LSR readings can be found in the free, online textbook, Learning Statistics with R by Danielle Navarro. For those interested in taking notes, I recommend the use of the Hypothes.is app to annotate webpages. I will note that the formatting of the book online is wonky in a few places. If this bothers you, or you prefer to work offline, you can download a PDF version of the book.
| Week | Date | Topic | Readings | Quiz | Homework |
|---|---|---|---|---|---|
| 1 | 09/29 | Introduction and overview | LSR Ch 1 & 3 | ||
| - | 10/01 | Threats to measurement validity | LSR CH 2 & 4, Cronbach & Meehl (1955) | ||
| - | 10/02 | Lab: Introduction to R | |||
| 2 | 10/06 | Describing data | LSR Ch 5 & 6 | Quiz 1 | |
| - | 10/08 | Describing data | Ozer & Funder (2019) | ||
| - | 10/09 | Lab: R basics and descriptives | |||
| 3 | 10/13 | Matrix algebra | Quiz 2 | ||
| - | 10/15 | Probability | LSR Intro to Part IV and Ch 9.1-9.3, Wetzels et al. (2011) | ||
| - | 10/16 | Lab: Matrix algebra | |||
| 4 | 10/20 | Probability | LSR Ch 9.4 | Quiz 3 | |
| - | 10/22 | Probability | LSR Ch 9.5-9.6 | ||
| - | 10/23 | Lab: Probability distributions | HW 1 Due | ||
| 5 | 10/27 | Sampling | LSR Ch 10 | Quiz 4 | |
| - | 10/29 | Hypothesis testing | LSR Ch 11 | ||
| - | 10/30 | Lab: Data wrangling | |||
| 6 | 11/03 | Hypothesis testing | Sainani (2012) | Quiz 5 | |
| - | 11/05 | Critiques of hypothesis testing | Simmons et al. (2011) Cumming (2014) | ||
| - | 11/06 | Lab: Graphing with ggplot2 | |||
| 7 | 11/10 | Open Science | Five Thirty Eight | Quiz 6 | |
| - | 11/12 | One-sample tests | LSR Ch 13.1-13.2 | ||
| - | 11/13 | Lab: TBA | HW 2 Due | ||
| 8 | 11/17 | One-sample tests | Quiz 7 | ||
| - | 11/19 | Comparing two means (paired) | LSR Ch 13.3-13.11 | ||
| - | 11/20 | Lab: One- and paired-samples t-tests | |||
| 9 | 11/24 | Comparing two means (independent) | Quiz 8 | ||
| - | 11/26 | Thanksgiving | |||
| - | 11/27 | Lab: No Lab | |||
| 10 | 12/01 | Effect sizes and assumptions | |||
| - | 12/03 | Wrap-up | Quiz 9 | ||
| - | 12/04 | Lab: Independent samples t-tests | |||
| Finals | 12/11 | HW 3 Due 1 9am |
Final: Oral exam will take place the week of December 7.
Your final grade is comprised of three components:
Quizzes are intended to assess your understanding of the theoretical principles underlying statistics. There will be a quiz every Tuesday, with the exception of the first week, when there will be no quiz, and the final week when the quiz will be on Thursday.
Quizzes may be resubmitted with corrections and receive full credit. To resubmit a quiz, attach a separate piece of paper to your quiz; for each question that was answered incorrectly, identify the correct answer, and explain why this is the correct answer. Only if the explanation sufficiently conveys an understanding of the theoretical principles will credit be given. There are no limits to the number of times a quiz may be resubmitted.
Homework assignments are intended to gauge your ability to apply the topics covered in class to the practice of data analysis. Homework assignments are to be done using R and RMarkdown; completed assignments should be emailed to Dr. Weston and students must attach both the .Rmd file and the compiled HTML file.
Homework assignments are due at the time the first lab starts on the day the assignment is listed. Homework assignments may be resubmitted with corrections and receive full credit. Please note, however, that corrections can only be made to problems that were answered at initial submission. There is no limit to the number of times a homework assignment may be resubmitted.
Late assignments will receive 50% of the points earned. For example, if you correctly answer questions totalling 28 points, the assignment will receive 14 points. If you resubmit this assignment with corrected answers (a total of 30 points), the assignment will receive 15 points.
You may discuss homework assignments with your classmates; however, it is important that you complete each assignment on your own and do not simply copy someone else’s code. If we believe one student has copied another’s work, both students will receive a 0 on the homework assignment and will not be allowed to resubmit the assignment for points.
Each week you’ll complete a short journal entry on Canvas. To earn full points, you’ll have to write at least 200 words about anything at all. This can include content related to the course, such as how well you feel you’re understanding the material or how you’ve seen the material come up in other work. This can also include anything related to your personal life or mental health that you would like for me to know, such as whether you’re struggling to balance classes and research, having trouble creating a workspace, or managing roommate drama. This can also be completely random things, like a news article you can’t stop thinking about, or musings on the mist-downpour continuum of Oregon weather. The content of what you write has no impact on your grade. In addition, what you write will be kept confidential.
You may skip 2 journal entries with no penalty.
The purpose of this assignment is to help facilitate communication between you and me. In a normal term, there would be many opportunities for us to chat casually, for you to hang back and ask a clarifying quesiton, or even for me to notice if you’ve lost a little pep and check in. Our ability to check in with each other becomes exponentially harder over Zoom. I created this assignment in Spring 2020 with another class and found it an excellent way to build relationships with students. Plus, I found that many students were more comfortable discussing questions and concerns in their journal assignments rather than through email or Zoom meetings.
The oral exam will take place during finals week. About two weeks prior, you will be asked to schedule a time to complete the exam. The exam will take roughly 15 minutes. You will be asked to explain basic and elemental concepts, as if you were teaching an advanced undergraduate or new graduate student.
We will be using R for all data wrangling, visulaization, and analysis. You may not use another statistical program in this course. Students must have the latest version of R, which can be downloaded here. It is strongly recommended that students also download the RStudio GUI, available here. Both softwares are free.
All reading assignments will be posted online.
Cheating and plagarism. Any student caught cheating on an assignment, quiz, or exam will receive a 0 on that assignment. Frankly, you’re in graduate school, and the purpose of work is to create opportunities to learn and improve. Even if cheating helps you in the short term, you’ll quickly find yourself ill-prepared for the career you have chosen. If you find yourself tempted to cheat, please come speak to Dr. Weston about an extension and developing tools to improve your success.
Students with special needs. The UO works to create inclusive learning environments. If there are aspects of the instruction or design of this course that result in disability-related barriers to your participation, please notify me as soon as possible. You may also wish to contact Disability Services in 164 Oregon Hall at 541-346-1155 or disabsrv@uoregon.edu.
Any student who has difficulty affording groceries or accessing sufficient food to eat every day, or who lacks a safe and stable place to live and believes this may affect their performance in this course is urged to contact the Dean of Students Office (346-3216, 164 Oregon Hall) for support.
This UO webpage includes resources for food, housing, healthcare, childcare, transportation, technology, finances, and legal support: https://blogs.uoregon.edu/basicneeds/food/
Final grades are due the following Tuesday, so there are limited opportunties to redo this particular assignment for points. I will do my best to grade these within 24 hours of receipt so you have a chance to retry any missed points. Consider Monday 12/14 at 9am that last possible moment to turn in this assignment.↩︎