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Unit of study_

PSYC3010: Advanced Statistics for Psychology

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

This unit of study expands upon students' knowledge of the general linear model and its applications in the analysis of data from psychological research. One half of the unit introduces students to contrast analysis and interaction analyses as an extension of ANOVA, which allows for more focused analysis of data where group comparisons are the primary interest. Another half focuses on multiple regression and its extensions, which are used when the primary interest is to predict or explain a particular variable based on a set of other variables.

Unit details and rules

Unit code PSYC3010
Academic unit Psychology Academic Operations
Credit points 6
PSYC2012 plus at least one other Intermediate Psychology Unit of Study from PSYC2010 and PSYC2910 and PSYC2011 and PSYC2911 and PSYC2013 and PSYC2014 and PSYC2015 and PSYC2915 and PSYC2016 and PSYC2017
Assumed knowledge


Available to study abroad and exchange students


Teaching staff

Coordinator Sabina Kleitman,
Lecturer(s) Daniel Costa,
Tutor(s) Alice Lo,
Matt Blanchard,
Oliver Tan,
Marvin Law,
Type Description Weight Due Length
Final exam (Take-home extended release) Type E final exam Final exam
Short answers
40% Formal exam period 48 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Online task On-line ANOVA and MR quizzes
20% Multiple weeks on-going and progressive
Outcomes assessed: LO1 LO6 LO4 LO3 LO2
Assignment ANOVA assignment
20% Week 07 1000 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment Regression assignment
Submitted work
20% Week 10 500-1000 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Type E final exam = Type E final exam ?

Assessment summary

  • ANOVA assignment: This assignment is based on lecture and tutorial material from weeks 1-6 (inclusive). You will be required to analyse a dataset of a factorial ANOVA using SPSS to answer contrast questions, interpret the output, and write a brief report. Reports will be evaluated on the basis of the appropriateness of analyses and contrasts to answer the research questions 
  • Regression assignment: This assignment is based on lecture and tutorial material from weeks 7-9 (inclusive). You will be required to analyse a dataset using SPSS, to perform and interpret regression analyses correctly and competently to address the postulated assignment requirements. You will be evaluated based on the appropriate use of SPSS to conduct regression analyses, valid interpretation of SPSS output and conclusions drawn, and competency of your overall output addressing assignment requirements.
  • ANOVA & Regression quizzes: There will be a number of multiple-choice on-line quizzes for each ANOVA and Regression parts of the course. The quizzes are based on lecture and tutorial material from weeks 1-6 (inclusive) for ANOVA part and weeks 7-12 (inclusive) for Regression part. You will be evaluated based on appropriate understanding and interpretation of information provided, including SPSS output in light of theory, research design and postulated questions. Quizzes will be available progressively on-line (to specified deadlines) throughout the semester for assessment, in weeks 1-6 for ANOVA part and in weeks 7-12 for Regression part. The quizzes are not compulsory.

More information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

For more information see

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 GLM and ANOVA Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Contrasts: formulation and testing Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
ANOVA and Contrasts Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 02 Contrasts: trend analysis, orthogonality Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Contrasts: adjusting for type 1 errors Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Contrasts Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 03 Two-way ANOVA model part 1 Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Two-way ANOVA model part 2 Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Trend Contrasts Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 04 Two-way ANOVA: interaction contrasts Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Repeated measures 1 Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Multifactor ANOVA Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 05 Repeated measures 2 Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Contrasts for 2-way ANOVA designs Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Repeated measures Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 06 Mixed designs 1 Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Mixed designs 2 and extensions Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Week 07 Simple linear regression: revision and extension Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Multiple regression 1: introduction Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Regression 1 Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Multiple regression 2: more detail Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Multiple regression 3: more detail Lecture (1 hr) LO1 LO2 LO3 LO4 LO5
Multiple regression 2 Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Week 09 Multiple regression 4: 3+ variables Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Different types 1 Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 10 Different types 2 Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Continuous variables and interactions Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Three types of multiple regression Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 11 Categorical and continuous variables, more on interactions Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Interactions and curves Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 12 Summary; reliability and assumptions Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Assumptions Lecture (1 hr) LO1 LO2 LO3 LO4
Summary and revisions Lecture (1 hr) LO1 LO2 LO3 LO4 LO5 LO6

Attendance and class requirements

F2F tutorials (in person, in a lab at the University) are provided on relevant weeks. Same contents on-line zoom tutorials are offered. This semester, students can choose which mode of delivery suits best their circumstances and register to either F2F or zoom tutorials accordingly.

Students taking zoom tutorials will need to upload their answers to the tutorial questions on Canvas by 5 pm on Friday of the relevant week. Serious attempts only will count as tutorial participation. 

Students attending F2F tutorials do not need to upload their answers on Canvas. Their tutorial participation will be marked as the attendance of a relevant tutorial. 

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

  • Keith, Z. T. (2006-2014). Multiple Regression and Beyond. Pearson New International Edition. USA: Pearson Education, Inc.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. develop a thorough understanding of techniques of statistical inference used in psychological research, including the ability to conduct and interpret analyses
  • LO2. understand, apply, and evaluate research methods in Psychology, including research design, advanced data analysis and interpretations, and the appropriate use of terminology
  • LO3. use critical thinking to solve problems related to psychological inquiry
  • LO4. value empirical evidence; act ethically and professionally
  • LO5. communicate effectively in a variety of formats and in a variety of contexts
  • LO6. develop an awareness of the applications of the statistical theory and research design in psychology to examine problems in everyday life and in society

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.
  • SPSS software: Purchasing SPSS software is strongly recomended but not essential for this course. The Learning Hub computers across campus have SPSS installed. Students may wish to purchase a copy of IBM SPSS Statistics from varous providers.  Discounted licences are often offered. Versions 26  and 27 for Mac and PC are the latest versions, but earlier versions are more than adequate. Please note that the SPSS Base Grad pack is a limited version that DOES NOT allow you to run all the analyses you need (i.e., it is not suitable for this course). It is your responsibility to check version/operating system compatibility. Note that SPSS is available via the ICT Virtual Desktops located in the Access labs and University Libraries, and can also be accessed online through Bring Your Own Device (BYOD). 
  • Contesting marks: Students do not have an automatic right to request re-marking of class work or exam papers, but they are
    encouraged to discuss the assessment of their work with members of the teaching staff. Before doing so, students must make sure they have read and understood any written comments already supplied by the marker.

Additional costs

There are no additional costs for this unit. Purchasing the SPSS licence is recommended but optional .


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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