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Unit outline_

HSBH4101: Research Design and Analysis in Health

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

In this unit of study you delve deeper into the methods used in health research, building on your knowledge from previous years (see the prerequisites). You will attend lectures and interactive workshops, and complete online study modules. After the common foundations, the unit will be split in streams so that each student will learn either quantitative or qualitative data analysis in depth (not both), depending on their prior learning. As part of quantitative methods, we cover experimental and observational (survey, case­control, cohort) study designs, and linear model and logistic regression for data analysis. Qualitative approaches include ethnography, grounded theory, phenomenology and narrative. Methods include interview, focus group and text based. The unit will help with your specific Honours project.

Unit details and rules

Academic unit Health Sciences
Credit points 6
Prerequisites
? 
HSBH3018 or HSBH3019
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

48cp of 3000 level units of study

Available to study abroad and exchange students

No

Teaching staff

Coordinator Tatjana Seizova-Cajic, tatjana.seizova-cajic@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Research methods exam
Online exam, open book
71% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO2 LO5
Assignment Mini-assignments
Written assessments (Weeks 3, 6, 6, 7 and 8)
29% Multiple weeks 300-500 words each
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO7
Presentation Present published result of your choice
See Week 12 tutorial
0% Week 12 10 min
Outcomes assessed: LO6 LO8
Type B final exam = Type B final exam ?

Assessment summary

  • Mini assessments: The five mini assessments are based on the required readings, material covered in the research methods seminar. (1) Bigger ideas behind my study, Week 3; (2) My study design, Week 6; (3) My peer’s study design, Week 6; (4) Statistics mini-quiz, Week 7; (5) Comparing qualitative and quantitative research, Week 8.
  • Research methods exam: This exam will test the understanding of concepts covered in class (excluding LEP classes) and compulsory readings. You will receive a detailed list of assessable concepts.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

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. Introduction to Honours; meet and greet; Introduction to the research methods seminar and assessments Lecture (2 hr)  
Types of research; research process; role of theory (online study; group discussion; self-test) Online class (2 hr) LO1 LO3 LO5 LO7
Week 02 2. Quantitative research: Introduction and basic concepts: Variables; measurement and uncertainty of measurement; distinction between observational and experimental research Individual study (4 hr) LO1 LO3 LO5 LO6 LO7
Do-me-together formative quiz: 60 min of unsupervised group work (each student prepares beforehand and offers an answer to one quiz question, followed by group discussion of relevant concepts) + 30 min with tutor for Q&A, peer allocation and reflection on class dynamics Tutorial (1.5 hr) LO1 LO5
Week 03 3. Qualitative research: introduction and basic concepts Lecture and tutorial (2 hr) LO1 LO5 LO6 LO7
'Bigger ideas behind my study': bring and exchange draft mini-assignment with your peer, discuss; also discuss with class (group study + 30 min Q&A with lecturer) Tutorial (1.5 hr) LO3 LO7
Week 04 4. Experimental research (randomized controlled trials and quasi experimental studies) Lecture (2 hr) LO3 LO5 LO6 LO7
Experimental research (with M Fiatarone Singh) Tutorial (1 hr) LO3 LO5 LO7
Causal reasoning in my project (unsupervised group discussion + 20 min Q&A with TSC) Tutorial (1 hr) LO1 LO3 LO5 LO7
Week 05 5. Use of interviews in qualitative research Lecture and tutorial (2 hr) LO3 LO4 LO5 LO7
TOPICS: 1. Observational research (read assigned chapter from Bruce et al textbook, think about observational designs, sampling, measurements, risk of bias); 2. Think about your own study design (experimental or observational), discuss with your peer via Zoom; draft mini assignment 2; post any questions on Discussions Independent study (3 hr) LO1 LO3 LO5 LO7 LO8
Week 06 6. Use of focus groups in qualitative research Lecture and tutorial (2 hr) LO3 LO4 LO5 LO7
Week 07 7. Descriptive statistics commonly used for (a) continuous data (b) categorical data; Quantitative skills: measures of central tendency and variability in Excel Lecture (2 hr) LO5 LO7
Week 08 8. Introduction to inferential statistics: (a) probability and probability distributions (b) statistical models, concept (c) statistical tests (NHST) Lecture (2 hr) LO5 LO7
(A) p value: what it means and what it doesn’t mean (introduction to the Inference Under Uncertainty module) (B) Confidence Intervals: why they tell us more than the p value, and how to compute them by hand/in Excel Tutorial (1.5 hr) LO5 LO7
Week 09 9. QUANT STREAM ONLY: From study design to statistics: How do we choose statistical tests? Statistical Decision Tree Analysis of continuous data (one continuous predictor): Correlation and regression Lecture (2 hr) LO5 LO7
QUANT STREAM ONLY: Intro to SPSS; Descriptive statistics; plotting histograms; correlation in SPSS; bootstrap Tutorial (1.5 hr) LO5 LO7
9. QUAL STREAM ONLY: How we evaluate qualitative research Lecture and tutorial (2 hr) LO5 LO6 LO7
Week 10 10. QUANT STREAM ONLY: Analysis of continuous data (one categorical predictor): Comparison between the means (t-test; ANOVA) Lecture and tutorial (2 hr) LO5 LO7
QUANT STREAM ONLY: Regression in SPSS; interpreting regression coefficients; t-test and ANOVA in SPSS Tutorial (1.5 hr) LO5 LO7
10. QUAL STREAM ONLY: Analysis of qualitative data, part 1 Lecture and tutorial (2 hr) LO5 LO7
Week 11 11. QUANT STREAM ONLY: Analysis of categorical data (one categorical predictor): OR, RR Analysis of categorical data (one continuous predictor): Logistic regression Lecture (2 hr) LO5 LO7
QUANT STREAM, optional class: Advanced topics (may change depending on demand): Understanding models with two or more predictors; nested study designs; random and fixed effects; Bayesian statistics Tutorial (1.5 hr) LO5 LO7
QUANT STREAM ONLY: Read a published report; identify type of data and statistics used, think about the result in the light of the research question; bring to class to present and lead discussion Individual study (2 hr) LO6 LO8
11. QUAL STREAM ONLY: Analysis of qualitative data, part 2 Lecture and tutorial (2 hr) LO5 LO7
QUAL STREAM ONLY: Read a published report, apply concepts discussed in class to identify and evaluate data analysis; bring to class to present and to lead discussion Individual study (2 hr) LO6 LO8
Week 12 12. QUANT STREAM ONLY: Your presentation of published results; Revision of quantitative stream (Weeks 9 – 12) via Q&A Lecture and tutorial (2 hr) LO5 LO8
12. QUAL STREAM ONLY: Your presentation of published results; Revision of qualitative stream (Weeks 9 – 12) via Q&A Workshop (2 hr) LO5 LO8

Attendance and class requirements

Attendance: Students are expected to attend all scheduled classes and to participate in discussions and activities. Attendance of less than 80% of the scheduled seminar classes must be supported by written documentation.

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 (all except #3 are available online). Feel free to use other texts if you prefer them or already have them, but compare them to some of the recommended sources to ensure they are of similar depth.

  1. Bourgeault, I., Dingwall, R. and deVries, R. (2010). The SAGE handbook of qualitative research in health research. London: Sage. Available online: https://sydney.primo.exlibrisgroup.com/permalink/61USYD_INST/2rsddf/cdi_askewsholts_vlebooks_9781473971172
  2. 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
  3. 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.
  4. 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
  5. 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 

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. study and work independently and in teams
  • LO2. manage schedules and resources
  • LO3. propose research that will increase knowledge in the area of interest
  • LO4. know the ethical principles of research and adhere to them
  • LO5. understand quantitative and qualitative approaches to research
  • LO6. conduct a literature review and write it up
  • LO7. investigate a topic under supervision, including data collection and analysis
  • LO8. demonstrate the ability to orally present ideas and research findings and respond to questions.

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.

Students responded well to structured group study, and this year more scheduled classes will have this format (students study and discuss assigned material on their own, followed by Q&A session with lecturer/tutor). This is aligned with learning objective 1: Study and work independently and in teams.

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.