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Statistics and Statistical Programming (Winter 2021)
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== Overview and learning objectives == <div style="float:right;" width=30%; class="toclimit-3">__TOC__</div> This course provides a get-your-hands-dirty introduction to inferential statistics and statistical programming for applications in communication research. 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—from 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 and proceed onward on the [https://www.csss.washington.edu/academics/phd-tracks/communication Statistics Concentration in the Communication for the MA/PhD program] offered between the Department of Communication and [https://www.csss.washington.edu/ Center for Statistics and the Social Sciences]. There will be readings on conceptualization and operationalization in quantitative research although these will overlap with reading in COM 501. The course will focus on a number of techniques, 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 have any prior training in 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, but nothing it is not assumed.
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