This paper examines scaling via conjoint measurement as an alternative to the usual multiple linear regression in the "bootstrapping" model. The research was based on synthetic data from a Monte Carlo simulation. It was found that, for data that cannot be presumed to be at least interval scale measures, scaling is superior to multiple linear regression as a method of approximating past data. In addition, it was found that assuming data to be ordinal measures when the data are actually interval scale measures is a much less severe error than is the error of assuming data to be interval scale measures when the data are actually only ordinal measures.
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