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Statistical Comparisons for HASS Researchers (Mar 2024)

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Event description

This practical workshop is designed to help HASS researchers choose and use the appropriate statistical test for their data by introducing key concepts of inferential statistics in SPSS. Using practical examples from the social sciences, 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. 

N.B. Prior knowledge of SPSS is mandatory as the basics of SPSS will not be covered. Please do not register unless you have this experience.

To participate in this workshop, you must have access to an SPSS licence; this can often be obtained through your university IT support system.

FAQs

What should I bring into the event?

This is an online workshop, so you will need to provide your own computer with SPSS pre-installed, and have access to a reliable internet connection sufficient for video conferencing. A second monitor is highly recommended.

Chromebooks and tablets are not suitable for this workshop; you will need a full-function computer.

What prior experience do I need?

Prior experience with SPSS interface is required, as the basics of SPSS will not be covered.

Who should attend?

HASS researchers wanting to understand how to choose the right statistical test for the context/condition and how to conduct the analysis by themselves using SPSS. The training material is based around examples from social sciences and humanties, although the statistical concepts behind these are relevant to researchers from all disciplines.

How can I contact the organiser with any questions?

You can email training@qcif.edu.au

What's the cancellation policy?

Cancellations are accepted up to three working days before the start of the workshop. If you do not join on the day without having cancelled beforehand, you may be blocked from attending any future QCIF training workshops.


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Refund policy

Refunds are available up to 3 working days prior to the event