PSYCH 744 : Experimental Design and Quantitative Methods for Psychology


2021 Semester One (1213) (15 POINTS)

Course Prescription

Covers applications of the general linear model to research design and analysis. Topics include: univariate techniques (analysis of variance, analysis of covariance, regression) and multivariate techniques (multivariate analysis of variance, discriminant analysis, multivariate regression, and factor analysis).

Course Overview

This course deals with the principles and practices relevant to psychological research. These include designing research, measurement of variables, describing and analysing data, and interpreting results. This course aims to prepare students for analysing and writing up data from their own research using statistical software. Emphasis will be on the practical application of both experimental and nonexperimental designs using more than two groups and designs with two or more variables. Everyone must have access to a computer in class and the course is discussion format. We use IBM SPSS version 24 software for all analyses though R will also be discussed as a viable alternative software. Basic (undergraduate) knowledge of statistics (e.g., multiple regression, factorial ANOVA, nonparametric statistics) and SPSS software is assumed and is required for this course.

Course Requirements

Prerequisite: PSYCH 306 or consent of School

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand basic statistical concepts used in quantitative data analysis such as the attributes of variables, different measurement scales and types of research designs. (Capability 1 and 2)
  2. Review the assumptions of parametric and non-parametric designs (from lectures, course book and text) and understand the advantages and disadvantages of both. Be able to screen data for assumptions of parametric tests. (Capability 1, 2 and 3)
  3. Become proficient in the use of SPSS software (windows and syntax options) to analyse data from psychological experiments and surveys. Following a statistical analysis, understand how to write a Results section using a set of conventions regarding the way that research information is presented. (Capability 1, 2, 3 and 5)
  4. Critically review the current research literature in your area of interest (via library database searches) and describe and critique the most commonly employed statistical techniques. (Capability 1, 2 and 4)
  5. Perform multiple regression analysis by computer software (simultaneous, hierarchical, logistic) and solve a regression equation by hand. Understand that multiple regression can be used to determine: how well a set of variables are able to predict the criterion; the relative contribution of each of the variables that make up the model; which variable is the best predictor over and above the other IVs included in the set; whether a particular predictor variable is still able to predict the criterion when the effects of covariate(s) are controlled for. (Capability 1, 2 and 3)
  6. Conduct a two or three-way split-plot ANOVA (with SPSS or R) and perform any necessary post-hoc and/or simple effects tests. Understand the main types of analysis of variance and when to perform each. (Capability 1, 2 and 3)


Assessment Type Percentage Classification
Assignment 1 30% Individual Coursework
Take home assessment 30% Individual Coursework
Assignment 2 40% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6
Assignment 1
Take home assessment
Assignment 2

Key Topics

Week One:
 - Introductions, terminology, access course data set.
Week Two: 
 - Hypothesis Testing: Independent and paired t-tests; Correlation; One-way ANOVA: between- & within-groups. 
Week Three:
- Simple linear regression; Multiple regression; Factor Analysis. 
Week Four: 
- Chi square; Logistic regression; Data transformation; Merge files. 
Week Five:
- Factorial ANOVA, Main effects, interactions;  Simple effects tests and syntax.
Week Six:
- Split Plot (mixed) ANOVA; Hierarchical regression.
Week Seven:
- Assumption testing for regression and ANOVA. 
Week Eight:
 - Advanced Split Plot ANOVA; Advanced Hierarchical regression; Advanced Logistic regression. 
Week Nine:
-Practise assessment.
Week Ten: 
-Take home (24 hour) assessment 
Week Eleven:
- Tables/Figures in APA; ANCOVA; Multiple regression using ANOVA; Excel to SPSS; SPSS to R .
Week Twelve:
- postgrad issues, careers, data issues: missing data, non-normal data. 

Special Requirements

Assignments and take-home assessment: should be in APA style, typed and handed in on-line. Double spacing, one-inch margins, and 12-point fonts must be used. Assignments handed in late (without an approved extension) will lose 10% of the total marks per day. Saturday and Sunday count as one day. Therefore, if your assignment is due on Friday 2pm and you hand it in at 4pm Monday it will count as two days late (Saturday/Sunday and Monday). 
Missed Class: If you miss a class it is your responsibility to find out what you missed (through your classmates or the coordinator during office hours). All the topics for each class in the semester will be in the course book, so you will know in general what was covered. 

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. 

During a typical teaching week there will be 2 hours of lectures. 

For the 12 teaching weeks, this totals to 24 hours. Since the course as a whole represents approximately 150 hours of study, that leaves a total of 126 hours across the entire semester for independent study, e.g. reading, reflection, preparing for assessments/exams, etc.

Delivery Mode

Campus Experience

Attendance is expected at each lecture to complete components of the course.
Lectures will be available as recordings if the computer lab we are allocated (303S-175 in 2021) has recording capability. 
The course will not include live online events.
Attendance on campus is not required for the test (mid-semester assessment).
The activities for the course are scheduled as a standard weekly timetable. 

This course is available to offshore students and students who have been exempted from in-person attendance. Different conditions will apply for these students.  

Learning Resources

Required text: Discovering Statistics Using IBM SPSS Statistics 5ed (2017) by Andy Field. University Bookstore new or used. Any edition/version by the same author is fine. 
A printed course book will be available on CANVAS.

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.

The structure and delivery method of this course continues to prove effective at developing the research methods and statistical proficiency of students who intend to undertake advanced postgraduate degrees (Masters, PhD). Based on feedback, we balance between lecture / course book instruction and we allow students the freedom to develop their own research questions on assignments. Students are informed throughout the course how the skills they acquire in this course advantage their preparation for postgraduate research. 

Other Information

Assignment grading criteria: You can obtain a passing mark by showing that you have a reasonable grasp of the topic. To obtain a “B” grade you must show that you have a good grasp of the topic, that you have read literature outside of that covered in class, and that you can provide an intelligent evaluation of the issues, with only minor errors in what you write. An “A” grade will require that you have an excellent grasp of the topic, that you provide an intelligent evaluation of the issues, and that you support your positions with relevant research or reasoned arguments. The higher “A” grades will be reserved for those students who supplement this level of accomplishment with some outstandingly creative and original insights.

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.

If the room we are allocated (303S-175 in 2021) allows lecture recording, this will be done and made available on Canvas.

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.


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.

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

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.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Under the government’s Covid-19 Alert Levels, we anticipate using the following delivery modes. Note this is subject to change depending on the specific circumstances. 
Level 1:  Delivered normally as specified in delivery mode 
Level 2: You will not be required to attend in person.  All teaching and assessment will have a remote option.  
Level 3 / 4: All teaching activities and assessments are delivered remotely

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 22/12/2020 05:44 p.m.