Outline

Overview

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

Academic unit	Psychology Academic Operations
Credit points	6
Prerequisites ?	PSYC2012 and (PSYC2X10 or PSYC2X11 or PSYC2013 or PSYC2014 or PSYC2X15 or PSYC2016 or PSYC2017)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Steson Lo, steson.lo@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final Exam See Canvas for details.	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	ANOVA Quizzes and MR Quizzes See Canvas for details.	20%	Multiple weeks	See Canvas for details.	AI allowed
Outcomes assessed:
Data analysis	MR Assignment Written assignment with statistical software output and syntax/ code.	15%	Week 05 Due date: 05 Sep 2025 at 23:59 Closing date: 19 Sep 2025	See Canvas for details.	AI allowed
Outcomes assessed:
Data analysis	ANOVA Assignment Written assignment with statistical software output and syntax/ code.	15%	Week 11 Due date: 24 Oct 2025 at 23:59 Closing date: 07 Nov 2025	See Canvas for details.	AI allowed
Outcomes assessed:
= hurdle task ?

Assessment summary

ANOVA Quizzes & MR Quizzes: There will be quizzes for the ANOVA and MR parts of the course with five summative quizzes for EACH part of the course. The quizzes are based on lecture and tutorial material from Weeks 1–6 (inclusive) for the MR part and Weeks 7–12 (inclusive) for the ANOVA part. You will be evaluated based on appropriate understanding and interpretation of information provided, including statistical software output in light of theory, research design, and postulated questions. The quizzes will have to be completed to their relevant deadlines. See Canvas for more details.

MR Assignment: This assignment is based on lecture and tutorial material from weeks 1–4 (inclusive). You will be required to analyse a dataset using statistical software, 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 statistical software to conduct regression analyses, valid interpretation of statistical software output and conclusions drawn, and competency of your overall output addressing assignment requirements. This assignment is not compulsory – if you do not complete this assignment in the specified time frame, you simply will not receive the marks associated with them. A successful application for Special Consideration for this assessment will result in an extension (up until the closing date) or submitting a replacement version of the assignment. See Canvas for more details.

ANOVA Assignment: This assignment is based on lecture and tutorial material from Weeks 7–9 (inclusive). You will be required to analyse a student-specific dataset of a factorial ANOVA using statistical software to answer contrast questions, interpret the output, and communicate the results. Assignments will be evaluated on the basis of the appropriateness of analyses and contrasts to answer the research questions as they are related to a) the given experimental design, b) the plausibility of the data, and c) the clarity and conciseness of the written interpretations. This assignment is not compulsory – if you do not complete this assignment in the specified time frame, you will simply not receive the marks associated with it. A successful application for Special Consideration for this assessment will result in an extension (up until the closing date) or submitting a replacement version of the assignment. See Canvas for more details.

Final Exam: A two-hour closed book exam will be held after the teaching period ends. If you miss the Final Exam and are approved by Special Consideration, you will sit a Replacement Exam during the Replacement Exam period. The Final Exam is a compulsory assessment, so if you do not attempt it, you will receive an Absent Fail (AF) grade, and it is also a hurdle assessment, so you will need to meet the required standard in order to pass the unit. See Canvas for more details.

Assessment criteria

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

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Simple linear regression: revision and extension	Lecture (1 hr)
Week 01	Multiple regression I	Lecture (1 hr)
Week 02	Multiple regression II	Lecture (1 hr)
	Multiple regression III	Lecture (1 hr)
	Simple linear regression	Tutorial (2 hr)
Week 03	Multiple regression IV	Lecture (1 hr)
	Multiple regression V	Lecture (1 hr)
	Multiple regression	Tutorial (2 hr)
Week 04	Three types of multiple regression	Lecture (1 hr)
	Categorical variables in multiple regression	Lecture (1 hr)
	Assumptions in regression	Tutorial (2 hr)
Week 05	Interaction with continuous variables I	Lecture (1 hr)
	Interaction with continuous variables II	Lecture (1 hr)
	Three types of multiple regression	Tutorial (2 hr)
Week 06	Interaction with continuous variables III	Lecture (1 hr)
	Multiple regression: summary and revision	Lecture (1 hr)
	Interaction with continuous variables	Tutorial (2 hr)
Week 07	One-way contrast analysis	Lecture (1 hr)
	Type I error correction	Lecture (1 hr)
	One-way contrast analysis	Tutorial (2 hr)
Week 08	Two-way analysis of variance	Lecture (1 hr)
	Two-way contrast analysis	Lecture (1 hr)
	Two-way analysis of variance	Tutorial (2 hr)
Week 09	Simple main effects for two-way designs	Lecture (1 hr)
	Non-parametric analysis of variance	Lecture (1 hr)
	Two-way contrast analysis	Tutorial (2 hr)
Week 10	One-way repeated measures analysis of variance	Lecture (1 hr)
	Two-way repeated measures analysis of variance	Lecture (1 hr)
	Simple main effects and non-parametric analysis of variance	Tutorial (2 hr)
Week 11	Mixed designs I	Lecture (1 hr)
Week 11	Mixed designs II	Lecture (1 hr)
Week 12	Psychological measurement	Lecture (1 hr)
	Analysis of variance: summary and revision	Lecture (1 hr)
	Repeated measures and mixed designs	Tutorial (2 hr)
Week 13	Advanced topics	Lecture (1 hr)
	Course overview and revision	Lecture (1 hr)
	Overview and revision	Tutorial (2 hr)

Attendance and class requirements

As per Section 60(5)(c), 68(2)(a), and 68(3) of the University's Coursework Policy, a student is required to comply with a Unit of Study's attendance requirements - for this Unit of Study, a student must be recorded as having attended 8 tutorials, and if a student does not meet this minimum standard, they will receive an Absent Fail (AF) grade for this Unit of Study.

Also, as noted in the Assessment table, the Final Exam is a compulsory assessment, so a student who does not attend it and is not approved to miss it will receive an Absent Fail (AF) grade.

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

Section 1: MR (Weeks 1 – 6)

Set Textbook:
- Keith, Z. T. (2006-2014). Multiple Regression and Beyond. Pearson New International Edition. USA: Pearson Education, Inc.
Recommended:
- Berk, R. A. (2006). Regression Analysis: A constructive Critique. Advanced Quantitative Techniques in the Social Sciences Series. Sage Publications, Inc.
- Pedhazur, E. J. (1997). Multiple Regression in Behavioral Research: Explanation and Prediction. Harcourt Brace College Publishers: New York.

Section 2: ANOVA (Weeks 7 – 13)

Set Textbook:
- No set textbook for this section
Recommended:
- Howell, D. C. (2013). Statistical Methods for Psychology (8th ed.). Belmont, CA: Wadsworth, Cengage Learning. (other editions will also be helpful)
- Maxwell, S. E. & Delaney, H. D. (2004). Designing experiments & analyzing data: a model comparison perspective. (2nd Ed) Belmont, CA: Wadsworth. NOTE: e-book
- Field, A. (2013-2018). Discovering Statistics Using IBM SPSS Statistics, 4th Edition. Sage.
- Keppel, G. & Wickens, T.D. (2004) Design and Analysis: A Researcher’s Handbook. (4th Ed) NJ: Prentice Hall.

Learning outcomes

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

All lectures will be held weekly in a 2-hour block on the same day instead of one-hour lectures twice a week. This change was implemented to facilitate student engagement.

Additional information

Purchasing statistical software is not essential for this course, as the computers across campus have statistical software installed - more information about how to access statistical software will be published on Canvas.

Disclaimer

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

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

PSYC3010: Advanced Statistics for Psychology

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links