Editing Statistics and Statistical Programming (Fall 2020) (section)

=== Overview and learning objectives ===

This course provides a get-your-hands-dirty introduction to inferential statistics and statistical programming mostly for applications in the social sciences and social computing. My main objectives are for all participants to acquire the conceptual, technical, and practical skills to conduct your own statistical analyses and become more sophisticated consumers of quantitative research in communication, human computer interaction (HCI), and adjacent disciplines.

I will consider the course a complete success if every student is able to do all of the following things at the end of the quarter:
* Design and execute a quantitative research project that involves statistical inference, start to finish.
* Read, modify, and create short programs in the R statistical programming language.
* Feel comfortable reading and interpreting papers that use basic statistical techniques.
* Feel prepared to enroll in more specialized and advanced statistics courses.

The course will cover a number of techniques, likely including the following: t-tests; chi-squared tests; ANOVA; linear regression; and logistic regression. We will also consider salient issues in quantitative research such as reproducibility and "the statistical crisis in science." We may cover other topics as time and interest allow.

The course materials will consist of readings, problem sets, assessment exercises, and recorded lectures and screencasts (some created by me, some created by other people). The course requirements will emphasize active participation, self-evaluation, and will include a final project focused on the design and execution of an original piece of quantitative research. We will use the R programming language for all examples and assignments.

You are not required to know much about statistics or statistical programming to take this class. I will assume some (very little!) knowledge of the basics of empirical research methods and design, basic algebra and arithmetic, and a willingness to work to learn the rest. In general we are not going to cover most of the math behind the techniques we'll be learning. Although we may do some math, this is not a math class. This course will also not require knowledge of calculus or matrix algebra. I will *not* do proofs on the board. Instead, the class is unapologetically focused on the application of statistical methods. Likewise, while some exposure to R, other programming languages, or other statistical computing resources will be helpful, it is not assumed.

'''Why this course? Why statistical programming? Why R?'''

Many comparable courses in statistics and quantitative methods do not emphasize statistical programming. So why bother? 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. I do most of my work with R, so that guides my choice for the course. 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.