Editing Statistics and Statistical Programming (Winter 2021) (section)

== Why statistical programming? Why R? ==

Some courses in statistics and quantitative methods do not emphasize statistical programming and rely on point-and-click tools like SPSS instead. Why bother learning R?

By learning statistical programming you will gain a deeper understanding of both the principles behind your analysis techniques as well as the tools you use to apply those techniques. In addition, a solid grasp of statistical programming will prepare you to create reproducible research, avoid common errors, and enable both greater durability and validity of your work. 

Other programming languages are also well suited to statistics, including Stata and Python. Ultimately, I'm teaching R because R is ascendant (i.e., it is increasing and is well on its way to "taking over") and there was consensus among the faculty in the department who were likely to teach statistics classes in the future that this made the most sense. I also do quite a lot of my own statistical work with R, so that also guides my choice. That said, I opt to use and teach with R for a few reasons:

* R is freely available and open source.
* R is the most widely used package in statistics and several social scientific fields.
* R (along with Stata) will be used in most of the advanced stats classes I hope you will take after this course.
* R is better general purpose programming language than Stata which means that R programming skills will let you solve non-statistical problems and may make it easier to learn other programming languages like Python.

For students with a strong psychometric focus or whose research will be limited to linear and logistic regression or ANOVA on small pre-collected datasets and similar, SPSS will likely be fine. R has a higher barrier to entry than SPSS but it's ceiling is much higher.