PSYCH 306 : Research Methods in Psychology


2020 Semester One (1203) (15 POINTS)

Course Prescription

Deals with principles and practices relevant to psychological research, including philosophy of science, research ethics, research design, measurement of dependent variables, describing and analysing data, and interpreting results. Participation in the laboratory component of this course is compulsory.

Course Overview

This course deals with principles and practices relevant to psychological research, best research practices, research design and measurements, describing and analysing data, and interpreting results. More specifically, the course covers common experimental designs in psychological science, the general linear model and its applications, the distinction between frequentist and Bayesian frameworks of analysis, and the major applications of both sets of methods. In addition, the course provides students with a unique opportunity to learn how to implement these analyses in R, a programming language that is becoming prominent in psychological science, but also in other fields of research and in industry. Students completing the course will thus possess a unique set of skills including statistical proficiency and competency in data analysis, broadly defined.

Course Requirements

Prerequisite: 45 points at Stage II in Psychology and 15 points from STATS 100-125

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 5: Independence and Integrity
Graduate Profile: Bachelor of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Have an overall understanding of current experimental designs and statistical techniques in psychology (Capability 1)
  2. Be able to formulate hypotheses and select appropriate analyses to test these hypotheses, and interpret results from these analyses (Capability 1)
  3. Be able to differentiate between frequentist and Bayesian approaches to data analysis, and understand their major strengths and weaknesses (Capability 1, 2 and 3)
  4. Be able to apply relevant tools and techniques to their own projects, in the professional or in the research domain (Capability 1, 2 and 3)
  5. Be able to use R to visualise data and to conduct a variety of statistical analyses, including t-tests, ANOVAs, and regressions (Capability 1, 2 and 3)
  6. Have a good understanding of best research practices to guide any future research activities, as well as to critically evaluate other studies (Capability 1, 2, 3 and 5)


Assessment Type Percentage Classification
Final Exam 40% Individual Examination
Test 30% Individual Test
Assignments 30% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6
Final Exam

Learning Resources

There is no specific required textbook for the course; lecturers will provide specific readings for each module including background reading if necessary.

See the reading list on CANVAS for the required and recommended readings for each module. Core readings will be available electronically. These will be specific to the content given in lectures and be assigned by the relevant lecturer.
When relevant, a list of reading material will also be recommended for each assignment and will be available electronically. You will also be expected to source your own material. This is an upper-level course and so we assume that you are able to use the library databases to search for relevant literature.

Special Requirements

The tutorials cover material that is directly relevant for the assignments or material that is examinable. It will be very difficult to complete the assignments without attending tutorials. For these reasons, the tutorials are compulsory.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures, a 2-hour tutorial, 3 hours of reading and thinking about the content and 2 hours of work on assignments and/or test preparation.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page:

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 11/01/2020 03:17 p.m.