About the Lab

This is a 10-week hands-on lab; we’ll be learning how to use data and data science to answer important and public-minded questions with an emphasis on justice and equity. You don’t need to be an expert in statistics or R or coding coming in - though the project will provide plenty of opportunities to become more adept. Good data-oriented work comes from the collaboration of people with diverse talents and perspectives and developing expertise. We need individuals data interested in data wrangling and analysis, working in R, and visualizing data effectively; but we also need people with an understanding or interest in child welfare, who can ask probing questions and think creatively, who can help teams work better and keep projects moving, who can dig into a new substantive area and synthesize information, and who can communicate about results and write effectively. That doesn’t mean everyone won’t be responsible for learning about and contributing to each step of the project, but you may find that at some points you are more of a learner and at other points you are more of a leader.

Throughout the project you’ll be working in smaller teams (of about 3) to complete assigned tasks for the fuller project. We’ll need to quickly develop a better understanding of the child welfare system and processes. We’ll need to understand the data we have, consider its provenance, and evaluate it’s limitations. There will be no ``right answers,’’ and we don’t have pre-determined outcomes. So it will be very representative of real-life data science work - embrace the ambiguity!

Expectations and Evaluations

Grades will depend on your learning and contribution to the project, which will be assessed by

• Three data exploration assignments (10%): In weeks 1-3, individuals will start to learn about the data while practicing using R. Individuals should take a stab at completing these on their own, but can ask a colleague or instructors for help or troubleshooting if they get stuck, ideally through slack so that the learning can be shared. If you’re in the position of helping a colleague, use it as a chance to deepen your own understanding by explaining ideas (don’t just share your work).

  • COVID revision: none, this was basically complete pre-pandemic.

• Class contributions and participation (25%): Since we are working as a group to complete a real project for a real partner/client, attending class prepared to be fully engaged will help you get the most out of the experience and give the most to our joint effort. Come prepared, do the reading, ask questions, chime in with ideas, help develop solutions, complete work you’ve been assigned.

  • COVID revision: To recreate the ability to discuss the remaining reading, we’ll all contribute to collaborative annotations and commenting of the text via Perusall. Similarly, team peer review will take place via slack to make sure we have the opportunity to understand one another’s work and provide suggestions.

• Team assignments (30%): From week 4 on, we’ll begin working in groups to complete parts of the overall project, including reviewing different parts of the literature, exploring different parts of the data set, analyzing different subsets of questions, writing different elements of the overall report. Once teams have been formed, I expect groups to meet each week between class to work together. Group members will have a chance to evaluate the contributions of one another as well. Each group will report on their progress each week, sometimes to the class, sometimes through documents submitted on slack. Based on that progress, we’ll determine the next steps that should be completed for the following class. Though I have something of an outline in my head of where I imagine we’ll be from week to week, applied research and data analysis often does not conform to planned timelines. In part, that’s what makes it exciting, but I know that can be uncomfortable for some, so I want to be upfront that the project will require some flexibility on all our parts.

  • COVID revision: This is pretty much the same, only groups may need to work differently (and more asynchronously).

• Weekly updates (10%): Beginning in week 5, individuals will submit a weekly update to me on slack briefly describing your individual contribution in the past week, what you’re anticipating completing in the coming week, and alerting me to any problems you or your team are facing.

  • COVID revision: scratching this – and folding it into the collaborative annotation, team progress reports, and team peer review.

• Final report (25%): Each group will be responsible for part of the final report, including some piece of the pre-analysis sections (overview, literature, and description of the data), as well as the communication of the processes and results around the research questions on which you took the lead.

  • COVID revision: This is pretty much the same.

On Data Science and R

We will be using R to do our analytical work. Some of us will have prior experience with R and with statistics, some of us will not – and that’s entirely fine.

• For R: We’ll spend time in class learning to use R, we’ll learn together and teach each other (some of your colleagues will be excellent resources), we’ll ask and answer questions on slack, and Charlotte and Prof. Claibourn will be available to help troubleshoot or explain. There’s really no better way to learn something like R than by diving in on a project you care about.

• For Data Analysis: You may find you need to learn about models or methods you don’t already know (it’s not yet clear what models we might need until we really get started). But we won’t let you go outside the bounds of appropriate statistical practice or leave you on your own to figure it out!

  • COVID revision: We’ll try to post examples or link to materials for learning new steps in R, and will be active in Slack and via online office hours in helping troubleshoot.

On Technology in the Lab¹

Some of our time together will be spent discussing the readings and projects, how we will practice responsible use of the data and the narrative we are creating, and providing feedback to our colleagues on their work. During these times, release yourself from your laptop and other electronic devices.

Some of our time together will be spent actively learning to use R for data wrangling, analysis, and visualization and working on the project in teams. During these times, of course, you’ll need your laptop.

1. Thanks to a past lab member for suggesting this.