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[ODP] The Elusive X-Factor: A Critique of J. M. Kaplan’s Model of Race and IQ

#41
Here's the latest version. I fixed the problems with alphabetical order and how Jensen's g x race meta-analysis was described and added a reference to te Nijenhuis's new study about g x h2 correlations. I also cite Rietvald's and Ward's studies at the very end of the paper, although I still think mentioning them is unnecessary.


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.pdf   The Elusive X-Factor A Critique of J. M. Kaplan’s Model of Race and IQ.pdf (Size: 519.29 KB / Downloads: 437)
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#42
I accept your reason for not including my study only on the grounds that you're not familiar with population genetics. Otherwise, I think it's very relevant to your paper and for refuting Kaplan's argument.

Apart from this, I approve publication
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#43
This ups the approval count to 3. Perhaps Meng Hu will agree to publication too?
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#44
(2014-Aug-19, 08:38:01)Emil Wrote: This ups the approval count to 3. Perhaps Meng Hu will agree to publication too?


I wanted to look at your study first because it came before Dalliard's, but what you're trying to do is becoming more and more complicated. Perhaps I should deal with Dalliard first. Do the simplest thing first. (Sorry about that, you have to wait a little bit for your paper...)

(2014-Aug-18, 09:50:28)Chuck Wrote: 1. Environmental effects such as schooling tend to be most pronounced on the least g-loaded sub-tests.
2. The B/W gap shows the reverse pattern.
Ergo: The B/W gap is not due to these types of effects.


Flynn dealt with that argument already. He computes correlation with MCV, then he calculates the g-score by summing the subtests weighted by their g-loadings. He compares the g-score with the IQ score (the mere sum of the subtests). He concludes that the difference in g-score and IQ score is minor with regard to FE gains and B-W IQ gap narrowing. You see this in Dickens & Flynn (2006) study of B-W IQ gap over time. That means the "g argument" is flawed. And it is true. MCV correlations imply that when the g-loading of the test increases, the BW gap is stronger, the educational gain is lower, the Flynn gain is lower, and so on. But we know already that the IQ tests are already very highly g-loaded. That means you can't increase g-loadings by much anymore. From Jensen's (1998) The g Factor, there is a (bivariate) regression analysis that almost no one has ever cited. It's on page 377. The dependent var is B-W gap, the independent var is g-loadings. The intercept was -.163. Remember, the intercept is the value of the dependent var when all independent var is(are) zero. In other words, the B-W gap is negative, i.e., in favor of blacks when g-loading is zero (assuming linearity assumption holds, that is, there is no floor or ceiling effects). Now the regression slope seems to be 1.47. So, 1.47-0.16=1.31. To which he concludes that when the g-loading of the IQ tests are at their maximum (100%), the expected B-W gap should be 1.31 SD difference, compared to what we see today, mostly around 1.0 or 1.1 SD. What does it tell us ? That 1.1 SD is less real than 1.3 SD ? Of course not. Or that increasing the amount of g-loading makes lot of difference ? Not even so. And that's what Flynn attempted to show in his book "Where Have All the Liberals Gone? Race, Class, and Ideals in America" page 87 box 14. There is an apparent negative correlation between IQ gap of blacks in 2002 versus whites in 1947-48 and the g-loadings. The IQ of blacks was 104.42 and their GQ was 103.53, which is lower, thus confirming MCV but at the same time killing the "g argument" you both make. This can be seen by the trivial (1 point) difference between IQ and GQ. This confusion concerning the idea that g and Flynn gain have different properties just because they load on different factors, through PCA, is similar to what I have pointed it elsewhere about the distinction we should make between correlation and means. If PCA "group" the variables and show you a pattern on which education/FE gains is not on the component with g-loading but on the other hand heritability and B-W gap load on the component along with g-loadings, it cannot prove that educ/FE gains are unreal gains. Back to Jensen's (1998) regression analysis, if the best we can have is to widen the gap by a mere 0.2 SD, this is a pretty weak argument.

If you want to show Flynn gain or educational gain to be devoid of g, there is only and only one way to do that : by way of transfer effect. Such as the Nettelbeck & Wilson (2004) or the Ritchie et al. (2012) for non-transferability of education gain to reaction times. Every other methods are flawed in their purpose of showing if the score change is g or not g. Even the MGCFA decomposition of g/non-g gains is irrelevant here.

As for the B-W gap, there is nothing I can say. If you're not looking for any pattern of score changes, it's clear that transfer effect studies can't be of help. At least, you can rely on MGCFA g/non-g decomposition along with its model fit.

Quote:Also, I strongly disagree with his characterization of the evidential status of SH. I consider SH to be well supported.

Then, I suppose you disagree with the fact that a g model ought to be preferred over non-g model(s) only on the basis of better model fit. In social sciences it is a well known fact that a model is to be preferred when and only when the model in question fits better than others. If you're not familiar with that, I can tell you a lot of scientists are not aware of that either. Or maybe they are, but they don't show it in their work. In economics in particular, I have read econometricians saying that lot of studies attempt to test a particular model but they don't oppose the studied model against the alternative ones. They say that if other models can predict the data as well, it's not obvious which one is the loser or the winner.

Based on that, I remain with my argument. There is no clear winner or no loser among g and non-g models. You only see g to be winner because you put more weight on the worst methodologies (MCV and PCA) but not on the best and recommended methods (CFA modeling). That' why I said to Dalliard earlier that the evidence for g is positive but only weak.

Quote:Also, I explained that different models in fact can be tested with MCV here.

Model testing should involve "model fit indices" but it's not what you did.

-----
-----

Concerning Piffer's method, I don't understand why some of you here reject it without giving any argumentation whatsoever. Just because it's not "vetted" does not mean the method is flawed. To prove it flawed, you should explain what's wrong in there. I always disliked argument from authority, and you know that.

By the way, you keep saying "Rietvald" but it's "Rietveld" ! There is no mispelling in the name among the list of references given the last version of the paper (Perhaps precise at the top of the first page it's a DRAFT) but there is a mispelling at page 35.

Also, when someone makes changes, especially if the article is lengthy, try to make explicit which pages have been modified, changed, or made them in color, or whatever. I don't want to re-read the entire article again.

And I'm not even sure what's being changed here. From what Dalliard says, he has added several studies (even though I don't see Ang et al. 2010). I found these passages already (CTRL+SHIFT+F is helpful sometimes) but what about my comments on measurement invariance and g models ? I want to be sure about what the author think of this issue, and he is planning to rewrite the relevant passages according to my comments, before I give my final opinion.
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#45
Quote:Flynn dealt with that argument already. He computes correlation with MCV, then he calculates the g-score by summing the subtests weighted by their g-loadings. He compares the g-score with the IQ score (the mere sum of the subtests)....If you want to show Flynn gain or educational gain to be devoid of g, there is only and only one way to do that : by way of transfer effect.

The argument doesn't deal with the magnitude of true score differences. It simply concerns itself with the pattern of effects on subtests. Thus Flynn's argument is irrelevant. Which premise, specially, do you disagree with:

1. Environmental effects such as schooling tend to be most pronounced on the least g-loaded sub-tests.
2. The B/W gap shows the reverse pattern.
Ergo: The B/W gap is not due to these types of effects.

Quote:Then, I suppose you disagree with the fact that a g model ought to be preferred over non-g model(s) only on the basis of better model fit. ]In social sciences it is a well known fact that a model is to be preferred when and only when the model in question fits better than others.

This sounds equivocal. Based on the totality of the data, Spearman's hypothesis should be preferred to the alternative(s). The MGCFA results are inconclusive; they don't speak for or against SH. Thus, one looks at other lines of evidence, which do speak for SH. So, yes you would be agnostic about SH based on MGCFA, but you would prefer it based on the totality of the evidence.

Quote:Based on that, I remain with my argument. There is no clear winner or no loser among g and non-g models. You only see g to be winner because you put more weight on the worst methodologies (MCV and PCA) but not on the best and recommended methods (CFA modeling).

Ok, let's weight the lines of evidence:

(-1 = against, 0 = neutral, 1 = for)

MGCFA x 50 = 0
Everything else x 50 = 1
......
evidential weight > 0

When I give MGCFA equal weight, SH is favored. If I double the weight, SH is still favored. If I triple the weight....

Quote:Model testing should involve "model fit indices" but it's not what you did.

Get out of here.

Quote:Concerning Piffer's method, I don't understand why some of you here reject it without giving any argumentation whatsoever. Just because it's not "vetted" does not mean the method is flawed. To prove it flawed, you should explain what's wrong in there. I always disliked argument from authority, and you know that.

A method not being well tested is a "flaw" when it comes to quality of evidence. The results are more uncertain because the method is.
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#46
(2014-Aug-20, 02:06:01)menghu1001 Wrote: Concerning Piffer's method, I don't understand why some of you here reject it without giving any argumentation whatsoever. Just because it's not "vetted" does not mean the method is flawed. To prove it flawed, you should explain what's wrong in there. I always disliked argument from authority, and you know that.


My papers have been published on peer reviewed journals (Mankind Quarterly, OBG, Interdisciplinary Bio Central) so they have been "vetted" by experts. All Chuck is doing is bringing discredit to the reviewers of OpenPsych in general (and as a reflection to himself) because my paper (Opposite selection pressures...) has been approved for publication on OpenPsych forum. This has made me doubt that he's qualified to be a reviewer for OBG, since obviously he doesn't consider himself expert enough in genetics to be able to evaluate my work on his own (but he's got to rely on the opinion of a team of people - the "signatories"- that he has consulted via email).
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#47
(2014-Aug-20, 02:06:01)menghu1001 Wrote: Flynn dealt with that argument already. He computes correlation with MCV, then he calculates the g-score by summing the subtests weighted by their g-loadings. He compares the g-score with the IQ score (the mere sum of the subtests). He concludes that the difference in g-score and IQ score is minor with regard to FE gains and B-W IQ gap narrowing. You see this in Dickens & Flynn (2006) study of B-W IQ gap over time. That means the "g argument" is flawed. And it is true. MCV correlations imply that when the g-loading of the test increases, the BW gap is stronger, the educational gain is lower, the Flynn gain is lower, and so on. But we know already that the IQ tests are already very highly g-loaded. That means you can't increase g-loadings by much anymore. From Jensen's (1998) The g Factor, there is a (bivariate) regression analysis that almost no one has ever cited. It's on page 377. The dependent var is B-W gap, the independent var is g-loadings. The intercept was -.163. Remember, the intercept is the value of the dependent var when all independent var is(are) zero. In other words, the B-W gap is negative, i.e., in favor of blacks when g-loading is zero (assuming linearity assumption holds, that is, there is no floor or ceiling effects). Now the regression slope seems to be 1.47. So, 1.47-0.16=1.31. To which he concludes that when the g-loading of the IQ tests are at their maximum (100%), the expected B-W gap should be 1.31 SD difference, compared to what we see today, mostly around 1.0 or 1.1 SD. What does it tell us ? That 1.1 SD is less real than 1.3 SD ? Of course not. Or that increasing the amount of g-loading makes lot of difference ? Not even so. And that's what Flynn attempted to show in his book "Where Have All the Liberals Gone? Race, Class, and Ideals in America" page 87 box 14. There is an apparent negative correlation between IQ gap of blacks in 2002 versus whites in 1947-48 and the g-loadings. The IQ of blacks was 104.42 and their GQ was 103.53, which is lower, thus confirming MCV but at the same time killing the "g argument" you both make. This can be seen by the trivial (1 point) difference between IQ and GQ. This confusion concerning the idea that g and Flynn gain have different properties just because they load on different factors, through PCA, is similar to what I have pointed it elsewhere about the distinction we should make between correlation and means. If PCA "group" the variables and show you a pattern on which education/FE gains is not on the component with g-loading but on the other hand heritability and B-W gap load on the component along with g-loadings, it cannot prove that educ/FE gains are unreal gains. Back to Jensen's (1998) regression analysis, if the best we can have is to widen the gap by a mere 0.2 SD, this is a pretty weak argument.

If you want to show Flynn gain or educational gain to be devoid of g, there is only and only one way to do that : by way of transfer effect. Such as the Nettelbeck & Wilson (2004) or the Ritchie et al. (2012) for non-transferability of education gain to reaction times. Every other methods are flawed in their purpose of showing if the score change is g or not g. Even the MGCFA decomposition of g/non-g gains is irrelevant here.

As for the B-W gap, there is nothing I can say. If you're not looking for any pattern of score changes, it's clear that transfer effect studies can't be of help. At least, you can rely on MGCFA g/non-g decomposition along with its model fit.


I have been thinking about this. I think Flynn is wrong here, not the MCV folks. Here's why. Look at the Nijenhuis test-retest training paper. The MCV gives gives close to -1, indicating no gain in g. I think we can safely postulate theoretically too, that there is no gain in general intelligence from taking the same test twice (and we have tested this with transfer tests too). However, using Flynn's method would show gains. Absurd conclusion, so his method must be flawed.

Transfer testing is the best test of whether a real change in GCA has occurred. MCV is a useful test because it does not require a new study, but it can be wrong. I was going to write up a small communication paper about this.
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#48
(2014-Aug-20, 09:21:48)Duxide Wrote:
(2014-Aug-20, 02:06:01)menghu1001 Wrote: Concerning Piffer's method, I don't understand why some of you here reject it without giving any argumentation whatsoever. Just because it's not "vetted" does not mean the method is flawed. To prove it flawed, you should explain what's wrong in there. I always disliked argument from authority, and you know that.


My papers have been published on peer reviewed journals (Mankind Quarterly, OBG, Interdisciplinary Bio Central) so they have been "vetted" by experts. All Chuck is doing is bringing discredit to the reviewers of OpenPsych in general (and as a reflection to himself) because my paper (Opposite selection pressures...) has been approved for publication on OpenPsych forum. This has made me doubt that he's qualified to be a reviewer for OBG, since obviously he doesn't consider himself expert enough in genetics to be able to evaluate my work on his own (but he's got to rely on the opinion of a team of people - the "signatories"- that he has consulted via email).


You know quite well that these journals are not top journals. Vetting would require examination of top experts, none of which are found on any of those three journals. If you want it vetted (this is a good idea), then I think you should perhaps contact some authors yourself.

For practical reasons, it is perhaps best to ask them only about the method based on the height data, since there are few feels involved in that. If they concur that it works well for height, we can be pretty sure it works well for any kind of highly polygenic trait.

PS. You really ought to stop the personal stuff in the forums. Stuff like that belongs in the private or comment section on the DailyMail.
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#49
Another version of the article is attached. It's the same as the previous one except that I added another variable (Positive affect) to Table 1, based on my analysis of Add Health data. This change has no substantial effect on any of my conclusions. I also fixed some language issues (e.g., Rietveld's name).

(2014-Aug-20, 02:06:01)menghu1001 Wrote: Then, I suppose you disagree with the fact that a g model ought to be preferred over non-g model(s) only on the basis of better model fit. In social sciences it is a well known fact that a model is to be preferred when and only when the model in question fits better than others.


No such thing is "well known" in social science. Model fit is only one piece of evidence, to be judged against the backdrop of other evidence. If the fit of two models is similar, then you use other evidence to choose between them.

As they say, nothing is as practical as a good theory. That's the point of my basing my arguments on the g model: it is the only theory that makes sense of the mess of evidence in this area. What you seem to advocating is blind, theory-free data analysis where you fetishize fit indices and ignore everything else. You won't get anywhere that way.

Quote:but what about my comments on measurement invariance and g models ? I want to be sure about what the author think of this issue, and he is planning to rewrite the relevant passages according to my comments, before I give my final opinion.

I'm not going to address them as I don't think they challenge my arguments in any way. Perhaps instead of these walls of text you could formulate your criticisms as syllogisms as Chuck does above, so I could discern what you are actually protesting against.


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#50
(2014-Aug-20, 04:26:13)Chuck Wrote: The argument doesn't deal with the magnitude of true score differences. It simply concerns itself with the pattern of effects on subtests. Thus Flynn's argument is irrelevant.


That's why I had the impression you misunderstand the implication of a correlation between g-loadings and score differences. A positive r does not make the score difference real, and a negative r does not make it less real or unreal. You shouldn't stop at the correlation. You must go further. What you must calculate is the impact of these correlational patterns on the score difference. A correlation is really abstract unless you can derive an effect size from it. As I said, Jensen's bivariate regression tells you why and where you're wrong. The B-W gap actually turns around 1 SD, and the expected B-W gap is 1.3 SD for g-loading at its maximum. Thus, increasing the g-loading simply adds a modest score difference.

If you want to prove Flynn effect has zero gain on g, you should better use (bivariate) regression and look at the magnitude of the cohort difference when the g-loading peaks at 100%.

(2014-Aug-20, 04:26:13)Chuck Wrote: This sounds equivocal. Based on the totality of the data, Spearman's hypothesis should be preferred to the alternative(s).


No. You have to distinguish between good and bad methods, between exploratory and confirmatory studies. I prefer the last ones. But most of the studies you cite are exploratory. Look at Dolan (2000) and you'll see there is no strong evidence of SH, just a meager one. I do not say MCV is bad. When you have large data points and combine with it meta-analysis and corrections, it's an acceptable one, but not like MGCFA. What is bad with MCV is that you do not test g-model against first-factor latent model. This is unsatisfactory because we know that below the level of 2nd-order g, you have some first-order factors. And yet, I don't see how you can test it with MCV. It seems to me that the "models" within MCV are extremely ill-specified. g model is not made explicit and the first order factors (below g) are totally absent. Given the absence of the latter, how is it possible to test g versus non-g models with MCV ? That is why I remain convinced you have not provided a good defense for MCV. And no one here did. I honestly think it not possible to defend MCV anymore. I once believed it was a good method, but the more I read Dolan studies, the more that belief has been shaken. MCV is a method you should use when you do not have any other things.

(2014-Aug-20, 11:14:41)Emil Wrote: I have been thinking about this. I think Flynn is wrong here, not the MCV folks. Here's why. Look at the Nijenhuis test-retest training paper. The MCV gives gives close to -1, indicating no gain in g. I think we can safely postulate theoretically too, that there is no gain in general intelligence from taking the same test twice (and we have tested this with transfer tests too). However, using Flynn's method would show gains. Absurd conclusion, so his method must be flawed.

Transfer testing is the best test of whether a real change in GCA has occurred. MCV is a useful test because it does not require a new study, but it can be wrong. I was going to write up a small communication paper about this.


That correlation of -1 was from white-white comparison if I'm not mistaken, whereas Flynn (2008) compared blacks and whites. Also, given his page 87, I calculate the correlation between g-loadings and black(2002)-white(1947) gap and it was -0.537. Yet, the difference IQ-GQ was 1 point. Honestly, I don't think a correlation of 100% will make it very different. But it's not clear to me what Flynn wanted to show, I admit. He did (with Dickens) a better job in his (2006) meta-analysis. See the link below, table 2.
http://www.brookings.edu/views/papers/di...619_iq.pdf

You have 3 columns of g gains :

1.17, 2.57, 4.67

You have 3 columns of IQ gains :

1.20, 2.82, 4.93

And the corresponding r(g-loadings*gains) :

-0.28, -0.73, -0.38

Look closely at that big value of -0.73. And yet, the difference between 2.57 (GQ) and 2.82 (IQ) is meaningless. What will be the value of the g gains when the correlation hit 100% ? Will it become zero g gains ? I don't think so. Then, what about the te Nijenhuis (2007) correlation of -1.00 ? I can't make sense of it. I don't think we can prove there is zero gain with that analysis. The Nettelbeck & Wilson (2004) study by way of comparison suffers no doubt about that, except its small sample size.

Also, te Nijenhuis has conducted other subsequent analysis after the (2007) meta-analysis, right ? I remember the 2013 study "Is the Flynn effect on g?: A meta-analysis". A fairly large sample, but a negative corr of -0.38.

Furthermore, Flynn's method is similar in its logic to the Jensen's bivariate regression. And I accept both, because, as I said to John, you should not stop at the correlation, but you must translate it into score difference, d gap, etc. That's what Jensen and Flynn did, using different methods. I think they can be complementary.

Dalliard Wrote:No such thing is "well known" in social science. Model fit is only one piece of evidence, to be judged against the backdrop of other evidence. If the fit of two models is similar, then you use other evidence to choose between them.


When you have difficulties choosing between models, you have to look elsewhere, I agree with this. But if the other evidence come from bad methods, you should understand that the evidence in favor of SH is far from being definitive, but only "encouraging" and "suggestive" for SH. I can change my mind if you or John or Emil or someone else here can answer my objection, which I quote here :

Quote:What is bad with MCV is that you do not test g-model against first-factor latent model. This is unsatisfactory because we know that below the level of 2nd-order g, you have some first-order factors. And yet, I don't see how you can test it with MCV. It seems to me that the "models" within MCV are extremely ill-specified. g model is not made explicit and the first order factors (below g) are totally absent. Given the absence of the latter, how is it possible to test g versus non-g models with MCV ?

Trust me. If you beat me on this, I will accept the argument that when MGCFA fails badly we should look at alternative methods such as MCV. And I will remove what I said about MCV. For real.
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