| Jump to: | Syllabus. | Schedule. | Oncourse (new window). |
| Instructor | Assistant | Assistant | |
| Name: | John K. Kruschke | ||
| Office Room: | PY 364 | PY | PY |
| Office Hours: | By appt. (please do ask) | (alternate weeks) | (alternate weeks) |
| Phone: | 855-3192 | 855- | 855- |
| E-mail: | kruschke@indiana.edu | @indiana.edu | @indiana.edu |
Course Description: Despite the official title ("Advanced Statistics in Psychology"), this course is an introduction to basic statistics. We will cover fundamental concepts of statistical inference. We will not deeply address issues in experimental design, nor the analysis of complex designs. (The subsequent course in the sequence, P554, addresses those issues.) For details of what is covered, read on.
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 emphasizes conceptual unification, not rote mechanics. You should have previously taken an undergraduate course in statistics. 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 this course, many students have told me that though 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 more advanced statistics courses instead.
Whereas most of the examples discussed in class will come from psychology, students from other fields are welcome.
Goals of the Course: There are two overarching goals for this course. The first goal is for you to understand the basic logic of statistical inference, and to understand how all the particular procedures we encounter, e.g., z-tests, t-tests, F-tests, chi-square tests, etc., are simply specific cases of the same general logic. (It is important for you to understand this logic of "null hypothesis significance testing" so that you can be conversant in the language of 20th-century social science. The 21st century, however, belongs to Bayesian statistical inference, and I strongly recommend you take my other course: P533/P534.)
The second goal is for you to understand that ``Science is a very human form of knowledge. ... Every judgment in science stands on the edge of error, and is personal. Science is a tribute to what we can know although we are fallible.'' (J. Bronowski, 1973.) The methods of statistics are the means by which we stake our claims to (scientific) knowledge, and these methods can be construed as a profound expression of human culture. Doing statistics is exercising our intellectual freedom, and statistical inference is based on the same premise as free society.
Computers:
Required Textbook:
Required Tools:
Laboratory sessions: You should attend a weekly "lab" session. The lab sessions will usually be devoted to participatory activities, solving example problems, computer demonstrations, and primarily addressing questions from students.
Lab session times:
Homework and Exams: There will be weekly homework assignments that cumulatively count for 40% of your grade. Homework assignments will be posted on the Web. Late homework will be severely penalized: Each day late, up to three days, reduces the total possible by 10%, and after three days no points are possible (unless you have a cogent excuse, in which case you should contact Prof. Kruschke as soon as possible, preferably the same day, by phone or 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 assistants, who will be grading the homework, must not be given a flood of late homework papers just before each exam.
You are encouraged to work together on the homework assignments to the extent that it enhances your learning of the material, but please write your own answers in your own words. Some of the homework exercises will have answers in the back of the book. You are encouraged to study these answers after you have attempted the problem yourself. 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.
There will be two mid-term exams and a final exam, each counting 20% of your grade. Each exam will heavily emphasize material from the immediately preceding part of the course, but will also involve material from earlier parts, so that the exams are partially cumulative. The exams require you to work individually and quickly, unlike the homework assignments.
If you know in advance that you must miss an exam, please let Prof. Kruschke know ahead of time, and it will usually be possible to take the exam one or two days before the scheduled date. If you unexpectedly miss an exam, discuss the absence with Prof. Kruschke as soon as possible, typically the same or next day, via phone or e-mail if not in person.
Course Grading Method: Grading is based on your overall percentile relative to the class. 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. It is Psychology Department policy to give incompletes ("I" grades) only with a valid medical excuse.
Lecture Notes: Neither lecture notes, nor copies of overhead projector slides, are available. If you must miss a lecture, get notes from a classmate, and then please see Prof. Kruschke or one of the assistants during office hours if you have questions.
Schedule: A detailed schedule is shown on a separate page, accessible from the web.
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 exam dates, grading policies, office hours, etc. Changes will be announced in class.