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Design and Analysis for Neurosciences B (NEUR90008)
Graduate courseworkPoints: 6.25On Campus (Parkville)
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About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
Overview
Availability | March |
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Fees | Look up fees |
This subject is an intensive 5 consecutive day program, introducing the main principles of scientific method, research design and scientific study validity using a structured approach. This subject will introduce the basic concepts of study design and layered topic-specific exercises and/or project-specific exercises. Students are taken through the initial ”building blocks” that form the basis for a sound research study. This includes:
- fundamentals of scientific methods;
- study validity;
- sampling;
- measurement;
- establishing cause and effect relationship;
- making statistical conclusions; and
- designing good experimental designs.
A second major component of this subject covers statistical analysis in some detail:
- hypotheses testing and statistical estimation;
- choosing a statistical test and/or model;
- power and sample size calculations as well as dealing with data;
- effective data management;
- analysis;
- presenting skills when using appropriate statistical software; and
- where and when to get statistical advice.
The subject also introduces meta-analysis and highlights the guidelines for quality experimental neuroscience research.
In-class discussions form an integral part of this subject and special emphasis is placed on the application of concepts taught to the student's individual research project through a multi-disciplinary group exercise. This culminates in class presentations at the end of the week; discussions concerning these presentations illustrate various types of study designs and analyses used for different types of research approaches (eg behavioural, molecular, cellular, imaging and clinical).
Intended learning outcomes
On completion of this subject students will be able to:
- Develop an overall understanding at basic to intermediate level of the main principles of good study design and analysis in basic and clinical neurosciences.
- Acquire basic to intermediate level competency in applying ‘building blocks’ of experimental design and analysis to a chosen research problem.
- Appreciate the ultimate importance of quality experimental design and analysis for the overall quality on neuroscience research.
- Become aware of the common pitfall areas where professional statistical expertise is required.
- Gain an awareness of different approaches to experimental study design as applied to different types of neurosciences’ research projects.
Generic skills
On completion of this subject, students will have developed the following generic skills:
- Research project design and analysis skills.
- Competency in applying experimental design and statistical analysis principles to a variety of research questions and approaches.
- Critical analysis capacity of different research areas in a multi-disciplinary field.
- Awareness of the value of quality research and the wide-ranging consequences of biased research.
- Advanced skills in formulating ideas clearly through oral, written and interpersonal communication.
- The capacity to work in teams and collaborate with other disciplines in the area of neurosciences.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
None
Corequisites
Students enrolled through the Florey Department and in this subject must also enrol in the following subjects at the same time:
NEUR90009 Brain Imaging and Neural Networks A (12.5)
or
NEUR90010 Brain Imaging and Neural Networks B (6.25)
NEUR90011 Molecular and Cellular Neuroscience A (12.5)
or
NEUR90012 Molecular and Cellular Neuroscience B (6.25)
NEUR90013 Neuroscience of Behaviour & Cognition A (12.5)
or
NEUR90014 Neuroscience of Behaviour & Cognition B (6.25)
Non-allowed subjects
Students cannot enrol in and gain credit for this subject and:
Code | Name | Teaching period | Credit Points |
---|---|---|---|
NEUR90007 | Design and Analysis for Neurosciences A | March (On Campus - Parkville) |
12.5 |
Recommended background knowledge
Basic statistical knowledge (ie means, standard deviation, confidence interval, distributions) is desirable but not essential.
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
Description | Timing | Percentage |
---|---|---|
Attendance taken twice daily. Full 5 days attendance of the subject and full participation in class exercises, group project, presentation and discussion are required Hurdle requirement: A minimum 85% attendance is required (=x1); a pro-rata attendance multiplier will apply to total assessment. | N/A | |
One group oral presentation
| End of the teaching period | 40% |
Written exercises, worth 60% times attendance multiplier
| End of the teaching period | 60% |
Last updated: 3 November 2022
Dates & times
- March
Mode of delivery On Campus (Parkville) Contact hours Total time commitment 85 hours Teaching period 2 March 2020 to 6 March 2020 Last self-enrol date 3 March 2020 Census date 20 March 2020 Last date to withdraw without fail 17 April 2020 Assessment period ends 11 May 2020
Time commitment details
85 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Doctor of Philosophy - Medicine, Dentistry and Health Sciences Course Ph.D.- Medicine, Dentistry & Health Sciences
Last updated: 3 November 2022