Design a Survey Rubric

	Wow!	Getting There	Needs Improvement
Brainstorming Phase and Survey Creation	We developed at least eight questions, and correctly identified which would be answered by categorical or quantitative data. We correctly determined which data type each question will produce, and created a digital version of our survey.	We developed eight questions, but weren’t always sure which would be answered by categorical vs. quantitative data. We couldn’t always determine which data type each question would produce, but we created a google form with our questions.	Our questions were often incorrectly categorized as categorical vs. quantitative, and we had a lot of confusion about which data type each question would produce. We did not finish making the digital survey.
Survey Hacking	We outlined several examples of realistic, dirty data that we entered on another group’s survey. We offered compelling and practical suggestions to guard against dirty data, and shared insights that could help us improve our own survey.	We outlined a few examples of dirty data that we entered on another group’s survey, but the examples were not always realistic. Our suggestions to guard against dirty data needed to be more specific. We shared one insight to help us improve our own survey.	Our examples of dirty data were not realistic. Our suggestions to guard against dirty data were not useful or helpful to the other group. We did not demonstrate that we learned how to improve our own survey.
Required Questions	We correctly indicated all questions that are required.	We sometimes indicated required questions.	We forgot to indicate required questions.
Question Format	We strategically used multiple choice answers, checkboxes, and dropdown menus when possible to prevent dirty data.	We missed one or more opportunities to use multiple choice answers, checkboxes, or dropdown menus to prevent dirty data.	We did not consider question format as a tool to prevent dirty data.
Description	Each question has appropriate and helpful instructions that help collect maximally clean data.	Most questions have helpful instructions and / or the instructions could be clearer.	We often forgot to include instructions with questions and / or our instructions were confusing.
Validation	When relevant, we specified answer data types and / or parameters to prevent dirty data.	We sometimes forgot to specify data types and / or parameters or we did not correctly specify data types.	We did not specify data types and / or parameters in order to guard against dirty data.

Wow!

Getting There

Needs Improvement

Brainstorming Phase and Survey Creation

We developed at least eight questions, and correctly identified which would be answered by categorical or quantitative data. We correctly determined which data type each question will produce, and created a digital version of our survey.

We developed eight questions, but weren’t always sure which would be answered by categorical vs. quantitative data. We couldn’t always determine which data type each question would produce, but we created a google form with our questions.

Our questions were often incorrectly categorized as categorical vs. quantitative, and we had a lot of confusion about which data type each question would produce. We did not finish making the digital survey.

Survey Hacking

We outlined several examples of realistic, dirty data that we entered on another group’s survey. We offered compelling and practical suggestions to guard against dirty data, and shared insights that could help us improve our own survey.

We outlined a few examples of dirty data that we entered on another group’s survey, but the examples were not always realistic. Our suggestions to guard against dirty data needed to be more specific. We shared one insight to help us improve our own survey.

Our examples of dirty data were not realistic. Our suggestions to guard against dirty data were not useful or helpful to the other group. We did not demonstrate that we learned how to improve our own survey.

Required Questions

We correctly indicated all questions that are required.

We sometimes indicated required questions.

We forgot to indicate required questions.

Question Format

We strategically used multiple choice answers, checkboxes, and dropdown menus when possible to prevent dirty data.

We missed one or more opportunities to use multiple choice answers, checkboxes, or dropdown menus to prevent dirty data.

We did not consider question format as a tool to prevent dirty data.

Description

Each question has appropriate and helpful instructions that help collect maximally clean data.

Most questions have helpful instructions and / or the instructions could be clearer.

We often forgot to include instructions with questions and / or our instructions were confusing.

Validation

When relevant, we specified answer data types and / or parameters to prevent dirty data.

We sometimes forgot to specify data types and / or parameters or we did not correctly specify data types.

We did not specify data types and / or parameters in order to guard against dirty data.

These materials were developed partly through support of the National Science Foundation, (awards 1042210, 1535276, 1648684, and 1738598). Bootstrap by the Bootstrap Community is licensed under a Creative Commons 4.0 Unported License. This license does not grant permission to run training or professional development. Offering training or professional development with materials substantially derived from Bootstrap must be approved in writing by a Bootstrap Director. Permissions beyond the scope of this license, such as to run training, may be available by contacting contact@BootstrapWorld.org.