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
Corequisites ?	None
Prohibitions ?	PSYC2012 or SCLG3603
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

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

Type	Description	Weight	Due	Length
Final exam (Record+)	Online Exam Online exam	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Foundations quiz (online) Closed-ended questions quiz	3%	Week 03 Due date: 19 Mar 2021 at 23:59 Closing date: 26 Mar 2021	Variable (conducted at home)
Outcomes assessed:
Small test	Quiz Quiz	15%	Week 07 Due date: 23 Apr 2021 at 09:00 Closing date: 23 Apr 2021	45 mins
Outcomes assessed:
Presentation	Class and online participation Contribution to group presentation, presentation and class participation	7%	Week 09 Due date: 04 May 2021 at 13:00 Closing date: 18 May 2021	15-20 min (presentation and questions)
Outcomes assessed:
Assignment	Individual report based on study Written assessment	25%	Week 12 Due date: 28 May 2021 at 23:59 Closing date: 11 Jun 2021	1200 words
Outcomes assessed:
= hurdle task ? = Type B final exam ?

Assessment summary

Group presentation (7%): Our aim here is to dig deeply into a small number of published papers. Together with your group, you will present a part of a paper, and 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% + 15%): Foundations quiz in Week 3 will assess online lectures and readings from Weeks 1-3 (part revision and part new material), which lay the foundation for further learning. Quiz conducted in class in Week 7 (15%) 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 (50%): 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. You must pass the exam in order to pass this unit of study.

More details can be found on Canvas.

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)
	Introductions; facts and interpretations; theories	Tutorial (1 hr)
	INTRODUCTION TO EXCEL AND SPSS; OUR DATA SETS	Practical (1 hr)
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)	Individual study (4 hr)
	Q&A about this week’s lecture materials/foundations quiz	Tutorial (1 hr)
	DATA EXPLORATION AND PLOTS IN SPSS	Practical (1 hr)
Week 03	3. Experimental design (exercise); Single case experiment	Lecture (2 hr)
	Introduction to the report; Introduction to Statistics Decision Tree	Tutorial (1 hr)
	NORMAL DISTRIBUTION AND Z SCORES IN SPSS; SD by hand	Practical (1 hr)
Week 04	4. Observational designs in health; overview of our survey	Lecture (2 hr)
	Descriptive statistics for continuous and categorical data (revision)	Tutorial (1 hr)
	SCATTERPLOTS 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 05	p value: what it means and what it doesn’t mean; Confidence Intervals: why they tell us more and how to compute them	Tutorial (1 hr)
Week 06	6.Data analysis, continuous outcomes: Correlation and regression	Lecture (2 hr)
	Interpreting regression coefficients	Tutorial (1 hr)
	CORRELATION AND REGRESSION IN SPSS; BOOTSTRAP	Practical (1 hr)
Week 07	7. Regression with two or more predictors; interaction	Lecture (2 hr)
	Revision with reference to published papers; Q&A	Tutorial (1 hr)
	QUIZ	Practical (1 hr)
Week 08	8. Data analysis, continuous outcomes: t-test and ANOVA	Lecture (2 hr)
	Writing a research report (reading)	Individual study (1 hr)
	When to use t-test and when not to use t-test; Q& A about group presentations and report	Tutorial (1 hr)
	t-test; ANOVA	Practical (1 hr)
Week 09	9. Data analysis, categorical outcomes: frequencies, proportions (risks), odds, OR and RR	Lecture (2 hr)
	Group presentations	Tutorial (1 hr)
	Two-way ANOVA	Practical (1 hr)
Week 10	10. Data analysis, categorical outcomes: logistic regression	Lecture (2 hr)
	Computing and interpreting OR, RR and risk difference	Tutorial (1 hr)
	Data analysis for the report	Practical (1 hr)
Week 11	11. Measurement in diagnostics: Sensitivity and specificity	Lecture (2 hr)
	Evaluate your peer’s data analysis for the report; Q&A	Tutorial (1 hr)
	Writing exercise (bring draft report or at least one paragraph)	Practical (1 hr)
Week 12	12. Context of research; Health and medical research in Australia	Lecture (2 hr)
	Read your peer’s report and give them feedback; Q&A	Tutorial (1 hr)
	READING A META-ANALYSIS	Practical (1 hr)
Week 13	13. Issues in Indigenous health research	Lecture (2 hr)
Week 13	Revision and practice exam questions; Q&A Feedback about the unit	Tutorial (1 hr)

Attendance and class requirements

Attendance:

LECTURES: We aim to have lectures recorded in multiple short segments, to be played in the allocated lecture time and interspersed with questions and activities. Lecture attendance is strongly recommended, in-person or virtual. Content accumulates quickly and we know from past experience that attendance makes it easier to keep up because you can ask questions and also get immediate feedback as you try to do activities/answer questions during the lecture.

TUTORIALS AND PRACTICALS: Attendance is compulsory, and your active participation is 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

The following recommended textbooks are available from the library, but feel free to use other texts on research design and statistics if you are already familiar with them or have them.

Books 1, 3 and 4 below cover both research designs and methods for data analysis and have a lot of overlap but each has its own style and you may want to explore and choose one as your go-to source (also read brief descriptions given below). These three books are all available online.
Book 2 (Andy Field) is very different; it’s focused on statistics and the software package SPSS.; unfortunately, it is not available online but you may find older editions or some tutorials freely available online.

Bruce N, Pope D & Stanistreet D (2018) Quantitative Methods for Health Research: a Practical Interactive Guide to Epidemiology and Statistics. Second edition. Hoboken, NJ: John Wiley & Sons, Inc. Available online: https://sydney.primo.exlibrisgroup.com/permalink/61USYD_INST/12rahnq/alma991005667659705106 A very thorough book on study design and methods for data analysis; uses published papers for examples, so you are immersed in real stuff from the start!
Field AP (2013) Discovering statistics using IBM SPSS statistics: and sex and drugs and rock 'n' roll. (4th ed.) London, SAGE Publications. Not available online – for hard copy, see: https://sydney.primo.exlibrisgroup.com/permalink/61USYD_INST/12rahnq/alma991005642139705106 An excellent introduction to statistics, with very detailed instructions on SPSS. It’s a big volume, because Andy Field makes many jokes.
Portney LG (2020) Foundations of Clinical Research: Applications to Evidence-Based Practice. Fourth edition. Philadelphia, PA: F.A. Davis Company. Available online https://sydney.primo.exlibrisgroup.com/permalink/61USYD_INST/1c0ug48/alma99103174078740510 This is a new edition by an experienced author of good books on research methods.
Saks M. & Allsop J. (2013) Researching health: Qualitative, quantitative and mixed methods. London: Sage. Available online: https://sydney.primo.exlibrisgroup.com/permalink/61USYD_INST/12rahnq/alma991014514489705106 You might have used this textbook in HSBH1007/2007.

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 statistical
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

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.

More lectures will be converted to shorter, multi-segment format, interleaved with periods of interaction.

Additional information

More information can be found on Canvas.

Student Administration Manual on the Faculty of Health Sciences

Additional costs

There are no additional costs for this unit.

Site visit guidelines

There are no site visit guidelines for this unit

Work, health and safety

There are no specific WHS requirements for this unit.

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

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

HSBH3018: Quantitative Research Methods in Health

Semester 1, 2021 [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

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect