# Attenuation bias explained (in diagrams)

Below is a simple representation of 45 pupils’ prior attainment on a simple 7-point score. Imagine these are KS2 scores used as the baseline to judge KS2-4 ‘Progress’, as in the Progress 8 measure. To create the Progress 8 measure the average KS4 score is found for each KS2 score (in this example, 7 KS4 ‘expected’ average scores would be found and used to judge pupils KS4 scores against others with the same KS2 score. Above are the ‘true’ scores, i.e. measured without any error. Let’s see what happens when we introduce random measurement error into this. Below are the same pupils on the same scale, but 1 in 5 of each group of 5 have received a positive error (+) and have been bumped up a point on the scale and 1 in 5 have received a negative error (-) and have been bumped down a point. There were originally 15 pupils with a score of 4. Now 3 of these have a higher score of 5 (see the 3 red pupils in the 5 box) and 3 have a lower score of 3 (see the 3 green pupils in the 3 box). Remember these are the observed scores. In reality, we do not know the true scores and will not be able to see which received positive or negative errors.

Now ask, what would happen if we were to produce a KS4 score ‘expectation’ from the average KS4 score for all pupils in box 5? There are some with the correct true score who would give us a fair expectation. There are some pupils whose true scores are actually 4 and would, on average, perform worse, bringing the average down. There is one pupil whose true score is higher and would pull the average up. Crucially, the less able pupils are more numerous than the more able and the average KS4 score for this group will drop.

The important thing to notice here is that – above the mean score of 4 – the number of pupils with a positive error outweighs the number with a negative error. Below the mean score of 4, the opposite is true. Calculating expected scores in this context will shrink (or ‘attenuate’) all expectations towards the mean score. As the expected scores shrink towards the mean score, what happens to value-added (‘Progress’)? It gets increasingly positive for pupils whose true ability is above average and increasingly negative for those who are below average. Lots of spurious ‘Progress’ variation is created and higher ability pupils are flattered.

## One thought on “Attenuation bias explained (in diagrams)”

1. […] you are interested in exactly what causes this – I have written a short explanation here (with a handy […]

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