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RESEARCH METHODS 251
or event can be modelled in a number of different ways. However, because each model is devised for a particular purpose, it is potentially dangerous to use it for some other, unrelated, purpose. It could also produce misleading information if it is used beyond the range or area of applicability for which it was designed. Models are never perfect because of the many difficulties faced by the researcher. The main factors which limit the capability of models to accurately mimic reality are as follows. Data limitations It is obviously impossible to incorporate a relevant variable into a quantitative model if you have no measurement of its values. If you have incomplete or only approximate values, then you will have to rely on guesswork to complete the information. Additionally, if you do not know in which ways the variables interact with each other, then your model will require much conjecture to complete it. However, it is easy to overstate the effects of insufficient information about the variables. If the purpose of the model is to study its behaviour rather than to predict future values of variables, the variables can be substantially changed (say 20% either way) without altering the behaviour significantly. Structural limits If incorrect assumptions are made about the relevance of the variables and the manner in which they interact, then the model will fail, sooner or later, to accurately reflect reality. For example, the early models of the solar system, devised on the basis of the earth at the centre, failed to represent the future motions of the planets accurately. Thus, it is wrong to assume, however closely the model reflects reality, that it is the ‘right one’. It is possible that the effects of other variables, not included in the model, could produce the same results. It is important to check predictions of the model against the field observations of the real situation. However, it is not necessary that the model be perfectly correct in order to be useful. Chaos It has long been assumed that if a system was accurately modelled, the behaviour of the model would closely reflect reality. Any small errors in the initial settings of the model (say of 1%) would produce only correspondingly small deviations in the results compared with reality (say 1–2%). It is now known that some sorts of systems do not behave in this way, and that tiny changes in the variables’ values can result in dramatic subsequent changes. These systems, which are infinitely sensitive to starting values, are called ‘chaotic’, not so much because they lack order but because they are unpredictable. Chaotic systems are virtually impossible to model in order to make long range predictions (as weather forecasters and investors in the stock market will be quick to tell you).