Course Title: Advanced Research Methods
Part A: Course Overview
Course Title: Advanced Research Methods
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
BESC1415 |
Bundoora Campus |
Undergraduate |
150H Health Sciences |
Face-to-Face |
Sem 2 2007, Sem 2 2008, Sem 2 2009, Sem 2 2010, Sem 2 2011, Sem 2 2012, Sem 2 2013, Sem 2 2014, Sem 2 2015, Sem 2 2016 |
BESC1415 |
Bundoora Campus |
Undergraduate |
173H School of Health and Biomed |
Face-to-Face |
Sem 2 2017, Sem 2 2018, Sem 2 2019, Sem 2 2020, Sem 2 2021, Sem 2 2022 |
BESC1416 |
Bundoora Campus |
Postgraduate |
150H Health Sciences |
Face-to-Face |
Sem 2 2007, Sem 2 2008, Sem 2 2009, Sem 2 2010, Sem 2 2011, Sem 2 2012, Sem 2 2013, Sem 2 2014, Sem 1 2016 |
BESC1465 |
Bundoora Campus |
Research |
150H Health Sciences |
Face-to-Face |
Sem 2 2007, Sem 2 2008, Sem 2 2009, Sem 2 2010, Sem 2 2011, Sem 2 2012, Sem 2 2013 |
Course Coordinator: Dr Russell Conduit
Course Coordinator Phone: +61 3 9925 6658
Course Coordinator Email: russell.conduit@rmit.edu.au
Course Coordinator Location: 201.03.007
Course Coordinator Availability: request via email
Pre-requisite Courses and Assumed Knowledge and Capabilities
None.
Course Description
The aim of the course is to provide students with the methodological skills necessary for them to carry out independent research. Throughout the year, methodological and design considerations are integrated with statistical techniques. Statistical theory is not emphasised; instead, students are trained to be consumers and users of statistics. Applied linkages are developed through the extensive use of the SPSS data analysis package. Advanced Research Methods can be divided into three components. Over the duration of the semester students will engage in topics including effect size measures and their associated confidence intervals, power analysis, clinical significance, advanced analysis of variance, regression modelling and regression diagnostics, bootstrapping, and dealing with missing data. Students are taught these techniques in the context of SPSS and other computer-based data analysis software. Qualitative methods are considered briefly.
Objectives/Learning Outcomes/Capability Development
This course contributes to the development of the Program Learning Outcomes:
- PLO 1: Understand appropriate and relevant fundamental and applied evidence based knowledge and undertake lifelong learning to improve personal and professional practice
- PLO 2: Demonstrate a capacity to employ a variety of approaches and procedures to research to permit judgements and decisions to be supported by appropriate evidence that places practice within a global and local context.
- PLO 3: Applies knowledge to diagnose and solve problems in a wide range of diverse situations, with an ability to work independently or with others and incorporate the analysis of evidence based scientific literature to solve psychological problems.
- PLO 4: Engage in dialogue with a diverse range of people and communicate in a broad range of forms (written, electronic, graphic, oral) to meet the circumstances of the situation and the capabilities of the audience.
- PLO 5: Maintains tolerance and respect for individuals and groups from diverse backgrounds, holding diverse values, adhering to professional expectations and demonstrating ethical behaviour.
Upon successful completion of this course, students will be expected to be able to:
- Describe the important methodological and design issues underlying applied human research.
- Conduct independent research using a range of research designs and methods.
- Enter, analyse, and interpret the results of data using SPSS for Windows.
- Describe the essential features of a range of advanced statistical techniques.
Overview of Learning Activities
You will be required to attend one lectorial or equivalent per week. Learning activities will build on your knowledge and provide a solid theoretical and practice driven basis for your learning. You will be expected to explore scientific literature to further support and consolidate your learning.
Overview of Learning Resources
Through lectures, students acquire a framework for and understanding of the theoretical and conceptual bases of the material. Further, this material is reinforced through students’ practical, applied out-of-class data analysis exercises, involving problem solving aided by computer-based statistical analysis software. As well as engaging students in critical thinking, this enables responsibility for drawing conclusions from, applying, and disseminating the knowledge acquired from data analysis.
Students will be required to access a range of learning resources for this course, including readings and websites. Further material will be provided to students in class.
Overview of Assessment
This Course has no hurdle requirements.
Assessment Tasks:
Assessment 1: 2 x Short answer questions.
Weighting 40%
This assessment task supports CLOs 1, 2 ,3 & 4
Assessment 2: 2 x Data analysis questions.
Weighting 45%
This assessment task supports CLOs 1, 2, 3 & 4
Assessment 3: Online Quiz.
Weighting 15%
This assessment task supports CLOs 1, 2, 3 & 4