Hi All, I am scheduled to teach a graduate course on research methods in health sciences at a university. While drafting the course proposal, I decided to include a brief introduction to R, primarily with an objective to enable the students to do data analysis using R. It is expected that enrolled students of this course have all at least a formal first level introduction to quantitative methods in health sciences and following completion of the course, they are all expected to either evaluate, interpret, or conduct primary research studies in health. The course would be delivered over 5 months, and R was proposed to be taught as several laboratory based hands-on sessions along with required readings within the coursework.
The course proposal went to a few colleagues in the university for review. I received review feedbacks from them; two of them commented about inclusion of R in the proposal. In quoting parts these mails, I have masked the names/identities of the referees, and have included just part of the relevant text with their comments. Here are the comments: Comment 1: "In my quick glance, I did not see that statistics would be taught, but I did see that R would be taught. Of course, R is a statistics programme. I worry that teaching R could overwhelm the class. Or teaching R would be worthless, because the students do not understand statistics. " (Prof LR) Comment 2: Finally, on a minor point, why is "R" the statistical software being used? SPSS is probably more widely available in the workplace – certainly in areas of social policy etc. " (Prof NB) I am interested to know if any of you have faced similar questions from colleagues about inclusion of R in non-statistics based university graduate courses. If you did and were required to address these concerns, how you would respond? TIA, Arin Basu ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.