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[OBG] Sexual selection explains sex and country differences in fluid g

#41
I have changed the title of my paper to "Sexual selection as an evolutionary mechanism behind sex and population differences in fluid intelligence". I will delete the word IQ from my paper as it has created confusion and the wrong impression that crystallized tests could be used to test my hypothesis. What I hate the most about science is misunderstanding one's ideas, and I'd rather not publish my work if it means going through harsh rounds of rudeness and incomprehension. I will not respond to critiques leveled at my thesis on the grounds of not having adopted measures such as PISA reading (and perhaps math), or other bad proxies for fluid g (such as age heaping) because my hypothesis has nothing to do with these abilities.
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#42
(2014-Jul-28, 00:00:56)nooffensebut Wrote: "Here is what Wikipedia says..."

Notice that the source of this passage is just another Wikipedia page in another language. No matter. It looks like we agree to change "intersexual competition" to "intersexual selection." That is a much less confusing term.


Ok we agree on this. I've made the corrections you suggested and added a disclaimer at the end. New paper is attached.

P.S.: As I said in a previous post, my hypothesis regards fluid intelligence. This is why I changed the title and I replaced IQ with g or gf where appropriate, to avoid generating confusion in the reader.


Attached Files
.docx   Sexualselection1.docx.docx (Size: 24.04 KB / Downloads: 594)
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#43
(2014-Jul-28, 12:31:54)Duxide Wrote: You shouldn't threaten to not approve of my paper. Your approval or not does not make my thesis right or wrong.


As you are so hostile to my commentary -- despite having personally requested my input in the first place -- I will accept your invitation to cease reviewing.

Before moving on, I will note, though, that Lynn and Irwing's 2004 and 2005 Raven's matrices data also seemed not to show a (positive) r (national IQ x M/F difference). Moreover, while it
has been said that PIAAC's Problem Solving test is "more related to" fluid intelligence, this test also failed to show the predicted national IQ x M/F diff association. Granted these indexes are imperfect: Lynn's data is unrepresentative; the PIAAC PS N is only 18 to 19, depending on how cases are counted. But it's not as if you found strong support from PISA CPS in the first place.

In general, you haven't established your explanandum. I would start there.
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#44
(2014-Jul-29, 23:10:37)Chuck Wrote:
(2014-Jul-28, 12:31:54)Duxide Wrote: You shouldn't threaten to not approve of my paper. Your approval or not does not make my thesis right or wrong.


As you are so hostile to my commentary -- despite having personally requested my input in the first place -- I will accept your invitation to cease reviewing.

Before moving on, I will note, though, that Lynn and Irwing's 2004 and 2005 Raven's matrices data also seemed not to show a (positive) r (national IQ x M/F difference). Moreover, while it
has been said that PIAAC's Problem Solving test is "more related to" fluid intelligence, this test also failed to show the predicted national IQ x M/F diff association. Granted these indexes are imperfect: Lynn's data is unrepresentative; the PIAAC PS N is only 18 to 19, depending on how cases are counted. But it's not as if you found strong support from PISA CPS in the first place.

In general, you haven't established your explanandum. I would start there.


You totally misunderstood my thesis. It's also partly my fault because I used the word IQ when I should have used the word fluid intelligence or gf throughout the paper, but I made it clear in the introduction and methods section that CPS measures fluid intelligence, and I had already replied to previous comments from another user asking me why I had not used PISA math,science,reading explaining why I was not gonna use these and you ignored my explanation, either because you didn't read it or because you didn't want to consider them, although a reviewer should always check previous comments in the thread and it's rude to dismiss an author's explanation. My hypothesis is not about scholastic apititude, because wide access to schooling is a recent phenomenon and there has not been enough time for selection to act on such skills; also, these are affected by effort and motivation; they are not a good measure of fluid g,particularly when looking at sex differences because females outperform males on reading, whereas males outperform females on math; I could continue,etc.. The samples you cited in the last post are not representative. An analysis of sex differences requires nationally representative samples and not estimates based on a bunch of different studies.
I admit my analysis needs replication but this will only be possible in 2015 when new CPS data will be released. I want to provide a theoretical framework and predictions that will have to be tested again with new representative data. It would probably add useful data to my analysis if someone could point out to me studies on sex differences in fluid g among Blacks, Whites and East Asians, to check whether the racial pattern confirms the country-level pattern.
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#45
It would probably add useful data to my analysis if someone could point out to me studies on sex differences in fluid g among Blacks, Whites and East Asians, to check whether the racial pattern confirms the country-level pattern.
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#46
Also you said:

Quote:Gross Domestic Product (World Bank, 2014) and purchasing power per capita (GDP (PPP)) was used as an independent variable due to its potential relationship with g and sex differences in country scores. That is, GDP is known to be positively related to country IQ (Lynn and Vanhanen, 2006, 2012; Rindermann, 2012; Sailer, 2012) and could predict sex difference in g, possibly with more economically developed countries showing lower sex difference (lower male advantage) due to females enjoying more enriched environments, since rich countries probably undertake efforts to raise female schooling and intelligence in an attempt to make men and women more equal.

Controlling for GDP to control for sex equality is also questionable. The correlation between your GDP variable and the UN's gender equality index was only ~ 0.3 to 0.5 depending on how missing data was handled -- granted that when gender equality is partialed out, the national g x sex difference correlation is somewhat more robust.
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#47
(2014-Jul-30, 04:33:03)Chuck Wrote: Also you said:

Quote:Gross Domestic Product (World Bank, 2014) and purchasing power per capita (GDP (PPP)) was used as an independent variable due to its potential relationship with g and sex differences in country scores. That is, GDP is known to be positively related to country IQ (Lynn and Vanhanen, 2006, 2012; Rindermann, 2012; Sailer, 2012) and could predict sex difference in g, possibly with more economically developed countries showing lower sex difference (lower male advantage) due to females enjoying more enriched environments, since rich countries probably undertake efforts to raise female schooling and intelligence in an attempt to make men and women more equal.

Controlling for GDP to control for sex equality is also questionable. The correlation between your GDP variable and the UN's gender equality index was only ~ 0.3 to 0.5 depending on how missing data was handled -- granted that when gender equality is partialed out, the national g x sex difference correlation is somewhat more robust.


In general I prefer "hard" indexes based on real data (such as GDP), gender equality index is based on soft data and that is why I didn't use it. Nonetheless, I may include it in my analysis, although I doubt it would dramatically alter the results. Chuck, you seem pretty knowledgeable about racial data so you may have data on sex differences among different races? It'd be a good test of my hypothesis.
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#48
In the abstract, you type "dimorpshism" and in your table 1, you type "Shangai-China". These should be corrected.

Piffer Wrote:A note of caution regarding this finding is necessary, as phenotypic variation is correlated to genetic variation but environmental noise due to population stratification (SES, ethnicity, etc.) can easily attenuate the genetic signal.


Concerning that problem, in general, behavior geneticists partial out the "race" variable. To this purpose they can use some sort of fixed effect approach. See here for how to do that in Stata. Very straightforward.
http://www.stata.com/support/faqs/statis...d-effects/

As mentioned in the above link, you can use dummy variables. In SPSS, here's the procedure:
https://www.youtube.com/watch?v=R0qc4rzr9ik

In your case, for instance, you can create dummy for european, arab, asian, african countries. You can end up with perhaps 4 or more dummies, (e.g., race1, race2, race3, race4, etc., all coded 1 if the country corresponds to the race category label and 0 for otherwise), and you should include all of them in the regression equation. (If you do that, your program will probably drop one variable due to collinearity or redundancy, and the variable dropped is treated as the reference category.)

See below (I assume here that "race" variable has been created already and is a numeric variable for which the countries have been categorized into the race category to which they should belong, e.g. white countries=1, arab countries=2, asian countries=3 etc...; I assume here you have 6 race categories, but there may be more, or less, probably... so that if you have 4 races, you should remove all the numbers '5' and '6' in the series shown below) :

RECODE race (1=1) INTO race_cat0.
RECODE race (2=1) (3,4,5,6,1=0) INTO race_cat1.
RECODE race (3=1) (2,4,5,6,1=0) INTO race_cat2.
RECODE race (4=1) (2,3,5,6,1=0) INTO race_cat3.
RECODE race (5=1) (2,3,4,6,1=0) INTO race_cat4.
RECODE race (6=1) (2,3,4,5,1=0) INTO race_cat5.

REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT PISA_Score_Total
/METHOD=ENTER Difference GDP
/METHOD=ENTER race_cat1 to race_cat5
/PARTIALPLOT ALL
/SCATTERPLOT=(*ZRESID ,*ZPRED)
/RESIDUALS DURBIN HISTOGRAM(ZRESID) NORMPROB(ZRESID)
/CASEWISE PLOT(ZRESID) OUTLIERS(3)
/SAVE MAHAL COOK LEVER PRED RESID.

REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT PISA_Score_Total
/METHOD=ENTER Difference GDP
/METHOD=ENTER race2 race3 race4 race5 race6 race1
/PARTIALPLOT ALL
/SCATTERPLOT=(*ZRESID ,*ZPRED)
/RESIDUALS DURBIN HISTOGRAM(ZRESID) NORMPROB(ZRESID)
/CASEWISE PLOT(ZRESID) OUTLIERS(3)
/SAVE MAHAL COOK LEVER PRED RESID.

(edit: I forget to precise, those are two different ways to perform this kind of fixed-effect regressions but they produce identical results, at least for the coefficients of variables other than the dummies which have coefficients differing only in function to which category is the reference variable.) Normally in logistic regression, what happens is that race2 to race6 have coefficients expressed in relation to race1 (reference category because entered last) but the point of interest here should be how they affect the other indep variables. It seems as far as I see that it works differently in linear regression, I don't know why but if the other independent var are unchanged, it's not necessarily a problem.

Can you do this analysis ? If you think it's useful of course (personally I think it's useful otherwise i woudn't bother).

Besides, do you have more information about PISA (CPS) ? You say it's like fluid intelligence, but it still is an achievement test. Does it really looks like the fluid subtests in most IQ tests ? (specifically, I'm talking about the items/questionnaires.)
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#49
(2014-Jul-30, 19:49:41)menghu1001 Wrote: In the abstract, you type "dimorpshism" and in your table 1, you type "Shangai-China". These should be corrected.

Can you do this analysis ? If you think it's useful of course (personally I think it's useful otherwise i woudn't bother).

Besides, do you have more information about PISA ? You say it's like fluid intelligence, but it still is an achievement test. Does it really looks like the fluid subtests in most IQ tests ? (specifically, I'm talking about the items/questionnaires.)


I'll fix the errors.Regarding race, I think doing that analysis would create more problems than it solves. I've explained in the paper that PISA is not an achievement test, instead it taps into fluid intelligence...the explanation is in the Methods section. If that is not enough, you can find example items from the CPS here: http://www.keepeek.com/Digital-Asset-Man...-en#page37
If you are satisfied with the paper, you can give your opinion concerning approving or not publication.
It's not really an achievement test (and it was built with the specific aim of not testing achievement, in contrast to PISA reading,math and science).
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#50
It's difficult for me. For the moment, I'm in full reflexion with comments made by Peter Frost and more importantly, the one made by John Fuerst. For the latter, it seems to me that whether Gf or Gc is better measure of intelligence is not clear at all. See here: "Minimally biased g-loadings of crystallized and non-crystallized abilities" (the authors say that Gc and Gf are equally important but that Gf seems to have a slightly higher loading than Gc). Of course this can still be debated but that's not the problem here. Normally, Gc and Gf should be highly correlated. If you find a correlation using Gf, there should be one with Gc, although in (much?) smaller magnitude. The fact that John has reported a correlation of just zero... I don't know how to interpret this in light of your hypothesis. Maybe you should add some comments as to why your hypothesis does not expect any relationship with crystallized (or non-fluid) measures of intelligence. Alternatively, you may want to look at the data on gender difference (across races) on fluid g. Of course if I had those data, I will give you them, but...
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