Hi Kym,

as far as I know, simply dividing the large number by some number should work. This has to be considered however when estimating unstandardized coefficients.

Best,

Holger

Hello Kym,

Classical factoring algorithms start with a correlation matrix; Pearson correlations standardize all variables as if you had transformed them to z-scores at the outset (indeed, that is the definitional version of the formula for the Pearson product-moment correlation--the product of the first moments about the mean).

The only technical concerns with variables having wildly different scales are: (a) possible loss of computational accuracy due to needing lots of digits (most software packages are likely to use 15.5 digits of accuracy in the internal representation of real number values); and (b) as Holger Steinmetz implies, the interpretation of unstandardized coefficients if you rescale/transform scores.

Starting with LISREL, the trend for SEM programs was to use covariance (not correlation) matrices as the scale information is considered important (e.g., for unstandardized coefficients), as is location (so, means are important as well). However, if your interpretations are based on standardized coefficients (in my experience, most published studies in my areas are), then correlations are fine. Hence, rescaled values are fine.

In general, if a specific variable manifested a spread in my data set of many orders of magnitude, I'd consider rescaling/transforming almost automatically.

Good luck with your work!

Holger Steinmetz and David Morse Thank you both for your answers. That was very helpful. My gut was to scale by the a divisor equal to the max possible score, but wanted confirmation. Thanks again.