Conduct peer review term paper2

[1Dezhenchen]

Summary: The paper by Justin Klip and Dhruv Gupta analyzes polling trends for Kamala Harris and Joe Biden during the 2024 U.S. Presidential Election using both linear and Bayesian logistic regression models. The study utilizes demographic and methodological predictors to provide a comprehensive view of support trends across different pollsters, regions, and timeframes, while effectively capturing uncertainty in predictions.

Strong Positive Points: The paper provides a thorough analysis of polling trends, using both linear and Bayesian models to capture variations in candidate support. Visualizations, such as Figures 1 and 2, effectively illustrate trends across different pollsters and states, making the results easy to follow. The use of Bayesian models, with confidence intervals, clearly conveys the uncertainty in the data.

Critical Improvements Needed:

• Clarify Scope and Goal: Clearly state that the paper aims to compare polling trends rather than make future predictions to avoid confusion.
• Improve Figure Captions: Simplify the captions, particularly for Figure 1, to improve readability. Correct Typos: Address minor spelling mistakes in the text.

Suggestions for Improvement:

• Title and Abstract: Add an appropriate title and an abstract (about four sentences) summarizing the research. Include a link to the GitHub repository for easy access to the source code and data.
• Introduction: Add an introduction of 3–4 paragraphs that explain the importance of election polling, mention previous studies or news events, and help the reader understand the broader problem the research is addressing.
• Data Visualization and Explanation: Provide tables or graphs that visualize the raw data, along with an explanation of why specific variables were chosen for the logistic regression model.
• Model Validation and Assumptions: Incorporate model diagnostics such as residual plots or checks for influential data points, and discuss any underlying assumptions of the logistic regression model.
• Results and Discussion: Add a Results and Discussion section that interprets the final model, highlights significant predictors, and discusses their impact on the election forecast.
• Appendix and References: Correct the appendix formatting and place the references on a separate page for better readability. Remove any unnecessary files in the paper folder for clarity.

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 non-specialists. Introduction: 3/4 - Streamline content and add motivation. Estimand: 1/1 - Clearly stated. Data: 6/10 - Needs more detail on data processing and variable analysis. Measurement: 3/4 - Generally complete. Model: 6/10 - Further explain model selection and assumptions. Results: 6/10 - Needs a deeper discussion of findings. Discussion: 4/10 - Expand comparisons with other studies. Prose: 4/6 - Could reduce redundancy. Cross-references: 1/1 - Correctly referenced. Captions: 1/2 - Simplify captions. Graphs/Tables: 3/4 - Effective but room for improvement. Methodology: 7/10 - Expand sampling and data collection descriptions. Survey Design: 3/4 - Needs more detail to ensure clarity. Pollster Methodology: 6/10 - Improve descriptions of all pollster methods. Referencing: 4/4 Commits: 2/2 Sketches: 2/2 Simulation: 4/4 Tests - Simulation: 3/4 - Could be improved. Tests - Actual: 3/4 - Could be improved. Parquet: 1/1 Reproducible Workflow: 3/4 - Improve reproducibility documentation. Miscellaneous: 2/3 Total: 87/126

The paper is overall well-written and informative but could be further enhanced by expanding the discussion section, clarifying data details, and improving methodology descriptions to increase its comprehensiveness and readability.

[huayan1998]

I am peer-reviewing Justin Klip's and Dhruv Gupta's paper on predicting Kamala Harris' popular vote support in the 2024 US Election.

Summary This paper utilizes Bayesian models to predict Kamala Harris' popular vote support in the 2024 US presidential election. The model is based on aggregated poll data from FiveThirtyEight and estimates voter preferences using rstanarm in R. The authors discuss the data sources, the setup of the model, and provide results that aim to capture Harris' potential support. They also include a section on the limitations of the data and future improvements.

Strong positive points: Clear Model Setup: The model setup is well-structured with a detailed explanation of the rationale behind using the Bayesian framework and the default priors from rstanarm. This provides a solid foundation for readers to understand the analytical process. Acknowledgment of Limitations: The authors explicitly discuss some challenges in predicting election outcomes, particularly the use of stale data and the need for better up-to-date polling information. This shows a good level of critical thinking about their approach. Critical improvements needed:

Abstract: The abstract lacks results and discussion of findings. Consider summarizing key outcomes and explaining their significance at a high level to make it more informative.

GitHub Link: The GitHub repository link is included, but there is no direct reference to the specific scripts used or how to replicate the results. Consider adding more detail about the data and code available.

Data Section: There is no clear description of how the polls were aggregated or cleaned. The paper would benefit from an exploratory data analysis (EDA) or summary statistics section, which would show relationships between variables.

Discussion of Results: The results section includes some discussion points, which should be moved to the discussion section. In addition, the interpretation of the findings could be expanded to provide deeper insights into what the results mean for the broader election context.

Figures: Figure 3 is difficult to interpret due to the density of labels on the X-axis. Consider using abbreviations, subplots, or focusing on fewer key variables to improve readability. Figure 4 lacks a discussion of the results, which would enhance the overall clarity.

Model Diagnostics: The paper lacks model diagnostics like RMSE or sensitivity analysis, which are critical for assessing the robustness of the model. Including these in the appendix would make the analysis more comprehensive.

Polling Methodology: While there is an explanation of how data from FiveThirtyEight was used, more detail on why this specific dataset was chosen over others would strengthen the argument. Additionally, a discussion of potential biases in the poll data would be beneficial. Suggestions for improvement:

Titles for Figures: Adding descriptive titles for each figure would help readers quickly understand the content, even without reading the entire paper.

Estimand Clarification: The introduction should clearly state the estimand (e.g., percentage of votes Harris is expected to win) to avoid ambiguity.

Typos and Formatting: There are minor typos (e.g., “it’s” instead of “its”) and formatting errors, particularly in the results section. Fixing these would enhance readability.

Measurement of Data: The measurement of data, such as how poll data was aggregated and what methods were used to clean it, is not sufficiently covered. This is a critical aspect that needs attention in the data section.

Reproducibility: While the GitHub link is provided, there are no clear instructions for replicating the analysis. Including a README file that outlines how to download, clean, and run the data through the model would make the research more accessible.

Estimated overall mark: 38/126 R is appropriately cited: 1/1 Data are appropriately cited: 1/1 Class paper: 1/1 LLM usage is documented: 0/1 Title: 1/2 Author, date, and repo: 0/2 Abstract: 1/4 Introduction: 1/4 Estimand: 0/1 Data: 4/10 Measurement: 0/4 Model: 6/10 Results: 4/10 Discussion: 6/10 Prose: 2/6 Cross-references: 1/1 Captions: 1/2 Graphs/tables/etc: 1/4 Idealized methodology: 0/10 Idealized survey: 0/4 Pollster methodology overview and evaluation: 0/10 Referencing: 4/4 Commits: 2/2 Sketches: 0/2 Simulation: 0/4 Tests-simulation: 0/4 Tests-actual: 0/4 Parquet: 0/1 Reproducible workflow: 1/4 Miscellaneous: 0/3 Any other comments: Please remember to rename the R project and add the LLM usage, otherwise, you will receive a score of 0 for this section.