2014-Oct-02, 04:28:11

I approve.

2014-Oct-02, 04:28:11

I approve.

Dear reviewers. A final revision.

https://osf.io/g2fsr/

I have added MCV analyses that I also used in the previous paper with international data. The results were similar (near unity). It is in a new section above the discussion. Please indicator whether this section is ok to you.

https://osf.io/g2fsr/

I have added MCV analyses that I also used in the previous paper with international data. The results were similar (near unity). It is in a new section above the discussion. Please indicator whether this section is ok to you.

2014-Oct-03, 22:26:31

Emil,

I'm having trouble understanding the new section, especially the first few sentences. It might be better to rewrite it as follows, but I'm not really sure:

Arthur Jensen invented the method of correlated vectors (MCV) to find out whether a variable correlates with the g factor (general cognitive ability) or with the remaining non-g variance.[22] The same method can be applied to other latent variables. I have previously used it to test whether various predictors (e.g. national IQ) correlate with the latent factor of international rankings (international S factor) or with the remaining variance.

I'm having trouble understanding the new section, especially the first few sentences. It might be better to rewrite it as follows, but I'm not really sure:

Arthur Jensen invented the method of correlated vectors (MCV) to find out whether a variable correlates with the g factor (general cognitive ability) or with the remaining non-g variance.[22] The same method can be applied to other latent variables. I have previously used it to test whether various predictors (e.g. national IQ) correlate with the latent factor of international rankings (international S factor) or with the remaining variance.

The thing is that it is not exactly checking for a correlation, but whether the latent trait is responsible for the observed correlation. For instance, when it is used on IQ gains over time (Flynn-Lynn effect), the effect is not found to be g-loaded (the subtests with the lowest g-loadings rise the most).

Here's a new version. The only change is in the MCV section (8). https://osf.io/g2fsr/ revision #6.

Here's a new version. The only change is in the MCV section (8). https://osf.io/g2fsr/ revision #6.

2014-Oct-04, 16:26:41

O.K. I would just change the last sentence of that paragraph to "The results strongly indicated that the S factor is responsible"

2014-Oct-04, 18:00:08

(2014-Oct-04, 13:26:57)Emil Wrote: The thing is that it is not exactly checking for a correlation, but whether the latent trait is responsible for the observed correlation. For instance, when it is used on IQ gains over time (Flynn-Lynn effect), the effect is not found to be g-loaded (the subtests with the lowest g-loadings rise the most).

Here's a new version. The only change is in the MCV section (8). https://osf.io/g2fsr/ revision #6.

This sentence is still confusing:

"Arthur Jensen invented the method of correlated vectors (MCV) in 1983 to find out whether the g factor (general cognitive ability) is responsible for correlations of cognitive test scores with a variable of interest (e.g. head size) or whether it is due to other factors.[22, 23, 24]"

How about:

"Arthur Jensen invented the method of correlated vectors (MCV) in 1983 to find out if the general factor of intelligence is responsible for mean differences in measures of intelligence [22, 23, 24]. Today, the method is mostly used with g (e.g. [25, 26, 27, 28]) and in context to mean differences, but it can also be used for any latent variable and in context to correlations between a predictor and criterion. To apply the method, one correlates the indicator variables’ loading on the latent variable of

interest (the vector of latent variable loading) with either the between group mean differences in the indicator or the relevant correlation between the indicator and criterion (the vector of difference). If the latent variable is ’driving’ the

association, then the correlation will be positive and strong.

"The standard deviation of loadings in the Norwegian and Danish datasets are .83 and .75, respectively, so range restriction does not appear to be a problem."

"In every case, the result is close to unity in the expected direction (Islam prevalence is negatively related to S factor scores, while the others are positively)."

Note: Could you just reverse the Islam sign as it's standard practice to report results such that a positive Jensen effect indicates that differences are greater on more general-factor loaded variables.

Thanks. On condition that you make the necessary alterations, I approve.

2014-Oct-04, 19:44:05

Here's a new draft. #7, https://osf.io/g2fsr/

(2014-Oct-04, 19:44:05)Emil Wrote: Here's a new draft. #7, https://osf.io/g2fsr/

This sentence still has trouble: "If the latent variable is ’driving’ the association and is positively correlated to the criteria variable, then the correlation will be positive, if it is the remaining variance, it will be negative, and if it is both or neither it will be near zero."

(It's both unclear and grammatically challenged.)

Maybe:

"If the general factor is ’driving’ the association and is positively correlated with the criteria variable, then the correlation between factor loadings and the effect sizes of the predictor -criterion associations will be positive. However, if the association is driven by the variance not attributable to the general factor, the correlation will generally be negative. And it will generally be somewhere in between if the association is driven by a mix of general and non-general factors."

2014-Oct-05, 01:08:29

I have replaced my sentence with Chuck's version above. Revision #8. https://osf.io/g2fsr/

2014-Oct-06, 19:35:06

(2014-Oct-05, 01:08:29)Emil Wrote: I have replaced my sentence with Chuck's version above. Revision #8. https://osf.io/g2fsr/

I approve this version. Who else does? For this latest version, we have:

Peter Frost

Chuck

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