Tag Archives: PedRIO

Chris Wild – Getting Further with Data Analysis Faster and Easier

In September 2015 Professor Chris Wild from the Department of Statistics at the
University of Auckland, New Zealand, gave a PedRIO (Pedagogic Research Institute and Observatory at the University of Plymouth) talk at Plymouth University entitled Getting Further with Data Analysis Faster and Easier.

Chris talked about his recent work with Future Learn developing a MOOC for the University of Auckland. The Future Learn platform and the introductory statistics course entitled ‘Data to Insight’ has been mentioned here before.

In addition, and the main focus of the presentation, Chris talks about the use of software to teach statistics faster and easier, using examples from iNZight, the data analysis software, to demonstrate thisinzight_transp.

The talk was very well attended and included academic staff from across the University. We would like to thank Chris for his presentation and all those who attended and made the event what it was, as well as PedRio for hosting the talk.

Chris was keen to collaborate with others on new and existing projects so please visit his site and email him should you have any further comments or questions.

Notes

The video covers the first 40 minuets of the presentation. Video clips from the MOOC, that are now freely available online (check the previous blog post), have been removed to shorten the video.  A further half hour has also been removed, this included an extended live demo of the iNZight software that was not clear enough to view in the video.

This video is a .MOV and may take a while to download.

Dr Michael A. Posner – Propensity Score Analysis

Recently Dr Michael A. Posner, Associate Professor of Statistics, Villanova University, USA, visited Plymouth University.

Dr. Posner gave two talks during his visit the first of which is now available to view online. The talk held on the 20th of January 2016 was entitled ‘Making Valid Inferences in Observational Studies using Propensity Score Analysis’, you can read the full abstract by downloading the leaflet for the day, linked below.

Making Valid Inferences in Observational Studies using Propensity Score Analysis

We would like to thank Michael for his visit and talk, we have had wonderful comments from attendees to both talks and have had many requests externally for links to the presentation. Incidentally, if you would rather view just the slides you can view Michael’s profile and previous talks on his profile page, linked to above and in the leaflet.

We would like to thank PedRIO (Pedagogic Research Institute and Observatory at the University of Plymouth) who supported the event and of course all those who attended, we had a great turnout with a wide cross section of academic staff from across the University.

Notes

The audience audio for the questions and answers segment was not totally clear so these are part of a separate video.

These videos are .MOV and large and may take a while to download.

There are more videos from other talks to follow, once these are done they will all be added to our resource page.

 

Dr Michael A Posner to visit the ICSE

We are very excited to announce that Dr Michael A Posner, Associate Professor of Statistics, Villanova University, USA and director of the Center for Statistical Education (not related), will be visiting Plymouth University early in the New Year to give a talk.

Dr. Posner is currently on sabbatical at The Department of Pure Mathematics and Mathematical Statistics Centre for Mathematical Sciences at the University of Cambridge.

The talk to be held on the 20th of January 2016 is entitled ‘Making Valid Inferences in Observational Studies using Propensity Score Analysis’, you can read the full abstract, get further information and book your place using the link below. All are welcome but priority will be for Plymouth University staff so please book early if you wish to attend.

The talk is supported by PedRIO (Pedagogic Research Institute and Observatory at the University of Plymouth).

Making Valid Inferences in Observational Studies using Propensity Score Analysis