Peer Review of the Paper “Forecasting the 2024 U.S. Presidential Election”
Summary
This paper presents a statistical model to predict the outcome of the 2024 U.S. Presidential Election using a multiple linear regression model based on polling data. The model forecasts support for Kamala Harris and Donald Trump by predicting the number of electoral college votes they are likely to win. The results indicate that neither candidate is likely to secure the required 270 votes. This analysis incorporates predictors such as pollster ratings and state-level demographics.
Strong Positive Points
Well-written and thoroughly explained.
Meets all the criteria for a comprehensive paper.
Excellent data cleaning and visualizations.
Critical Improvements Needed
Missing Data Handling: Missing data is not well documented, and there is no clear explanation of how it is addressed.
Dynamic Factors: A linear regression model may not be the best choice for predicting election results. Voter behavior is often influenced by dynamic factors such as campaign events and regional demographics, which linear models are limited in capturing. Polling biases and the U.S. Electoral College system are not well addressed by linear regression.
Result Interpretation: The results section could go further in discussing battleground states, voter behavior, or potential swing states.
Suggestions for Improvement
Demographic Predictors: Adding additional variables like age, education, and race into the analysis could help enrich state-level predictions.
Time Analysis: Including time in your model can help show trends in voter preferences over time, which can improve forecasts as election day approaches.
Alternative Models: Bayesian approaches may provide more accurate predictions by addressing complexities and capturing the non-linear factors in elections.
Evaluation
Citation of Software Tools (0-1): [1] Tools like R, tidyverse, and ggplot2 are properly cited.
Citation of Data Sources (0-1): [0.5] Some data sources are mentioned, but specific polling data sources are missing.
Title (0-2): [1] Clear and descriptive, but it could benefit from more detail about the approach.
Abstract (0-2): [2] A clear and concise overview of the study, objectives, and findings.
Introduction (0-2): [2] Well-structured and provides solid context for the research.
Data Section (0-6): [6] The data is well-described, including key variables and limitations.
Model (0-5): [3] A solid start, but further elaboration on model assumptions, validation, and multicollinearity is needed.
Results (0-10): [6.5] Results are presented effectively with visuals, but a deeper discussion on state-level results and further analysis would enhance this section.
Discussion (0-10): [6] Thorough, but more detail on key findings and addressing model limitations would strengthen it.
Prose (0-5): [4] Clear and well-structured, with a few minor grammatical issues.
Visuals (0-5): [5] Effective visuals that support the paper’s points.
Reproducibility (0-5): [5] The provided code and repository ensure reproducibility, and the README includes detailed instructions.
Estimated Mark
[42] out of [54], 70%
Comments
This paper is really well written, meets the criteria, and provides a strong foundation. If it incorporated more sophisticated models and richer predictors, it could serve as a powerful forecasting tool for the 2024 election.
Peer Review of the Paper “Forecasting the 2024 U.S. Presidential Election”
Summary
This paper presents a statistical model to predict the outcome of the 2024 U.S. Presidential Election using a multiple linear regression model based on polling data. The model forecasts support for Kamala Harris and Donald Trump by predicting the number of electoral college votes they are likely to win. The results indicate that neither candidate is likely to secure the required 270 votes. This analysis incorporates predictors such as pollster ratings and state-level demographics.
Strong Positive Points
Critical Improvements Needed
Suggestions for Improvement
Evaluation
R,tidyverse, andggplot2are properly cited.Estimated Mark
[42] out of [54], 70%
Comments
This paper is really well written, meets the criteria, and provides a strong foundation. If it incorporated more sophisticated models and richer predictors, it could serve as a powerful forecasting tool for the 2024 election.