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

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
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:
Presentation	Research proposal and questions Group presentation and online submission	5%	Week -05 Due date: 21 Mar 2023 at 23:59 Closing date: 21 Mar 2023	10 min
Outcomes assessed:
Assignment	Foundations quiz Online quiz	3%	Week 03 Due date: 10 Mar 2023 at 23:59 Closing date: 17 Mar 2022	10 questions, no time limit
Outcomes assessed:
Online task	Mid-semester quiz Small test	20%	Week 07 Due date: 04 Apr 2023 at 13:00 Closing date: 04 Apr 2023	Approx. 25 questions, 50 min
Outcomes assessed:
Presentation	Survey results Group presentation	5%	Week 09 Due date: 24 Apr 2023 at 23:59 Closing date: 24 Apr 2023	Results of the survey (7-10 min)
Outcomes assessed:
Assignment	Research report Assignment	22%	Week 12 Due date: 19 May 2023 at 23:59 Closing date: 02 Jun 2023	1,200 words
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Three assessments in this unit of study are linked. Their aim is that you get hands-on research experience and apply concepts and skills we learn immediately, using also your creativity and lived experience to enrich the study. You will start off working in a group, and finish by writing an individual research report based on the data you collected together.

Group presentation 1, due in Week 5 (5%): With your group, you will develop a research question and propose how to investigate it using a survey. You will also develop some questions for the survey. You need to submit a recorded presentation of the research proposal (7-10 min) and survey questions in writing.
Group presentation 2, due in Week 9 (5%): Following data collection, you need to extract and analyze the results with your group. You will then present them in another recorded, short presentation (7 – 10 min).
Individual report based on the survey (22%)

There are also two quizzes:

Foundations quiz in Week 3 (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 (20%) 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. You will be allowed to bring in one hand-written page of your own notes to class.

Final pen-and-paper exam in the exam period (45%) will assess your familiarity with concepts, depth of your understanding, and the ability to apply knowledge to examples of published research; you will also be asked to interpret the SPSS output for procedures we covered in class. You will be allowed to bring in two hand-written pages of your own notes to this exam. No electronic devices are allowed. This is a hurdle task – you must pass the exam in order to pass this unit of study.

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.

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.

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 EXCEL, SPSS AND OUR DATA SETS	Practical (1 hr)
Week 02	2. Basics of quantitative research design; link between design and data analysis	Lecture (2 hr)
	T2. Variables - what are they? Can you spot variables in a paper?	Tutorial (1 hr)
	P2. TYPES OF PLOTS AND PLOTTING IN SPSS	Practical (1 hr)
Week 03	3. Experimental designs; observational designs (revision)	Lecture (2 hr)
	T3. Introduction to group assignment; survey design	Tutorial (1 hr)
	T4. Develop your research question and design a survey (group work)	Tutorial (1 hr)
	T5. Complete your proposal and survey questions (submit today - see Assignments)	Tutorial (1 hr)
	P3. NORMAL DISTRIBUTION, SD AND Z SCORES	Practical (1 hr)
Week 04	4. Descriptive statistics	Lecture (2 hr)
Week 04	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)
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	Lecture (2 hr)
	T7. Revision, quiz practice; Q&A	Tutorial (1 hr)
	P7. QUIZ	Practical (1 hr)
Week 08	8. Data analysis, continuous outcomes: t-test and ANOVA	Lecture (2 hr)
	T8. Interpreting interaction in ANOVA; Q&A	Tutorial (1 hr)
	P8. t-test; ANOVA	Practical (1 hr)
Week 09	Public holiday - see Canvas for recommended activities for this week	Independent study (2 hr)
	See Canvas for group assignment submission on Monday (no class due to public holiday)	Independent study (1 hr)
	Public holiday - see Canvas for recommended SPSS revision for this week	Independent study (1 hr)
Week 10	10. Data analysis, categorical outcomes: frequencies, proportions/risks, odds, OR and RR; survival analysis	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	Lecture (2 hr)
	T11. Data analysis for the report: examples and discussion	Tutorial (1 hr)
	P11. Reporting your results: formats for presenting data sourced from SPSS	Practical (1 hr)
Week 12	12. Broader context of health research	Lecture (2 hr)
	T12. Writing a report: examples and discussion	Tutorial (1 hr)
	P12. Understanding meta-analysis, its advantages and limitations	Practical (1 hr)
Week 13	13. Revision; preparation for the exam	Lecture (2 hr)
Week 13	T13. Practice exam questions	Tutorial (1 hr)

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

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.

This is the second time this unit is offered under its current code and within Science. It has been running for a long time under a different name and with different cohorts of students. Student feedback is generally very good and where students reported glitches we pay attention and smooth them over. For example, one online module seemed to have been difficult and we replaced it with a modified live lecture. With many students now taking data science units, your background knowledge seems to be higher than before but there is still plenty to learn so please engage and don't hesitate to ask questions. "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."

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

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, 2023 [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

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