Statistical comparisons using SPSS (DSCRP) [St Lucia]

This is a Data Science Collaborative Research Platform (DSCRP) course and places are highly limited. If you are unable to attend, please de-register (or email graduateschool@uq.edu.au if you are unable to do so). An unexplained absence could result in all your future DSCRP registrations becoming Waitlisted. You may only attend the workshop once. Repeat bookings will be removed and placed on the Waitlist.

This hands-on workshop introduces key principles of inferential statistics and supports participants in selecting and applying the appropriate standard statistical tests using SPSS. Through guided exercises, participants will learn how to define and interpret hypothesis tests commonly used in research, including correlation, contingency tables, chi-square tests, t-tests, and ANOVA.

Recommended Participants
This workshop is designed for researchers who want to learn how to select the appropriate statistical test for their specific research context and carry out the analysis independently using SPSS. It is suitable for participants from any discipline, though the examples and exercises will focus on a clinical study. Please note that prior experience with SPSS is required, as the session will not cover introductory SPSS skills.

Note: This is an in-person workshop held at the St Lucia campus.

Learning Outcomes

• Choose appropriate statistical tests based on the type of data and research question
• Perform a range of inferential statistical tests using SPSS
• Generate plots, tables, and figures to summarise statistical results using the SPSS interface
• Interpret and report outcomes from commonly used statistical analyses

Workshop Topics

• Introduce key terminology and concepts related to hypothesis testing
• Explore common statistical tests used in research, including correlation, chi-square tests, t-tests, and ANOVA
• Provide practical experience in applying statistical methods to real-world data using SPSS
• Guide participants in interpreting and reporting statistical results accurately