Course Title: Quantitative Research Techniques

Part A: Course Overview

Course Title: Quantitative Research Techniques

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2256

City Campus

Research

145H Mathematical & Geospatial Sciences

Internet

Sem 1 2014,
Sem 1 2015,
Sem 2 2015

MATH2336

RMIT University Vietnam

Research

171H School of Science

Face-to-Face

Viet2 2018,
Viet3 2019

Flexible Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2256

City Campus

Research

171H School of Science

Internet

RSCHYr2019 (RT93)

Course Coordinator: Dr James Baglin

Course Coordinator Phone: +61 3 9925 6118

Course Coordinator Email: james.baglin@rmit.edu.au

Course Coordinator Location: 8.9.69

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

This course assumes that you have: 
-  completed a discipline specific research methods course.
-  have collected or plan to collect data related to your research during the course of the teaching period.
-  basic IT skills including the ability to use web browsers, e-mail, discussion boards and word processing programs (e.g. Microsoft Word and PowerPoint)
-  regular access to the internet
-  fundamental mathematical skills to a graduate high-school standard.
 


Course Description

This course aims to deliver you with the skills needed to collect, organise, summarise, analyse and communicate quantitative data related to your higher degree by research. This course builds on quantitative research methods by developing fundamental skills related to gathering, managing and statistically analysing quantitative data using a technology-based approach.


Objectives/Learning Outcomes/Capability Development

Upon completion of this subject you should be able to: 

  1. Explain concepts and principles foundational to quantitative research and data collection.
  2. Plan and collect quantitative data for the purpose of research.
  3. Evaluate methods of investigating and collecting quantitative data.
  4. Organise and manage quantitative data for the purpose of statistical analysis.
  5. Demonstrate an ability to analyse and interpret quantitative data.
  1. Read, interpret and communicate statistical information related to your area of research.
  2. Work with different types of quantitative data and understand how the nature of quantitative data changes statistical analysis.
  3. Effectively communicate and present quantitative data analysis outcome.
  4. Apply basic and advanced statistical methods to quantitative data using a statistical software package.  



Overview of Learning Activities

This course is delivered online using CANVAS platform. This will give you access to staff communication tools, discussion forums, the teaching schedule, learning materials, course notes, suggested reading, project details, exercises, assessment timelines and online tests. The course features case study videos, video demonstrations and forums all conducted online. This online material will make your individual study activity more flexible. Weekly exercises should be submitted online to check your understanding and to provide additional information.

 

Total study hours

You are required to spend up to 4 hours per week working on the suggested readings and exercises on your own.


Overview of Learning Resources

You will be expected to have access to a current copy of the SPSS statistical package or the free statistical package R.

All RMIT HDR students can obtain free licenses for SPSS by contacting ITS team through the live chat, the link can be found here.  In your live chat request, state that you’re currently enrolled as an HDR student and require SPSS for conducting your research.

To access and install the free statistical package R, please click here.

You will also need access to a spreadsheeting program such as Microsoft Excel or Office 365.

Students must regularly access the course site on CANVAS, which includes announcements, course documents (learning material, video resources, video demonstrations and exercises), practice quizzes, graded quizzes, assignments, project information and a very active discussion board.

The prescribed text for the course is based on which statistical package you plan to use. There are two options:
Field, A. (2013) Discovering Statistics using IBM SPSS Statistics. Sage Publication Ltd, London.
Field, A. (2012) Discovering Statistics using R. Sage Publication Ltd, London


Overview of Assessment

The assessment of this course is based on the following tasks:

Assessment Task 1: Module Exercises

Course module exercises that aim students to reflect upon and apply important concepts covered in each module. This assessment task comprises of short exercises and online quizzes.

Weighting 20%

The assessment task supports CLOs 1,2,5,6,7,9

Assessment Task 2: Online Tests

There will be two web tests available via the CANVAS. The first test is typically during the mid-semester and final test during the exam period. The tests require students to reflect upon course material, computer-based exercises, submitted work and the application of theory. These web tests will act as formal acknowledgement of students’ mastery of course concepts.

Weighting 30%

The assessment task supports CLOs 1,2,5,6,7,9

AssessmentTask 3: Project

The project requires students to submit various tasks that demonstrate the application and related concepts to their own research. This will involve the submission of a research summary, data analysis plan, data file and an online poster presenting statistical analysis of their data.

Weighting 50%

The assessment task supports CLOs 1,2,3,4,5,6,8,9

Final Grading

Final grading for this course is only Pass/Fail with a pass mark of 50%.