For the past couple of months, I have had hundreds of emails – or at least feels that way – back and forth with research students regarding a School-wide introductory quantitative methods course. Some emailed me because they were eager to join the course, while others were not really keen but feeling compelled to learn. Some needed training but they were not able to commit to this 10-week provision because of other prior commitments, while others attended the first week of it and found that it wasn’t pitched at the right level for them. At the end of all this, 30+ students are now taking the course and have just passed halfway. Many others decided to give it a miss this time and asked me for pointers to other opportunities instead. Those in the last category are the ones for whom I thought I’d whip out this post.
As I explicitly indicated here and there on this blog, I see myself as a qualitative researcher – not because of the range of methods that I have employed in my work but because of the kind of questions that I end up pondering. So, when it comes to statistics, I have a working knowledge of it and I would never claim anything more than that. To be more specific, I am comfortable reading papers that draw statistical conclusions and co-authoring a paper with more numerically-oriented colleagues, but I wouldn’t feel adequate to teach a course. (I have taught SPSS to undergraduates before, but that was more to do with software proficiency.)
And I must say I have come a long way to reach where I am now. I have never had a math anxiety in school – believe it or not, I was once set for a STEM major – but for some reason I was never able to work through to the end of a statistics curriculum during high school and undergraduate years. My attention span seemed to wear out by the time we reached the ‘confidence interval’ chapter.
More recently I have had some good training opportunities along the way, but the main source of training in this area for me has still been self-learning. Against this backdrop, for fellow self-learners of statistics out there who aim at just as much as developing a working understanding and skills (in the sense of knowing how to drive but without knowing auto mechanics), I have listed below the learning materials that I have personally found to be accessibly written and helpful. I will add on if I come across more.
- Discovering Statistics using IBM SPSS Statistics (Andy Field, 2013, 4th edition, SAGE)
- IBM SPSS by Example (Alan C. Elliott & Wayne A. Woodward, 2015, SAGE)
- Statistics for Humanities (John Canning, 2013, free web-book)
- The Tao of Statistics: A Path to Understanding (With No Math) (Dana K. Keller, 2006, SAGE)
- Points of Significance: Statistics for Biologists (a monthly column by Nature Methods since September 2013)
- Statistics: Making Sense of Data (Alison Gibbs & Jeffrey Rosenthal, University of Toronto, 2013, a 8-week MOOC hosted by Coursera)
- Data Analysis and Statistical Inference (Mine Çetinkaya-Rundel, Duke University, 2015, a 10-week MOOC hosted by Coursera)