Last updated: 30 April 2017
Data collection is an integral component of promoting solar cooking. In 2017, Solar Cookers International drew upon a literature review of solar cooker project evaluations, and a thirty year period of sector experience, to provide the following checklist to assist in planning new projects.
Data Collection Part I: A necessity, not an option
- Include data sharing as part of the project partner selection process. Clearly communicate that data collection and sharing is expected, not optional.
- Include a detailed plan for data analysis. Who will collect the data? Where? When? How often?
- Have the costs of data collection and project evaluation been included in the project budget and grant applications?
- Data quality control: who will follow up if some of the respondents’ answers don’t seem clear?
- Solar Cookers International recommends using the SCI Adoption & Impact Survey developed by the global network. It harmonizes with surveys used by cookstove and international organizations.
- SCI recommends conducting the baseline survey before starting the intervention. We recommend conducting the post-intervention survey one year after the group began solar cooking.
- Add data to SCI’s solar cooker distribution map.
- April 2017: Share your best practices with email@example.com.
Data Collection Part II: Successful Solar Cooking Projects
- With the Solar Cooking Adoption and Impact Survey, we recommend doing the baseline survey (before people start solar cooking) and the post distribution questions 1 year after they started solar cooking.
- Make sure the data gets added to SCI’s map of solar cooker distribution.
- Include evaluation costs in initial project budget and grant applications.
- Include agreement to data sharing as part of the initial project participation selection. (participants should understand this is an expectation for being a part of the project)
- Include regular meeting times in the project design for the project participants to problem solve, develop community, and share data.
- Make sure the surveyor understands the questions and expected answers.
- Gather individual personal success stories AND facts and figures. Include requests for photographs and/or video and/or quotes as part of the grant agreement (and budget) with the implementing organization with a specific # and deadline.
- Make sure there is a plan for data analysis (who is doing it? Where? When? Is it included in the budget) and data quality control (a way to follow up if some of the respondents answers don’t seem clear or might have been communicated incorrectly).
- Make sure there is a way to understand local units of measure of fuel (like bags of crop waste) in universal terms (like kgs).
- Consider the format that the survey answers are recorded in. Excel sheets would be much easier for data analysis, but so far I’ve only been able to receive answers in Microsoft word format. Account for time to transfer data from Word to Excel formats if that is the case. If possible, use the Google Form version of the survey (but that requires internet at some point in time).