Many of the questions that matter most in educational science are questions about patterns: whether pupils from different backgrounds move through the school system in different ways, whether an intervention makes a difference and for whom, whether what we observe in one classroom holds across many. Quantitative reasoning is a powerful way of addressing such questions. It allows us to look beyond the single case, to compare groups systematically and to distinguish what is general from what is particular.
Numbers are valuable for this purpose. They allow patterns to be seen across many cases at once, they make claims precise enough to be checked by others, and they make it possible to estimate how far a finding might extend beyond the cases observed in our own research. At the same time, counting something requires translating a complex social phenomenon into a measurable form, and that process can simplify complex realities. Used well, quantitative analysis is an extremely powerful tool available to us for understanding educational and social life. Used poorly, it lends authority to weak evidence. A central concern of this course is therefore both what quantitative analysis can show and what it cannot, and how quantitative analysis complements rather than replaces other ways of investigating the social world.
No prior statistical knowledge and no previous experience with statistical software are assumed. The course is deliberately not a course about equations and hand calculations. The mathematics is kept to the minimum needed to understand what a procedure is doing. What is emphasised instead is the logic of statistical analysis, which includes recognising what kind of problem you are facing, choosing an analysis that is appropriate to that problem and to the data you have, understanding the assumptions each technique makes and what follows when they are not met, and interpreting statistical outputs, including both inferential results and effect sizes. Analyses are carried out in SPSS, which is introduced from the beginning and used throughout on real datasets.
- Teacher: Tobia Fattore