Student and tutor module reviews

This module covers a wide variety of statistical concepts, and staying close to its title, does so from an applied point of view.

The material is outstanding, and it is impressive how difficult concept we introduced without much mathematical background. For example, introducing principal components analysis (PCA) without any matrix algebra was actually clearer than the more theoretical way previous course introduced the concept (at a different university).

The main "issue" with this course is not the developed material, which again is great. The problem with this course is that it does not provide much theory (or any) that allows students to tie together concepts or provide first principle bases. From experience, such background is essential to apply these concepts in a creative and flexible manner and move away from "cookbook" analyses. For example, while the PCA section is fabulous, it really is only an introduction and an additional chapter on the general matrix algebra underlying this would very much strengthen a student's understanding, and increase their ability to use it in new (and potentially different) settings.

I realise this goes beyond the "partial" side of things. But the course is a (mandatory) part of my maths degree. After following multiple courses involving matrix algebra and other concepts that directly underpin these statistical methods it just is very disappointing that the course does not follow through with the actual, more generalizable theory. Closely related, this has been the least arduous module I have followed with the OU (including any of my first year modules), and I had to pace myself not to finish the work in 4 weeks (working only on weekends).

Finally, the software (SPSS) is hardly relevant for modern statisticians or anybody doing statistical analysis. In my day job I supervise medical PhD student who use programming languages such as R or python to analyse their data. I hate to say it but if medical student (who are not trained to program and only get a small degree of statistics training) can use these programs, why does the OU think we can only use a point and click system. In fact because programming (in R for example) is reasonably expressive, computer assignment can be much closer to pen and paper questions. For example, calculating a t-statistic in R:
xbar <- 2
se <- 1.2
tstat <- (xbar-0)/se # assuming the null-hypothesis value for xbar is zero.
Which is identical to variable substitution all student enrolled in this course will know.

So in summary really great material, but it lacks depth, the software is more archaic than pragmatic, and the course is very light (there should be more material!).

Course starting: October 2019

Review posted: August 2020