Statistics as a Distillation of Everyday Experience

Gerald van Belle, University of Washington, Seattle, WA

Comments by Victor van Daal Universitetet i Stavanger, Norge

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Why it is good…

• The link of methodology and statistics with everyday experience – We look for consistency in a noisy system: we look for ways to categorise things, we look for causes – Variation makes it harder to find out whether a thing belongs to a certain category or not • Establishing a link between everyday-life reasoning and doing research is important because we can use it to train our students – How to tackle issues of variation and causation:the methodology – How to chose proper tools to examine variation and causation: the statistics

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…and it’s good because…

• I recognise a lot in it – I’m biased… • Personally: – The example from a study on Alzheimer’s • Professionally: – British Cohort Studies • Very large data set (17,000 births in week 20 in 1970) with a follow-up at ages 6, 10, 16, 21, and 30 • Look for predictors and consequences of developmental disorders – PIRLS (2001) and (2006) • Comparison of 43 countries on reading comprehension performance in 10-year-old children, including measures of home, school and teacher characteristics

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…but…

• I don’t think it’s just variation and causation, but also measurement, especially because a lot of attention is given to IRT (Item Response Theory) • Or could it be that measurement is in fact classification?

• ‘We sort into genres’ could be extended that we use more than one dimension to sort into (with again lots of individual variation) • How do we know that we have a random sample?

– It must be random to avoid bias – Small samples tend to be biased – Therefore big samples?

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More variations on variation…

• There is no end to controlling variation – Just by trying to match on very many variables, you may unintentionally create differences on an unknown variable – My question would therefore be ‘Why do you wish to control variation?’ – May be it’s better to find the sources of variation • What’s there so slippery about representativeness?

• Missing values – When to impute and how to impute? – When not to impute?

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For the discussion…

• Everyday experience may lead us astray in many ways – There are more ingredients to a lethal mix – How to assess that you’re on the same track?

• The great divide – Anticipate possible problems – In hindsight: how to fix the problems afterwards?

• Theory Example: developmental disorders – Progressive modularisation, genetic constructivism, levels involved: genetics/biology, cognitive, behavioural • Research strategy – Problem solving, responsibilities, …

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