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There are two errors in causal reasoning that have to do with mixing different levels of analyses, which are illustrated in Figure 1.4. The first is known as the ecological fallacy and has to do with generalizing group characteristics to individuals. If we analyse the effect that average neighbourhood income has on the crime rates of that neighbourhood, we are comparing group characteristics with group characteristics. To extend this argument to particular individuals in the neighbourhood can be misleading. It is not appropriate to apply group-level characteristics to individual-level inferences. We may well find that as average income in a neighbourhood decreases, the crime rate increases – but we cannot say that if an individual’s income decreases, he or she is more likely to commit crime!

The ecological fallacy can be demonstrated in a number of ways. Another common misinterpretation of group characteristics is to look at the average income in two very different communities that are about the same size. In Wealthyville, the average household income is $500,000 per year. In Poorville, the average household income is only $15,000 per year. If 10,000 households live in each community, we would say that the average household income across both communities is $257,500 per year. This would give a completely inaccurate representation of the communities, however, because it doesn’t represent the household income of anyone. It is far too little to represent Wealthyville (just about half the actual household income) and too high to represent Poorville (over 17 times the actual household income). By taking group characteristics and trying to generalize to individual households, we have committed the ecological fallacy.

Figure 1.4 Units of analysis and making generalizations

Keeping your units of analysis comparable also applies to arguments made in the opposite direction – generalizing individual processes to group processes.This problem is known as the atomistic fallacy (or individualist fallacy). People make this mistake when they take results from individual-level data and apply them to groups, where the context may be very different. We may find, for example, that being an immigrant is associated with an increased risk of mental health problems. A policy solution, however, of creating mental health programmes for all immigrants may be misguided, if contextual variables (at the group level) are not taken into account. It may be that immigrants in large cities have better mental health than immigrants in small communities (where they may be isolated) (Courgeau, 2003). If we simply take individual-level characteristics and apply them to groups, failing to take contexts into consideration, we may come to conclusions based upon flawed logic.

Both the ecological and atomistic fallacies are errors that researchers make when they take data at one level and try to make generalizations to another level. As social scientists, we know that individual characteristics (e.g. age, gender, race) and contextual-level variables (e.g. school, neighbourhood, region) are important determinants for many different outcomes of interest. In multilevel modeling, we use both individual and group characteristics and our outcomes can be modeled in ways that illustrate how individual and group characteristics both affect outcomes of interest, and how group characteristics may influence how individual characteristics affect the outcome of interest, given certain contexts.