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Unit outline_

FMHU3001: Quantitative Research Methods in Health

Semester 1, 2022 [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 Health 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
Type Description Weight Due Length
Final exam (Record+) Type B final 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
Assignment Foundations quiz
Online quiz
3% Week 03
Due date: 11 Mar 2022 at 23:59

Closing date: 11 Mar 2022
10 questions, no time limit
Outcomes assessed: LO2 LO3 LO4
Online task Mid-semester quiz
Small test
20% Week 07
Due date: 08 Apr 2022 at 09:00

Closing date: 08 Apr 2022
Approx. 25 questions, 50 min
Outcomes assessed: LO2 LO6 LO5 LO4 LO3
Online task group assignment Group presentation
In-depth presentation of published work
7% Week 09
Due date: 26 Apr 2022 at 13:00

Closing date: 26 Apr 2022
15 - 20 min
Outcomes assessed: LO1 LO7 LO4 LO3 LO2
Assignment Research report
Report of research findings
25% Week 12
Due date: 20 May 2022 at 23:59

Closing date: 03 Jun 2022
1,200 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Group assignment with individually assessed component = group assignment with individually assessed component ?
Type B final exam = Type B final exam ?

Assessment summary

Group presentation (7%): Our aim here is to carefully read and discuss a published paper. With your group, you will present a part of one paper and choose few critical concepts to teach the class; another group will present other sections of the same paper.

Individual report based on our study (25%): We will conduct a study on ourselves, and you will write an empirical research report based on the study. The study topic and design will be discussed in class.

Quizzes (3% + 20%): Foundations quiz in Week 3 will assess online lectures and readings from Weeks 1-3, which lay the foundation for further learning (part revision and part new material). In-class quiz in Week 7 will assess your understanding of concepts in research design and statistics introduced in the previous weeks. You will be asked to choose the appropriate analysis for a given research design, interpret output from a simple anaysis in SPSS (or statistical software of your choice), and interpret information from a published empirical study. Online practice quizzes are available.

Exam (45%): Questions will assess your familiarity with concepts, the depth of your understanding, and the ability to apply your knowledge to examples of published research; you will also be asked to interpret the SPSS output for procedures we covered in class. 

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.

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 EXCEL, SPSS AND OUR DATA SETS Practical (1 hr) LO4 LO6
Week 02 2. Variable, the fundamental concept (24-min video lecture); Observational vs experimental research 17-min video lecture); Revision readings: experimental study design; observational design (NOTE: no f2f lecture this week) Independent study (4 hr) LO3 LO4 LO5
T2. Q&A about this week’s materials and Week 3 foundations quiz Tutorial (1 hr) LO3 LO4
P2. TYPES OF PLOTS AND PLOTTING IN SPSS Practical (1 hr) LO4 LO6
Week 03 3. Experimental design; Small-n experiments Lecture (2 hr) LO1 LO2 LO3
T3. Report; Statistics Decision Tree Tutorial (1 hr) LO5 LO7
T4. Descriptive statistics for continuous and categorical data (revision) Tutorial (1 hr) LO4 LO5 LO6
T5. p value: what it is and what it isn't; Confidence Intervals: how good are they! Tutorial (1 hr) LO4 LO6 LO7
P3. NORMAL DISTRIBUTION, SD AND Z SCORES Practical (1 hr) LO4 LO6
Week 04 4. Observational designs in health; overview of our survey Lecture (2 hr) LO1 LO2 LO3
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
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 Lecture (2 hr) LO4 LO5 LO6 LO7
T7. Revision, quiz practice; Q&A Tutorial (1 hr) LO4 LO5 LO6 LO7
P7. QUIZ Practical (1 hr) LO2 LO3 LO4 LO5 LO6 LO7
Week 08 8. Data analysis, continuous outcomes: t-test and ANOVA Lecture (2 hr) LO4 LO5 LO6 LO7
T8. Interpreting interaction in ANOVA; group presentations draft slides; Q& A about report Tutorial (1 hr) LO4 LO5 LO6 LO7
P8. t-test; ANOVA Practical (1 hr) LO4 LO5 LO6
Week 09 9. Data analysis, categorical outcomes: frequencies, proportions (risks), odds, OR and RR (also, maybe: group presentations) Lecture (2 hr) LO4 LO5 LO6 LO7
T9. Group presentations Tutorial (1 hr) LO1 LO2 LO3 LO4 LO7
P9. Analysis of categorical outcomes from simple, 2 x 2 tables Practical (1 hr) LO4 LO5 LO6
Week 10 10. Data analysis, categorical outcomes: survival analysis Lecture (2 hr) LO4 LO6
T10. Interpreting categorical outcomes in published research Tutorial (1 hr) LO4 LO5 LO7
P10. Data analysis for the report Practical (1 hr) LO4 LO5 LO6
Week 11 11. Searching and understanding health literature: PICO and its variants; systematic reviews Lecture (2 hr) LO3 LO4 LO8
T11. Evaluate your peer’s data analysis for the report; Q&A Tutorial (1 hr) LO3 LO4 LO5 LO6
P11. Writing exercise: bring at least one paragraph from your draft report Practical (1 hr) LO7
Week 12 12. Australian research context: Issues in Indigenous health research Lecture (2 hr) LO1 LO2 LO3
T12. Read your peer’s report and give them feedback; class discussion of common issues Tutorial (1 hr) LO2 LO3 LO4 LO5 LO7
P12. UNDERSTANDING A FOREST PLOT Practical (1 hr) LO4 LO5
Week 13 13. Australian research context: Issues in Indigenous health research Lecture (2 hr) LO1 LO2 LO3
T13. Revision and practice exam questions; Q&A Feedback about the unit Tutorial (1 hr) LO1 LO2 LO3 LO4 LO5 LO6

Attendance and class requirements

LECTURES: Most lectures are split into short segments, interspersed with questions and activities. 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.

Also, content in this unit accumulates quickly and we know from experience that 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.

This is the first time this unit is offered under its current code, but it is the same as its predecessor HSBH3018. Students in 2021 were quite happy with the unit - example response below - so we continue in the same format. "I thought this was a great unit that extended on what i previously knew but made me more confident in how to understand research. Tanya is also super helpful and gives great feedback. I think the assessment tasks are great because they are open book so it is application based." One suggestion for improvement concerned availability of the course reader: "The only major issue was that the course reader was not updated which would have been helpful to have during the weeks that we needed them." The purpose of the reader is to complement lecture slides and/or to provide topic summaries for reading ahead and to aid revision. It is not a textbook and should not detract from using the textbook (the textbook is good - Portney, 2020). We also have guest lecturers who may not provide summary entries for the reader, but we will aim to provide them for most weeks this year.

Teaching staff:

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

Dr Rachel Thompson (rachel.thompson@sydney.edu.au)

Dr Vanessa Lee (vanessa.lee@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.