Dr. Shrout – Data Cultures

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Class 14.2

Posted on December 3, 2018December 6, 2018 by Dr. Shrout

Ethical Concerns When Working with Data

Data is not objective, and is not neutral. (Noble, O’Neill)
Human motives influence the bias in the data. (Eviction Lab)
Readers might not be aware of these biases. (Simpson’s Paradox, Asimov)
Data needs to be looked at in context. (Klein, Klein and D’Ignazsio)
Lack of depth/context can lead to unintentional bias. (Eviction Lab, O’Grada and Guinnane)
In order for users to feel safe, they should understand how/where data is being collected. (Eviction Lab, Force 11)
Not enough people know that data is not your own. (Gabourey)
Verifying your data
Make sure your survey design is good (Eviction Lab)
Consent (What you Can’t and Shouldn’t do with social media)
It is not always clear what your audience is when using social media to collect data (Gabourey, Klein, Healy)
Think about the financial incentives (Gabourey)
Publishing accurate statistics makes it possible to infer personal information (Healy)

Posted on December 3, 2018December 6, 2018 by Dr. Shrout

Best Practices for Sharing Data and Code

Make sure that your data was ethically collected. (Pew, Healey)
Use common language (Binder)
Have variables that make sense
Make sure that the layout of your interface is mobile accessible
Use keys
Make sure that your data that can be analyzed in many softwares (i.e. non-proprietary format)
Pre-usage instructions (e.g. read me document)
Being respectful – anticipating the expectations of the people whose data you are using (Noble)
Sticking to larger datasets
Only share the data you need/used for replication
Account for the Hawthorn effect
Do not share names and location.

Posted on December 3, 2018December 6, 2018 by Dr. Shrout

Best Practices for Making Public-Facing Data Projects

Make the presentation of the data understandable by anyone. Enough so that it is not necessary for you to be there to describe the data
Use a shared vocabulary so people can understand your work without doing too much learning
Make sources of information clear and available. Be clear about where the data is coming from and how you collected it (Klein and D’Ignazsio)
Protect any personal information provided by people that you used for your data
Be mindful of the purposes of your research
Create visual / easy to understand methodology along with data (Drucker)
Always cite our sources to ensure credibility & give credit to people/services that are typically overlooked
Make sure any visualizations accurately represents the data
Being transparent enough for replication purposes *(Force 11, Ramsey and Rockwell, Pew, Eviction Lab)
Consider context when looking at data (Klein and D’Ignazsio)
Being overall aware of potential subjectivity of data and how you yourself are able to skew the data.

Posted on November 29, 2018November 29, 2018 by Dr. Shrout

Practice Exercise #7 – Mapping

Use the mapping notebook to map a subset of the interviewees (i.e. all Americans, all women, all mill workers).
Now, map a different but related set (i.e. all Canadians, all men, all shoe workers)
Describe the differences between the two. Include images.