Include tables or graphs visualizing the raw data and offer explanations for variable selection in the logistic regression models. Model Validation and Assumptions: Include model diagnostics, such as residual plots or checks for influential data points, and discuss the assumptions behind the logistic regression models. Results and Discussion: Add sections to interpret final model outcomes, highlight significant predictors, and discuss their implications for the election forecast. Appendix and References: Improve appendix formatting, separate references onto a distinct page, and remove any unnecessary files in the paper folder for better organization. Evaluation R is Appropriately Cited: 1/1 Data Are Appropriately Cited: 1/1 Class Paper: 1/1 LLM Usage is Documented: 1/1 Title: 2/2 Author, Date, and Repo: 2/2 Abstract: 3/4 – Could be more concise for a broader audience. Introduction: 3/4 – Needs clearer motivation and streamlined content. Estimand: 1/1 – Clearly stated. Data: 6/10 – Requires more details on data processing and variable selection. Measurement: 3/4 – Generally complete, but could be expanded. Model: 6/10 – Further explanation of model selection and assumptions is needed. Results: 6/10 – Needs a deeper discussion of findings. Discussion: 4/10 – Expand comparisons with other studies. Prose: 4/6 – Could be more concise and reduce redundancy. Cross-references: 1/1 – Correctly referenced. Captions: 1/2 – Simplify for better clarity. Graphs/Tables: 3/4 – Effective, but room for improvement. Methodology: 7/10 – Expand descriptions of sampling and data collection methods. Survey Design: 3/4 – Needs more detail for better clarity. Pollster Methodology: 6/10 – Improve explanations of each pollster's methods. Referencing: 4/4 – Well done. Commits: 2/2 – Sufficient commit history. Sketches: 2/2 – Clear and relevant. Simulation: 4/4 – Well-documented. Tests - Simulation: 3/4 – Could be more comprehensive. Tests - Actual: 3/4 – Could be improved. Parquet: 1/1 – Correctly used. Reproducible Workflow: 3/4 – Improve reproducibility documentation. Miscellaneous: 2/3 – Solid, but room for minor refinements. Total: 87/126 Overall Feedback The paper is well-structured and informative, offering a comprehensive analysis of polling trends. It could be further enhanced by expanding the discussion section, clarifying data details, and improving descriptions of the methodology, making it more complete and easier to understand.