Thanks for the opportunity to review this interesting methodological, brief-report communication. I think it has significant value both as a methodological refresher for seaoned investigators as well as a novel pedagogic utility for junior scientists, particular in fields where relative magnitudes of predictors or indicator variables often break along biological (genetic) and environmental (social) conceptual and empirical lines. Below I make a few suggestions that I think will sharpen the manuscript.
- Most broadly, the piece gives the impression that the author was motivated by attention to a particular literature--namely the BG literature--when crafting it. Heritabilities feature prominently as pedagogic examples (albeit not exclusively--i.e.,the Big Five/social-personality literature) and it occurred to me that perhaps drawing out more sharply the author's motivation for the piece would contextualize and provide increased sharpness to the cogent points made in the piece.
- 2nd paragraph, 3rd sentence reads improperly. I believe "when" should be replaced with "while" to clarify the intended meaning of the sentence.
-A more general issue is whether this confusion is truly as widespread as the author implies. I don't recollect comparisons in social sciences--or at least the work I have done and am most familar with--using R^2 to evalate relative comparisons of predictors or indicator components; Rather, comparisons (again that I am most familar with) have always used standardized beta to rival predictors. This may be a feature of the work under my purview, but I also would note that standardized betas are produced as default in SPSS MLR output (along with the model ANOVA which will provide variance components and R^2 etc.). Related: in what way does this piece move beyond/advance information (or serve as an anciliary to) that can be gleaned in Cohen's (1988-2nd edition) treatise? A sentence or two addressing these points would be useful, I think.
-Some clarification on the author's meaninging of standardized effect ratio as it relates to scenarios *across different outcomes* would be useful. This is becuase in the case of disparate outcomes, effects in MLR (and SEM) will often be standardized as a function of different Y outcome variances, so unless the two 'standardized' variables being compared share the same Y (i.e., within model), the meaning may be more or less informative
-On p. 3, the reciprocal of the effect ratio (as a unit of interpretation) should be introduced in the 1st full paragraph (first didactic example) rather than the 2nd full paragraph as this way of interpretating the relationship may be more intuitive to a given reader. Its mention can be retained in the 3rd paragraph (second didactic example) for consistency.
- Related to the point above: It may be useful to draw a juxtaposition with the IRT literature to further clarify. In IRT, reliability can be computed as 1 - the squared reciprocal of the square root of the information value for scores in a specified latent trait range; the reciprocal of the of the square root of the information value provides an estimate of the standard error of latent trait ability measurement in the specified latent trait range, which, when squared, provides an index of error variance in the same latent trait range.
I say this as to my surprise, in some of the pedagogic examples in the piece, 1 - the squared reciprocal does approximately yield the discussed effect ratio percentage difference noted (e.g., the Polderman et al. example and the criminality and substance abuse example), whereas in others it does not (e.g., the following 50/5 heritable, shared environment example). The distinction would be pedagogically useful, I think, to show how similar ways of parsing data can be used to calculate different metrics that share some underlying formal operations (also since CTT reliability is discussed in the piece). Of course, one may also take the view it complicates the focal issue, but a brief reaction to the suggestion would be informative nonetheless.
One suggestion more generally is to include a mock-up of a bivariate data table demonstrating that the sqrt of variance ratio is equivalent to the beta ratio for a given pair of values.