BeST - Bespoke e-Style Statistical Training for AfricaMiranda Y. Mortlock, Vincent A Mellor
School of Agriculture and Food Science, Faculty of Science, University of Queensland, Australia
Good Design Good Research
Figure 1: Field trials of maize in Harare, Zimbabwe–Scientists require good statistical skills for design and anal-ysis of trials
Poor field study design and the inadequateanalysis of resultant data (Mouyelo-Katoula2006) can lead to expensive research with littleor no useful outcome. In Africa, which isresource rich but wealth poor, this issue is ofgreat importance. Also with the vast size of thecontinent it is difficult and a major commitmentto access trained Statistical Consulting support(Thabane et al. 2008).
Our project is designed to produce an end-to-end statistical training programme, easilyaccessible on both computers and mobile devices,specifically aimed at Agricultural Research inAfrica. We structure our modules from initialdesign methods, all the way to the presenta-tion of results in an accessible manner for allmathematical abilities.
Filling a need
As the Statistical Training programme was a com-ponent of the larger research on Sustainable Intensi-fication of Maize and Legume Systems for Food Se-curity in Eastern and Southern Africa (SIMLESA)supported by the Australian Centre for InternationalResearch (ACIAR), the project would focus mainlyon applications to crop science. Before starting todesign the modules, a small interest study was un-dertaken, with the results showing strong interest ina statistical training programme are shown below.
The Training Programme
The development of ‘BeST’, an online course forAfrican early career researchers and scientists, wasstarted in 2014 by a team of Agricultural Re-searchers, some with a background of teaching inAfrica, Mathematicians, Physicists and Engineers.With approaches from different fields and back-grounds, the course was designed around the use ofR software (R Core Team 2016) using the popularRStudio interface RStudio Team (2015) to provideresearchers of variable mathematical ability com-plete methods for designing, then analysing and re-porting their experiments. The focus is on a graphi-cal modular format which provides online and down-loadable resources. As the programme’s primary fo-cus is on those with limited backgrounds, extensionsto topics are provided for those wishing to know the“mechanics” behind the methods. The programmehas also been designed with an open source approachand is licensed under Creative Commons
Modular Design
Special Issues
In African Internet Affordability is poor and cover-age of technology can be difficult, especially at fieldstations. With this in mind, this online programmehas been designed to be lightweight and providesdownloadable resources in the form of pdf ‘Walk-thoughs’ of particular analysis methods along withpresentations of key points. When code is presented,the syntax is explained so that it may be used againwith different data easily. Modules on Data Collec-tion, Storage and Processing are provided as theseare key issues for any study. As time may be lim-ited, the programme’s modular design aims to let re-search learn about specific topics, while seeing howthey can link together to give ‘toolbox’ of powerfulstatistical methods.
Acknowledgements
This project is part of the research into Sustainable Intensifica-tion of Maize and Legume Systems for Food Security in Easternand Southern Africa (SIMLESA), and the team would also liketo thank the following organisations for the continued support:
References
Mouyelo-Katoula, M. (2006), ‘Rethinking statistics for na-tional development in Africa’, The African Statistics Jour-nal 2, 140.
R Core Team (2016), R: A Language and Environment forStatistical Computing, R Foundation for Statistical Comput-ing, Vienna, Austria.URL: https://www.R-project.org/
RStudio Team (2015), RStudio: Integrated Development En-vironment for R, RStudio, Inc., Boston, MA.URL: http://www.rstudio.com/
Thabane, L., Chinganya, O. & Ye, C. (2008), ‘Training youngstatisticians for the development of statistics in africa’, TheAfrican Statistical Journal 7, 125.
Contact Information• Web: http://www.yieldingresults.org
• Twitter: @BeST_MYM• Facebook: @BeST4AAR• G+: +YieldingresultsOrg
