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

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

5 July 2022 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.

Statistics for comparisons using R

14 July 2022 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 14 May AT 12PM.

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

20 July 2022 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 WILL OPEN ON 20 MAY AT NOON.

Longitudinal and mixed model analysis using R [webinar]

2 August 2022 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 WILL OPEN ON 2 JUNE at NOON.

Statistics for comparisons using SPSS (QCIF) [webinar]

12 September 2022 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. REGISTRATIONS IS REQUIRED AND WILL OPEN ON 12 JULY AT NOON.

Basic statistical ideas for researchers [webinar]

11 November 2022 10:00am12:00pm
Learn ideas relating to research design, descriptive statistics and inferential statistics. REGISTRATION IS REQUIRED AND WILL OPEN APPROX 6-8 WEEKS PRIOR.

Introduction to Regression modelling (ISSR) [webinar]

2 June 2022 9:00am12:00pm
Learn about simple and multiple regression models. REGISTRATION IS REQUIRED AND WILL OPEN ON 2 APR AT 12PM.

Introduction to longitudinal data analysis (ISSR) [webinar]

27 April 2022 9:00am12:00pm
Learn about longitudinal data analysis. REGISTRATION IS REQUIRED AND WILL OPEN ON 27 FEB AT 12PM.

Statistics for comparisons using R (QCIF) [webinar]

12 April 2022 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 12 MARCH AT 12PM.

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

6 April 2022 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 WILL OPEN ON 6 MARCH at NOON.

Statistics for comparisons using SPSS (QCIF) [webinar]

9 March 2022 9:00am5:00pm
Participants will learn how to compute, report, and interpret hypothesis tests for popular statistical models such as correlation, contingency tables, chi-square test, t-test and ANOVA. Knowledge of SPSS is required.