Outline

Overview

Research Methods is a component of the Honours elective, and is designed to extend students' knowledge and skills in research methods and problem solving, as well as oral and written scientific communication. The workshop and seminar series in the unit will equip students with the advanced research skills needed for their research projects. Research projects will commence in Semester 1 and will be completed in Semester 2 under the direct supervision of an academic staff member or supervisory team.

Unit details and rules

Academic unit	Pharmacy
Credit points	6
Prerequisites ?	PHAR3100 and PHAR3815 and PHAR3825 and PHAR3816 and PHAR3817 and PHAR3818 and PHAR3819 and PHAR3826 and PHAR3827 and PHAR3820
Corequisites ?	PHAR4811 and PHAR4812 and PHAR4823 and PHAR4100
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Wojciech Chrzanowski, wojciech.chrzanowski@sydney.edu.au
Lecturer(s)	Betty Chaar, betty.chaar@sydney.edu.au
	Edwin Tan, edwin.tan@sydney.edu.au
	Lorraine Smith, lorraine.smith@sydney.edu.au
	Monika Dzidowska, monika.dzidowska@sydney.edu.au
Tutor(s)	Kaiser Hamid, kaiser.hamid@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Literature review search strategy and outline Proposal	5%	Week 06 Due date: 04 Apr 2025 at 23:59	3 pages
Outcomes assessed:
Presentation	Scientific presentation Oral presentation	5%	Week 07 Due date: 11 Apr 2025 at 14:00	5 minutes
Outcomes assessed:
Presentation	Literature review and research protocol Oral presentation	25%	Week 13 Due date: 30 May 2025 at 12:00	15 minutes
Outcomes assessed:
Assignment	Literature review manuscript in project research area Literature review	55%	Week 13 Due date: 30 May 2025 at 23:59	4500 words
Outcomes assessed:
Assignment	Data management Assignment	10%	Week 13 Due date: 30 May 2025 at 23:59	6 problems
Outcomes assessed:
= hurdle task ? = restricted AI ?

Assessment summary

Data management assignment: Assignment on the principles of data management.
Literature review search strategy and outline: A written outline of the project literature review paper.
Scientific presentation: Students are required to present on a research article, covering the background, aims, methods, results and conclusion of a published research study.
Literature review manuscript in project research area: Students will submit a narrative review (or a systematic review) and compilation of previous research on a specific research topic. The review should crtitically analyse and interpret established findings from previous studies, conflicting evidence from the literature, and identify gaps in published work. This assessment must be passed to pass the unit of study.
Literature review and research protocol: Students will present on their research study literature review and the proposed research design.
Limited use of AI tools (Literature review and research protocol, Literature review search strategy and outline, Literature review manuscript in project research area, Data Management, Scientific Presentation)
In this assessment you are allowed to use automated writing tools and artificial intelligence (AI) for the following purposes:
  Editing assistance
You may use automated writing or AI tools such as to provide feedback on your work and suggest readability improvements to your text in terms of grammar and expression. You cannot use generative AI tools to generate content.
Creating media for assessment
You may use AI tools such as to generate that you use as part of your submission.
Suggesting a structure or outline
You may use AI tools such as to help you .
Searching and summarising literature
You may use AI tools such as to find and summarise research articles. The generated summary should not be included in the submission. You need to incorporate the scholarship yourself into your submission.
Your final submitted work must be your own, original work. You must acknowledge any use of AI tools that have been used in the assessment, and any material that forms part of your submission, must be appropriately referenced. For guidance on how to reference and acknowledge the use of AI, please refer to the AI in Education Canvas site.
Please adhere to the following guidelines:

•   Do not enter confidential, personal, copyrighted or otherwise sensitive information into any AI tool.
•   Do not rely on the accuracy of outputs. You remain responsible for your work. This means you must independently verify and edit AI-generated content to ensure the integrity, accuracy, and suitability of the output.
•   If you use these tools, you must be aware of their limitations, biases, and propensity for fabrication.
Failure to declare the use of AI tools is considered a breach of the Academic Integrity Policy and may result in penalties, which can include a fail for the assessment.
You are advised to keep copies of the prompts you provided and AI outputs as evidence of your research and writing process. This allows you to produce an accurate acknowledgement of AI use when you submit your work. This can be requested by the unit coordinator if there is any uncertainty about the originality of your work.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (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	At HD level, a student demonstrates an aptitude for the subject and a well-developed understanding of the unit material. A ‘High Distinction’ reflects exceptional achievement and is awarded to students who demonstrate the ability to apply their subject knowledge and understanding to produce original solutions for novel or highly complex problems and/or comprehensive critical discussions of theoretical concepts.
Distinction	75 - 84	At DI level, a student demonstrates an aptitude for the subject and a well-developed understanding of the units material. A ‘Distinction’ reflects excellent achievement and is awarded to a student who demonstrates an ability to apply their subject knowledge and understanding of the subject to produce good solutions for challenging problems and/or a reasonably well-developed critical analysis of theoretical concepts.
Credit	65 - 74	At CR level, a student demonstrates a good command and knowledge of the unit material. A ‘Credit’ reflects solid achievement and is awarded to a student who has a broad general understanding of the units material and can solve routine problems and/ or identify and superficially discuss theoretical concepts.
Pass	50 - 64	At PS level, a student demonstrates proficiency in the material. A ‘Pass’ reflects satisfactory adequately referencing the original source of the work.
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) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

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	Orientation and Ethics	Lecture (5 hr)
Week 03	Literature search 1	Workshop (2 hr)
	Seminar	Seminar (1 hr)
	Literature reviews/Academic writing	Workshop (2 hr)
	Honours overview	Workshop (2 hr)
	Data management 1	Workshop (2 hr)
Week 04	Literature search 2	Workshop (2 hr)
	Seminar	Seminar (1 hr)
	Literature search 3	Workshop (2 hr)
Week 05	Seminar	Seminar (1 hr)
	Research methods - questions and study design	Workshop (2 hr)
	Data management 2	Workshop (2 hr)
Week 06	Journal club 1	Workshop (1 hr)
	Seminar	Seminar (1 hr)
	Systematic reviews	Workshop (2 hr)
	Data Management 3	Workshop (2 hr)
Week 07	Seminar	Seminar (1 hr)
	Research Ethics and Integrity	Workshop (2 hr)
	Journal Club 2	Workshop (1 hr)
	Data Management 4	Workshop (2 hr)
Week 08	Seminar	Seminar (1 hr)
	Scientific Presentation Skills	Workshop (2 hr)
	Data Management 5	Workshop (2 hr)
Week 09	Seminar	Seminar (1 hr)
	Scientific Presentation - Student Presentations	Presentation (3 hr)
	Journal Club 3	Workshop (1 hr)
	Data Management 6	Workshop (2 hr)
Week 10	Seminar	Seminar (1 hr)
	Qualitative Research 1	Workshop (2 hr)
	Journal Club 4	Workshop (1 hr)
Week 11	Data Management 7	Workshop (2 hr)
	Seminar	Seminar (1 hr)
	Qualitative Research 2	Workshop (2 hr)
	Writing a scientific abstract	Workshop (1 hr)
Week 12	Data Management 8	Workshop (2 hr)
	Seminar	Seminar (1 hr)
	Qualitative Research 3	Workshop (2 hr)
	Journal Club 5	Workshop (1 hr)
Week 13	Data Management 9	Workshop (2 hr)
	Seminar	Seminar (1 hr)
	Student Presentations of Lit Review	Presentation (4 hr)
Weekly	Learning independently including pre-work, reports and assignments	Independent study (46 hr)

Attendance and class requirements

Attendance: Students are expected to attend a minimum of 85% of compulsory activities for a unit of study, unless granted exemption by the Program Director.
If a seminar/workshop is missed due to illness or misadventure, students are to submit a special consideration application along with supporting documentation. Students arriving more than 10 minutes after the commencement of the seminar/ workshop will be marked as unprofessional. Students swapping a class without prior approval from the UoS co-ordinator will be marked as unprofessional.

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. search databases and retrieve, evaluate and report information of significance for a research project
LO2. plan and write a critical literature review
LO3. show independence in carrying out and managing a literature review
LO4. work efficiently in a research team
LO5. demonstrate high level research skills
LO6. deliver high quality work by oral and written presentations
LO7. understand basic statistical analyses

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.

In response to student feedback, data management content has been revised

Additional information

Policy Register http://sydney.edu.au/policies/)

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) and automated writing tools

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

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

PHAR4815: Research Methods

Semester 1, 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) and automated writing tools

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

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