CommunityData:StatsGaps

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:
 * 7/15 -- Discuss weeks 1, 2, and 3
 * 7/22 -- Discuss weeks 4 and 5
 * 7/29 -- Discuss week 6
 * 8/5 -- Discuss week 7
 * 8/12 -- Discuss week 8
 * 8/19 -- Discuss weeks 9 and 10
 * 8/26 -- Circle back and pick up dropped threads, discuss next steps -- what's out there, what else do you need to know to meet your goals, etc.

Week 1
All: PNAS May 14, 2019 116 (20) 9785-9789; first published April 29, 2019 -- Mattias--Harassment Prevention
 * Read: Preventing harassment and increasing group participation through social norms in 2,190 online science discussions J. Nathan Matias

Learn R:
 * Week 1 R lecture materials (.zip file)
 * Week 1 screencast (part 1, 23 minutes) (the video should load directly in browser window)
 * Week 1 screencast (part 2, 27 minutes)

Learn Stats:
 * Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data)
 * Do Problem Set 1

Refresh:
 * Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data)
 * Read Problem Set 1

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:
 * Mine Çetinkaya-Rundel's OpenIntro §1 Lecture Notes
 * OpenIntro Video Lectures including some for §1

Week 2: Probability and Visualization
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.

Learn R:
 * Week 2 R lecture materials (.Rmd file)
 * Week 2 screencast (17 minutes)

Learn Stats:
 * Diez, Barr, and Çetinkaya-Rundel: §2 (Probability)
 * Do problem set 2 -- Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2

Refresh:
 * Diez, Barr, and Çetinkaya-Rundel: §2 (Probability)
 * Read problem set 2 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2

Stronger:
 * Skim problem set 2 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2

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
All: (N/A)

Learn R:
 * Week 3 R lecture materials (.Rmd file)
 * Week 3 screencast (19 minutes)

Learn Stats:
 * Read Diez, Barr, and Çetinkaya-Rundel: §3.1-3.2, §3.4 (Aaron says: You should read the rest of the chapter (§3.3 and §3.5). I won't assign problem set questions about it but it's still important to be familiar with.)
 * Do Problem Set 3 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3

Refresh:
 * Read Problem Set 3 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3

Stronger:
 * Skim Problem Set 3 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3

Extra Resources:
 * Seeing Theory §3 (Probability Distributions).
 * Mine Çetinkaya-Rundel's OpenIntro §3 Lecture Notes
 * OpenIntro Video Lectures including 2 videos for §3.1 and §3.2

Week 4: Statistical significance and hypothesis testing
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:
 * Do Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 4

Refresh:
 * Read Problem Set 4 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 4

Stronger:
 * Skim Problem Set 4 Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 4

Resources:
 * Week 4 R lecture materials (.Rmd file)
 * Mine Çetinkaya-Rundel's OpenIntro §4 Lecture Notes
 * OpenIntro Video Lectures including 7 videos for nearly all of §4
 * Verzani: §7 (Statistical inference), §8 (Confidence intervals)
 * Seeing Theory §4 (Frequentist Inference)

Week 5: Continuous Numeric Data & ANOVA
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:
 * Week 5 R lecture screencast: t-tests (~22 minutes)
 * Week 5 R lecture screencast: for loops and if statements (~12 minutes)

Learn Stats:
 * Read Diez, Barr, and Çetinkaya-Rundel: §5 (Inference for numerical data)
 * Do Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 5

Refresh:
 * Skim Diez, Barr, and Çetinkaya-Rundel: §5 (Inference for numerical data)
 * Do Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 5

Stronger:
 * Skim Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 5

Resources:
 * Mine Çetinkaya-Rundel's OpenIntro §5 Lecture Notes

Week 6: Categorical data
All:
 * Gelman, Andrew and Eric Loken. 2014. “The Statistical Crisis in Science Data-Dependent Analysis—a ‘garden of Forking Paths’—explains Why Many Statistically Significant Comparisons Don’t Hold Up.” American Scientist 102(6):460. [Available through Library Subscription] (This is a reworked version of this unpublished manuscript which provides a more detailed examples.) Also note the correction here: https://statmodeling.stat.columbia.edu/2014/10/14/didnt-say-part-2/

Learn R:
 * Week 6 R lecture materials (.Rmd file)

Learn Stats:
 * Read Diez, Barr, and Çetinkaya-Rundel: §6.1-6.4 (Inference for categorical data).
 * 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 available on Hill's personal website]
 * Do Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 6

Refresh and get Stronger:
 * Skim Diez, Barr, and Çetinkaya-Rundel: §6.1-6.4 (Inference for categorical data).
 * Read over Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 6

Resources
 * Diez, Barr, and Çetinkaya-Rundel: §6.5-6.6 (Small samples and randomization inference)
 * Verzani: §3.4 (Bivariate categorical data); §10.1-10.2 (Goodness of fit)
 * Mine Çetinkaya-Rundel's OpenIntro §6 Lecture Notes
 * OpenIntro Video Lectures including 4 videos for §7

Week 7: Linear Regression
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:
 * Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 7

Learn R:
 * Week 7 R lecture materials

Resources:
 * Seeing Theory §5 (Regression Analysis)
 * Mine Çetinkaya-Rundel's OpenIntro §7 Lecture Notes
 * Mine Çetinkaya-Rundel's OpenIntro §8 Lecture Notes
 * OpenIntro Video Lectures including 4 videos for §7 and 3 videos on the sections §8.1-8.3

Week 8
Polynomial Terms, Interactions, and Logistic Regression

All:

 * Diez, Barr, and Çetinkaya-Rundel: §8 (Multiple and logistic regression)
 * Lesson 8: Categorical Predictors and Lesson 9: Data Transformations from the PennState Eberly College of Science STAT 501 Regression Methods Course. There are several subparts (many quite short), please read them all carefully.
 * Mako Hill wrote this document which will likely be useful for many of you: Interpreting Logistic Regression Coefficients with Examples in R

Learn Stats:

 * Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 8

Learn R:

 * Week 8 R lecture materials

Resources

 * Verzani: §11.3 (Linear regression), §13.1 (Logistic regression)
 * Ioannidis, John P. A. 2005. “Why Most Published Research Findings Are False.” PLoS Medicine 2(8):e124. [Open Access]
 * Head, Megan L., Luke Holman, Rob Lanfear, Andrew T. Kahn, and Michael D. Jennions. 2015. “The Extent and Consequences of P-Hacking in Science.” PLOS Biology 13(3):e1002106. [Open Access]
 * Mine Çetinkaya-Rundel's OpenIntro §8 Lecture Notes
 * OpenIntro Video Lectures including a video on §8.4