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[ODP]Reassessment of Jewish Cognitive Ability:

#1
Hello, I would like to submit the attached manuscript for review. Thank you, Curtis Dunkel


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#2
Quote:The results suggest that parental fluency in Hebrew or Yiddish is a valid measure Jewish within group differences and further research using the measure and the Project Talent data file is proscribed.

Proscribed means "forbid, especially by law.", but that doesn't seem right. Is the author suggesting that no further analyses should be made based on these data? Perhaps you meant "prescribed"? :)

The cognitive profile of Jews is odd. They are basically like smart women with their low spatial ability. Even politically correct, generally anti-hereditarian Richard Nisbett thinks it is due to genetics (see: Nisbett 2009: Footnote 173):

Quote:Before leaving the topic of Jewish IQ, I should note that there is an anomaly concerning Jewish intelligence. The major random samples of Americans having large numbers of Jewish participants show that whereas verbal and mathematical IQ run 10 to 15 points above the non-Jewish average, scores on tests requiring spatial-relations ability (ability to mentally manipulate objects in two- and three-dimensional space) are about 10 points below the non-Jewish average (Flynn, 1991a) . This is an absolutely enormous discrepancy and I know of no ethnic group that comes close to having this 20 to 25-point difference among Jews. I do not for a minute doubt that the discrepancy is real. I know half a dozen Jews who are at the top of their fields who are as likely to turn in the wrong direction as in the right direction when leaving a restaurant. The single ethnic difference that I believe is likely to have a genetic basis is the relative Jewish incapacity for spatial reasoning. I have no theory about why this should be the case, but I note that it casts an interesting light on the Jews' wandering in the desert for forty years!

My girlfriend suggested that it is mediated by a different testosterone level, assuming that testosterone boosts spatial ability, which seems plausible.

The evidence is not so strong it seems. There appears to be a nonlinear relationship. Some cites:

Silverman, Irwin, et al. "Testosterone levels and spatial ability in men." Psychoneuroendocrinology 24.8 (1999): 813-822.

Gouchie, Catherine, and Doreen Kimura. "The relationship between testosterone levels and cognitive ability patterns." Psychoneuroendocrinology 16.4 (1991): 323-334.

Shute, Valerie J., et al. "The relationship between androgen levels and human spatial abilities." Bulletin of the Psychonomic Society 21.6 (1983): 465-468.

Quote:Sixteen tests of cognitive ability were administered to participants. The scores from the full base year sample on the sixteen tests were submitted to an Exploratory Factor Analysis using Principal Axis Factoring. The first unrotated factor, with an Eigenvalue of 7.71 and accounting for 48.19% of the variance among scales, was used to compute g. The individual tests with their factor loadings are as follows: Abstract Reasoning (.70), Advanced Math (.52), Arithmetic Reasoning (.78), Creativity (.72), Disguised Words (.66), English Total (.78), High School Math (.77), Information (.88), Mechanical Reasoning (.65), Memory for Sentences (.33), Memory for Words (.54), Reading Comprehension (.86), Visualization in Two Dimensions (.49), Visualization in Three Dimensions (.61), Vocabulary (.85), Word Functions in Sentences (.72).

A table would better show these data.

Quote:If one assumes that the data is interval, the correlation between parental fluency in Hebrew or Yiddish and g was, r (10,578) = .10.

Can you calculate the average ability levels of the children by their parents fluency levels? This way the reader will know how large r=.10 is in IQ points (µ=100, SD=15).

Quote:Table 4 presents the scores on individual cognitive tests by White Jew and White gentile and myopic or not myopic. For both Jews and gentiles the pattern is the same. Myopia was associated with higher scores with the exceptions of mechanical reasoning and two-dimensional visualization.

This would better be presented visually. One could do the same with all the tables.

I don't have any particular objections to publication, just the above comments. It's a fine paper.

The data are public yes? They need to be linked to or attached before the paper can be published as this journal has mandatory data sharing.
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#3
I believe "proscribed" can also mean "commanded".

When conducting MCV, you should correct for artifacts such as unreliability of the vectors (te Nijenhuis et al., 2014 have a full list of such corrections).

Also, given the lower spatial ability amongst Jewish populations, one would expect spatial tests to have higher g-loadings (Michael Woodley pointed this out in his review of te Nijenhuis et al., 2014). An analysis should be conducted to confirm or disconfirm this.
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#4
(2014-May-02, 19:36:29)Philbrick Bastinado Wrote: When conducting MCV, you should correct for artifacts such as unreliability of the vectors (te Nijenhuis et al., 2014 have a full list of such corrections).

Also, given the lower spatial ability amongst Jewish populations, one would expect spatial tests to have higher g-loadings (Michael Woodley pointed this out in his review of te Nijenhuis et al., 2014). An analysis should be conducted to confirm or disconfirm this.


For this to be possible however, it must have IQ data at several waves. Then he can correlate the vectors and estimate the reliability. I'm sure, given the description, that this set of tests is not common.

I don't think it will change the result however. As he hypothesized. The positive correlation was only effective in Hebrew/Yiddish language, not the others where you have negative r, and in one group, you have null r. Even correction for vector unreliability would not change the null r for that group. Nearly all of the correlation are very high, i.e., correction for the listed artifacts by Schmidt/Hunter would increase the r but by not much.

I'm more concerned, however, about the subtest reliability. Is there no data on subtest reliability for that battery ? Jensen (1998) always recommends doing this, because positive correlation between g-loadings and other variables of interest can be "faked" by differential subtest reliability. For example, if you have 6 tests with rtt=0.80 and 6 others with rtt=0.60, you might obtain incorrect effect sizes (i.e., correlations) because g-loadings are stronger for more reliable subtests. In general, however, IQ tests are highly reliable, and their subtests too. And their reliability don't differ that much across subtests. So, hypothetically, you can assume you won't have any troubles with MCV. But having the subtest reliability correction done is better than not.

Is it possible to conduct such analysis ? If not, I would like to validate the publication of this study. I believe it really worths it. (although I recommend to add a little note about the impossibility of doing subtest reliability correction, assuming you can't do it of course)

P.S. (If you can do and plan on doing subtest reliability correction, there are 2 ways to do it, either by dividing each column by SQRT of the reliability, or by correlating the two variables with subtest reliability partialed out, using the method of partial correlation. Jensen seems to believe the last method is better.)
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#5
(2014-May-04, 05:19:25)menghu1001 Wrote: I'm more concerned, however, about the subtest reliability. Is there no data on subtest reliability for that battery ? Jensen (1998) always recommends doing this, because positive correlation between g-loadings and other variables of interest can be "faked" by differential subtest reliability.


As an alternative, communalities could be used. The problem is that doing so over corrects and tends to diminish correlations.

Because reliability coecoefficients of these tests have not been determined directly in a comparable subject sample, each test's communality (i.e. the proportion of its total variance accounted for by the common factors) is used as a lower-bound estimate of the test's reliability. Partialling out the vector of communalities (as surrogate reliability coecients) is an extremely stringent procedure, because generally the largest proportion of the communalities is contributed by the PC1, so some part of the PC1 vector's correlation with d is removed in the partial correlation, thereby tending to work against the outcome predicted by Spearman's hypothesis. If the rs is nonsigni®cant ( p > 0.10, 2-tailed test), the partial correlation is not computed. Controlling variation in test reliabilities (estimated by the communalities), however, seems preferable to no control whatsoever. These results are shown in Table 2. (Jensen and Nyborg, 2000).


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#6
Thanks. I almost forgot this thing. But I remembered I have never been convinced by this method. Why it reverses your correlation, and it behaves unexpectedly, etc. Perhaps as you say, overcorrection. The problem is that Nyborg/Jensen explain it's better than no correction at all. But why so ? I want the explanation. I remember I have asked Nyborg himself but he told me that he's definitely done with psychometrics. In the end, I don't really know what to think about it.
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#7
I'll ask Nyborg to comment on this, since I know him personally.
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#8
The estimates of the subtest reliabilities are available. I can include them in a Table Emil recommended with the factor loadings. To recalculate correcting for reliablity menghu wrote, "by correlating the two variables with subtest reliability partialed out, using the method of partial correlation."

Is it as simple as running a partial correlation between the factor loadings with fluency-score correlations while controlling for the subtests reliability?

Thanks, Curt
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#9
(2014-May-05, 15:12:17)csdunkel Wrote: The estimates of the subtest reliabilities are available. I can include them in a Table Emil recommended with the factor loadings. To recalculate correcting for reliablity menghu wrote, "by correlating the two variables with subtest reliability partialed out, using the method of partial correlation."

Is it as simple as running a partial correlation between the factor loadings with fluency-score correlations while controlling for the subtests reliability?

Thanks, Curt


Yes, I think so.

The alternative method is to correct for unreliability as done in a psychometric meta-analysis. See: https://www.goodreads.com/book/show/8957...a_Analysis

You can find the book for free here: http://gen.lib.rus.ec/book/index.php?md5...407&open=0

The really short version is that you use the formula found on Wikipedia here (https://en.wikipedia.org/wiki/Correction...ttenuation) if you have the reliability of both measurements, and then you run the correlation with subtest g-loadings on the corrected correlations.

Furthermore, one can correct for measurement error in g-loadings, that is, the g-loadings are not always estimated correctly by latent variable methods (principle components, factor analysis, maximum likelihood estimation, etc) and this might bias findings. The expert in this area is Jan te Niejenhus, a Dutch psychologist. You can contact him at nijen631 [removethis_replacewith@] planet.nl.
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#10
(2014-May-05, 15:12:17)csdunkel Wrote: Is it as simple as running a partial correlation between the factor loadings with fluency-score correlations while controlling for the subtests reliability?
Thanks, Curt


I would just use Jensen's basic method. See 589 of the g-factor. One thing that Jensen notes elsewhere is to use the square root of the reliability (see also KJ Kan (2012)) -- so just divide both vectors by this.


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