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
Validity
Reliability
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
Correlations
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
T-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