Teachers + students: why isn’t the equation adding up?

Q&A at the University of Canberra Research Festival on 19 March. I attended the session on “Teachers + students: why isn’t the equation adding up?” Issues arose around rote learning versus creative thinking in cultures around the world; and declining numbers in advanced maths unit and engagement in maths. Solutions revolved around drawing the links between maths and other subjects; and growing great advocates. Unfortunately I couldn’t stay till the end, but hope that something actionable will come of the meeting of minds that took place.

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Inequality within wealthy nations: A comparison of the gender wealth gap in Switzerland and Australia

I attended this talk at UC given by PhD student Laura Ravazzini and Faculty researcher Jenny Chesters. The Australian HILDA data was pressed into service, along with similar Swiss data, that enabled quantile regressions to be fitted and some interesting comparisons made. I’m looking forward to the results of fitting single models with an offset for gender rather the trying to compare two models for males and females separately – some stronger statistical conclusions should be the reward.

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Goulburn 27

If I had to summarise Goulburn27 (on Wednesday March 18) in six words (as in “I came, I saw, I conquered”) it would be “space and time, models, open problems”. Where in the past talks t Goulburn have revolved around the concept of likelihood, this time I thought the model was more prominent as a way to commence the analyses. The notions of space and time capture not only the time-series data of Han Lin Shang and the spatio-0tempral data of Andrew Zammit_Mangion, but also Ray Chambers’ more discursive talk on survey sampling in official statistics. All the speakers mentioned open problems in their area of discussion, Nikos Tzavidis being the remaining speaker who I’ve not yet mentioned.

The weather is also possible to summarise in six words – torrential rain, hail like marbles, sunshine!

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Chronic Fatigue Syndrome – ME: from clinical to cellular and protein pattern identification to unravel a mysterious disease

Brett Lidbury grasped the nettle around chronic fatigue syndrome at the John Curtin School of Medical Research seminar on 27 February. He described the work we have done as dimension reduction and hypothesis generation rather than hypothesis testing. The weighted standing time got a airing, as did the work on the protein activin and the random forests. Need to go searching for a paper on orthographic intolerance in depressed patients! Don’s clinic URL is cfsdiscovery.com.au

 

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Issues in evaluation of public health interventions

On 24 February Catherine d’Este from the Research School of Public Health gave this talk to kick off the year of talks at the Canberra Branch of the Statistical Society. She gave a really clear presentation, drawing on a number of interesting public health research questions dealt with in her group. I learnt that broken stick models are still current, and have been joined by bent cables in the ongoing attempts to model step changes in response to an intervention. I also learnt about step-wedge experiment designs, which also have a place in the staged introduction of changes in ABS-style surveys.

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MathsAssess: A guide to implementing criteria based assessment in undergraduate mathematics

The guide was released in October 2014 as part of an OLT funded project “Developing a shared understanding of assessment criteria and standards for undergraduate mathematics”. Statistics has not been ignored in the project, and the guide includes a handy rubric for assessment of your typical first year statistics project.

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Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society

The American Statistical Association has released a white paper on this meaty topic, and I really enjoyed reading it. The bits I highlighted for future use were this definition of statistics: “the science of learning from data, and of measuring, controlling and communicating uncertainty” and this request for the future: “it is essential that these data scientists possess a statistical perspective, which emphasizes the quality of the data, the power of sampling, the characteristics of the sample, the validity of generalization, and the balance of humans and machines.”

That’s the kind of graduate I’d like to see from my University – roll on the first semester of 2015 when we start the process again!

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Statistics for a more scientific science

How much of published laboratory science should we believe? This was John’s subtitle for the talk he gave at ANU on 11 December. He started with delightful quote from the “Journal of Irreproducible Results” about the height of hats worn by witches, priests and chefs. Just like Terry Speed spoke about earlier this year, john emphasised the importance of estimating batch effects. I plan to keep an eye also on the Many Labs project, reproducing 13 classic psychological studies; Centreforopenscience.org, replicating Cancer biology studies; and validation.scienceexchange.com who will arrange for replication of your study for you. john also highlighted new guidelines for reporting research such as ARRIVE and REMARK. I’d like to find out more about the Harvard Institute for Quantitative Social Science too.
John will be returning to New Zealand soon – we will miss easy access to his R programming skills, and will have to keep in touch through his thoughtful and sometimes provocative ANZSTAT postings.

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Does school attended make a difference for primary school children?

Jenny Chesters and Anne Daly managed to attract nearly a dozen people to this seminar on 10 December – Christmas may be coming but maybe the release of the 2014 NAPLAN report today inspired those still at work to come out and hear the talk.

They did have individual child-level NAPLAN data with the approval of DET, and fitted a nice hierarchical linear model in Stata. The claim was made that because this was population data (or at least 98% of the population as claimed in the newspapers today) then no confidence intervals needed to be presented, just the parameter estimates. Those estimates mostly looked quite significant in terms of percentage increase or decrease in scores though. Jenny did point out that if you’re desperate for a standard deviation of scores within a school, you could do worse than take the range ( which is apparently given on the MySchool website) and divide by 6. So long as you thought the NAPLAN score were normally distributed of course!

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Past, Present and Future of Statistical Science III

Part III of this book is on “Perspectives on the field and profession” and it’s been quite w hike since I posted on Parts I and II. This book has continued to be an absorbing read. Authors, chapter titles and a one-sentence reaction to each one follow.
Stephen Fienberg. Statistics in service to the nation. Four nice take-home messages after discussion of log-linear models.
Iain Johnstone. Where are the majors? Very short and puzzling.
Peter Hall. We live in exciting times. Recounts his rather charmed career and incredibly mathematical and theoretical nature of his research, though I’m sure he would beg to differ!
Rafael Irizarry. The bright future of applied statistics. Some good inspirational stuff for first lectures here.
Nilinjan Chatterjee. The road travelled: from statistician to statistical scientist. Pertinent comments about other researchers grabbing discoveries but statisticians carefully characterising results.
Xihong Lin. A journey into statistical genetics and genomics.
Mary Thompson. Reflections on women in statistics in Canada. Very Canadian focus, historical overview.
Nancy Reid. “The whole women thing”. Loved the call to get nominating, and to not be so hard on each other.
Louise Ryan. Reflections on diversity. Extremely valuable reflections on the experience of women in science in Australia even touching on overseas travel.

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