Title: Data = fit + residual
Course: UGQuants (SSPS) > Section: Univariate Analysis

Data = fit + residual

The notion that in quantitative analysis our data often contains two elements – a ‘fit’ and a ‘residual’ – offers a useful starting point even when discussing the representation of data for one variable and is very significant when we come to look at more complicated ways of representing the relationships between variables. This is because in data analysis (both quantitative and other forms of analysis) we usually attempt to simplify what is often an immensely complex situation with respect to the social and political world and how we might try to explain it.

In quantitative analysis we often talk of creating a ‘model’ of our data. This model is usually a kind of summary that we judge to be the best way (or at the very least a good way) of representing our data in a useful and intelligible fashion, finding a compromise between excessive complexity and excessive simplicity. As such, models of data in social and political research seldom purport to fully represent our data or to provide perfect explanations. Within a model, that part of the data that is represented or explained is described as the ‘fit’, and the unrepresented or unexplained element is described as the ‘residual’.  Because the social and political world does tend to be very complicated and difficult to explain, you will often encounter quite large ‘residuals’. You should not be discouraged by this – imperfect representations and explanations are better than none at all. But it does not follow that any model is better than none. We should endeavour to avoid models that are very flawed or weak.

Watch the video below to see an example of why we don't often see the bigger picture when conducting research.