Outline

Overview

The aim is to introduce students to fundamental concepts in statistics and research design as applied to psychological research. These include summary descriptive statistics, an introduction to the principles and practice of research design (both quantitative and qualitative approaches), and the use of inferential statistics. Building upon this framework, the unit of study aims to develop each student's expertise in understanding the rationale for, and application of, a variety of statistical tests to the sorts of data typically obtained in psychological research.

Unit details and rules

Academic unit	Psychology Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Previous or concurrent completion of PSYC5111 or PSYC5112
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Daniel Costa, daniel.costa@sydney.edu.au
Lecturer(s)	Rebekah Laidsaar-Powell, rebekah.laidsaar-powell@sydney.edu.au
	Steson Lo, steson.lo@sydney.edu.au
	Alissa Beath, alissa.beath@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam See the 'Assessment summary' below and Canvas site for details.	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Quizzes See the 'Assessment summary' below and Canvas site for details.	10%	Multiple weeks	15 minutes ea	AI allowed
Outcomes assessed:
Contribution	Lecture engagement Lecture engagement	5%	Ongoing	N/A	AI allowed
Outcomes assessed:
Out-of-class quiz	Quizzes 1 and 2 Early feedback task	0%	Week 02	15 minutes ea	AI allowed
Outcomes assessed:
Data analysis	Assignment 1 See the 'Assessment summary' below and Canvas site for details.	20%	Week 06 Due date: 03 Apr 2026 at 23:59 Closing date: 01 May 2026	See Canvas for details.	AI allowed
Outcomes assessed:
Data analysis	Assignment 2 See the 'Assessment summary' below and Canvas site for details.	20%	Week 11 Due date: 15 May 2026 at 23:59 Closing date: 29 May 2026	See Canvas for details.	AI allowed
Outcomes assessed:
Written work	Reflective task Students reflect on their own experience with applications of research methodology and data analysis to a real-world problem.	5%	Week 13 Due date: 29 May 2026 at 23:59 Closing date: 29 May 2026	See Canvas for details.	AI allowed
Outcomes assessed:
= hurdle task ? = early feedback task ?

Assessment summary

Reflective task: More information will be provided about this task, which is due at the end of Week 13, after semester commences.
Quizzes: A total of 12 quizzes will be delivered online throughout semester. Each quiz will be made available from Tuesday 12pm – Monday 11:59pm the following week, with the best 10 of 12 contributing to your final score. Each quiz will test the knowledge acquired in previous weeks tutorials. If you miss any of the Quizzes, you may apply for Special Consideration, from which the only outcome is a 'mark adjustment', applying only to the Quiz(zes) you were approved to miss (N.B: if you miss seven or more quizzes and are approved for Special Consideration for all of them, you will be required to complete an oral assessment). If you miss any of the Quizzes and do not receive Special Consideration, you will simply not receive the marks associated with the Quiz(zes).
Assignments 1 and 2: Students will be provided with a dataset and will need to answer questions by critically applying the knowledge and skills learnt in previous weeks to said dataset. If you do not complete the Assignment by the closing date, you may apply for Special Consideration, from which the only outcome is a 'replacement', details of which will be sent to you by the Unit of Study Coordinator by the end of the semester. If you do not complete the Assignment and are not approved by Special Consideration, you will simply receive 0 for the Assignment.
Final Exam: Each lecture series and the Assignment will be assessed in a two-hour closed book exam held after the teaching period ends. If you miss the Final Exam, you may apply for Special Consideration, from which the only outcome is a 'replacement', which will be held in the University's Replacement Exam period. If a Second Replacement Exam is required, the format will be at coordinator discretion and may be an oral exam held outside of the formal period. If you miss the Final Exam and are not approved by Special Consideration, you will receive an Absent Fail (AF) grade for this unit, as the Final Exam is a compulsory assessment.

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.

Result name	Mark range	Description
High distinction	85 - 100	This work shows excellent understanding of the topic and clear evidence of independent critical thought.
Distinction	75 - 84	This work shows a very good understanding of the relevant content. Some calculations or interpretations may be flawed, but a serious and sustained attempt has been made.
Credit	65 - 74	This work shows a clear understanding of the relevant material; it contains only small gaps or minor errors.
Pass	50 - 64	This work shows evidence of a satisfactory level of understanding of the relevant material; it may contain gaps, errors or other kinds of blemishes, but it is obvious that the student has read and digested material from lectures and/or tutorials.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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	Introduction and research questions in psychology	Lecture (3 hr)
Week 02	Variables and measurement properties	Lecture (3 hr)
Week 02	Research questions in psychology	Tutorial (2 hr)
Week 03	Descriptive statistics and the normal distribution	Lecture (3 hr)
Week 03	Introduction to R	Tutorial (2 hr)
Week 04	Null hypothesis significance testing	Lecture (3 hr)
Week 04	Descriptive statistics, tables and graphs	Tutorial (2 hr)
Week 05	Qualitative research: design and data collection	Lecture (3 hr)
Week 05	Qualitative research: design and data collection	Tutorial (2 hr)
Week 06	Related and independent-samples t-tests	Lecture (3 hr)
Week 07	One-way analysis of variance	Lecture (3 hr)
Week 07	One-, related and independent-samples t-tests	Tutorial (2 hr)
Week 08	Two-way analysis of variance	Lecture (3 hr)
Week 08	One-way analysis of variance	Tutorial (2 hr)
Week 09	Correlation	Lecture (3 hr)
Week 09	Two-way analysis of variance	Tutorial (2 hr)
Week 10	Regression	Lecture (3 hr)
Week 10	Correlation	Tutorial (2 hr)
Week 11	Design, data collection and analysis in qualitative research	Lecture (3 hr)
Week 11	Regression	Tutorial (2 hr)
Week 12	Chi-square tests and other research methods topics	Lecture (3 hr)
Week 12	Design, data collection and analysis in qualitative research	Tutorial (2 hr)
Week 13	Ethics and limitations of research	Lecture (3 hr)
Week 13	Interpretation and summary	Tutorial (2 hr)

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.

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. Analyse and apply foundational research methods to design and evaluate basic psychological studies using quantitative and qualitative approaches.
LO2. Summarise and interpret data using appropriate numerical, graphical, and descriptive techniques.
LO3. Conduct and interpret foundational statistical analyses appropriate for common psychological research questions, using statistical software appropriately.
LO4. Communicate research processes and findings clearly and accurately, using discipline-specific conventions and tailoring communication to different audiences or purposes.
LO5. Describe and apply appropriate ethical principles and best practices in the design, conduct, analysis, and reporting of psychological research, including implications for diverse populations.
LO6. Identify, explain, and reflect on common errors and misconceptions in the design, analysis, and interpretation of psychological research.
LO7. Apply foundational research methods to analyse and address everyday psychological issues, demonstrating awareness of real-world contexts and the broader implications of research findings.

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.

This unit of study is new in 2026.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

PSYC5212: Applied Psychology Research Methods

Semester 1, 2026 [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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links