CommunityData:StatsGaps

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Welcome to the StatsGaps StudyGroup page -- a set of suggested learning pathways making use of course resources produced by community data faculty, meant to be used by folks who have a mixture of familiarity and non-familiarity with R, statistics, and research processes. The primary text is: Open Intro to Statistics

We borrow heavily from the course most recently taught by Aaron: Statistics_and_Statistical_Programming_(Spring_2019)

To support participation from people with ranging prior experiences and learning goals for this summer, we've organized this content into the following strands:

  • Learn R -- you don't know R
  • Learn Stats -- you haven't taken much if any statistics, or otherwise feel you're mostly starting from scratch
  • Refresh -- overview and shore up your stats knowledge if it feels rusty
  • Stronger -- your stats knowledge is strong but your class stopped before you got to good stuff you see used in lots of the papers in this group (e.g. regression)

Depending on which strand(s) best apply to you, we provide different recommended readings and assignments each week.

Meet at http://meet.jit.si/cdsc on Monday at 11:00 Pacific, 1:00 Central.

Schedule:

Week 1[edit]

All:

  • Read: Preventing harassment and increasing group participation through social norms in 2,190 online science discussions J. Nathan Matias

PNAS May 14, 2019 116 (20) 9785-9789; first published April 29, 2019 -- Mattias--Harassment Prevention

Learn R:

Learn Stats:

Refresh:

Stronger:

  • Skim Problem Set 1 -- since we may discuss it f2f. Take a look at the text's Chapter 1 if you find any of the questions to be confusing or the answer you came up with is different than the key.


Extra Resources:

Week 2: Probability and Visualization[edit]

All:

  • Shaw, Aaron and Yochai Benkler. 2012. A tale of two blogospheres: Discursive practices on the left and right. American Behavioral Scientist. 56(4): 459-487. [1]

Learn R:

Learn Stats:

Refresh:

Stronger:


Extra Resources:

  • Seeing Theory §1 (Basic Probability) and §2 (Compound Probability). (Note: this site provides a beautiful visual introduction to core concepts in probability and statistics).
  • Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in Proceedings of the 8th ACM Conference on Designing Interactive Systems. Aarhus, Denmark: ACM. PDF
  • Mine Çetinkaya-Rundel's OpenIntro §2 Lecture Notes
  • Video Lectures including 2 short videos for §2

Week 3: Distributions[edit]

All: (N/A)

Learn R:

Learn Stats:

Refresh:

Stronger:


Extra Resources:

Week 4: Statistical significance and hypothesis testing[edit]

All:

  • Read Diez, Barr, and Çetinkaya-Rundel: §4 (Foundations for inference) (I suggest everyone read this chapter -- this topic is a source of much confusion. -khc)
  • Gelman, Andrew and Hal Stern. 2006. “The Difference Between ‘Significant’ and ‘Not Significant’ Is Not Itself Statistically Significant.” The American Statistician 60(4):328–31. [Available via your library]

Learn R: N/A

Learn Stats:

Refresh:

Stronger:

Resources:

Week 5: Continuous Numeric Data & ANOVA[edit]

All:

  • Sweetser, K. D., & Metzgar, E. (2007). Communicating during crisis: Use of blogs as a relationship management tool. Public Relations Review, 33(3), 340–342. [Available through NU Libraries]

Learn R:

Learn Stats:

Refresh:

Stronger:

Resources:

Week 6: Categorical data[edit]

All:

Learn R:

Learn Stats:

Refresh and get Stronger:

Resources

Week 7: Linear Regression[edit]

All:

  • Diez, Barr, and Çetinkaya-Rundel: §7 (Introduction to linear regression)
  • OpenIntro eschews a mathematical approach to correlation. Look over the Wikipedia article on correlation and dependence and pay attention to the formulas. It's tedious to compute, but you should be aware of what goes into it.
  • Lampe, Cliff, and Paul Resnick. 2004. “Slash(Dot) and Burn: Distributed Moderation in a Large Online Conversation Space.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '04), 543–550. New York, NY, USA: ACM. doi:10.1145/985692.985761. [Available via library]

Learn Stats:

Learn R:

Resources:


Week 8[edit]

Polynomial Terms, Interactions, and Logistic Regression

All:[edit]

Learn Stats:[edit]

Learn R:[edit]

Resources[edit]