title	UW Resilience Lab Poster Project Proposal
author	Elisa Truong, Hayden Hong, Kassandra Franco, Jared Praino

Overview

I. Purpose

• What is the purpose of your research project?

  • In our research project, we are aiming to scope out the effectiveness of the Resilience Lab’s signs intervention, wherein they distributed signs with positive messages on them in Winter Quarter 2018 as well as the beginning of Spring Quarter 2018 through grassy areas on campus. The general student response to it was particularly interesting, because the signs led to the creation of many memes that denoted a perception of the signs as sarcastic. This meme creation for the Resilience Lab’s signs intervention was what intrigued our group about the signs. The original purpose of the signs as stated by Anne Browning (the head of the UW Resilience Lab) is “to try something new in our efforts to promote compassion on campus,” and as a way “to combat microaggressions with microcompassions.” We want to scope out sentiments towards the signs, by distributing a survey targeted towards UW students. At the end, we want to be able to draw relationships between student demographics/profiles and perception of the signs. We want to provide a resource for the Resilience Lab to use as a way to gain insight regarding their intervention, and ultimately to be able to inform whatever next steps they decide to take.

II. Background

• What other research has been done in this area? Make sure to include 3+ links to related works.

  • There hasn’t been much research about “micro-compassion” to combat microaggression, thus we focused a lot of our background research on microaggression in general since microaggression is a distal factor in someone’s health including what we are ultimately addressing when it comes to the effectiveness of the signs. In one research study, they evaluated the perception of microaggressions in the classroom by teachers and students, and the effectiveness of an authoritative figure to address microaggression. In another study, researchers evaluated and measured the racial microaggression experienced in the University of Illinois by students through a survey. The rest of the resources describe where microaggression stems from as well as how people should approach microaggression.
  1. https://www.tandfonline.com/doi/abs/10.1080/87567555.2012.654831
  2. http://www.racialmicroaggressions.illinois.edu/files/2015/03/RMA-Classroom-Report.pdf
  3. http://www.apa.org/monitor/2017/01/microaggressions.aspx
  4. https://www.psychologytoday.com/us/blog/culturally-speaking/201711/are-racial-microaggressions-college-campuses-harmful

III. Dataset

• What is the dataset you'll be working with?

  • Our group will be collecting data on the effectiveness of the resilience signs through a survey, thus our dataset that we will be working with will be our own.

• Please include background on who collected the data, where you accessed it, and any additional information we should know about how this data came to be.

  • We are college students at the University of Washington conducting a survey to discover the effectiveness of the resilience signs placed throughout campus during Winter and the beginning of Spring quarter. This survey was conducted as a final project for a population health informatics class (INFO 498); the project was to define, explore, and answer a question about a public health topic. The survey is 23 questions with 2 sections: a demographics and a resilience sign section. We will measure the effectiveness of the resilience signs towards achieving the UW resilience objective: to promote compassion on campus to address the microaggressions experienced by students. This will be determined by asking several questions focused around how the students felt after viewing the signs.Some of these questions were “Did the signs change how you feel?” and based on their answer of “yes,” the description that they gave for why they were feeling what they were feeling would determine its effectiveness

• To view the survey: https://goo.gl/forms/eWwTCU8Mxo19XB2X2

IV. Audience

• Who is your target audience? Depending on the domain of your data, there may be a variety of audiences interested in using the dataset. You should hone in on one of these audiences.

  • Our main target audiences would be the Resilience Lab, as they were the main implementers of the intervention, and may want feedback and evaluation regarding it so they can better scope out how to improve future iterations of the intervention.

Objectives

• What should your audience learn from your resource? Please list out at least 3 specific questions that your project will answer for your audience.

  • We want our audience, the Resilience Lab, to be able to use our resource to gain more understanding on the strength and impact of their intervention. Ideally, they would gain insight from our resource and use it to inform their next steps (what should be changed, what should be added, etcetera).

  • Here are specific questions that our project will answer for our audience:

    • Do UW students typically perceive these signs as positive or negative?
    • What is the effect of seeing the signs first on the UW Memes for Boundless Teens as opposed to seeing them on campus first?
    • Is there a correlation between a student’s rating of their own mental health and a perception of the signs?

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Presentation

I. Format

• What will be the format of your final product (Shiny app, HTML page or slideshow compiled with KnitR, etc.)?

  • We are making an R Shiny application for the format of our final product. This application will have a ta b that goes into greater detail for each major question we have about it. These tabs will have visualizations of correlations we find and interactive ways to manipulate and visualize the data. (For example, allowing filtering by grade, gender, or race, using plotly to allow our users to filter specific data out -- point removal)
  • For the word cloud, we might have to use JavaScript and a word cloud NPM package to generate an image.

II. Required Skills

• What new technical skills will you need to learn in order to complete your project?

  • Making the word cloud is something that we do not know how to do. After some quick Googling, it doesn’t look like there is an easy R package to do it, so we might have to find a cool new way of doing that.
  • Some of us do not remember how to do R Shiny at all, so creating and hosting an R Shiny app is something we will have to remember.
  • Identifying and cleaning up “messy” data to ensure our R scripts work well.

III. Challenges

• Do you anticipate any specific data collection / data management challenges?

  • Because we do not have any good datasets to go off of, we had to create our own survey and collect the data ourselves.

• What major challenges do you anticipate?

  • Identifying and cleaning up “messy” data within the dataset will be difficult.
  • Collaboration is difficult sometimes because of Git merge conflicts. We will have to be clear about who is working on what part to reduce redundant work.