  Percentage coefficient (bp) has emerged in recent publications as an
additional and alternative estimator of effect size for regression analysis.
This paper retraces the theory behind the estimator. It's posited that an
estimator must first serve the fundamental function of enabling researchers and
readers to comprehend an estimand, the target of estimation. It may then serve
the instrumental function of enabling researchers and readers to compare two or
more estimands. Defined as the regression coefficient when dependent variable
(DV) and independent variable (IV) are both on conceptual 0-1 percentage
scales, percentage coefficients (bp) feature 1) clearly comprehendible
interpretation and 2) equitable scales for comparison. The coefficient (bp)
serves the two functions effectively and efficiently. It thus serves needs
unserved by other indicators, such as raw coefficient (bw) and standardized
beta.
  Another premise of the functionalist theory is that "effect" is not a
monolithic concept. Rather, it is a collection of concepts, each of which
measures a component of the conglomerate called "effect", thereby serving a
subfunction. Regression coefficient (b), for example, indicates the unit change
in DV associated with a one-unit increase in IV, thereby measuring one aspect
called unit effect, aka efficiency. Percentage coefficient (bp) indicates the
percentage change in DV associated with a whole scale increase in IV. It is not
meant to be an all-encompassing indicator of an all-encompassing concept, but
rather a comprehendible and comparable indicator of efficiency, a key aspect of
effect.
