| dc.contributor.author | Hannington Ochieng1, 2* , John Ojiem1 , Joyce Otieno2 | |
| dc.date.accessioned | 2022-01-30T08:14:26Z | |
| dc.date.available | 2022-01-30T08:14:26Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | https://repository.maseno.ac.ke/handle/123456789/4768 | |
| dc.description | DOI:https://osf.io/preprints/africarxiv/ncw8a/ | en_US |
| dc.description.abstract | The number of non-experts (such as farmers) participating in research activities has increased
over the years, with the aim of them addressing their heterogeneous conditions. The situation
has resulted in them being engaged in data collection through a process called crowdsourcing.
The study examined the level of variation between data sets and the conclusions drawn from
data collected using researcher (expert) and farmer (non-expert) methodologies, and also
determined the associated trade-offs for using either methodology. The results showed a low
convergence between individual observations of the methodologies on most variables with
coefficients ranging from |0.39| to |0.60|. However, there was stronger convergence in the
conclusions drawn when the results were aggregated (r>|0.80|) for all the variables tested in
this study. Therefore, expert and non-expert data were equivalent for average results.
However, data may not be comparable for understanding variations in technology
performance due to lack of precision in the subjective assessments of farmers relative to the
objective measurements of the researcher | en_US |
| dc.subject | farmer, researcher, methodology, crowd science, crowdsourcing, participatory research | en_US |
| dc.title | Farmer versus Researcher data collection methodologies: Understanding variations and associated trade-offs | en_US |
| dc.type | Article | en_US |
