The measure of goodness of fit in multidimensional scaling is called Stress! It measures the difference between the observed (dis)similarity matrix e.g. reaction time between semantic pairs and the estimated one using one or more dimensions. The lower the stress the better the fit. Dimensions are similar to factors as they give the number of facets of the relationships between stimuli and may be plotted. Hair et al(1998) have rules of thumb for what values of stress make a good fit.
100 x Stress |
Goodness of Fit |
|
20% or above |
Very Poor (not worth doing) |
|
10%-19.9% |
Fair |
|
5%-9.9% |
Good |
|
2.5%-4.9% |
Excellent |
|
0%-2.4% |
Near Perfect Fit |
|