Proposal Review

[LaurieLBaker]

Proposal Review

Reviewer: Professor Baker Date: 03/14/2022

Section 1 - Introduction:

The introduction should introduce your general research question and your data (where it came from, how it was collected, what are the cases, what are the variables, etc.).

What is their general research question: Is there a correlation between the racial majority of the neighborhood and the number of allegations against police officers? Is there a correlation between the racial majority of the neighborhood and how likely the allegation is to be disciplined? Is the general research question clear? If it is not clear, what questions do you have?

• Well-defined questions and subquestions!

Section 2 - Data:

• [X] Is the data in the /data folder?
• [ ] Does the README include the dimensions and codebook for the data set? (Yes, you'll want to move this to the data folder thought).
• [ ] Does the proposal include the output of glimpse() or skim() of the data frame. (Yes, you'll want to move this to the proposal folder).

Data suitability:

• [X] Does the dataset have at least 50 observations and between 10 to 20 variables (exceptions can be made).
• [X] Does the data set include a mix of categorical variables, discrete numerical variables, and continuous numerical variables.
• [X] What variables does the data include (list below): (See file).

  Section 3 - Data analysis plan:

• [X] Does the proposal include outcome (response, Y) and predictor (explanatory, X) variables they will use to answer your question? And/or the comparison groups they will use, if applicable.
• Great work! Very clear.

Do the outcome and predictor variables and/or comparison groups make sense in the context of the question? Why or Why not?

• Yes. I think that it will look really interesting.
  • [X] Does the proposal include some very preliminary exploratory data analysis, including some summary statistics and visualizations, along with some explanation on how they help you learn more about your data. (They can add to these later as they work on their project.)

• [X] Does the proposal include the statistical method(s) that they believe will be useful in answering your question(s). (They can update these later as they work on their project.)

• [X] Do they include what results from these specific statistical methods that are needed to support their hypothesized answer?

Reflections

1. What was something you found interesting about the project?

   • Great work finding the various datasets. I think this dataset and question are hugely relevant and I look forward to seeing what you create.

2. What ideas/feedback do you have for other things they may explore?

   • I mentioned to Ella that you might look at changes overtime and explore gganimate and heat maps. I'd be interested to know whether the spatial data matches up across the different datasets and what you have found challenging.

3. What kinds of plots should they consider to complete the project goal to create some kind of compelling visualization(s) of this data in R?

   • gganimate, heat maps and/or maps of the data. You have a time and space component that will be really interesting to explore.
   • I think looking at data for completeness and also patterns in what is missing will be interesting and important.

4. Any additional feedback you'd like to give the other group:

   • Great work! The proposal is looking great. Just need to shift things into different folders.

[SadieKriegler]

Reflections What was something you found interesting about the project?

• I found it super interesting that they were focusing on a relevant topic - one that is applicable to the world we live in today. This will make it easier for them to capture our interest and share their visualizations.

What ideas/feedback do you have for other things they may explore?

• It might be of interest for them to explore seeing how this data impacts us. If there is some way to explore the data within the Lewiston/Auburn community and then do something with it, even just circulating it around campus for awareness.

What kinds of plots should they consider to complete the project goal to create some kind of compelling visualization(s) of this data in R?

• They should consider actually mapping the data by location. It might be interesting to visualize actual hot spots in a country/state/city; this might be easier to digest than raw numbers.

Any additional feedback you'd like to give the other group:

• I would encourage this group to limit what they want to do. They have a lot of data and it seems like an easy place to get lost and distracted; narrowing your focus (or maybe even your data) might lead to a more coherent project.

[LaurieLBaker]

Great work! Nice job @ellabeiser, @smsokona, @EllyBeckerman. See feedback above.