Calling it a rhetorical “strategy” implies there is an actual skill to master there. They’re literally just keeping a list of everything that confirms their priors and pasting it any time they’re challenged.
The response is to just call them a stupid asshole and not treat the Gallop like it's valid. Hell, call out the Gallop for what it is. Responding to the Gallop on its own terms is conceding defeat.
The HBD-ers are *by far* the worst at this. I once got linked a 600 page unsearchable PDF when I asked for **TrannyPorn0** to back up his bullshit.
The problem is; it takes them thirty seconds to google for and post a dozen papers that would take you hours of work to read and refute. The only way to win is not to play.
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(edit) I looked back through my post history and the HBD shithead who linked me a 600 page unsearchable PDF was actually **stucchio**, not **TrannyPorn0**. Both are HBD shitheads who love pulling this bullshit tactic, though.
OTOH, there are the HBDers that link the same few Pioneer Fund studies that you've seen a zillion times if you've ever had the misfortune of arguing with HBDers on the tubes.
Or basically the last 6 years of mediocre/flawed papers from Intelligence and/or the Ulster Institute gang e.g Emil, John Fuerst, Davide Piffer, and Michael Woodley. It’s an entirely self-contained world of people producing shit work to support other people’s shit work
Some of the shittiest shit I've seen (among recent papers) are by te Nijenhuis and other Dutch coauthors. For example, [this](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.694.8353&rep=rep1&type=pdf) meta-analysis is cited by HBDers non-stop, but it *removed dissenting papers from the meta-analysis under the pretext that the papers are "outliers"*. [Here](https://www.gwern.net/docs/iq/2015-tenijenhuis.pdf) is another such "meta-analysis", in which te Nijenhuis et al. remove *one out of four papers* as an "outlier", and give a meta-analysis of the remaining three. (Of course, the removed outliers are those that disagree with the HBD narrative.)
Anyway, all this is to say that te Nijenhuis belongs on any decent list of HBD pseudoscientists.
Yeah, I have deep skepicism over much of the *g* loadings literature because so much of it relies on the method of correlated vectors which is widely known to be woefully insufficient to actually detect these sorts of things.
And as we know from Lynn's work, these authors are definitely not above shoddy practices of throwing out studies for meta-analyses that allow them to conclude whatever they want
It's worse than woefully insufficient. The definition of g depends on the battery of tests you're considering; therefore, by manipulating the battery you can do whatever you want.
For example, suppose there are two imperfectly correlated IQ tests, say math and vocab. Further, suppose a change in X is associated with an increase in both (here X can be the Flynn effect or adoption or race or having a certain polygenic score). And let's say the math test increases more than the vocab under X (though again, both increase).
Now, if I want to claim X-related IQ gains are on g, I'll simply pick a battery with more tests similar to math and fewer tests similar to vocab. On the other hand, if I wanted to claim X-related IQ gains are not on g, I can pick a battery with more vocab tests than math tests.
In fact, if all the IQ tests show a Flynn effect, then under mild assumptions it is a mathematical guarantee that the Flynn effect is on g for *some* choice of battery. Therefore, such meta-analyses are harebrained to begin with. I just brought up the outliers thing because that's *obviously* inexcusable - no understanding of statistics is necessary to see the dishonesty there.
(This is a separate objection from the adequacy of the statistical tools used to estimate the g loadings; even with unlimited subjects taking an unlimited number of tests, the above objection would still apply.)
>In the first paper (on the Flynn effect), if the outlier is included, the correlation between g loadings and the Flynn effect is -.26. In the second, if the outlier is included, the correlation is -.66. So the outliers don't radically change the results.
Uh huh, and how do the confidence intervals change?
In any case, if removing outliers does not change the outcomes then do not remove the outliers.
They keep citing Hunter and Schmidt (1990) who recommend removing outliers, except I went and looked up that book to see what they are talking about, and the recommendation is (1) to remove outliers from **both the top and bottom end**, perhaps 5% each, and (2) the 5% refers to 5% of *papers*, not 5% of *data points*, at least as far as I can tell. The idea is that you're only removing outliers because of the non-trivial chance that they are simply typos; but typos/mistakes in the final outcome are about as likely in a study of 10 people as in a study of 1000 people unless you have reason to believe the latter was more carefully done. In other words, when they say "we chose to leave out two outliers comprising 4%
of the research participants" they are misunderstanding the purpose. And if your meta analysis is of 4 papers, you don't get to remove anything, that's just ridiculous.
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>You're right to point out that the g loadings of tests are dependent on the other tests in the battery. To my knowledge, no empirical test has been conducted of how much this alters MCV results, though I doubt it affects them much.
Again, it's a mathematical guarantee that by changing the battery I can cause the Flynn effect to be positively loaded on g. There's nothing to test here, though I can show this to you with data if you have a favorite battery showing a Flynn effect. You just have to allow me to (1) remove some tests from the data set, and (2) duplicate or mix together some other ones (with added noise to simulate a brand new test). I can turn any negative g-loaded gains into positive g-loaded gains by doing so, and the statistical method won't matter much either. It follows from how g is defined.
>Thorndike found a pretty high correlation between the g loadings of the same tests randomly inserted in different batteries.
Some ground-clearing is in order. Spearman originally hypothesized that intelligence is truly one-dimensional, in the sense that every IQ test is simply the g factor plus noise. This hypothesis is testable: we simply have to check whether (say) two math tests correlate with each other better than a math test and a vocab test, assuming all tests are equally noisy (measured by their correlation with yet another test, for example). A similar way is to test whether the tetrad differences vanish.
Spearman's original g hypothesis is now considered falsified, as is frankly common sense: *of course* two math tests correlate better than a math test and a vocab test. I mean, is this your first day on planet Earth, Spearman?
Anyway, back to the point. If we assume Spearman's g hypothesis is false (duh), then **it is a mathematical guarantee that I can have two different batteries where one test will have different g loadings in the batteries**. If Thorndike found a "high correlation" between g loadings, this is because the batteries were not so different to begin with. **There is no such thing as the one true IQ battery, nor any approximation to it**. If I only cared about memory tests, I could have a memory test battery in which Raven's would have a low g-loading, and I could call that the True IQ Battery, and that would be just as valid as the current IQ batteries with no way to choose between them.
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>Most of the literature on MCV vs. MGCFA is frankly above my head; I'm no statistician. I would love to do an empirical test of how much MCV and MGCFA results in fact parallel each other!
I hardly know anything about MCV or MGCFA. My criticism is independent of how you measure g-loadings, however; I have beef with the underlying concept these methods are attempting to measure.
Your analogy is perfect, except that it is the Jensen folks who are handpicking their battery. After all, a wide variety of IQ tests (all except like 0.5%) showed extremely large increases up until the 90s; in order to conclude that the Flynn effect is fake, you have to twist the data by emphasizing that 0.5% which did not increase, or else you have to resort to statistical trickery like the g factor to get your desired result.
In other words, in real life reviewer 2 is trickier than what you suggest: he realizes that picking 0.5% of the tests looks bad, so instead he picks a battery of IQ tests and shows that their g factor is negatively correlated with the Flynn effect (this isn't even saying that the g factor decreased, but it *sounds like* it's saying the g factor decreased, which is what's important, lol. As a matter of fact, if you project the Flynn gains onto the g factor, I believe you should still see increases in g in the common batteries, despite the negative correlation. Don't tell HBD folk though.)
The Flynn effect of the 70s-90s was indeed robust across many different types of IQ tests, so you should take my side in this by your own logic.
Look, do you have a favorite IQ battery that showed a negative correlation between Flynn effect and g-loadings? I will happily reproduce it by **randomly** deleting half the tests and duplicating the other half (with some added noise). I predict that will already be enough to get a positive correlation between g-loadings and Flynn, at least for some decent percent of the battery-modification trials (say, at least 30% of them). Do you want to bet on this? I assume this answers your objection regarding robustness. **I am aware of no publication that tested such robustness, whether for Flynn effects, adoption gains, BW gap, or any other correlated-with-g-factor result**. I think this is statistical malpractice, frankly. I am highly confident negative correlations with g-loadings are non-robust, on purely mathematical grounds. (Positive correlations with g-loadings are more likely to be robust; the natural tendency of "real" effects should be a positive correlation with g-loadings, simply based on the fact that some tests are noisier than others.)
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>MCV demonstrates that the criterion validity of a subtest, and its correlation with intuitively "biological" variables (whatever that means) like most neural variables and test heritabilities, are positively correlated with its g loading.
Not surprising, because most effects should be positively correlated with most g factors (plural due to the battery choice). This is because some tests are noisier than others, and the noisy tests have both (1) low correlation with other tests, and hence low g loadings, and (2) low "effect" for whichever effect you're testing. But this should be true for Flynn too: the noisier tests should have lower g-loading AND lower Flynn effect. To get a negative correlation of anything with g-loading, you have to pick your battery carefully.
>It demonstrates that, weirdly enough, tests like vocabulary and information are (in some samples*) very highly g loaded, though they're clearly quite culturally influenced and don't seem to require much reasoning––which is a useful datum for environmentalist theories of the positive manifold.
I can modify a battery that has a high loading on vocab by adding a second variant of Raven's, and suddenly Raven's will be more g-loaded than vocab (and since Raven's had a large Flynn effect, that would change the Flynn-g correlation). These things are more brittle than you're pretending.
>And it demonstrates that most environmental variables––adoption, test-retest gains, the Flynn effect, family environment (c2), TBI and fetal cocaine exposure––are not positively correlated with g.
All that means - literally **all** it means - is that some tests, like vocab, have less test-retest gains and less Flynn effect than other tests, like Raven's (and that the battery you chose happened to favor vocab by having more vocab-like tests). It does **not** elevate the vocab-type tests to be a more "real" measure of intelligence than the Raven's type, and if you ask a person on the street which between Raven's and vocab corresponds to the intuitive notion of intelligence, they will say Raven's.
>(Incidentally, I intend to empirically test the latter issue by factor analyzing an enormously broad set of cognitive tests, such as emotional intelligence, creativity, practical intelligence, etc., along with more usual measures of g. My prediction is that the loadings of the usual measures of g on the PC1 of the usual g measures vs. on the PC1 of the entire battery will correlate highly. I'll let you know what I find, if you're interested :))
Correlate highly is not good enough. If factor g1 correlates highly with factor g2, it may still be the case that g1 has a positive correlation with the Flynn effect and g2 has a negative correlation.
>I think to do that analysis, introducing noise, you'd need raw data, which I don't have (maybe you do? maybe it's in Wicherts, 2004)
I believe you can do everything with only the correlation matrix, though it's been a while since I've thought about it and I'm less sure about the adding noise part. Of course, not all papers provide the correlation matrix (though it only takes half a page) - another gripe I have with the community. Authors like Rushton generally do not provide it, while Wicherts provides several (for different datasets). Then again, Wicherts's data shows a positive correlation between g loadings and Flynn for some of its datasets (and those were excluded as outliers by te Nijenhuis et al., of course).
[Funny how the higher transparency data agrees with me; if I was a psychometrician I'd be tempted to draw up a correlation between transparency of data and how much it agrees with me about g-Flynn correlations, maybe even find a general factor somewhere...]
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>So this evidence supports your hypothesis: these MCV results, at least, are not robust when the Wechsler is split by Verbal x Performance subtests. That's an extremely important finding if it can be replicated in general for the Wechsler test! Thanks for getting me to look that Flynn study up!
Thanks for looking it up! Cheers! Also thanks for linking to some data I was not aware of.
>Even granting that negative FEs are less likely to be robust, why is it that negative correlations with g loadings fall in such a predictable cluster––roughly, "environmental" effects? Why would environmental effects systematically be more likely to correlate negatively with g loadings?
Suppose we had two underlying factors of intelligence (or clusters of IQ tests), one exemplified by vocab and one by Raven's. The first will be more genetic, the second more environmental. The first will have lower Flynn effects, the second higher. The first will have low test-retest gains, the second higher. The first has higher BW gap than the second. The first has lower adoption gains.
Aha, but which has a higher g loading? That depends on the battery: in a battery that includes more vocab-type tests, the vocab-type tests will have a higher g loading. In a battery with more Raven's-type tests, the Raven's-type test will have higher g loading.
What's not OK is to say that the vocab cluster is the true "general intelligence" and the other one is merely "hollow". (These aren't my terms, as I'm sure you know; Jensen uses them everywhere.) No, you don't get to say that! In fact, if you ask people on the street, they may well tell you that the *second* cluster is the one that intuitively corresponds to intelligence.
But hold on, I hear you say: what about validity tests? Doesn't the first cluster (the traditionally higher-g-loading cluster) predict outcomes more? Doesn't it predict brain size more?
My response is: no, I don't think so, because there's a complication. There's actually a third cluster of tests: the crappy, noisy ones that have little intelligence-related signal. They have low g-loadings in BOTH the vocab-heavy battery AND the Raven's-heavy battery. And they are terrible at predicting outcomes and brain size. Now, if you do a correlation of g-factor with outcomes, you'd get that the higher g-loaded tests are more predictive of outcomes... for BOTH the g factor of the vocab-centered battery AND the g-factor of the Raven's-centered battery.
If you directly showed me that vocab is more predictive of outcomes, brain size, job performance, etc. than Raven's, and if this was corrected for attenuation and if it remained robust among different populations, *then* I'd be convinced vocab is a better measure of intelligence, and *then* it will start to be relevant that the adoption gains and Flynn effect are less on vocab than Raven's. But I do not believe such a result has been shown; let me know if I'm wrong.
Huge props to you for taking the time to refute the HBD stuff, by the way. It's always fun seeing how mad they get when an actual expert calls them on their bullshit.
I don't like to be an "expert" because I'm really just a PhD student who's been working in this area for several years. Compared to actual faculty I'm far from an expert, but they don't browse Reddit so here I am!
Wonderful demonstration of the obverse side of Dunning-Krueger-- you know enough to know you're not an expert, so you don't pretend like one for being able to link to papers that you sought to support your pre-existing biases!
> Wonderful demonstration of the obverse side of Dunning-Krueger-- you know enough to know you're not an expert, so you don't pretend like one for being able to link to papers that you sought to support your pre-existing biases!
That's not quite what I meant. Obviously there's a wide gap between not being an expert and just cherry-picking papers. I'm active in this field, I've published papers in genomics/population genetics/evolutionary genomics, I've worked with faculty in the field, I regularly speak to faculty in the field, etc. Knowing that I am not at the level of those faculty, who are unquestionably experts, does not mean I don't have some level of knowledge or expertise in the field. It does mean that the explanations I can provide might not always be as refined or elaborated as theirs and that it'd be better to just be able to have them respond in my place; I recognize that. I also recognize that most of the arguments or claims I am making are echoed by people in the field though, so the least I can do it attempt to use my knowledge base in the field and the conversations I know they are having to pierce through some of the less-informed online conversations being used for largely ideological and political purposes.
> "Here is a paper" is such a pet peeve of mine.
Yeah, though, I mean, on the one hand, it's like, if you know the argument has been made thoroughly and completely and don't want to waste your own time, why not point to it? Like, I know a fair bit about climate change, I've read dozens of scientific papers on it, understand the physics, etc, but I'm a statistician and it's not my field of interest, it's been a few years since I've been up to date on it (when was the last time I read a climate paper? I don't know) or even worked with any climate data for fun. So I'm just going to point at skepticalscience.com or, if I know a specific paper, point to that.
But, yeah, on the other hand, if all you're doing is googling, taking the first result, reading the abstract, and saying, "read it, plebe", that's just disrespectful.
If you're gonna link a fucking paper in an online argument (absolutely the lowest trick in the art of online arguing) at least have the decency to use a metaanalysis
Pride is pretty decadent if you've never been to a party that didn't center around a game console or peak at the arrival of Papa John's. Or involved more than one person.
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I love when right wingers point to pride as some kind of highly transgressive festival. It’s literally just gay people being out in the open