Author: Meng Hu

The Bell Curve, 20 years after

Or nearly so. I was planning to publish that blog article for the 31th December 2014. As you can see, I failed in this task, and didn’t finish in the right time. Anyway, I wrote this article, mainly because I am bothered that when people cite The Bell Curve the typical opponent responds with a link toward Wikipedia, specifically the part related to the “controversy” of The Bell Curve. It goes without saying that these persons did not read the books written in response to The Bell Curve. In fact, they have certainly read none of them. It is ridiculous to cite a book you didn’t read, but apparently, it does not bother many people, as I see.

For the 20 years of the book, I found appropriate to write a defense of the book. Or more precisely, a critical comment on the critics. I have decided to read carefully one of these books I can have access, and for what I have read here and there, it is probably the best book ever written against The Bell Curve. I know that Richard Lynn (1999) has already written a review before. But I wanted to go into the details. The title of the book I’m reviewing is :

Devlin, B. (1997). Intelligence, Genes and Success: Scientists Respond to the Bell Curve. Springer.

In fact, I have read that book some time ago, but didn’t find the need to read everything in detail. And I was unwilling to write a lengthy review. But I have changed my mind because of some nasty cowards.
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How to calculate and use predicted Y-values in multiple regression

I was planning to publish this article after my paper on the black-white vocabulary gap in the GSS is released, but I have changed my mind. So, here, I will explain how to use the so-called “Yhat” or predicted values of Y when doing regression (OLS, logistic and multilevel).
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The study of deaf people since Braden (1994)

Earlier, I have reviewed Braden’s (1994) book, Deafness, Deprivation, and IQ. Considerable amount of studies have been conducted since then. The focus is on the validity of measures of intelligence among the deaf population, such as reliability, predictive validity, measurement properties of the tests.
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MH’s book review of Deafness, Deprivation, and IQ (Braden 1994)

Jeffery P. Braden. (1994). Deafness, deprivation, and IQ. Springer.

The book is a compilation of studies on deaf people, which concludes that cultural deprivation due to deafness lowers verbal IQ but not nonverbal IQ. Braden sought to prove Arthur Jensen wrong about his conclusions on the genetic component in racial differences in IQ. At the end, his research culminated in a trauma well known to scientific history, namely, his perfectly good theory was ruined by his data. Being born deaf does not affect g. And genetic theories are the most powerful arguments to account for the pattern of the data.
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Racial differences in the long-term trend NAEP scores (1975/78-2012)

I analyze the LTT NAEP achievement scores, a public data set available at NCES. In general, minority-majority ethnic groups show a secular decline in d gap, for both math and reading tests, and this occurs at all ages of assessment (9, 13, 17), and at all percentile levels. Some exceptions are noteworthy. There is no secular gain at age 17 among whites, and no meaningful decline in black-white difference for the NAEP math at ages 13 and 17. Within each year of assessment, no evidence is provided for the hypothesis that the racial gaps (notably, the black-white gap) widen with age after entering schools. There was simply no trend at all.
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The Fallacy of Significance Tests

It must be known that a p-value, or any other statistics based on the Chi-Square, is not a useful number. It has two components : sample size and effect size. Its ability to detect a non-zero difference increases when either sample size or effect size increases. If only sample size increases, even with the other left constant, the statistics become inflated. There is also a problem with the assumption. If it is about the detection of “non-zero” difference, it is of no use if the magnitude, i.e., effect size, is of no importance. I will provide several examples of the dangerosity of the significance tests.
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Multiple Regression, Multiple Fallacies

It goes without saying that multiple regression is one of most popular and applied statistical methods. Thus, it would be odd if most practitioners among scientists and researchers do not understand and misapply it. And yet, this provocative conclusion seems most likely.

Because a simple bivariate correlation does not disentangle confounding effects, the multiple regression is said to be preferred. The technique attempts to evaluate the strength of an independent (predictor) variable in the prediction of an outcome (dependent) variable, when controlling, i.e., holding constant, every other variables entered (included) as independent variables into the regression model, either progressively step by step or altogether at the same time. The rationale is to get the effect of an independent variable that only belongs to it. But this is a fallacy.
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Research on genetic g and differential heritabilities

Multivariate genetic analyses and simple correlational analyses have been conducted to evaluate the extent to which the general factor (g) of intelligence is differentially heritable, compared to, for example, group factors. A positive correlation would be supportive of Jensen’s view, notably advanced in The g Factor (1998), of the heritable g. This can be interpreted to say that what makes people being good at all tests has more genetic component than what make people being good at one specific test. On the other hand, if environmental effects are smaller at the g level, it would mean that what make people being good at all tests has less environmental component than what make people good at one specific test. Similarly, if heritability is large at the g level and environment is small at the g level, then g differences between persons are largely genetic, not environmental (Plomin, 2003, p. 186).

The present article is a review of the studies published so far and can be seen as a complement to my article on the genetics of intelligence. Brody (2007) and Deary (2006) have already reviewed a large part of the existing studies. But some features need to be highlighted. The article can be subjected to modifications if I happen to read some more studies not listed here (I prefer not to publish a new article each time I read a new research paper). Shortly, there seems to be some proof of differential heritabilities, higher for g. But it’s not overwhelming.

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What does it mean to have a low R-squared ? A warning about misleading interpretation

A common argument we read everytime, everywhere. All with the same common mistake. It consists in squaring the correlation. For example : “Your brain-IQ correlation is r=0.40, so if you square it, that only amounts to a tiny 16% (r²=0.40*0.40=0.16) of variance explained which is not impressive”. Or something in this vein. R² use and abuse caused enough damage. It is more than time to put an end to this utter fallacy.

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What happened with the Abecedarian study ? IQ-malleability theories in danger.

In an attempt to equalize social opportunities, several large-scale studies have been launched. These studies were of special interest because they sampled a large portion of black people, since a study on white people can’t be generalized over other ethnic minorities. Among those projects, the Abecedarian (ABC) has the particularity to have generated conflicting interpretations. This needs to be discussed thoroughly.
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