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Unit outline_

FMHU3001: Quantitative Research Methods in Health

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

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

Academic unit Participation Sciences
Credit points 6
Prerequisites
? 
HSBH1007 or HSBH2007 or FMHU2000
Corequisites
? 
None
Prohibitions
? 
PSYC2012 or SCLG3603 or HSBH3018
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
The census date for this unit availability is 2 April 2024
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: LO1 LO2 LO3 LO4 LO5 LO6 LO8
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: LO5 LO6 LO8 LO2 LO3 LO4
Presentation group assignment 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: LO1 LO3
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: LO2 LO3 LO4 LO5 LO6
Presentation group assignment 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: LO4 LO5 LO6 LO7
Presentation group assignment 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: LO1 LO2 LO3 LO6 LO7
group assignment = 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 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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative 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 acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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

WK Topic Learning activity Learning outcomes
Week 01 1. Looking for a black cat in a dark room: Reason and imagination in research; role of theory Lecture (2 hr) LO1 LO2
T1. Introductions; facts and interpretations; theories Tutorial (1 hr) LO1 LO2
P1. INTRODUCTION TO SPSS AND OUR DATA SETS Practical (1 hr) LO4 LO6
Week 02 2. Basics of quantitative research design; link between design and data analysis (ONLINE CLASS) Online class (2 hr) LO1 LO3
T2. Variables - what are they? Spot-a-variable in a published paper Tutorial (1 hr) LO3 LO5
P2. INTRODUCTION TO AI FOR RESEARCH AND AI-PROMPTING STRATEGIES Practical (1 hr) LO2 LO3 LO4 LO5 LO8
Week 03 3. Experimental designs; observational designs - revision (ONLINE CLASS) Online class (2 hr) LO1 LO2 LO3
T3. Introduction to group assignment; building AI agent (Cogniti) knowledgeable in survey design Tutorial (1 hr) LO1 LO3
P3. NORMAL DISTRIBUTION, SD AND Z SCORES Practical (1 hr) LO4 LO6
Week 04 4. Descriptive statistics Lecture (2 hr) LO4 LO5 LO6 LO7
T4. Group work: Develop your research question and survey questions (let our AI agent - Cogniti - help you) Tutorial (1 hr) LO1 LO3 LO5
P4. DATA EXPLORATION IN SPSS Practical (1 hr) LO4 LO5 LO6 LO7
Week 05 5. Inferential statistics: (a) probability and probability distributions (b) statistical models (c) statistical tests (NHST) Lecture (2 hr) LO4 LO5 LO6 LO7
T5. Present and submit your survey questions (8 min + 7 min for questions) Tutorial (1 hr) LO1 LO3 LO4
P5: PRESENTATIONS, continued (see T5); Data exploration - last year's survey Practical (1 hr) LO4 LO5 LO6
Week 06 6. Data analysis, continuous outcomes: Correlation and regression Lecture (2 hr) LO4 LO5 LO6 LO7
T6. Interpreting regression coefficients Tutorial (1 hr) LO4 LO6 LO7
P6. CORRELATION AND REGRESSION IN SPSS; BOOTSTRAP Practical (1 hr) LO4 LO5 LO6 LO7
Week 07 7. Regression with two or more predictors; interaction Online class (2 hr) LO4 LO5 LO6 LO7
T7. QUIZ Tutorial (1 hr) LO2 LO3 LO4 LO5 LO6
P7. REGRESSION IN SPSS: CONDUCT AND INTERPRET Practical (1 hr) LO4 LO5 LO6 LO7
Week 08 8. Data analysis, continuous outcomes: t-test and ANOVA Online class (2 hr) LO4 LO5 LO6 LO7
T8. Interpreting t-test and ANOVA; Q&A about Week-9 presentations Tutorial (1 hr) LO4 LO5 LO6 LO7
P8. GROUP WORK: ANALYSIS AND REPORTING OF SURVEY RESULTS Practical (1 hr) LO4 LO5 LO6 LO7
Week 09 9. Data analysis, categorical outcomes: frequencies, proportions, risks, odds; Survival analysis Lecture (2 hr) LO4 LO5 LO6
T9. Present and submit your results (8 min + 7 min for questions) Tutorial (1 hr) LO4 LO5 LO6 LO7
P9: PRESENTATIONS, continued (see T9); t-test; ANOVA Tutorial (1 hr) LO2 LO4 LO6 LO7
Week 10 10. Data analysis, categorical outcomes: logistic regression Lecture (2 hr) LO4 LO5 LO6
T10. Interpreting categorical outcomes in published research Tutorial (1 hr) LO4 LO5 LO7
P10. FREQUENCY DATA (OR, RR) in published studies Practical (1 hr) LO4 LO5 LO6
Week 11 11. Searching and understanding health literature: PICO and its variants; systematic reviews Online class (2 hr) LO3 LO4 LO8
T11. Group work: Prepare the presentation and abstract for Week 12 Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
P11. GROUP WORK: Use AI to help with the presentation and abstract for Week 12 Practical (1 hr) LO2 LO7
Week 12 12. Contemporary Indigenous health research Lecture (2 hr) LO1 LO2 LO3
T12. Present your interpretation and submit abstract (8 min + 7 min for questions) Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
P12. PRESENTATIONS, continued (see T12); Discussion of AI, its advantages and limitations Practical (1 hr) LO2 LO3 LO4 LO5 LO6 LO7
Week 13 13. Revision; preparation for the exam Online class (2 hr) LO1 LO2 LO3
T13. Q& A; Practice exam questions Online class (1 hr) LO1 LO2 LO3 LO4 LO5 LO6

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 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. 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.

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.