Farmer versus Researcher data collection methodologies: Understanding variations and associated trade-offs [post]

Hannington Odido Ochieng, John Ojiem, Joyce Otieno
2019 unpublished
The number of non-experts e.g. farmers, participating in research activities have increased over the years with an aim of addressing their heterogeneous conditions. This has resulted into 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
more » ... fs for using either methodology. The results showed 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 equivalent for understanding variations in technology performance, due to lack of precision in the subjective assessments of farmer relative to the objective measurements of the researcher.
doi:10.31730/osf.io/ncw8a fatcat:tulqbonwpbhhdevjw4ygmfjfaq