|Name:||John K. Kruschke||**|
|Office Room:||PY 364||**|
|Office Hours:||By appt. (please do ask)||**|
Course Description: This course is an introduction to basic statistics (despite the official title, "Advanced Statistics..."). We will cover fundamental concepts of statistical inference, focusing on classical "frequentist" methods but also getting some exposure to Bayesian methods. We will explore some of the most commonly used models, including t-tests, ANOVA, regression, etc. More information about content is provided below, and on the schedule.
Prerequisites: This course is intended to bring all the incoming graduate students in Psychology "up to pace," so it is not intended to "weed out" students with relatively weak previous training in statistics. On the other hand, this course is definitely not remedial --- it moves quickly and covers a lot of material, so expect to devote a lot of time to the course. You should have previously taken an undergraduate course in statistics. The course emphasizes conceptual unification, not rote mechanics. A purpose of P553 is to enrich and solidify your understanding of the conceptual underpinnings of methods to which you were previously exposed. (After taking various previous instantiations of this course, many students have told me that although they have taken stats courses before, this is the first time they have understood statistics! My hope is that regardless of your previous level of understanding, you come away from this course with a better understanding.)
Students with relatively strong previous training in statistics should also find this course useful to refresh their knowledge and to gain a deeper understanding of the basic concepts. If you are a Psychology major and have already taken a comparable graduate-level course, and feel that you are already thoroughly familiar with the material in P553, please see the instructor to discuss a possible exemption from the P553 requirement. Students exempted from P553 are encouraged to take other statistics courses, such as Prof. Kruschke's Bayesian course.
Students from all disciplines are welcome in this course.
After taking this course you should be able to...
Homework: There will be weekly homework assignments. All assignments are mandatory. Late homework is exponentially penalized (unless you have a cogent excuse, in which case you should contact Prof. Kruschke as soon as possible, preferably in advance or on the due date, by e-mail). There are two reasons for this policy: First, the course moves quickly and the material is largely cumulative, so the late penalty acts as an extra incentive to keep up. Second, the assistant, who will be grading the homework, must not be given a flood of late homework papers at the end of the semester.
You are encouraged to use whatever resources help you understand the homework and complete it with full comprehension, but ultimately you must write your own answers on your own and in your own words. Each homework assignment begins with an honor statement indicating that you are writing your answers on your own in your own words. In your answers that you submit, please provide explanations and thoroughly show all your computations, with annotation that explains what you are doing. An unannotated succession of computations will not get full credit, even if it is numerically correct.
Course Grading Method: Grading is based on your total homework score, as a percentile relative to the class. (There are no exams, and no projects.) Scores tend to be very high, so do not think that, say, 96% must be a grade of A --- it could end up being an A- if, say, two thirds of the class does better than 96%. Typically the late penalties turn out to be a bigger deduction than points missed due to errors, so don't fall behind. As this is a graduate course, grades are typically in the A to high B range, and only rarely is a C or less assigned.
Software: We'll be using software called R and RStudio. Both are free to download and install on your personal computer. Details will be provided in class. Both are also on all IU computers.
Lecture Notes: Lecture materials will be posted online. The early weeks have some extensive written notes, but the later weeks have only slides without annotation. Therefore, if you must miss a lecture, please get notes from a classmate and then see one of the assistants during office hours or Prof Kruschke if you have questions.
Recommended Book: We will not be following this book chapter by chapter, but it is a very accessible reference for many of the topics we'll be covering. I highly recommend it as a very useful resource for this course and for your future data analysis with R. The book is:
Fischetti, Tony (2015). Data Analysis with R. Birmingham, UK: Packt Publishing. ISBN: 978-1-78528-814-2.
Other Reference Materials: There are many online materials about R, including the official R documentation. Another nice resource is Using R for psychological research by the Personality Project. Another useful online site is Quick-R (which also promotes a book that shows examples of R but does not explain statistical concepts).
Canvas: We will use an online system called Canvas for posting announcements, discussion, and grades. To get to Canvas, go to https://canvas.iu.edu/ and click on the Login button. You need to have an IU computer account, and you need to be enrolled in this course.
Schedule: Weekly homework is assigned on Mondays and is due the following Monday. Mondays and Wednesdays are mostly lecture-style presentations. Fridays are computer activities that will address the homework that is due the following Monday. Please study the homework assignment before arriving at the computer session so that you can ask questions.
|1||Describing noisy data with mathematical models. Getting started with R.|
|2||Finding the parameter values of a model that best fit the data: maximum likelihood estimation. Examples: Single group, two groups, linear regression.|
|3||Sampling distributions and p values (null hypothesis significance testing).|
|4||Sampling distributions and confidence intervals.|
|5||Sampling distributions (hence p values and confidence intervals) depend on stopping and testing intentions.|
|6||Model comparison and deciding among models.|
|7||The generalized linear model. Linear regression.|
|8||Multiple linear regression.|
|9||Oneway ANOVA. Multiple comparisons.|
|10||Multi-factor ANOVA. Interaction.|
|11||Dichotomous predicted variable: Logistic regression.|
|12||Nominal predicted variable: Softmax regression.|
|13||Ordinal predicted variable: Ordinal probit regression.|
|14||Count predicted variable: Log-linear models.|
|15||Bayesian methods: |
Bayesian for newcomers: published article, final manuscript.
Bayesian and frequentist, hypothesis testing and estimation: published article, final manuscript.
Two-group comparison: JEP:G article.
Linear regression: ORM article.
Hierarchical models: chapter.
Disclaimer: This syllabus is meant to be suggestive, not absolute. Any and all of the information on this syllabus is subject to change at any time, including due dates, grading policies, etc. Changes will be announced in class.