Lectures Tuesdays: 4:10 - 5:00 pm Room: EA402 Room: EA407
Office hours Thursdays 11-11:50 Room: EA421 (or by appointment)
Workshops Wednesdays: 3:30 – 5:20 PM Room: EA402
Course coordinator: Prof. Joseph Bulbulia Email: joseph.bulbulia@vuw.ac.nz
Course administration: Johannes Karl Email: johannes.karl@vuw.ac.nz
This course is introduction to the R statistical programming language with practical applications for postgraduate students in psychological science. We start with basic coding and workflow protocols, data wrangling and visualisation, and elementary regression, and gradually progress to more complex multilevel models. The practical skills you acquire will prepare you for advanced research in psychological science as well as for work in other areas of science, government, and industry. No prior familiarity with R or computer programming is required.
Weekly lectures and course resources are located under the lecture tab here
** Weekly workbooks can be found under the workbook tab [here] (https://go-bayes.github.io/psych-447/index_workbooks.html)
See information under the Assessments tab here: https://go-bayes.github.io/psych-447/assessments.html
Lectures will be posted to: https://github.com/go-bayes/psych-447
Course-related notices will appear on VUW Blackboard
Any information relating to Psyc 447 or any last-minute changes to the course will be posted on Blackboard.
Written work will be submitted via Blackboard, unless otherwise specified.
The problem sets and journal are due every Friday during term at midnight.
You will be marked out of your best 10 x submissions for the problem sets Only 10 x journal entries will count.
Trimester dates: 22 February to 20 June Teaching dates: 22 February to 28 May
Mid-trimester break: 5 April to 18 April
Last assessment item due: Final assessment (project) will need to be submitted on 31st May (see Assessments)
Study Examination/Assessment Period: Note: students who enrol in courses with examinations must be able to attend an examination at the University at any time during the scheduled examination period. Withdrawal dates: Refer to Withdrawing If you cannot complete an assignment or sit a test or examination (aegrotats), refer to Aegrotats
Psyc 447 is a 15pt course which represents approx. 150 hours of total work for an average student.
In-class time involves (approx. 22hrs):
Lectures by academic staff: Lectures will introducing the topics. These will be based on weekly readings.
Class participation: you need to follow the lectures or else you will risk falling behind.
Workshops: the weekly workshops are where you will receive hands on training. Again, you need to attend or follow these or else you will risk falling behind.
There are no mandatory course requirements.
Given that Psyc 447 is a 15 point course, the expected workload is no less than 150 hours in total. This would mean roughly 15 hours per week. This should be viewed as a minimum - how much you get out of this course will depend on the work you put in.
Students must turn in all assignments on time by due dates. If students do not turn in an essay or assignment at a designated time, they will receive a zero for assignment this will be counted toward the final grade.
Computer a personal computer will be essential for completing this course. If you do not have access to a personal computer let your instructor know as soon as possible.
A github education account This will be useful, even if not strictly necessary (i.e. you can set up an ordinary github account)
You should apply for a github education account as soon as possible here: https://education.github.com/pack/offers
Most of the material students will be expected to read is published in journals academic websites and, in some cases, in books. Students will generally need to read between one and five articles per week. Set readings will mostly be supplied throughout the course (please check this website). However, students will also be required to conduct their own independent reviews of the literature for assignment purposes.
The information above is specific to this course. There is other important information that students must familiarise themselves with, including:
Special passes Refer to the Assessment Handbook
Lecture dates are:
There will be a 1.5-hour practicum each week in which students cultivate their skills by applying what the learn to data. Learning applied statistics in R is like learning a language. Progress comes from immersing yourself in the language. You need to speak often, and you need to make lots of mistakes. The weekly workshops will provide you with opportunity to do R. The workshops will consist of your weekly problem sets + additional support. The are the place were we will help you individually and in groups, and were you will help each other.
The weekly labs on the Wednesdays after the Tuesday lectures.
Workshop dates:
Weekly assignments allow you to further develop you skills, and to build the statistical intuitions that will enable them to flexibly address a broad range of scientific problems.
The focus of these assignments will be on thinking about coding and statistics, documenting requests for help, and offers for help.
The course culminates with an in-depth project in which students work on data from their own area of interest combining R with contemporary tools of collaborative (GitHub) and open science (OSF). Student CVs will afford students with the opportunity to present themselves for employers or graduate school admissions committees.
If you see mistakes or want to suggest changes, please create an issue on the source repository.
Text and figures are licensed under Creative Commons Attribution CC BY-NC-SA 4.0. Source code is available at https://go-bayes.github.io/psych-447/, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".