The article presents a new method for estimating individual differences in multidimensional scaling. Instead of fitting different parts of the model separately, the researchers developed a way to estimate all parameters at once using least squares principles. By applying matrix calculus, they found equations that give the best estimates for the model's parameters. This simultaneous approach can provide a good starting point for algorithms used in this type of analysis.