Resources

Learn how to improve your data practices, make informed decisions, and enhance your impact.

How to ask good questions
What makes a good survey, interview, or focus group question? This video and article provides guidance for how to think about what you really want to ask.
Webinar: Applying Trauma-Informed Practices to Workplace Cultural Assessments
The B-Word: What nonprofits should know about biases in research | The Data Coach |
Five tips to help boost your survey response rates! | The Data Coach | Nonprofit consultant
The B-Word: Types of bias in research and how to avoid them
For nonprofits interested in using data for strategizing, evaluation, and advocacy, it is essential to understand the different types of bias and how to avoid them.
What makes research "valid"?
Ensuring validity means that the project team has taken steps to avoid making common mistakes in research design, data collection, and data analysis.

Upcoming Events

April 16th-18th
NTEN 'S Nonprofit Technology Conference

I'll be hosting a workshop called, "Using trauma-informed principles in data collection: Stressed-out staff edition." The workshop will cover how to use trauma-informed principles, such as transparency, collaboration, and choice, to gather critical feedback from their staff members.

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Resources in

Data Collection

How to ask good questions
What makes a good survey, interview, or focus group question? This video and article provides guidance for how to think about what you really want to ask.
Webinar: Applying Trauma-Informed Practices to Workplace Cultural Assessments
The B-Word: What nonprofits should know about biases in research | The Data Coach |
Five tips to help boost your survey response rates! | The Data Coach | Nonprofit consultant
The B-Word: Types of bias in research and how to avoid them
For nonprofits interested in using data for strategizing, evaluation, and advocacy, it is essential to understand the different types of bias and how to avoid them.
What makes research "valid"?
Ensuring validity means that the project team has taken steps to avoid making common mistakes in research design, data collection, and data analysis.
illustration of two people looking through various data models

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