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.