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Thread for researchers (especially white researchers who often sit in rooms full of white researchers).
Four tips for building an ethical and equitable #research practice
#EconTwitter #PolicyTwitter #Nonprofits #philanthropy https://twitter.com/SMMcKernan/status/1273087685301882880
Four tips for building an ethical and equitable #research practice
#EconTwitter #PolicyTwitter #Nonprofits #philanthropy https://twitter.com/SMMcKernan/status/1273087685301882880
#1 PROVIDE AN ALTERNATIVE
If a funder/board member/colleague asks you for data that could cause harm to collect, recommend a better way.
Say this: "It would a breach of trust to include that question on a survey. Here's an existing data source that can speak to your question."
If a funder/board member/colleague asks you for data that could cause harm to collect, recommend a better way.
Say this: "It would a breach of trust to include that question on a survey. Here's an existing data source that can speak to your question."
#2 INTERROGATE RESEARCH QUESTIONS
Many social science "research questions" are motivated by racial stereotypes and other biases.
Regarding unrestricted cash payments, you could ask: "Why is it important to you to know how families spend the funds?"
#MoneyForThePeople
Many social science "research questions" are motivated by racial stereotypes and other biases.
Regarding unrestricted cash payments, you could ask: "Why is it important to you to know how families spend the funds?"
#MoneyForThePeople
#3 MAKE SPEAKER REFERRALS
When you're invited to speak on social policy research, unless the panel is about research methods or otherwise specific to your role, recommend someone who was interviewed or surveyed for the project to join you (or replace you) on the panel.
When you're invited to speak on social policy research, unless the panel is about research methods or otherwise specific to your role, recommend someone who was interviewed or surveyed for the project to join you (or replace you) on the panel.
#4 CALL THEM OUT
If someone misrepresents the data, first check your bias. If it feels wrong, ask questions. If it still feels wrong, Let them know you disagree and can't defend their take. This is our responsibility as researchers.
Using statistics =/= learning from data.
If someone misrepresents the data, first check your bias. If it feels wrong, ask questions. If it still feels wrong, Let them know you disagree and can't defend their take. This is our responsibility as researchers.
Using statistics =/= learning from data.
I've found these strategies effective, but the work continues.
I've learned a ton from @Chicago_Beyond's guidebook!
It's a resource for community organizations, researchers, and funders committed to conducting equitable research and evaluation. https://chicagobeyond.org/researchequity/
I've learned a ton from @Chicago_Beyond's guidebook!
It's a resource for community organizations, researchers, and funders committed to conducting equitable research and evaluation. https://chicagobeyond.org/researchequity/
