This paper examines how we might test the continuum theory against the community unit theory. Adherence to one or other of these models without testing is simply an assignment of an extreme prior probability to the preferred option. The question can be rephrased to ask whether, for a set of observations, a single model is adequate or whether a mixture of models would be preferable. To judge between them involves first defining the nature of the model(s) to be fitted in each case and then comparing the complexity and quality of fit. Occam's razor suggests that we should seek the simplest model with adequate fit, with parameters estimated with optimal precision. The simplest comparison of the two theories thus requires only the estimation of the number of clusters for the chosen model(s) of within-cluster variation. If a single cluster is of adequate quality then the continuum model is appropriate, while if several are needed then the community model is preferable for that particular dataset. To establish universal applicability of either model involves investigation of many datasets. There are several ways in which model quality can be assessed, and here I concentrate on the minimal message length principle which is a function of the prior probability of the model and its fit to the observed data, assuming the model to be correct. This principle has been shown to perform well when compared with other possibilities. I first illustrate the procedure for making a choice between models, using a simple model, then examine two alternative formulations of within-cluster models which seem more appropriate, one static, the other dynamic.
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