1) I'd like to see more details of the factor/PC analyses. Were the sub-component intercorrelations explained by a single factor by the usual standards (e.g., only one factor with eigenvalue>1)? If not, how many other factors were there, can you give a substantive interpretation to them, and are they correlated with national IQ? KMO and Bartlett's test are pretty superfluous and aren't usually reported; I would mention them in a footnote only.

2) "individuals have a general socioeconomic factor"

This language is confusing. 'Factor' refers to a source of variance among individuals, so individuals cannot have factors. Individuals are located at different points on a factor, or have factor scores.

3) "national measures of country well-doing or well-being"

excise "well-doing"

4) "(see review national in [5]."

reword that

5) Figure 1 describes the structure of the SPI, why is there no corresponding figure describing the DP?

6) "principle components analyses", "principle axis factoring"

principal, not principle

7) "In some cases PCA can show a general factor where none exists (Jensen and Weng, 1994 [13]). For this reason, I compared the first factor extracted via PCA to the first factors using minimum residuals, weighted least squares, generalized least squares, principle axis factoring and maximum likelihood estimation"

I don't see how the similarity of factor loadings based on different extraction methods can tell us anything about the existence of a general factor. You don't say what a 'general factor' is, but I assume it means a factor with all-positive indicator loadings regardless of extraction method. There is no such factor in your data, as indicated by the many negative loadings listed in the Appendix. Even if you reverse coded the variables so that higher values on all variables would have positive valence (e.g, "Adequate nourishment" instead of "Undernourishment"), which I think would be a good thing to do, there'd still be negative loadings on the first factor/PC (e.g., suicide rate).

8) How did you compute the correlations between factor loadings? The congruence coefficient rather than Pearson's r should be used: http://en.wikipedia.org/wiki/Congruence_coefficient (Or did you use factor scores?)

9) Sections 4-6 have nice graphs, but I don't see their purpose. The fact that the correlation between two linear combinations of correlated elements gets higher the more there are shared elements is self-evident. The graphs might be of use if there was a practical need to estimate the S factor using only a limited number of components, but I don't see why anyone would want to do that.

I assume that the results in sections 4-6 are based on correlations of factor/component scores, but it's nowhere specified. Are the factors extracted with replacement?

Are the results from all the 54 components based on PCA? If so, the higher correlations with PCA components could be artefactual, due to common method variance.

10) MCV correlations of 0.99 are unusual in my experience. I'd like to see the scatter plots to ascertain that there's a linear association across the range. It's possible that the high correlations are due to outliers. What happens to the MCV correlations if you reverse score the variables with negative valence?

11) "The analyses carried out in this paper suggest that the S factor is not quite like g. Correlations between the first factor from different subsets did not reach unity, even when extracted from 10 non-overlapping randomly picked tests (mean r’s = .874 and .902)."

Your analysis is based on different sets of observed variables, while the g studies that found perfect or nearly perfect correlations were based on analyses of latent factors which contain no error or specific variance, or on analyses of the same data set with different methods. So the results aren't comparable.

12) The biggest problem in the paper is that it seems to be pretty pointless. Yes, most indicators of national well-being are highly correlated with each other and with national IQ, which means that the first PC from national well-being data must be highly correlated with national IQ, but so what? That was obvious from the outset.

What is the S factor? Do you believe it is a unitary causal factor influencing socioeconomic variables? That interpretation would give some meaning to the paper, but I think it's not a very promising idea. Socioeconomic status is normally thought of as a non-causal index reflecting the influence of various factors. Clark speaks of a "social competence" that is inherited across generations, but I don't think he views it as a unitary causal force but rather as a composite of different influences (such as IQ and personality).

I think national IQs and national socioeconomic indices are so thoroughly causally intermingled that attempting to say anything about causes and effects would require longitudinal data.

13) "It is worth noting that group-level correlations need not be the same or even in the same direction as individual-level correlations. In the case of suicide, there does appear to be a negative correlation at the individual level as well."

This means that the s and S factors aren't the same. Why is suicide an indicator of socioeconomic status anyway?

14) I couldn't find a correlation matrix of all the variables in the supplementary material. It would be useful (as an Excel file).

15) PCA and factor analysis are really different methods, and PCA shouldn't be called factor analysis.

2) "individuals have a general socioeconomic factor"

This language is confusing. 'Factor' refers to a source of variance among individuals, so individuals cannot have factors. Individuals are located at different points on a factor, or have factor scores.

3) "national measures of country well-doing or well-being"

excise "well-doing"

4) "(see review national in [5]."

reword that

5) Figure 1 describes the structure of the SPI, why is there no corresponding figure describing the DP?

6) "principle components analyses", "principle axis factoring"

principal, not principle

7) "In some cases PCA can show a general factor where none exists (Jensen and Weng, 1994 [13]). For this reason, I compared the first factor extracted via PCA to the first factors using minimum residuals, weighted least squares, generalized least squares, principle axis factoring and maximum likelihood estimation"

I don't see how the similarity of factor loadings based on different extraction methods can tell us anything about the existence of a general factor. You don't say what a 'general factor' is, but I assume it means a factor with all-positive indicator loadings regardless of extraction method. There is no such factor in your data, as indicated by the many negative loadings listed in the Appendix. Even if you reverse coded the variables so that higher values on all variables would have positive valence (e.g, "Adequate nourishment" instead of "Undernourishment"), which I think would be a good thing to do, there'd still be negative loadings on the first factor/PC (e.g., suicide rate).

8) How did you compute the correlations between factor loadings? The congruence coefficient rather than Pearson's r should be used: http://en.wikipedia.org/wiki/Congruence_coefficient (Or did you use factor scores?)

9) Sections 4-6 have nice graphs, but I don't see their purpose. The fact that the correlation between two linear combinations of correlated elements gets higher the more there are shared elements is self-evident. The graphs might be of use if there was a practical need to estimate the S factor using only a limited number of components, but I don't see why anyone would want to do that.

I assume that the results in sections 4-6 are based on correlations of factor/component scores, but it's nowhere specified. Are the factors extracted with replacement?

Are the results from all the 54 components based on PCA? If so, the higher correlations with PCA components could be artefactual, due to common method variance.

10) MCV correlations of 0.99 are unusual in my experience. I'd like to see the scatter plots to ascertain that there's a linear association across the range. It's possible that the high correlations are due to outliers. What happens to the MCV correlations if you reverse score the variables with negative valence?

11) "The analyses carried out in this paper suggest that the S factor is not quite like g. Correlations between the first factor from different subsets did not reach unity, even when extracted from 10 non-overlapping randomly picked tests (mean r’s = .874 and .902)."

Your analysis is based on different sets of observed variables, while the g studies that found perfect or nearly perfect correlations were based on analyses of latent factors which contain no error or specific variance, or on analyses of the same data set with different methods. So the results aren't comparable.

12) The biggest problem in the paper is that it seems to be pretty pointless. Yes, most indicators of national well-being are highly correlated with each other and with national IQ, which means that the first PC from national well-being data must be highly correlated with national IQ, but so what? That was obvious from the outset.

What is the S factor? Do you believe it is a unitary causal factor influencing socioeconomic variables? That interpretation would give some meaning to the paper, but I think it's not a very promising idea. Socioeconomic status is normally thought of as a non-causal index reflecting the influence of various factors. Clark speaks of a "social competence" that is inherited across generations, but I don't think he views it as a unitary causal force but rather as a composite of different influences (such as IQ and personality).

I think national IQs and national socioeconomic indices are so thoroughly causally intermingled that attempting to say anything about causes and effects would require longitudinal data.

13) "It is worth noting that group-level correlations need not be the same or even in the same direction as individual-level correlations. In the case of suicide, there does appear to be a negative correlation at the individual level as well."

This means that the s and S factors aren't the same. Why is suicide an indicator of socioeconomic status anyway?

14) I couldn't find a correlation matrix of all the variables in the supplementary material. It would be useful (as an Excel file).

15) PCA and factor analysis are really different methods, and PCA shouldn't be called factor analysis.