A Conversation About Data Transparency

The integrity of the research process serves as the foundation for excellence in research on nonprofit and voluntary action. While transparency does not guarantee credibility, it guarantees you will get the credibility you deserve. Therefore we are developing criteria for transparency standards with regards to the reporting of methods and data.

We started this important conversation at the 48th ARNOVA Conference in San Diego, on Friday, November 22, 2019. In the session, we held a workshop to survey which characteristics of data and methods transparency that help review research and utilize past work as building blocks for future research.

This session was well attended and very interactive. After a short introduction by the editors of NVSQ, the leading journal in the field, we split up in three groups of researchers that work with the same type of data. One group for data from interviews, one for survey data, and one for administrative data such as 990s. In each group we first took 10 minutes for ourselves, formulating criteria for transparency that allow readers to assess the quality of research. All participants received colored sticky notes, and wrote down one idea per note: laudable indicators on green notes, and bad signals on red notes.

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Next, we put the notes on the wall and grouped them. Each cluster received a name on a yellow note. Finally, we shared the results of the small group sessions with the larger group.

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Though the different types of data to some extent have their own quality indicators, there were striking parallels in the match between theory and research design, ethics, sampling, measures, analysis, coding, interpretation, and write-up of results. After the workshop, we collected the notes.

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We will write up a report to be shared later.

Best wishes,

The Working Group on Data Transparency: René Bekkers, Lehn Benjamin, Jesse Lecy, Paloma Raggo and Kelly LeRoux

NVSQ Editors: Chao Guo, Susan Phillips, and Angela Bies

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Filed under data, experiments, methodology, open science, survey research

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