Category: data curious (Page 2 of 3)

Odd ratios for the different levels of stomach inflammation.

GM-fed pigs, chance and how research works

2013-06-21 / Luis

Following my post on GM-fed pigs I received several comments, mostly through Twitter. Some people liked having access to an alternative analysis, while others replied with typical anti-GM slogans, completely ignoring that I was posting about the technical side of the paper. This post is not for the slogan crowd (who clearly are not interested in understanding), but for people that would like to know more about how one would evaluate claims from a scientific article. While I refer to the pig paper, most issues apply to any paper that uses statistics.

In general, researchers want to isolate the effect of the treatments under study (diets in this case) from any other extraneous influence. We want control over the experimental conditions, so we can separate the effects of interest from all other issues that could create differences between our experimental units (pigs in this case). What could create ‘noise’ in our results? Animals could have different genetic backgrounds (for example with different parents), they could be exposed to different environmental conditions, they could be treated differently (more kindly or harshly), etc.

Ordinal logistic GM pigs

2013-06-14 / Luis

This week another ‘scary GMO cause disease’ story was doing the rounds in internet: A long-term toxicology study on pigs fed a combined genetically modified (GM) soy and GM maize diet. Andrew Kniss, a non-smokable weeds expert, mentioned in Twitter that the statistical analyses in the study appeared to be kind of dodgy.

Curious, I decided to have a quick look and I was surprised, first, by the points the authors decide to highlight in their results, second, by the pictures and captioning used in the article and, last, by the way of running the analysis. As I’m in the middle of marking assignments and exams I’ll only have a quick go at part of the analysis. As I see it, the problem can be described as ‘there is a bunch of pigs who were fed either non-GM feed or GM feed. After some time (approximately 23 weeks) they were killed and went through a CSI-like autopsy’, where part of the exam involved the following process:

Some regressions on school data

2012-09-26 / Luis

Eric and I have been exchanging emails about potential analyses for the school data and he published a first draft model in Offsetting Behaviour. I have kept on doing mostly data exploration while we get a definitive full dataset, and looking at some of the pictures I thought we could present a model with fewer predictors.

The starting point is the standards dataset I created in the previous post:

Updating and expanding New Zealand school data

2012-09-25 / Luis

In two previous posts I put together a data set and presented some exploratory data analysis on school achievement for national standards. After those posts I exchanged emails with a few people about the sources of data and Jeremy Greenbrook-Held pointed out Education Counts as a good source of additional variables, including number of teachers per school and proportions for different ethnic groups.

The code below call three files: Directory-Schools-Current.csv, teacher-numbers.csv and SchoolReport_data_distributable.csv, which you can download from the links.
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New Zealand school performance: beyond the headlines

2012-09-24 / Luis

I like the idea of having data on school performance, not to directly rank schools—hard, to say the least, at this stage—but because we can start having a look at the factors influencing test results. I imagine the opportunity in the not so distant future to run hierarchical models combining Ministry of Education data with Census/Statistics New Zealand data.

At the same time, there is the temptation to come up with very simple analyses that would make appealing newspaper headlines. I’ll read the data and create a headline and then I’ll move to something that, personally, seems more important. In my previous post I combined the national standards for around 1,000 schools with decile information to create the standards.csv file.
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