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:'''Statistics and Statistical Programming''' | |||
:Media, Technology & Society (MTS) 525 | ::Media, Technology & Society (MTS) 525 | ||
:Tuesdays & Thursdays | ::Tuesdays & Thursdays 10-11:50am CT (synchronous sessions) | ||
:Fall 2020 | ::Fall 2020 | ||
:Northwestern University | ::Northwestern University | ||
:'''Instructor:''' [http://aaronshaw.org Aaron Shaw] ([mailto:aaronshaw@northwestern.edu aaronshaw@northwestern.edu]) | |||
: | ::Office Hours: Thursday 12-2pm and by appointment | ||
: | ::Meeting location: [https://meet.jit.si/aaronoffice Jitsi] | ||
: | |||
: [https:// | |||
:'''Teaching Assistant:''' [http://nickmvincent.com Nick Vincent] ([mailto:nickvincent@u.northwestern.edu nickvincent@u.northwestern.edu]) | |||
:Office Hours: | ::Office Hours: I'll try to respond to any asynchronous questions in a timely fashion during "business hours" (9a-5p Central Time), and will also have OH by appointment. I'll also try to schedule some fixed time during which I'll hang out on a video call, hours TBA. | ||
: | ::I'll likely use whatever conference we use for class sessions, but am happy to try out alternative communication software for OH! | ||
:'''Course Websites''': | |||
: | :* We will use [https://canvas.northwestern.edu/courses/90927 Canvas] for [https://canvas.northwestern.edu/courses/90927/announcements announcements], [https://canvas.northwestern.edu/courses/90927/assignments turning in most assignments], and maybe [https://canvas.northwestern.edu/courses/90927/discussion_topics discussions] the other possibility is [https://discord.com Discord]. | ||
: | :* Everything else will be linked on this page. | ||
== 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. | 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. | ||
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* 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. | * 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. | ||
== Format and structure == | |||
<!--- | <!--- | ||
I expect everybody to come to class, every week, with a laptop and a power cord, ready to answer any question on the problem set and having uploaded code related the the programming questions. The class is listed as nearly 3 hours long and, with the exception of short breaks, I intend to use the entire period. Please be in class on time, plugged in, and ready to go. | I expect everybody to come to class, every week, with a laptop and a power cord, ready to answer any question on the problem set and having uploaded code related the the programming questions. The class is listed as nearly 3 hours long and, with the exception of short breaks, I intend to use the entire period. Please be in class on time, plugged in, and ready to go. | ||
---> | ---> | ||
This course will proceed in a '''remote''' format that includes ''asynchronous'' and ''synchronous'' elements | This course will proceed in a '''remote''' format that includes ''asynchronous'' and ''synchronous'' elements. In general, the organization of the course assumes a "flipped" approach where you consume instructional materials on your own or in groups and we use synchronous meetings to answer questions, address challenges or concerns, and hold semi-structured discussions. A brief description of how I expect it all to work follows below. We'll talk about it all more during the first class session. | ||
===Asynchronous elements of the course=== | |||
These include all readings, recorded lectures/slides, tutorials, and assignments. I expect you to complete (or at least attempt to complete!) these asynchronous on your own time outside of our class meeting times. For nearly all of the "instructional" material introducing particular statistical concepts and techniques, you are expected to use the textbook and lecture materials created by the textbook authors. Note that this means I will not deliver lectures during our class meetings! This also means that you are responsible for coordinating your problem set groups and any collaborative work with other members of the class outside of our synchronous class meeting times. | |||
===Synchronous elements of the course=== | |||
The synchronous elements of the course will be the two weekly class meetings that will happen via video conference (platform TBD). These are scheduled to run for a maximum of 110 minutes. I plan for this to include short breaks and some extra time at the end. | |||
Although the day-to-day routine will vary, each class session will generally include the following: | Although the day-to-day routine will vary, each class session will generally include the following: | ||
* Quick updates about assignments, projects, and meta-discussion about the class. | * Quick updates about assignments, projects, and meta-discussion about the class. | ||
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* Discussion of '''statistics questions''' related to new material in Diez, Barr, and Çetinkaya-Rundel. | * Discussion of '''statistics questions''' related to new material in Diez, Barr, and Çetinkaya-Rundel. | ||
* Discussion of any exemplary empirical paper we have read and the '''empirical paper questions'''. | * Discussion of any exemplary empirical paper we have read and the '''empirical paper questions'''. | ||
== Textbook, readings, and resources == | |||
This class will use a freely-licensed textbook: | This class will use a freely-licensed textbook: | ||
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* Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon]) | * Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. ([https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Various Sources]; [https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Amazon]) | ||
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]) | * Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. ([https://link.springer.com/book/10.1007%2F978-3-319-24277-4 Springer/NU Libraries]; [https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Various Sources]) | ||
There are also some invaluable non-textbook resources: | There are also some invaluable non-textbook resources: | ||
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* If you are planning to analyze large-scale data (i.e., data that won't fit in memory on your laptop) then you will want to sign up for a research allocation on Quest, which is Northwestern's high-performance computing cluster. Instructions on how to do that are [[Statistics_and_Statistical_Programming_(Spring_2019)/Quest_at_Northwestern|here]]. | * If you are planning to analyze large-scale data (i.e., data that won't fit in memory on your laptop) then you will want to sign up for a research allocation on Quest, which is Northwestern's high-performance computing cluster. Instructions on how to do that are [[Statistics_and_Statistical_Programming_(Spring_2019)/Quest_at_Northwestern|here]]. | ||
== | == Assignments == | ||
The assignments in this class focus on applied statistical research design, analysis, and interpretation. Unless otherwise noted, all assignments are due at the end of the day (i.e., 11:59pm on the day they are due). | |||
=== Weekly problem sets and participation === | |||
Each week I will post a problem set incorporating three kinds of questions: | |||
* '''Statistics questions''' about statistical concepts, principles, and interpretation. | |||
* '''Programming challenges''' that you must solve using R. | |||
* '''Empirical paper questions''' about other assigned readings. | |||
Some of these (usually just the statistics questions) will be taken from the textbooks and some will not. In general, we will cover the statistical concepts and principles in the first session of the week and the empirical paper questions in the second session. The programming material will likely span both sessions depending on the week. You will need to submit your solutions to the relevant questions ahead of the relevant class session. Details of exactly how this will work will be provided in the course schedule and we'll go over them during the first class. | |||
Throughout the course you will be assigned to a (rotating) problem set group. This will be a group of 2-3 students (exact numbers will depend on the final enrollment) with whom you will meet outside of class time to discuss, complete, and/or review your problem sets. The groups will change roughly every two weeks during the quarter to ensure that you get to work with different members of the class. The main idea is to support collaborative learning, peer teaching, and accountability. While the specifics of exactly when and how you work with your problem set group will largely be up to you, the teaching team will provide a template that you can use as a starting point. | |||
Because randomness is extremely important in statistics, I will use a small R program to '''randomly assign''' different problem set groups to share and discuss their solutions to select questions during class sessions. These assignments will be announced at least a few days ahead of time so that the group has an opportunity to prepare. The idea here is not to put people on the spot, but to ensure an equitable distribution of the responsibility to discuss questions, answers, points of confusion, and alternatives. | |||
For the programming challenges, you should submit code for your solutions (more on how in a moment) so we can walk through the material together. If you get completely stuck on a problem, that's okay, but please share whatever code you have so that you can tell us what you did and what you were thinking. | |||
Attendance in the synchronous portion of the class will be important to supporting your mastery of the material. Although the problem sets will not be assigned a letter grade, it is critical that you be present and able to discuss your answers to each of the questions. Your ability to do so will figure prominently in your participation grade for the course (see the section on grading and assessment below). | |||
=== Research project | === Research project === | ||
As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all: | As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all: | ||
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''I strongly urge you'' to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, use as pilot analysis that you can report in a grant or thesis proposal, and/or use to fulfill a degree requirement. | ''I strongly urge you'' to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, use as pilot analysis that you can report in a grant or thesis proposal, and/or use to fulfill a degree requirement. | ||
There are several intermediate milestones | There are several intermediate milestones and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Canvas. | ||
==== | ==== Project plan and dataset identification ==== | ||
;Due date: | ;Due date: TBA | ||
;Maximum length: 500 words (~1-2 pages) | ;Maximum length: 500 words (~1-2 pages) | ||
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* Use the [http://scientificdata.isa-explorer.org/index.html ISA Explorer] to find datasets. Keep in mind the large majority of datasets it will search are drawn from the natural sciences. | * Use the [http://scientificdata.isa-explorer.org/index.html ISA Explorer] to find datasets. Keep in mind the large majority of datasets it will search are drawn from the natural sciences. | ||
* The City of Chicago has one of the best [https://data.cityofchicago.org/ data portal sites] of any municipality in the U.S. (and better than many federal agencies). There are also numerous administrative datasets released by other public entities (try searching!) that you might find inspiring. | * The City of Chicago has one of the best [https://data.cityofchicago.org/ data portal sites] of any municipality in the U.S. (and better than many federal agencies). There are also numerous administrative datasets released by other public entities (try searching!) that you might find inspiring. | ||
<!--- | |||
* <TODO fix/update accordingly> Set up a meeting with Jennifer Muilenburg — Data Curriculum and Communications Librarian who runs [https://www.lib.washington.edu/digitalscholarship/services/data research data services at the UW libraries]. Her email is: libdata@uw.edu I've have talked to her about this course and she is excited about meeting with you to help. | |||
--> | |||
* [http://fivethirtyeight.com FiveThirtyEight.com] has published a [https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html GitHub repository and an R package] with pre-processed and cleaned versions of many of the datasets they use for articles published on their website. | * [http://fivethirtyeight.com FiveThirtyEight.com] has published a [https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html GitHub repository and an R package] with pre-processed and cleaned versions of many of the datasets they use for articles published on their website. | ||
* If you interested in studying online communities, there are some great resources for accessing data from Reddit, Wikipedia, and StackExchange. See [https://files.pushshift.io/reddit/ pushshift] for dumps of Reddit data, [https://meta.wikimedia.org/wiki/Research:Data here] for an overview of Wikipedia's data resources, and [https://data.stackexchange.com/ Stack Exchange's data portal]. | * If you interested in studying online communities, there are some great resources for accessing data from Reddit, Wikipedia, and StackExchange. See [https://files.pushshift.io/reddit/ pushshift] for dumps of Reddit data, [https://meta.wikimedia.org/wiki/Research:Data here] for an overview of Wikipedia's data resources, and [https://data.stackexchange.com/ Stack Exchange's data portal]. | ||
==== | ==== Project planning document ==== | ||
;Due date: | ;Due date: TBA | ||
; | ;Maximum length: ~5 pages | ||
The project planning document is a shell/outline of an empirical quantitative research paper. Your planning document should should have the following sections: (a) Rationale, (b) Objectives; (b.1) General objectives; (b.2) Specific objectives; (c) (Null) hypotheses; (d) Conceptual diagram and explanation of the relationship(s) you plan to test; (e) Measures; (f) Dummy tables/figures; (g) anticipated finding(s) and research contribution(s). Longer descriptions of each of these planning document sections (as well as a few others) can be found [[CommunityData:Planning document|on this wiki page]]. | The project planning document is a basic shell/outline of an empirical quantitative research paper. Your planning document should should have the following sections: (a) Rationale, (b) Objectives; (b.1) General objectives; (b.2) Specific objectives; (c) (Null) hypotheses; (d) Conceptual diagram and explanation of the relationship(s) you plan to test; (e) Measures; (f) Dummy tables/figures; (g) anticipated finding(s) and research contribution(s). Longer descriptions of each of these planning document sections (as well as a few others) can be found [[CommunityData:Planning document|on this wiki page]]. | ||
I will also provide three example planning documents via our Canvas site (links to-be-updated for 2020 edition of the course): | I will also provide three example planning documents via our Canvas site (links to-be-updated for 2020 edition of the course): | ||
* [https://canvas.northwestern.edu/files/ | * [https://canvas.northwestern.edu/files/6908602/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/ | * [https://canvas.northwestern.edu/files/6919735/download?download_frd=1 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. | ||
* [https://canvas.northwestern.edu/files/ | * [https://canvas.northwestern.edu/files/6908606/download?download_frd=1 One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and incredibly long for our purposes, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation. | ||
==== Research paper ==== | |||
;Paper due date: TBA | |||
;Paper due date: | |||
;Maximum length: 6000 words (~20 pages) | ;Maximum length: 6000 words (~20 pages) | ||
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I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you might aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software like Zotero to handle your bibliographic sources. | I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, ''your paper must follow a standard format'' (e.g., [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format] or [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you might aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software like Zotero to handle your bibliographic sources. | ||
==== Project presentation ==== | |||
;Presentation due date: TBA | |||
;Maximum length: 7 minutes | |||
<!-- TODO revisit old presentations page to update/adapt | |||
[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations]] | |||
---> | |||
You will also create and record a short (7-8 minute) presentation of your final project. The presentation will provide an opportunity to share a brief summary of your project and at least preliminary 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 [https://canvas.northwestern.edu Creating a Successful Scholarly Presentation] (file will be posted to Canvas) may be useful. | |||
More details about the presentation goals, format suggestions, and more will be provided later in the quarter. | |||
==== Human subjects research, IRB, and ethics ==== | ==== Human subjects research, IRB, and ethics ==== | ||
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Research ethics are broad and complex topic. We'll talk about issues related to ethics and quantitative empirical research a bit more during class, but will likely only scratch the surface. I strongly encourage you to pursue further reading, conversation, coursework, and reflection as you consider how to understand and apply ethical principles in the context of your own research and teaching. | Research ethics are broad and complex topic. We'll talk about issues related to ethics and quantitative empirical research a bit more during class, but will likely only scratch the surface. I strongly encourage you to pursue further reading, conversation, coursework, and reflection as you consider how to understand and apply ethical principles in the context of your own research and teaching. | ||
== Grading and assessment == | |||
I will assign grades (usually a numeric value ranging from 0-10) for each of the following aspects of your performance. The percentage values in parentheses are weights that will be applied to calculate your overall grade for the course. | I will assign grades (usually a numeric value ranging from 0-10) for each of the following aspects of your performance. The percentage values in parentheses are weights that will be applied to calculate your overall grade for the course. | ||
* Weekly participation: 40% | * Weekly participation (includes problem sets): 40% | ||
* Proposal identification: 5% | * Proposal identification: 5% | ||
* Final project planning document: 5% | * Final project planning document: 5% | ||
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* Final project paper: 40% | * Final project paper: 40% | ||
The teaching team will jointly | The teaching team will jointly evaluate your participation along four dimensions: attendance, preparation, engagement, and contribution. These are quite similar to the dimensions described in the "Participation Rubric" section of [https://mako.cc/teaching/assessment.html Benjamin Mako Hill's assessment page] and [https://reagle.org/joseph/zwiki/Teaching/Assessment/Participation.html Joseph Reagle's participation assessment rubric]. Exceptional participation means excelling along all four dimensions. Please note that participation ≠ talking more and I encourage all of us to seek [https://reagle.org/joseph/zwiki/Teaching/Best_Practices/Learning/Balance_in_Discussion.html balance in our discussions]. | ||
The teaching team's assessment of your final project proposal, planning document, presentation, and paper will reflect the clarity of the work, the effective execution and presentation of quantitative empirical analysis, as well as the quality and originality of the analysis. A more detailed assessment rubric will be provided. Throughout the quarter, we will talk about the qualities of exemplary quantitative research. In general, I expect your final project to embody these exemplary qualities. | The teaching team's assessment of your final project proposal, planning document, presentation, and paper will reflect the clarity of the work, the effective execution and presentation of quantitative empirical analysis, as well as the quality and originality of the analysis. A more detailed assessment rubric will be provided. Throughout the quarter, we will talk about the qualities of exemplary quantitative research. In general, I expect your final project to embody these exemplary qualities. | ||
== Policies == | |||
==== Teaching and learning in a pandemic | === General course policies === | ||
[[User:Aaronshaw/Classroom_policies|General policies]] on a wide variety of topics including classroom equity, attendance, academic integrity, accommodations, late assignments, and more are provided [[User:Aaronshaw/Classroom_policies|on my class policies page]]. Below are some policy statements specific to this course and quarter. | |||
=== Teaching and learning in a pandemic === | |||
The Covid-19 pandemic will impact this course in various ways, some of them obvious and tangible and others harder to pin down. On the obvious and tangible front, we have things like a mix of remote and (a)synchronous instruction, the fact that many of us will not be anywhere near campus or each other this year, and the unusual academic calendar. These will reshape our collective "classroom" experience in major ways. | The Covid-19 pandemic will impact this course in various ways, some of them obvious and tangible and others harder to pin down. On the obvious and tangible front, we have things like a mix of remote and (a)synchronous instruction, the fact that many of us will not be anywhere near campus or each other this year, and the unusual academic calendar. These will reshape our collective "classroom" experience in major ways. | ||
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I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I will try to be transparent and direct with you throughout—both with respect to the course material as well as the pandemic and the university's evolving response to it. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time (directly related to the pandemic or not), please contact the teaching team. | I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I will try to be transparent and direct with you throughout—both with respect to the course material as well as the pandemic and the university's evolving response to it. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time (directly related to the pandemic or not), please contact the teaching team. | ||
=== Syllabus revisions === | |||
This syllabus will be a dynamic document that will evolve throughout the quarter. Although the core expectations are fixed, the details will shift. As a result, please keep in mind the following: | This syllabus will be a dynamic document that will evolve throughout the quarter. Although the core expectations are fixed, the details will shift. As a result, please keep in mind the following: | ||
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# '''The course design may adapt throughout the quarter.''' As this is a new format for this course, I may iterate and prototype course design elements rapidly along the way. To this end, I will ask you for voluntary anonymous feedback — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback and I expect to do so again. | # '''The course design may adapt throughout the quarter.''' As this is a new format for this course, I may iterate and prototype course design elements rapidly along the way. To this end, I will ask you for voluntary anonymous feedback — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback and I expect to do so again. | ||
=== Statistics and power === | |||
The subject matter of this course—statistics and statistical programming—has historical and present-day affinities with a variety of oppressive ideologies and projects, including white supremacy, discrimination on the basis of gender and sexuality, state violence, genocide, and colonialism. It has also been used to challenge and undermine these projects in various ways. I will work throughout the quarter to acknowledge and represent these legacies accurately, at the same time as I also strive to advance equity, inclusion, and justice through my teaching practice, the selection of curricular materials, and the cultivation of an inclusive classroom environment. Please see my [[User:Aaronshaw/Classroom_policies|general classroom policies]] for more on some of these topics. | The subject matter of this course—statistics and statistical programming—has historical and present-day affinities with a variety of oppressive ideologies and projects, including white supremacy, discrimination on the basis of gender and sexuality, state violence, genocide, and colonialism. It has also been used to challenge and undermine these projects in various ways. I will work throughout the quarter to acknowledge and represent these legacies accurately, at the same time as I also strive to advance equity, inclusion, and justice through my teaching practice, the selection of curricular materials, and the cultivation of an inclusive classroom environment. Please see my [[User:Aaronshaw/Classroom_policies|general classroom policies]] for more on some of these topics. | ||
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When reading the schedule below, the following key might help resolve ambiguity: §n denotes chapter n; §n.x denotes section x of chapter; §n.x-y denotes sections x through y (inclusive) of chapter n. | When reading the schedule below, the following key might help resolve ambiguity: §n denotes chapter n; §n.x denotes section x of chapter; §n.x-y denotes sections x through y (inclusive) of chapter n. | ||
=== Week 1 | === Week 1: Introduction, Setup, Data, and Variables === | ||
==== September 17 | ==== September 17 ==== | ||
'''Required''' | |||
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html pre-course assessment of statistical concepts] (access code TBA VIA email). '''Submission deadline: September 18, 11:00pm Chicago time''' | |||
* Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data). | |||
* Review [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §1.1-3 (Videos 1-4 in the playlist)]. | |||
* Confirm access to software and web-services for course (Discord, Canvas, this wiki, R, RStudio). | |||
=== Week 2: === | |||
==== September 22 ==== | |||
'''Required''' | '''Required''' | ||
* Read | * <TODO> Read and work through Introduction to R and RStudio | ||
* Complete | * Complete Problem set #1 | ||
* | ** SQ from OpenIntro §1: 1.6, ''1.9'', 1.10, 1.16, ''1.21'', 1.40, 1.42, ''1.43'' | ||
* | ** Installing R and RStudio, getting help, creating/saving .Rmd, weaving code and text, knitting output into html or pdf. | ||
** <TODO> Refactor 2019 PS1 PC1-4. Update R and Rstudio installation. Add a calculator problem. Clarify task to write code and text and knit output. | |||
''' | '''Resources''' | ||
* Verzani §1 (Getting started) and Healy §2 (Get started) provide helpful background for working with R and RStudio. | |||
==== September 24 | ==== September 24 ==== | ||
'''Required''' | '''Required''' | ||
* Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). | * Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). | ||
* Review [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §2.1 and §2.2 (Videos 6-7 in the playlist)]. | * Review [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 Lecture materials for §2.1 and §2.2 (Videos 6-7 in the playlist)]. | ||
* | * R lecture materials from 2019 W02 | ||
* | * Problem set #2 | ||
** OpenIntro questions: | |||
'''Resources''' | |||
==== September 29 | === Week 3: === | ||
==== September 29 ==== | |||
'''Required''' | '''Required''' | ||
* | * Problem set #3 | ||
** Empirical paper/data (UCB admissions. Police stops in IL.) | |||
** See PS2 Programming challenges from 2019 | |||
'''Resources''' | '''Resources''' | ||
* [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper] | * [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper] | ||
* [https://openpolicing.stanford.edu Stanford OpenPolicing Project] | * [https://openpolicing.stanford.edu Stanford OpenPolicing Project] | ||
==== October 1 | ==== October 1 ==== | ||
'''Required''' | '''Required''' | ||
* Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). | * Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). | ||
* | |||
* | * Problem Set #4 | ||
** OpenIntro questions: | |||
'''Resources''' | '''Resources''' | ||
* [https://seeing-theory.brown.edu/index.html#secondPage Seeing Theory §1-2 | * [https://seeing-theory.brown.edu/index.html#secondPage Seeing Theory] §1-2 | ||
=== Week 4 | === Week 4: === | ||
==== October 6 ==== | |||
'''Required''' | |||
* | |||
==== October | '''Resources''' | ||
==== October 8 ==== | |||
'''Required''' | '''Required''' | ||
* | * | ||
'''Resources''' | |||
''' | === Week 5: === | ||
* | ==== October 13 ==== | ||
'''Required''' | |||
* | |||
'''Resources''' | |||
==== October 15 ==== | |||
'''Required''' | |||
* | |||
'''Resources''' | |||
==== October | === Week 6: === | ||
==== October 20 ==== | |||
'''Required''' | |||
* | |||
'''Resources''' | |||
==== October 22 ==== | |||
'''Required''' | '''Required''' | ||
* | * | ||
'''Resources''' | |||
=== Week 7: === | |||
==== October 27 ==== | |||
'''Required''' | |||
* | |||
'''Resources''' | |||
==== October 29 ==== | |||
'''Required''' | |||
* | |||
'''Resources''' | '''Resources''' | ||
==== | === Week 8: === | ||
*''' | ==== November 3 ==== | ||
'''Election Day (U.S.): No class meeting today''' | |||
==== November 5 ==== | |||
'''Required''' | |||
* | |||
'''Resources''' | |||
=== Week | === Week 9: === | ||
==== November 10 ==== | |||
==== | |||
'''Required''' | '''Required''' | ||
* | * | ||
'''Resources''' | |||
''' | ==== November 12 ==== | ||
==== | |||
'''Required''' | '''Required''' | ||
* | * | ||
'''Resources''' | '''Resources''' | ||
=== Week | === Week 10: === | ||
==== November 17 ==== | |||
==== | |||
'''Required''' | '''Required''' | ||
* | * | ||
'''Resources''' | |||
==== November 19 ==== | |||
==== | |||
'''Required''' | '''Required''' | ||
* | * | ||
'''Resources''' | '''Resources''' | ||
=== Week | === Week 11: === | ||
==== November 24 ==== | |||
==== | |||
'''Required''' | '''Required''' | ||
* | * Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html post-course assessment of statistical concepts] (access code TBA VIA email). '''Submission deadline: December 1, 11:00pm Chicago time''' | ||
'''Resources''' | '''Resources''' | ||
==== | == 2019 course schedule == | ||
'''Required''' | |||
* | When reading the schedule below, the following key might help resolve ambiguity: §n denotes chapter n; §n.x denotes section x of chapter; §n.x-y denotes sections x through y of chapter n. | ||
* | === Week 1: Thursday April 4: Introduction, Setup, and Data and Variables === | ||
* | |||
* [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 1]] | |||
Please complete the readings and assignment prior to class so that we can discuss them and start talking through some of the examples in R together. | |||
'''Required Readings:''' | |||
* Diez, Barr, and Çetinkaya-Rundel: §1 (Introduction to data) | |||
* 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. [[http://www.pnas.org/content/111/24/8788.full Open Access]] | |||
'''Recommended Readings:''' | |||
* Verzani: §1 (Getting Started), §2 (Univariate data) [[https://canvas.northwestern.edu/verzani_ch1-ch2.pdf Available via Canvas]] | |||
* Verzani: §A (Programming) | |||
* Healy: §2 (and skim the preferatory material as well as §1) | |||
'''Assignment (Complete before class):''' | |||
* [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 1]] | |||
'''Lectures:''' | |||
* [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w01-R_lecture.zip Week 1 R lecture materials] (.zip file) | |||
* [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s01-intro.webm Week 1 screencast (part 1, 23 minutes)] (the video should load directly in browser window) | |||
* [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s02-intro.webm Week 1 screencast (part 2, 27 minutes)] | |||
'''Resources:''' | |||
* [https://www.openintro.org/download.php?file=os3_slides_01&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §1 Lecture Notes] | |||
* [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including some for §1 | |||
=== Week 2: Thursday April 11: Probability and Visualization === | |||
* [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 2]] | |||
* Questions? Topics you'd like to discuss? Add them to the [https://canvas.northwestern.edu/courses/90927/discussion_topics/601700 Canvas discussion] for this week's material. | |||
'''Required Readings:''' | |||
* Diez, Barr, and Çetinkaya-Rundel: §2 (Probability) | |||
* 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. [[https://doi.org/10.1177%2F0002764211433793 available via NU libraries]] | |||
'''Recommended Readings:''' | |||
* Verzani: §3.1-2 (Bivariate data), §4 (Multivariate data), §5 (Multivariate graphics) <!---[[https://faculty.washington.edu/makohill/com521/verzani-usingr-ch3.1-2_ch4_ch5.pdf Available with UW NetID]]---> | |||
* [https://seeing-theory.brown.edu/ 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. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on my personal website]] | |||
---> | |||
* Healy: §3. | |||
'''Assignment (Complete Before Class):''' | |||
* [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 2]] | |||
'''Lectures:''' | |||
* [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w02-R_lecture.Rmd Week 2 R lecture materials] (.Rmd file) | |||
* [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w02.webm Week 2 screencast (17 minutes)] | |||
'''Resources:''' | |||
* [https://www.openintro.org/download.php?file=os3_slides_02&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §2 Lecture Notes] | |||
* [https://www.openintro.org/stat/videos.phpOpenIntro Video Lectures] including 2 short videos for §2 | |||
=== Week 3: Thursday April 18: Distributions === | |||
* [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 3]] | |||
'''Required Readings:''' | |||
* Diez, Barr, and Çetinkaya-Rundel: §3.1-3.2, §3.4: 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. | |||
'''Recommended Readings:''' | |||
* Verzani: §6 (Populations) | |||
* [https://seeing-theory.brown.edu/ Seeing Theory] §3 (Probability Distributions). | |||
'''Assignment (Complete Before Class):''' | |||
* [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3]] | |||
'''Lectures:''' | |||
* [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w03-R_lecture.Rmd Week 3 R lecture materials] (.Rmd file) | |||
* [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w03.webm Week 3 screencast (19 minutes)] | |||
'''Resources:''' | |||
* [https://www.openintro.org/download.php?file=os3_slides_03&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §3 Lecture Notes] | |||
* [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 2 videos for §3.1 and §3.2 | |||
=== Week 4: Thursday April 25: Statistical significance and hypothesis testing === | |||
* [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 4]] | |||
'''Required Readings:''' | |||
* Diez, Barr, and Çetinkaya-Rundel: §4 (Foundations for inference) | |||
'''Recommended Readings:''' | |||
* Verzani: §7 (Statistical inference), §8 (Confidence intervals) | |||
* [https://seeing-theory.brown.edu/ Seeing Theory] §4 (Frequentist Inference) | |||
'''Assignment (Complete Before Class):''' | |||
* [https://docs.google.com/forms/d/e/1FAIpQLScMkAPwWQUjB4C5wtbkemkNZYjNl3ipO4Dg5wsORFmdfduEtA/viewform?usp=sf_link Mid-quarter course evaluation survey] (by Monday please!) | |||
* [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 4]] | |||
'''Lectures:''' | |||
*[https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w04-R_lecture.Rmd Week 4 R lecture materials] (.Rmd file) | |||
*(No screencast for this week) | |||
'''Resources:''' | |||
* [https://www.openintro.org/download.php?file=os3_slides_04&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §4 Lecture Notes] | |||
* [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 7 videos for nearly all of §4 | |||
=== Week 5: Thursday May 2: Continuous Numeric Data & ANOVA === | |||
* [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 5|Session plan]] | |||
'''Required Readings:''' | |||
* Diez, Barr, and Çetinkaya-Rundel: §5 (Inference for numerical 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. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF from Hill's website]]---> | |||
* Sweetser, K. D., & Metzgar, E. (2007). Communicating during crisis: Use of blogs as a relationship management tool. ''Public Relations Review'', 33(3), 340–342. [[https://doi.org/10.1016/j.pubrev.2007.05.016 Available through NU Libraries]] | |||
* Reinhart, §1 | |||
'''Recommended Readings:''' | |||
* Verzani: §9 (significance tests), §12 (Analysis of variance) | |||
* 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. [[http://dx.doi.org/10.1198/000313006X152649 Available through NU Libraries]] | |||
'''Assignment (Complete Before Class):''' | |||
* [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 5]] | |||
'''Lectures:''' | |||
* No new R material for this week. | |||
<!--- | |||
* [[Statistics and Statistical Programming (Spring 2019)/R lecture outline: Week 5]] | |||
* [https://communitydata.cc/~mako/2017-COM521/com521-week_05-ttests_and_anova.ogv Week 5 R lecture screencast: t-tests] (~22 minutes) | |||
* [https://communitydata.cc/~mako/2017-COM521/com521-week_05-for_if.ogv Week 5 R lecture screencast: for loops and if statements] (~12 minutes) | |||
---> | |||
'''Resources:''' | |||
* [https://www.openintro.org/download.php?file=os3_slides_05&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §5 Lecture Notes] | |||
=== Week 6: Thursday May 9: Categorical data === | |||
* [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 6|Session plan]] | |||
'''Required Readings:''' | |||
* 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. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf PDF available on Hill's personal website]] | |||
* Reinhart, §4 and §5. | |||
'''Recommended Readings: | |||
* 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) | |||
* 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. [[https://www.americanscientist.org/issues/pub/2014/6/the-statistical-crisis-in-science/1 Available through NU Libraries]] (This is a reworked version of [http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf this unpublished manuscript] which provides a more detailed examples.) | |||
'''Assignment (Complete Before Class):''' | |||
* [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 6]] | |||
'''Lectures:''' | |||
*[https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w06-R_lecture.Rmd Week 6 R lecture materials] (.Rmd file) | |||
*(No screencast for this week) | |||
'''Resources:''' | |||
* [https://www.openintro.org/download.php?file=os3_slides_06&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §6 Lecture Notes] | |||
* [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 4 videos for §7 | |||
=== Week 7: Thursday May 16: Linear Regression === | |||
* [[Statistics and Statistical Programming (Spring 2019)/Session plan: Week 7|Session plan]] | |||
'''Required Readings:''' | |||
* Diez, Barr, and Çetinkaya-Rundel: §7 (Introduction to linear regression) | |||
* OpenIntro eschews a mathematical approach to correlation. Look over [https://en.wikipedia.org/wiki/Correlation_and_dependence 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. [[http://dx.doi.org/10.1145/985692.985761 Available via NU libraries]] | |||
'''Recommended Readings:''' | |||
* Verzani: §11.1-2 (Linear regression). | |||
* [https://seeing-theory.brown.edu/ Seeing Theory] §5 (Regression Analysis) | |||
'''Assignment (Complete Before Class):''' | |||
* [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 7]] | |||
* Final project planning document (see details above!) | |||
''' | '''Lectures:''' | ||
* [https:// | * [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w07-R_lecture.Rmd Week 7 R lecture materials] | ||
'''Resources:''' | |||
* | * [https://www.openintro.org/download.php?file=os3_slides_07&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §7 Lecture Notes] | ||
* [https://www.openintro.org/download.php?file=os3_slides_08&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §8 Lecture Notes] | |||
* [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including 4 videos for §7 and 3 videos on the sections §8.1-8.3 | |||
=== Week 8 | === Week 8: Thursday May 23: Polynomial Terms, Interactions, and Logistic Regression === | ||
* [[Statistics_and_Statistical_Programming_(Spring_2019)/Session plan: Week 8|Session plan]] | |||
'''Required Readings:''' | |||
* | * Diez, Barr, and Çetinkaya-Rundel: §8 (Multiple and logistic regression) | ||
* [https://onlinecourses.science.psu.edu/stat501/node/301 Lesson 8: Categorical Predictors] and [https://onlinecourses.science.psu.edu/stat501/node/318 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. | |||
* (Revisit) 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. [[http://dx.doi.org/10.1145/985692.985761 Available via NU libraries]] | |||
* Reinhart, §8 and §9. | |||
'''Recommended Readings:''' | |||
''' | * 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. [[http://dx.doi.org/10.1371%2Fjournal.pmed.0020124 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. [[http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002106 Open Access]] | |||
''' | '''Assignment (Complete Before Class):''' | ||
* [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 8]] | |||
''' | '''Lectures:''' | ||
* | *[https://communitydata.science/~ads/teaching/2019/stats/r_lectures/w08-R_lecture.Rmd Week 8 R lecture materials] | ||
'''Resources''' | '''Resources:''' | ||
* [https://www.openintro.org/download.php?file=os3_slides_08&referrer=/stat/slides/slides_0x.php Mine Çetinkaya-Rundel's OpenIntro §8 Lecture Notes] | |||
* [https://www.openintro.org/stat/videos.php OpenIntro Video Lectures] including a video on §8.4 | |||
* | * Mako Hill wrote this document which will likely be useful for many of you: [https://communitydata.cc/~mako/2017-COM521/logistic_regression_interpretation.html Interpreting Logistic Regression Coefficients with Examples in R] | ||
* | |||
* | |||
=== Week | === Week 9: Thursday May 30: Loose ends and Final Presentations (part 1) === | ||
=== | |||
* [[Statistics_and_Statistical_Programming_(Spring_2019)/Session plan: Week 9|Session plan]] | |||
* [ | |||
''' | '''Required readings:''' | ||
* Reinhart, §10 and §11. | |||
'''[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations|Final presentations]]: (part 1)''' | |||
* First batch today. The rest next week. | |||
* | |||
'''Resources:''' | |||
* [https://communitydata.cc/~ads/teaching/2019/stats/r_lectures/w09-R_lecture.html Week 9 R-lecture] (we will use this in class) | |||
=== | === Week 10: Thursday June 6: Fully reproducible research example, Replications, Final Presentations (part 2), and wrap-up === | ||
* | * Fully [https://www.overleaf.com/read/tkdpdcspwtkp reproducible research example]. | ||
* [https://canvas.northwestern.edu/courses/90927/files/folder/resources/Straub-Cook%20Replication Research replication study] by Polly Straub-Cook (UW Comm. Ph.D. student) | |||
:: (n.b.: cluster & heteroscedasticity robust standard errors!) | |||
* | |||
* '''[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations|Final presentations]]: (part 2)''' | |||
:: Second batch of presenters today. | |||
* Closing thoughts | |||
:: What next? Beyond your final projects... | |||
:: Class social gathering | |||
Followed by much rejoicing! | |||
== Credit and Notes == | == Credit and Notes == | ||
This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. | This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. I also based most aspects of the course design on Benjamin Mako Hill's [[Statistics_and_Statistical_Programming_(Winter_2017)|COM 521 class]]. |