If your research study employs quantitative or mixed methodologies, you would need to understand the nuts and bolts of statistics and modelling. The Graduate School works with different providers to provide a range of sessions covering descriptive and inferential statistics as well as modelling with esteemed providers such as ISSR and Student Services.

Useful links

Basic statistical ideas for researchers [webinar]

11 June 2024 10:00am12:00pm
Learn ideas relating to research design, descriptive statistics and inferential statistics.

Predicting Binary Outcomes using Logistic Regression (QCIF) [webinar]

28 November 2023 9:00am12:30pm
The course will cover the principles of logistic regression and includes practical sessions getting hands-on experience of logistic regression in R. You will require prior expertise in R and a basic familiarity with statistical hypothesis testing and regression analysis to attend. REGISTRATION IS REQUIRED AND WILL OPEN 28 SEPTEMBER AT NOON.

Statistical comparisons using SPSS (QCIF) [webinar]

17 November 2023 9:00am5:00pm
This practical workshop will help participants to choose and use the appropriate standard statistical test for their data by introducing key concepts of inferential statistics in SPSS. Participants will learn how to compute and interpret hypothesis tests for popular statistical models such as correlation, contingency tables, chi-square test, t-test and ANOVA. Prior knowledge of SPSS is required. REGISTRATION IS REQUIRED AND OPENS 17 SEPTEMBER AT 12PM.

Exploring and predicting using linear regression in SPSS (QCIF) [webinar]

19 October 2023 9:00am5:00pm
This hands-on SPSS workshop introduces principles and methods of regression models using SPSS, and how to interpret relationships between variables. It covers basic principles of regression methods through to interpreting the output of statistical analyses. Prior expertise with SPSS is required. Participants are also expected to have a basic familiarity with the concepts of descriptive statistics and elementary statistical hypothesis testing. REGISTRATION IS REQUIRED AND OPENS 19 AUGUST AT 12PM

Longitudinal and mixed model analysis using R (QCIF) [webinar]

4 October 2023 9:00am5:00pm
This interactive online workshop deals with longitudinal data and its analysis. Participants MUST know R and be familiar with the concepts of statistical hypothesis testing and regression analysis. REGISTRATION IS REQUIRED AND WILL OPEN ON 4 AUGUST AT 12 PM.

Exploring and predicting using linear regression in R (QCIF) [webinar]

19 September 2023 9:00am5:00pm
This highly interactive online workshop will provide attendees with a friendly, gentle introduction to the theory behind linear regressions in R. Prior experience with R and the RStudio interface is required, as well as familiarity with the concepts of descriptive statistics and elementary statistical hypothesis testing. REGISTRATION IS REQUIRED AND OPENS 19 JULY AT 12PM.

Statistical comparisons using R (QCIF) [webinar]

15 September 2023 9:00am5:00pm
This practical hands-on workshop will help participants to choose and use the appropriate statistical test for their data by introducing key concepts of inferential statistics in R. Prior knowledge of R is required.

Statistical comparisons using R (QCIF) [webinar]

8 August 2023 9:00am5:00pm
This practical hands-on workshop will help participants to choose and use the appropriate statistical test for their data by introducing key concepts of inferential statistics in R. Prior knowledge of R is required. REGISTRATION IS REQUIRED AND OPENS 8 JUNE AT 12PM.

Introduction to longitudinal data analysis (ISSR) [webinar]

28 June 2023 9:00am12:00pm
Learn about longitudinal data analysis. REGISTRATION IS REQUIRED AND WILL OPEN ON 28 APRIL AT 12PM.

Introduction to Regression modelling (ISSR) [webinar]

21 June 2023 9:00am12:00pm
Learn about simple and multiple regression models. REGISTRATION IS REQUIRED AND WILL OPEN ON 25 APRIL AT 12PM.

Statistical comparisons using R (CDF) [webinar]

20 June 2023 9:00am5:00pm
This practical hands-on workshop will help participants to choose and use the appropriate statistical test for their data by introducing key concepts of inferential statistics in R. Prior knowledge of R is required.