PSYCH 744 : Experimental Design and Quantitative Methods for Psychology


2023 Semester One (1233) (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 aims to prepare students for analysing and writing up data from their own research using SPSS. 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.

This course also 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 will cover a wide range of quantitative parametric statistical techniques, with emphasis on Factorial Analysis of Variance and Multiple Regression (simultaneous, hierarchical, logistic).

The course is discussion and computer-based format.

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

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand and describe basic statistical concepts used in quantitative data analysis such as the attributes of variables, different measurement scales and types of research designs. (Capability 1)
  2. Review the assumptions of parametric and non-parametric designs, while understanding the advantages and disadvantages of both, as well as screen data for assumptions of parametric tests. (Capability 1)
  3. Be proficient in the use of SPSS software (windows and syntax options) to analyse data from psychological experiments. 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 3)
  4. Review the current research literature in your area of interest and describe and critique the most commonly employed statistical techniques. (Capability 2)
  5. Perform and interpret multiple regression analysis by computer (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 3)
  6. Conduct and report on a three-way split-plot ANOVA 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 3)


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


Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at

This course is supported by the Tuākana in Science Programme, which facilitates the success and wellbeing of our Māori and Pacific students. The foundation of the Tuākana Programme is the Tuākana-Teina principle an integral relationship in which older or more expert Tuākana (traditionally brother, sister or cousin) guides a younger or less expert Teina (traditionally younger sibling or cousin). This is a reciprocal relationship which fosters safe learning and teaching environments. Read more here:

Key Topics

NOTE that the content of the classes below may change. 

Class 1:  
  • Introductions, coursebook & SPSS overview, basic terminology
  • Access course data set
  • Discuss assessments 1-3
Class 2: 
  • Hypothesis Testing: Independent and paired t-tests
  • Correlation
  • One-way ANOVA: between- & within-groups 
Class 3: 
  • Simple linear regression
  • Multiple regression (simultaneous)
  • Exploratory Factor Analysis 
Class 4:  
  • Chi square
  • Logistic regression
  • Data transformation
Class 5:  
  • Factorial ANOVA, Main effects, interactions, simple effects tests and syntax 
Class 6:   
  • Assignment 1 due 
  • Introduce Split Plot (mixed) ANOVA
  • Introduce Hierarchical regression
 Class 7:   
  • Assumption testing for regression
  • Assumption testing for ANOVA
  • Multiple regression using ANOVA, ANCOVA
 Class 8:
  • Advanced Split Plot ANOVA
  • Advanced Hierarchical regression
  • Advanced Logistic regression 
 Class 9:
  • Practice mid-semester assessment
Class 10: Mid-Term Assessment 

Class 11:
  • The new statistics; Tables/Figures in APA; Excel to SPSS; SPSS to R; Data issues: missing data, non-normal data

Special Requirements

Students must have prior and basic knowledge of statistics and research methods from an advanced undergraduate course. 

Everyone must have access to a computer (in a University of Auckland lab and at home for online learning).

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. Following University workload guidelines, a standard 15-point course represents approximately 150 hours of study. 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 scheduled activities to complete the course.
  • Lectures will not be available as recordings. 
  • Attendance on campus is required for the exam.
  • The activities for the course are scheduled as a standard weekly timetable.

Learning 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.

Required Text: 
  • Discovering Statistics Using IBM SPSS Statistics by Andy Field. University Bookstore new or used NOTE: any edition/version is fine: 5ed (2017) or other. 

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 PG research. Familiarising students with postgraduate research processes increases their confidence that they can be successful at the PG level.

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.

Upon reasonable request, students may be required to provide an electronic version of their work for computerised review, even if an electronic version was not required as a part of the submission process.

If a student deliberately cheats and receives a penalty, the case will be recorded in a University-wide register. The record of the offence will normally remain until one year after the student graduates.

NOTE that correctly quoted material used appropriately and sparingly will enhance your assignments, but quoted material should not be over-used.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


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 course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

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 students may be asked to submit 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. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 28/10/2022 11:28 a.m.