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

MATH2256

City Campus

Research

171H School of Science

Internet

 Sem 2 2020

MATH2336

RMIT University Vietnam

Research

171H School of Science

Face-to-Face

 Viet2 2018,
Viet3 2019,
Viet2 2020

Flexible Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2256

City Campus

Research

171H School of Science

Internet

 RSCHYr2019 (RT93)

MATH2256

City Campus

Research

171H School of Science

Internet

 RSCHYr2021 (RT13)

MATH2256

City Campus

Research

171H School of Science

Internet

 RSCHYr2022 (RT23)

MATH2256

City Campus

Research

171H School of Science

Internet

 RSCHYr2023 (RT33)

MATH2256

City Campus

Research

171H School of Science

Internet

 RSCHYr2024 (RT43)

Course Coordinator: James Baglin

Course Coordinator Phone: +61 3 9925 6118

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

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

  • You are enrolled in a Higher Degree by Research (HDR) program. 
  • You have previously completed or are concurrently enrolled in a HDR research methods course. 
  • You are in the early stages of your HDR and plan to undertake quantitative research.  
  • You have a good knowledge of general IT used in research.
  • You have fundamental mathematical skills to a graduate high-school standard. 

Candidates not fitting all the above criteria should discuss their enrolment by contacting the course coordinator.


 


Course Description

Quantitative research aims to answer research questions through systematic collection and analysis of count or measurement-based data taken from many occurrences of a phenomena under observation or experimental manipulation. Quantitative research is a powerful research modality if carefully planned and implemented. This course introduces quantitative research techniques starting with defining research questions, hypotheses, and operationalising variables. You will also learn about different research designs, measurement reliability and validity, sampling, sample size determination, data management, ethics and research integrity. Modules covering data preprocessing, exploratory data analysis, statistical inference, hypothesis testing, and statistical modelling will develop a statistical foundation for quantitative data analysis. The course will also discuss effective strategies for summarising and communicating the findings of quantitative research. You will demonstrate your understanding through reflection and application of course content to the context of your higher degree by research. By the end of the course will have a deeper and more critical awareness of the strengths and weaknesses of quantitative research.  


Objectives/Learning Outcomes/Capability Development

  


Upon completion of this course you should be able to:

  1. Apply  fundamental concepts, assumptions, ethical considerations and   codes of conduct related to undertaking quantitative research
  2. Evaluate and select appropriate quantitative research designs, measures and samples based on research objectives and constraints
  3. Plan, collect, manage, prepare and explore data for the purpose of reproducible statistical analysis
  4. Critically review, select, and apply common statistical models used to analyse combinations of different variable types with the assistance of statistical software
  5. Analyse and interpret the results of statistical analysis and clearly communicate results textually, visually and verbally


Overview of Learning Activities

This course will be delivered online using Canvas. The course will include a combination of online learning resources and regular virtual classes. Online learning resources will include detailed notes and readings, worked examples, short video demonstrations, discussion boards for Q&A and detailed assessment instructions. Virtual classes will facilitate a social learning environment to replace traditional face-to-face teaching. During these classes, you will discuss your research, important course concepts and assessment with the lecturer and your peers.  


Overview of Learning Resources

All course learning resources will be available through Canvas. This will include detailed notes, readings, worked examples and video demonstrations. Canvas will also host the course discussion board, assessment details and virtual class access.  

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

The quickest way to access SPSS for free as an RMIT student is through myDesktop.  

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

You will also need access to a spread sheeting program such as Microsoft Excel or Google Drive (Spreadsheet). 

The prescribed texts for the course (both available from the RMIT Library) was based on which statistical package you plan to use. There are two options: 


Overview of Assessment

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

Assessment Task 1: Proposal: Written report using proposal template 

Weighting 20% 

The assessment task supports CLOs 1 & 2 

Assessment Task 2: Data Preprocessing and Exploration: Written Data Analysis Report, Data Dictionary, and Dataset 

Weighting 20% 

The assessment task supports CLO 3 

Assessment Task 3: Statistical Analysis Report: Written report summarising statistical analysis

Weighting 30% 

The assessment task supports CLO 4 

Assessment Task 4: Poster Presentation: Academic poster and screencast (video) 

Weighting 30% 

The assessment task supports CLO 5