Surely for a given dataset there is an optimal transformation of the form
f(x) = (x + a)^b for reducing heterogeneity of variance (or skew or both),
where a is an offset equal to the minimum score. Does anyone know how the
optimal value of b can be found? This transformation would encompass the
reciprocal, square root and square transformations. Perhaps a more general
form could incorporate the log transformation. Should the degrees of freedom
be reduced when using such a method? If such a method exists why do people
use the "suck it and see" approach?
Dr Graham D. Smith
Psychology Division
School of Behavioural Studies
University College Northampton
Boughton Green Road
Northampton
NN2 7AL
Tel (01604) 735500 Ext 2393
Email [EMAIL PROTECTED]
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