Category: Psychometrics (Page 1 of 3)

Fair and Square: A Conclusion on IQ Test Bias

This is a 2-part article. In this first part, the most important studies on internal test bias with respect to racial groups in the item-level, subtest-level and construct-level are reviewed. The proposed causes will be discussed. Generally, the most commonly used IQ tests aren’t biased or only minimally biased as to be of no practical value.

The best methodologies with an application using the Wordsum GSS for the Black-White group will be discussed in the second part of the article : (link to the forthcoming article will be provided soon).
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Schooling enhances IQ, not intelligence

The idea that schooling raises intelligence still prevails. The influential study review of Ceci (1991) concluded that schooling has a strong impact on IQ scores despite his final warning that observed score does not equate real intelligence. After, many more studies were published, including latent factor modeling and quasi-experimental designs. It is unclear whether education truly improves general intelligence modeled as latent factor or whether long-lasting IQ gain involves far transfer effect. More likely, the answer to all of these questions is negative.

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Re-analysis of Willerman’s Study: Race of Mother’s Hypothesis

It’s been almost 50 years now that the famous study of Willerman et al. (1974) has been published. This study is regularly cited as one of the most convincing evidence against the hereditarian hypothesis, despite strong emphasis by hereditarians on the failure of experimental efforts to raise IQ (more specifically, g) and population differences magnifying during adolescence or adulthood due to increasing heritability with age (Jensen, 1998, pp. 333-344, 359, 474; See Malloy [2013] for a case of a stability model with respect to the Black-White gap). Caution about this study is now vindicated. The data used by Willerman also revealed a pattern: the IQ deficits related to having a Black mother seem to vanish over time (Hu, 2022). Continue reading

Measurement Error, Regression to the Mean, and Group Differences

Regression to the mean, RTM for short, is a statistical phenomenon which occurs when a variable that is in some sense unreliable or unstable is measured on two different occasions. Another way to put it is that RTM is to be expected whenever there is a less than perfect correlation between two measurements of the same thing. The most conspicuous consequence of RTM is that individuals who are far from the mean value of the distribution on first measurement tend to be noticeably closer to the mean on second measurement. As most variables aren’t perfectly stable over time, RTM is a more or less universal phenomenon.

In this post, I will attempt to explain why regression to the mean happens. I will also try to clarify certain common misconceptions about it, such as why RTM does not make people more average over time. Much of the post is devoted to demonstrating how RTM complicates group comparisons, and what can be done about it. My approach is didactic and I will repeat myself a lot, but I think that’s warranted given how often people are misled by this phenomenon.
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Into the IQ shredder

Wang, M., Fuerst, J., Ren, J. (2016). Evidence of dysgenic fertility in China. Intelligence, 57, 15-24.

From the discussion: “We’ve seen, in Table 4, that urban populations in China exhibited a relatively high dysgenic fertility trend in the 1951–1970 birth cohort. For this same cohort, the trend was much smaller in the rural populations. It suggests that dysgenic selection is related to urbanity. This supports Pan’s (1923) observation that “modern urbanization has had so many dysgenic effects upon the race.”

IQ and Personality: What James Heckman Got Wrong

A few years ago James Heckman, together with some other economists, published a study arguing that “achievement tests” and “IQ tests” are different beasts: the former, they claim, are better predictors of criterion outcomes (such as grade point averages) and are more strongly influenced by personality differences than the latter. Like most of Heckman’s forays into psychometrics — he has been obsessed with trying to shoot down Bell Curve -type arguments ever since the book was released — the study leaves much to be desired. David Salkever has published a nifty reanalysis of Heckman and colleagues’ study, showing that their results stem from faulty imputation and a failure to take into account age effects. Continue reading

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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