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

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

To be eligible to enrol in this course you must have successfully completed and achieved a Distinction grade in the research component of:

  • BESC1419 Professional Issues and Research

Contact your course coordinator if you think you may be eligible for recognition of prior learning. For further information go to: www.rmit.edu.au/students/enrolment/credit/he


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

Upon successful completion of this course, students will be expected to be able to: 

  • CLO1: Describe the important methodological and design issues underlying applied human research. 
  • CLO2: Carry out independent research using a range of research designs and methods. 
  • CLO3: Enter, analyse, and interpret the results of data using SPSS for Windows. 
  • CLO4: Describe the essential features of a range of advanced statistical techniques.

 


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.


Overview of Learning Activities

Students will engage in a range of learning activities, with an emphasis on problem-based learning focusing on the application of data analysis techniques for addressing the research questions at the heart of their own research projects.

Teacher Guided Hours: 24 per semester
Learner Directed Hours: 96 per semester


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

Assessment Tasks:

 

Early Assessment Task:  Short answer questions. (3 questions x 10%)

Weighting 30%

This assessment task supports CLOs 1

 

Assessment Task 2:  Data analysis (3 questions x 10%)

Weighting 30%

This assessment task supports CLOs 2,3 & 4

 

Assessment Task 3: Online discussion activity 

Weighting 10%

This assessment task supports CLO 4

 

Assessment 4: Test

Weighting 30% 

This assessment supports CLOs 1-4