Affecting Machines

INCLUSIVE DATA COLLECTION

Inclusive data collection is a critical step in assessing the efficiency of your organization’s internal processes and gender equity initiatives. Collecting data on gender disparities will allow you to gain a comprehensive understanding of the numerous – yet often understudied – challenges faced by women and 2SLGBTQIA+ collaborators both within your organization and the tech sector more broadly. 

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Addressing Gender Disparities

Aids in spotting gender disparities and ensures equal opportunities.

Tailoring Gender Equity Initiatives

Helps in designing specific gender equity programs.

Retaining Employees

Understands employee needs, especially from marginalized backgrounds.

Boosting Employees’ Wellbeing

Enhances the sense of belonging and wellbeing of employees.

Attracting New Talent

Reflects an organization’s commitment to gender equity.

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DATA YOU
CAN READ

Qualitative data is broadly defined as “data you can read.” It is appropriate when you want to assess the effectiveness of a program or understand your employees’ particular needs, concerns, and aspirations. See below how to collect qualitative data inclusively and respectfully.

Interviews

Ensure diversity, use inclusive language, and maintain privacy. 

Focus Groups

Recognize group dynamics and promote marginalized voices. 

Quantitative data is defined as “data you can count.” Quantitative data is useful when you want to measure how widespread an issue is or evaluate the general impact of a program. See below how to collect quantitative data inclusively and respectfully.

DATA YOU
CAN
COUNT

Sampling

Represent the diversity in the sample population.

Inclusive Survey Design

Use accessible language and accommodate diverse identities. 

Inclusive Data Analysis and Transparency

When analyzing data, consider subgroups and avoid generalizing findings on a dominant gender group. Disaggregate data to identify patterns and disparities within different demographics. Clearly communicate your limitations and acknowledge any potential biases or gaps in your findings.

STEPS + CONSIDERATIONS

  • Identify Goals: Understand what you want to measure. 
  • Choose Respondents: Know your target audience. 
  • Design Tools: Keep them simple and understandable. 
  • Conduct Study: Ensure a safe environment. 
  • Analyze Data: Adopt a community-inclusive approach. 
  • Share Results: Disseminate using accessible means. 
  • Gather Feedback: Welcome criticism and opinions. 
  • Plan for Adaptation: Implement feedback as per resource availability. 
  • Disposition: Choose to either destroy or archive data with transparency. 
  • Protect Participants: Prioritize their rights and anonymity. 
  • Maintain Transparency: Explain the purpose and approach of data collection. 
  • Avoid Categorization: Use open-ended questions. 
  • Acknowledge Power Dynamics: Ensure everyone’s freedom to express. 
  • Make Tools Understandable: Ensure clarity and accessibility. 
  • Limit Burden: Minimize nonessential collection. 
  • Accountability: Be responsible and open to feedback. 
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