Editing Statistics and Statistical Programming (Winter 2021)
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;Instructor: [[Benjamin Mako Hill]] ([mailto:makohill@uw.edu makohill@uw.edu]) | ;Instructor: [[Benjamin Mako Hill]] ([mailto:makohill@uw.edu makohill@uw.edu]) | ||
:Office Hours: By appointment (I'm usually available via chat during "business hours.") You can view out [https://mako.cc/calendar/ my calendar] and/or [https://harmonizely.com/mako put yourself on it] | :Office Hours: By appointment (I'm usually available via chat during "business hours.") You can view out [https://mako.cc/calendar/ my calendar] and/or [https://harmonizely.com/mako put yourself on it]. | ||
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* [https://depts.washington.edu/acelab/proj/Rstats/index.html Statistical Analysis and Reporting in R] — A set of resources created and distributed by Jacob Wobbrock (University of Washington, School of Information) in conjunction with a MOOC he teaches. Contains cheatsheets, code snippets, and data to help execute commonly encountered statistical procedures in R. | * [https://depts.washington.edu/acelab/proj/Rstats/index.html Statistical Analysis and Reporting in R] — A set of resources created and distributed by Jacob Wobbrock (University of Washington, School of Information) in conjunction with a MOOC he teaches. Contains cheatsheets, code snippets, and data to help execute commonly encountered statistical procedures in R. | ||
* [https://www.datacamp.com DataCamp] offers introductory R courses. Northwestern usually has some free accounts that get passed out via Research Data Services each quarter. Apparently, if you are taking or teaching relevant coursework, instructors can [https://www.datacamp.com/groups/education request] free access to DataCamp for their courses from DataCamp. If folks are interested in this, I can reach out. | * [https://www.datacamp.com DataCamp] offers introductory R courses. Northwestern usually has some free accounts that get passed out via Research Data Services each quarter. Apparently, if you are taking or teaching relevant coursework, instructors can [https://www.datacamp.com/groups/education request] free access to DataCamp for their courses from DataCamp. If folks are interested in this, I can reach out. | ||
== Assignments == | == Assignments == | ||
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I will also provide example planning documents via our Canvas site: | I will also provide example planning documents via our Canvas site: | ||
* [https://canvas.northwestern.edu/files/9439380/download?download_frd=1 One by public health researcher Mika Matsuzaki]. The first planning document I ever saw and still one of the best. It's missing a measures section. It's also focused on a research context that is probably very different from yours, but try not to get bogged down by that and imagine how you might map the structure of the document to your own work. | * [https://canvas.northwestern.edu/files/9439380/download?download_frd=1 One by public health researcher Mika Matsuzaki]. The first planning document I ever saw and still one of the best. It's missing a measures section. It's also focused on a research context that is probably very different from yours, but try not to get bogged down by that and imagine how you might map the structure of the document to your own work. | ||
* [One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and over-long for the purposes of this assignment, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation. | * [One by Jim Maddock] created as part of a qualifying exam early in 2019. Jim doesn't provide dummy tables or anticipated findings/contributions, but he has an especially phenomenal explanation of the conceptual relationships and processes he wants to test. {{forthcoming}} | ||
* [One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and over-long for the purposes of this assignment, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation. {{forthcoming}} | |||
==== Research project presentation ==== | ==== Research project presentation ==== | ||
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[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations]] | [[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations]] | ||
---> | ---> | ||
You will also create and record a short presentation of your final project. The presentation will provide an opportunity to share a brief overview of your project and findings with the other members of the class. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills. The document [ | You will also create and record a short presentation of your final project. The presentation will provide an opportunity to share a brief overview of your project and findings with the other members of the class. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills. The document [Creating a Successful Scholarly Presentation] (file posted to Canvas) may be useful. {{forthcoming}} | ||
Additional details about the presentation goals, format suggestions, resources, and more will be provided later in the quarter. | |||
==== Research project paper ==== | ==== Research project paper ==== | ||
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=== Day 9: Wednesday February 3: Reinforced foundations for inference === | === Day 9: Wednesday February 3: Reinforced foundations for inference === | ||
'''Required | '''Required:''' | ||
* COM520 R Tutorial #6: Distributions in R and more [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-06.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-06.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-06.pdf PDF | * COM520 R Tutorial #6: Distributions in R and more [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-06.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-06.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-06.pdf PDF]] | ||
* Read Reinhart, §1. {{avail-uw|1=https://alliance-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=CP71226818410001451&context=L&vid=UW&lang=en_US&search_scope=all&adaptor=Local%20Search%20Engine&tab=default_tab&query=any,contains,statistics%20done%20wrong}} | * Read Reinhart, §1. {{avail-uw|1=https://alliance-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=CP71226818410001451&context=L&vid=UW&lang=en_US&search_scope=all&adaptor=Local%20Search%20Engine&tab=default_tab&query=any,contains,statistics%20done%20wrong}} | ||
* Read the following paper (it will be familiar to those of you in COM501): Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111 (24): 8788–90. https://doi.org/10.1073/pnas.1320040111. {{avail-uw|https://doi.org/10.1073/pnas.1320040111}} | * Read the following paper (it will be familiar to those of you in COM501): Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” ''Proceedings of the National Academy of Sciences'' 111 (24): 8788–90. https://doi.org/10.1073/pnas.1320040111. {{avail-uw|https://doi.org/10.1073/pnas.1320040111}} | ||
'''Homework:''' | '''Homework:''' | ||
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'''Recommended tasks:''' | '''Recommended tasks:''' | ||
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5Gn-sHTw1NF0e8IvMxwHDW&v=_iFAZgpWsx0 inference for categorical data] (videos 1-3 in the playlist) OpenIntro lectures. | * Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5Gn-sHTw1NF0e8IvMxwHDW&v=_iFAZgpWsx0 inference for categorical data] (videos 1-3 in the playlist) OpenIntro lectures. | ||
* [https://gallery.shinyapps.io/CLT_prop/ OpenIntro Central limit theorem for proportions demo]. | * [https://gallery.shinyapps.io/CLT_prop/ OpenIntro Central limit theorem for proportions demo]. | ||
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'''Required tasks:''' | '''Required tasks:''' | ||
* COM520 R Tutorial #7: | * COM520 R Tutorial #7: Distributions in R and more [[https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-07.html HTML], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-07.rmd RMarkdown], [https://www.dropbox.com/preview/COM520-shared_files-UW-2021-Q1/r_tutorials/com520-r_tutorial-07.pdf PDF]] | ||
* Read Reinhart, §4 and §5 (both are quite short). | * Read Reinhart, §4 and §5 (both are quite short). | ||
* Skim the following (all are referenced in the problem set) | * Skim the following (all are referenced in the problem set) | ||
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=== NO CLASS: Monday February 15: Presidents' Day === | === NO CLASS: Monday February 15: Presidents' Day === | ||
=== Day 12: Wednesday February 17: Inference for numerical data | === Day 12: Wednesday February 17: Inference for numerical data === | ||
'''Class material:''' | |||
* [[/Day 12 session plan]] | |||
* [https://www.openintro.org/go/?id=stat_better_understand_anova&referrer=/book/os/index.php OpenIntro supplement on ANOVA calculations] (particularly useful if you think you'll be doing more ANOVAs). | |||
'''Required tasks:''' | '''Required tasks:''' | ||
* Read Diez, Çetinkaya-Rundel, and Barr: §7.1-5 (Inference for numerical data: differences of means; power calculations, ANOVA, and multiple comparisons). | * Read Diez, Çetinkaya-Rundel, and Barr: §7.1-5 (Inference for numerical data: differences of means; power calculations, ANOVA, and multiple comparisons). | ||
* Complete '''Problem set 12''': exercises from OpenIntro §7: 7.12, 7.24, 7.26, 7.42, 7.44, 7.46 | |||
'''Recommended tasks:''' | '''Recommended tasks:''' | ||
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 1-8 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!). | * Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 inference for numerical data] (videos 1-8 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!). | ||
* | * Check out [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central limit theorem for means demo]. | ||
=== Day 13: Monday February 22: t-tests, power analysis, ANOVA === | |||
'''Class material:''' | |||
* [[/Day 13 session plan]] | |||
'''Required tasks:''' | |||
* Complete [[/Problem set 13]] (programming challenges) <!-- Statistics_and_Statistical_Programming_(Winter_2020)/pset6 --> | |||
* COM520 R Tutorial #7 {{forthcoming}} <!-- w09-R_tutorial.html --> | |||
=== Day 14: Wednesday February 24: Linear regression === | |||
'''Class material:''' | |||
* [[/Day 14 session plan]] | |||
* [[/Day | |||
'''Required tasks:''' | '''Required tasks:''' | ||
* Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression). | * Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression). | ||
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM63ikRfN41DNIhSgzboELOM linear regression] (videos 1-4 in the playlist) OpenIntro lectures. | |||
* Read [https://www.openintro.org/go/?id=stat_more_inference_for_linear_regression&referrer=/book/os/index.php More inference for linear regression] (OpenIntro supplement). | * Read [https://www.openintro.org/go/?id=stat_more_inference_for_linear_regression&referrer=/book/os/index.php More inference for linear regression] (OpenIntro supplement). | ||
* Complete '''Problem set 14''': exercises from OpenIntro §8 (8.6, 8.36, 8.40, 8.44) and OpenIntro supplement (4 and 5). Answers to the latter and provided in the supplement. | |||
'''Recommended tasks:''' | '''Recommended tasks:''' | ||
* Read [https://seeing-theory.brown.edu/index.html#secondPage/chapter6 Seeing Theory §6 (Regression analysis)] | * Read [https://seeing-theory.brown.edu/index.html#secondPage/chapter6 Seeing Theory §6 (Regression analysis)] | ||
=== Day 15: Monday March 1: Applied linear regression === | |||
'''Class material:''' | '''Class material:''' | ||
* [[/Day | |||
* [[/Day 15 session plan]] | |||
'''Required tasks:''' | '''Required tasks:''' | ||
* COM520 R Tutorial #8 {{forthcoming}} <!-- w10-R_tutorial.html --> | |||
* Complete [[/Problem set 15]] <!-- Statistics_and_Statistical_Programming_(Winter_2020)/pset7 --> | |||
=== Day 16: Wednesday March 3: Multiple and logistic regression === | |||
'''Class material:''' | |||
* [[/Day 16 session plan]] | |||
* [[/Day | |||
'''Required tasks:''' | '''Required tasks:''' | ||
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* Read [https://www.openintro.org/go/?id=stat_interaction_terms&referrer=/book/os/index.php Interaction terms] (OpenIntro supplement). | * Read [https://www.openintro.org/go/?id=stat_interaction_terms&referrer=/book/os/index.php Interaction terms] (OpenIntro supplement). | ||
* Read [https://www.openintro.org/go/?id=stat_nonlinear_relationships&referrer=/book/os/index.php Fitting models for non-linear trends] (OpenIntro supplement). | * Read [https://www.openintro.org/go/?id=stat_nonlinear_relationships&referrer=/book/os/index.php Fitting models for non-linear trends] (OpenIntro supplement). | ||
* Complete '''Problem set 16''': exercises from OpenIntro §9: 9.4, 9.13, 9.16, 9.18, | |||
'''Recommended tasks:''' | '''Recommended tasks:''' | ||
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I multiple and logistic regression] (videos 1-4 in the playlist) OpenIntro lectures. | * Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I multiple and logistic regression] (videos 1-4 in the playlist) OpenIntro lectures. | ||
=== Day 17: Monday March 8: Applied multiple and logistic regression === | |||
'''Class material:''' | |||
* [[/Day 17 session plan]] | |||
* [[/Day | |||
'''Required tasks:''' | '''Required tasks:''' | ||
* COM520 R Tutorial # | * COM520 R Tutorial #9: Tutorial on interpreting logistic regression in R {{forthcoming}} <!-- logistic_regression_interpretation.html --> | ||
* Complete [[/Problem set 17]] <!-- Statistics_and_Statistical_Programming_(Winter_2020)/pset8 --> | |||
=== Day 18: Wednesday March 10: Final Presentations === | |||
=== Day | |||
'''Class material:''' | '''Class material:''' | ||
* [[/Day 18 session plan]] | * [[/Day 18 session plan]] | ||
'''Post your video via this "Discussion" on Canvas]''' {{forthcoming}} — Please view and provide constructive feedback on other's videos! | |||
* '''Post videos directly to the "Discussion."''' The Canvas text editor has an option to upload/record a video. That's what you want. | * '''Post videos directly to the "Discussion."''' The Canvas text editor has an option to upload/record a video. That's what you want. | ||
* '''Please remember not to over-work/think this.''' I mentioned this in class, but just to reiterate, the focus of this assignment should not be your video editing skills. Please do what you can to record and convey your ideas clearly without devoting insane hours to creating the perfect video. | * '''Please remember not to over-work/think this.''' I mentioned this in class, but just to reiterate, the focus of this assignment should not be your video editing skills. Please do what you can to record and convey your ideas clearly without devoting insane hours to creating the perfect video. | ||
* '''Some resources for recording presentations:''' There are a bunch of ways you might record/share your video. Some ideas include using the embedded media recorder in Canvas (!) that can record with with your webcam (maybe attach a few visuals to accompany this?); recording a "meeting" with yourself in Zoom; and "Panopto," a piece of high-end video recording, sharing, and editing software that UW licenses for campus use. Here are some pointers: | * '''Some resources for recording presentations:''' There are a bunch of ways you might record/share your video. Some ideas include using the embedded media recorder in Canvas (!) that can record with with your webcam (maybe attach a few visuals to accompany this?); recording a "meeting" with yourself in Zoom; and "Panopto," a piece of high-end video recording, sharing, and editing software that UW licenses for campus use. Here are some pointers: | ||
** You should be able to use your UW zoom account to create a zoom meeting, record your meeting (in which you deliver your presentation and share your screen with any visuals), and then share a link to the recording via the "Recordings" item in the left-hand menu of your | ** You should be able to use your UW zoom account to create a zoom meeting, record your meeting (in which you deliver your presentation and share your screen with any visuals), and then share a link to the recording via the "Recordings" item in the left-hand menu of your [https://northwestern.zoom.us/ https://northwestern.zoom.us/] account page. | ||
** If nothing works, please get in touch. | ** If nothing works, please get in touch. | ||
== Special Notes == | == Special Notes == |