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Some cross‐sectional studies aim to shed light on cause and effect by recording whether people with a disease were more likely than people without the same disease to have experienced exposure to a risk factor. For example, for more than half a century researchers have recognized that patients with schizophrenia, when compared with the general population, are more likely to come from lower socio‐economic classes. What is a lot less clear is whether lower socio‐economic status is a risk factor for schizophrenia or, conversely, whether schizophrenia causes a slide down the socio‐economic scale. The cross‐sectional study design – in which the researcher measures in each subject a supposed risk factor at the same time as recording the presence of a condition – will nearly always have this chicken or egg problem (which comes first?).

In a study of bullying (Figure 6.2), the researchers persuaded 904 co‐educational secondary school pupils aged 12–17 years to declare whether they were bullied or not and to self‐report their feelings – including a scale that measured their level of anxiety. They found that those who reported being bullied also reported more anxiety. Notice that the study design does not preclude either possibility: that bullying exacerbates anxiety or that anxious children are more likely to be targets for bullies.

Figure 6.2 Extract from table of summary statistics from cross‐sectional study of bullying and self‐reported anxiety (values are numbers of schoolchildren unless stated otherwise).

Source: From Salmon et al. (1998), © 1998, BMJ Publishing Group Ltd.

It is a limitation of cross‐sectional designs that the direction of any effect cannot be determined because the supposed risk factor and the outcome are identified at the same time. The next two analytic study designs tackle this weakness and are able to identify the direction of any effect.