Title
Seeing the forest, not the trees – Crowdsourced data collection methods for sector-wide research
Author(s)
Voiklis, John; Flinner, Kate; Field, Shaun; Gupta, Rupanwita; Fraser, John; Dwyer, Joseph de la Torre; Rank, Shelley; Nock, Kathryn
Published
2023
Publisher
Visitor Studies
Abstract
Research that involves a large and broad sample of museums can produce a representative picture of the entire museum sector and lead to global insights that may not be attainable through a more local lens. However, many museum research projects use a small sample of museums, meant to represent the entire field. We propose a research method that distributes data collection across a broad swath of museums to provide local detail that can be used to assemble a collective picture on a topic of interest to the field. This method, called crowdsourced data collection, was used in a yearlong study of zoos and aquariums in North America, in which 95 institutions were asked to collect data for one to two survey modules per month. We hoped this approach would produce data comparable to data gathered with conventional methods and reduce burden on participating institutions. We found the method replicated nationally representative studies with two validated scales. While only one third of the institutions completed all modules, institutions typically did 8-9 modules, with only slight decreases in the probability of completing the study over time. These results suggest researchers can use crowdsourced data collection to reliably study the museum sector. We also discuss the challenges of this method for researchers and institutions participating as data collection sites.
Keywords
attrition; crowdsource; data collection; methods; replication; sector study
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PUB35924