Outline

Overview

This unit will deepen your knowledge about design of observational and experimental studies in health, current issues in health research and statistical procedures for data analysis. We will discuss published studies and analyse our own data using correlation, linear regression, t test, ANOVA, odds ratio, relative risk, etc., with understanding of fundamentals of statistical theory. You will develop the ability to draw a sound conclusion about the research question taking into account both statistical result and study design. You will learn to use Statistical Package for Social Sciences (SPSS), and how to write concise research reports. The unit will prepare you to be a critical reader of health research and to engage in further research training should you wish to do so

Unit details and rules

Unit code	FMHU3001
Academic unit	Participation Sciences
Credit points	6
Prohibitions ?	PSYC2012 or SCLG3603 or HSBH3018
Prerequisites ?	HSBH1007 or HSBH2007 or FMHU2000
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Tatjana Seizova-Cajic, tatjana.seizova-cajic@sydney.edu.au
Lecturer(s)	Tatjana Seizova-Cajic, tatjana.seizova-cajic@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Open-book exam; Multiple choice questions and short answers	45%	Formal exam period	2 hours
Outcomes assessed:
Small continuous assessment	Mini quizzes At-home and in-class online quizzes	11%	Multiple weeks Due date: 08 Mar 2024 at 23:59 Closing date: 17 May 2024	Multiple
Outcomes assessed:
Presentation	Research proposal and questions Group presentation and online submission	8%	Week -05 Due date: 19 Mar 2024 at 13:00 Closing date: 19 Mar 2024	8 min + 7 min for questions; submission
Outcomes assessed:
Online task	Mid-semester quiz Small test	20%	Week 07 Due date: 09 Apr 2024 at 13:00 Closing date: 09 Apr 2024	Approx. 25 questions, 50 min
Outcomes assessed:
Presentation	Survey results Group presentation; submission	8%	Week 09 Due date: 23 Apr 2024 at 13:00 Closing date: 23 Apr 2024	8 min + 7 min for questions; submission
Outcomes assessed:
Presentation	Interpretation and discussion of study results Presentation; abstract submission, 150 words	8%	Week 12 Due date: 14 May 2024 at 13:00 Closing date: 14 May 2024	8 min + 7 min discussion; submission
Outcomes assessed:
= group assignment ?

Assessment summary

1. Group project presentations and submissions (24% Total):

- Week 5: Research question & survey design involving AI (8%)

- Week 9: Data analysis with SPSS & AI (8%)

- Week 12: Interpreting results and drawing concusions (8%)

Group marks are based on quality of work, and individual marks will be scaled if necessary.

2. Mini-quizzes on Canvas (11% Total):

- Revision materials will be assessed in Week 3 (3%)

- Understanding and participation assessed during 5 face-to-face lectures (8%)

3. In-class Canvas quiz (20% Total):

- Conducted during Week 7 tutorial (limited notes allowed)

4. Final exam (45% Total):

- Comprehensive exam (limited notes allowed)

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

Distinction 75 - 84

Credit 65 - 74

Pass 50 - 64

Fail 0 - 49 When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

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.

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 2023 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 2023. 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	1. Looking for a black cat in a dark room: Reason and imagination in research; role of theory	Lecture (2 hr)
	T1. Introductions; facts and interpretations; theories	Tutorial (1 hr)
	P1. INTRODUCTION TO SPSS AND OUR DATA SETS	Practical (1 hr)
Week 02	2. Basics of quantitative research design; link between design and data analysis (ONLINE CLASS)	Online class (2 hr)
	T2. Variables - what are they? Spot-a-variable in a published paper	Tutorial (1 hr)
	P2. INTRODUCTION TO AI FOR RESEARCH AND AI-PROMPTING STRATEGIES	Practical (1 hr)
Week 03	3. Experimental designs; observational designs - revision (ONLINE CLASS)	Online class (2 hr)
	T3. Introduction to group assignment; building AI agent (Cogniti) knowledgeable in survey design	Tutorial (1 hr)
	P3. NORMAL DISTRIBUTION, SD AND Z SCORES	Practical (1 hr)
Week 04	4. Descriptive statistics	Lecture (2 hr)
	T4. Group work: Develop your research question and survey questions (let our AI agent - Cogniti - help you)	Tutorial (1 hr)
	P4. DATA EXPLORATION IN SPSS	Practical (1 hr)
Week 05	5. Inferential statistics: (a) probability and probability distributions (b) statistical models (c) statistical tests (NHST)	Lecture (2 hr)
	T5. Present and submit your survey questions (8 min + 7 min for questions)	Tutorial (1 hr)
	P5: PRESENTATIONS, continued (see T5); Data exploration - last year's survey	Practical (1 hr)
Week 06	6. Data analysis, continuous outcomes: Correlation and regression	Lecture (2 hr)
	T6. Interpreting regression coefficients	Tutorial (1 hr)
	P6. CORRELATION AND REGRESSION IN SPSS; BOOTSTRAP	Practical (1 hr)
Week 07	7. Regression with two or more predictors; interaction	Online class (2 hr)
	T7. QUIZ	Tutorial (1 hr)
	P7. REGRESSION IN SPSS: CONDUCT AND INTERPRET	Practical (1 hr)
Week 08	8. Data analysis, continuous outcomes: t-test and ANOVA	Online class (2 hr)
	T8. Interpreting t-test and ANOVA; Q&A about Week-9 presentations	Tutorial (1 hr)
	P8. GROUP WORK: ANALYSIS AND REPORTING OF SURVEY RESULTS	Practical (1 hr)
Week 09	9. Data analysis, categorical outcomes: frequencies, proportions, risks, odds; Survival analysis	Lecture (2 hr)
	T9. Present and submit your results (8 min + 7 min for questions)	Tutorial (1 hr)
	P9: PRESENTATIONS, continued (see T9); t-test; ANOVA	Tutorial (1 hr)
Week 10	10. Data analysis, categorical outcomes: logistic regression	Lecture (2 hr)
	T10. Interpreting categorical outcomes in published research	Tutorial (1 hr)
	P10. FREQUENCY DATA (OR, RR) in published studies	Practical (1 hr)
Week 11	11. Searching and understanding health literature: PICO and its variants; systematic reviews	Online class (2 hr)
	T11. Group work: Prepare the presentation and abstract for Week 12	Tutorial (1 hr)
	P11. GROUP WORK: Use AI to help with the presentation and abstract for Week 12	Practical (1 hr)
Week 12	12. Contemporary Indigenous health research	Lecture (2 hr)
	T12. Present your interpretation and submit abstract (8 min + 7 min for questions)	Tutorial (1 hr)
	P12. PRESENTATIONS, continued (see T12); Discussion of AI, its advantages and limitations	Practical (1 hr)
Week 13	13. Revision; preparation for the exam	Online class (2 hr)
Week 13	T13. Q& A; Practice exam questions	Online class (1 hr)

Attendance and class requirements

LECTURES: Lecture attendance is warmly recommended and desperately needed: we teach best when we interact with you – and we believe that you learn best when interacting with us and with each other.

This year we split the lectures in two groups to have the best of both worlds: some lectures are given face-to-face and they are compulsory, and others are live online lectures, based on pre-recorded materials. Most compulsory lectures have a mini-quiz at the end, based on the key concepts in the lecture.

Online lectures are split into short segments, interspersed with questions and activities.

Note that content in this unit accumulates quickly. Attendance makes it easier to keep up because you can ask questions and get immediate feedback as you try to do activities and answer questions during the lecture.

TUTORIALS AND PRACTICALS: Attendance is compulsory, and your active participation expected. Please don’t hesitate to ask questions in class.

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

Title: Foundations of clinical research: applications to evidence-based practice

Author: Portney, Leslie Gross, author.

ISBN: 9780803661165

Fourth edition.

Publication Date: 2020

Publisher: F.A. Davis Company

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. understand social context of research and that research questions arise from theory and practical needs
LO2. distinguish research findings (facts) from interpretations by researchers or the media
LO3. understand basic design characteristic of health studies
LO4. understand and apply basic concepts of descriptive and inferential statistics
LO5. identify an appropriate method of data analysis for a given (simple) study design
LO6. conduct and interpret simple data analysis using SPSS (or other statistical software)
LO7. present research findings clearly and succinctly in written and oral form
LO8. conduct structured search of health literature

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.

6/24students (25%) filled the unit experience survey in 2023. Satisfaction with the unit was high (4.53 overall), with the highest rating for "I have been guided by helpful feedback on my learning.", at 4.83. Qualitative feedback from a few students echoes this enthusiasm for our teaching (e.g., "Tanja was an amazing lecturer and tutor. The way she explained concepts were intuitive and helpful. She understood the knowledge levels of students in the class and made sure to redefine and reexplain concepts to facilitate learning of concepts. The pace of the unit was great.") There was no notable weakness in the unit, although one students said that transition to online tutorials in May did not suit them ("The abrupt transition to online-learning made me feel similar to COVID-19 times. It disrupted my learning and I believe it has caused me to become less engage in topics and class discussion."). OUR RESPONSE: The transition occurred due to circumstances outside our control, which we do not have this year. All tutorials except that in Week 13 are face-to-face. Lecture attendance, in spite of the expressed enthusiasm for face-to-face teaching, was poor. This year we split the lectures in two groups to have the best of both worlds: some lectures are given face-to-face and they are compulsory, and others are live online lectures, based on pre-recorded materials.

Additional information

Teaching staff:

Dr Tatjana Seizova-Cajic (tatjana.seizova-cajic@sydney.edu.au)

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

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

Media

Student links

About us

Connect

Current students

FMHU3001: Quantitative Research Methods in Health

Semester 1, 2024 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

Media

Student links

About us

Connect