From the reader's perspective, enhancing the presentation of counts and summary statistics through visual representation would improve clarity. The map's inference about clustering of points needs validation, which could be achieved by providing constituency population data and urban/rural classification. Interpreting regression results on a log scale might be challenging for readers, suggesting a switch to the odds scale (odds ratios or odds ratio percentages) for better comprehension. In terms of the overall analysis, providing justifications for incorporating median wage and devolution variables, and explaining the focus on questions post-May 10, 2022, particularly with a reduced emphasis on oral questions, would create a more cohesive and transparent framework. While Multinomial Regression and the choice of the Treasury as the reference category aligns with the research focus but lacks an explanation. Additionally, using the Conservative party as the reference category for political party requires justification. Despite generally low p-values, adding the likelihood ratio test would enhance the reader's understanding of the overall model fit.
Employing partial name matching in scraping majority and coordinates data could have prevented data loss for 11 and 26 constituencies respectively, ultimately avoiding the removal of 72 observations during mapping. Instead of scraping the nations , it could have been integrated during the majority data scraping by adding a column with the respective nation names. Overcounting occurred while summarizing majority and median wage data due to multiple questions asked by some parliamentarians. Optimizing the use of a relational database for analysis through SQL queries would be more effective, as the current approach involves retrieving the entire table with a single query and using the dplyr package, rendering the relational database setup redundant.
From the reader's perspective, enhancing the presentation of counts and summary statistics through visual representation would improve clarity. The map's inference about clustering of points needs validation, which could be achieved by providing constituency population data and urban/rural classification. Interpreting regression results on a log scale might be challenging for readers, suggesting a switch to the odds scale (odds ratios or odds ratio percentages) for better comprehension. In terms of the overall analysis, providing justifications for incorporating median wage and devolution variables, and explaining the focus on questions post-May 10, 2022, particularly with a reduced emphasis on oral questions, would create a more cohesive and transparent framework. While Multinomial Regression and the choice of the Treasury as the reference category aligns with the research focus but lacks an explanation. Additionally, using the Conservative party as the reference category for political party requires justification. Despite generally low p-values, adding the likelihood ratio test would enhance the reader's understanding of the overall model fit.
Employing partial name matching in scraping majority and coordinates data could have prevented data loss for 11 and 26 constituencies respectively, ultimately avoiding the removal of 72 observations during mapping. Instead of scraping the nations , it could have been integrated during the majority data scraping by adding a column with the respective nation names. Overcounting occurred while summarizing majority and median wage data due to multiple questions asked by some parliamentarians. Optimizing the use of a relational database for analysis through SQL queries would be more effective, as the current approach involves retrieving the entire table with a single query and using the dplyr package, rendering the relational database setup redundant.