Statistics and data journalism: seasonal adjustment for journalists

seasonal adjustment image from Junk Charts

When you start to base journalism around data it’s easy to overlook basic weaknesses in that data – from the type of average that is being used, to distribution, sample size and statistical significance. Last week I wrote about inflation and average wages. A similar factor to consider when looking at any figures is seasonal adjustment.

Kaiser Fung recently wrote a wonderful post on the subject:

“What you see [in the image above] is that almost every line is an inverted U. This means that no matter what year, and what region, housing starts peak during the summer and ebb during the winter.

“So if you compare the June starts with the October starts, it is a given that the October number will be lower than June. So reporting a drop from June to October is meaningless. What is meaningful is whether this year’s drop is unusually large or unusually small; to assess that, we have to know the average historical drop between October and June.

“Statisticians are looking for explanations for why housing starts vary from month to month. Some of the change is due to the persistent seasonal pattern. Some of the change is due to economic factors or other factors. The reason for seasonal adjustments is to get rid of the persistent seasonal pattern, or put differently, to focus attention on other factors deemed more interesting.

“The bottom row of charts above contains the seasonally adjusted data (I have used the monthly rather than annual rates to make it directly comparable to the unadjusted numbers.)  Notice that the inverted U shape has pretty much disappeared everywhere.”

The first point is not to think you’ve got a story because house sales are falling this winter – they might fall every winter. In fact, for all you know they may be falling less dramatically than in previous years.

The second point is to be aware of whether the figures you are looking at have been seasonally adjusted or not.

The final – and hardest – point is to know how to seasonally adjust data if you need to.

For that last point you’ll need to go elsewhere on the web. This page on analysing time series takes you through the steps in Excel nicely. And Catherine Hood’s tipsheet on doing seasonal adjustment on a short time series in Excel (PDF) covers a number of different types of seasonal variation. For more on how and where seasonal adjustment is used in UK government figures check out the results of this search (adapt for your own county’s government domain).

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