Regression Analysis in SPSS (online) (Medicine and Health Sciences Faculty)
Duration 1 day
Training Units 2.0
Team Researcher Academy
Target Audience

This course is part of the M&HS Faculty Training Programme and N-trans training programme, which are convened by Researcher Academy.

Postgraduate Research Students and Research Staff in the Faculty of Medicine and Health Sciences only. Researchers who are based in other faculties, and who are undertaking health and medical-related research may access the online lecture capture resources. If you wish to join the practical web-based session, please email to request a place, as soon as possible and no later than 3 working days before the course date.


This course is aimed at students and researchers who have a good grasp of basic statistics including the ideas of sampling variability, measures of effect (e.g. odds ratio and mean difference) and tests of statistical significance (e.g. chi-squared test and t-test) and familiarity with SPSS.

This course covers advanced level statistical methods and advanced level usage of statistical software. There will be no opportunity to cover the basics of statistics or SPSS during the workshop. If you are not familiar with SPSS you must complete prior training in SPSS (visit the Researcher Academy Training pages for suggested online course).  

Participants who have not attended these introductory courses or have prior working knowledge of the software are unlikely to benefit from attending this course until they have the basics. There is no time during the session to cover introductory questions.


Online learning environment- lectures and practical computer based tasks.

Participants will need their own laptop/PC with SPSS software downloaded and an internet connection. The course will be delivered via Moodle and Microsoft Teams.

Course Description


  • To provide understanding of the purpose of multivariate regression models
  • To provide knowledge of two important regression models; linear and logistic regression
  • To provide practical skills in building and interpreting linear and logistic regression models in SPSS

Learning outcomes

  • Appreciate the different situations in which we might need to use multivariable regression analyses
  • Know when it is appropriate to use two of the common multivariable regression models, linear and logistic regression.
  • An understanding of how to build, and interpret the output from a multiple linear regression model in SPSS
  • An understanding of how to build, and interpret the output from, a multiple logistic regression model in SPSS
  • An understanding of approaches to building a model to adjust for confounders
  • An understanding of approaches to building a model to best predict an outcome

This online version of ‘Regression Analysis in SPSS’ is scheduled for a full day and is recommended to be completed on the scheduled day. However, the pre-recorded lectures may be watched in advance and the participant join the practical sessions at the designated time on 4th May. 

To gain training points/attendance mark for completion of this course you must have accessed the lecture materials on or before the 4th May AND then log in online to Teams and complete the practical sessions on 4th May during the designated timeslots. The timings of the session will be sent to participants in their booking confirmation email.

How to receive training points/full attendance mark

Complete all 4 blocks of lecture recordings, and the practical/Q&A live sessions on 4th May.

For participants with work/caring commitments who need greater flexibility in their day, the online course may be completed in smaller sections on 4 May. In this case, the lecture recordings should be watched in advance and then log in to participate in the 4 live-streamed practical sessions taking place on 4th May.

Please note, the classroom based version of this course was 1 day in duration.



How to Access Course Resources

To access the online training course resources, live streamed practical session timings on 4 May, and full instructions on how to join the course visit Moodle here:

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