The USC offers two introductory courses that deal with the most commonly used methods in quantitative research. 

Pre-Data Collection Course

The first course provides guidance in structuring a well designed quantitative study. This will ensure that your study design and subsequent data collected will be appropriate for the statistical methods required to address your research questions. We suggest signing up to participate in the Pre-data collection course while you are in the initial planning stages of your study (Proposal phase).

Post-Data Collection Course

The second course provides guidance on selecting and performing the appropriate statistical analyses for your study to ensure that your research question is adequately addressed. The Post-data collection section should be completed as soon as your data collection process has been completed.

Introduction to Quantitative Research Methodology


These courses aims to equip researchers with the skills necessary to facilitate their own data collection, management and analysis. Currently the courses are offered online (self-paced) or through multiple 3-day online block course run through Microsoft Teams.

The aim of both these courses is to empower and educate participants on the basic methodologies, terminologies and statistical techniques required for quantitative research. By the end of the courses, participants should have the confidence and skills to undertake their own quantitative research independently, from start to finish. This includes everything from conceptualizing the research to performing the statistical analyses. International standards require that postgraduate students perform all their own statistical analyses for their research. Although one may seek the guidance of a statistician, the actual work needs to be done by the student themselves. It may seem a little daunting at first but being the primary person responsible for all aspects of the study will make interpreting and writing up the results that much quicker and easier!

The lectures of these courses are based on commonly used research approaches and will provide an intuitive understanding of the statistics used as opposed to a heavily theoretical approach filled with formulas and unfamiliar jargon! This approach will help you understand why we do each type of analysis and how you could make use of it in your own research. The post-data collection course concludes with a final assessment that will demonstrate your proficiency in basic quantitative techniques. Once you have passed this assessment you will have access to individual consultations to discuss any further questions about your quantitative research in detail.

To apply for either, or both, courses please select the application form link to the right. You will then select your preferred course and dates.

The outlines for both courses can be found below. 


Pre-Data Collection Course Outline

Introduction to Statistics in Research

How does the course work?  

Why are statistics/statistical analysis important for research?        

Why is it important to understand the statistics that you use?       

Important statistical concepts          

Quantitative/Qualitative Methods and Data Types          

Quantitative analysis versus Qualitative analysis                 

Quantitative Research Methods       

Qualitative Research Methods         

Advantages and disadvantages        

Mixed Methods Research Design      

Data Types     

Research Methodology - Questionnaire Design     

Questionnaire structure       



Difference between validated and self-validated scales      

Sampling Techniques

Sampling method      

Types of probability sampling

Types of non-probability sampling   

Research Methodology - An introduction to Experimental Design      

Introduction to Experimental Design

Basic methods of Experimental Design         

Basic Principles of Experimental Designs      

Local Control 

Data capturing and software    

Capturing data          

Cleaning data

Missing data  

Types of statistical software 

How to download SPSS and the data analysis toolpak in Excel   

SPSS Installation Process        

Excel Data Analysis Toolpak  


Post-Data Collection Course Outline

Descriptive Statistics       

Numerical descriptive statistics

Graphical descriptive statistics

Basic inferential tests and concepts   

Inferential statistics   


Understanding p-values        

Confidence intervals 

Exploratory Factor Analysis (EFA)     

Factor scores and Cronbach alpha’s 

Commonly used inferential methods

Parametric versus Non-parametric methods

Chi-square tests        


One Sample T-test     

Paired Sample T-test (Also known as Dependent Samples T-test)   

Independent Samples T-test (Also known as Two-Sample T-test)   

Mann-Whitney U Test

Wilcoxon Test (Signed Rank Test)     

One-way ANOVAs      

Kruskal-Wallis Test    

Simple Linear regression       

Practical Significance vs Statistical Significance        

Revision and Case study applications

Case Study 1  

Case Study 2  


Contact information
Unit for Statistical Consultation

USC Application Form

Click here to apply