Peer Review by Vidhan

[vidhanshar]

The report showcases an adept understanding of statistical analysis and the research domain, evidenced by the highly nuanced and comprehensive selection of plausible factors that could explain the distinction between MPs and the appropriateness of all the employed statistical techniques. The author adeptly applies web scraping and data wrangling techniques, sourcing data from diverse ancillary sources to accurately represent and train independent variables in the regression model. Further, demonstrating a strong statistical understanding, the author aptly navigates various data types, including indexes, categorical, and continuous variables. Logistic regression model certainly lends increased statistical substantiveness and accuracy to the explanatory impact of predictor variables on the probability of a MP focusing on welfare issues, by computing average marginal effects, and isolating the impact of each variable from the average profile and observing it across 1000 simulations of the model. However, to enhance accessibility, simplifying complex statistical findings for a broader audience is recommended. Discussion discrepancies for Fig.4 exist; it indicates only Age and Seat Safety negatively impact the average probability, yet the marginal effect for Socioeconomic Index is -0.079, unaddressed in the discussion. Additionally, the marginal effect is mentioned to be 0.3% for Age and Seat Safety, but it is -0.003 and -0.029 respectively, as per Fig 4. Clarification on these inconsistencies may be needed.

The code executed seamlessly, ensuring reproducibility of outputs. Processed API responses and web-scraped data were consolidated as data frames and saved as CSV files in a designated ‘data’ folder, enhancing efficiency by eliminating the need for repeated data gathering. Additionally, well-annotated code and descriptive variable names facilitated easy comprehension.

[methodology-asds]

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