Talk:Twin study

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Why is it correlation, not r-squared?[edit]

Why does the article refer to correlation (under Methods? Why not the Coefficient of determination, also called R-squared? I thought this was the correct measure to use, if one was interested in slicing up the correlation into components. Mere correlation looks like a mathematical error to me. What am I missing? IBE (talk) 17:50, 13 March 2017 (UTC)[reply]

This is a very common source of confusion. The confusion stems from the fact that familial correlations are often estimates of biometric variance components in behavior genetic designs. For example, let's say the IQ correlation between MZ twins reared apart in independent environments is 0.80. This correlation is equal to the heritability of IQ (=80%), which is the proportion of phenotypic IQ variance that is explained by genetic differences (equivalent to the R squared). Here's[1] an article that should make it clear. Maybe this issue would warrant some discussion in the article.--Victor Chmara (talk) 09:14, 14 March 2017 (UTC)[reply]

Thanks, but I don't have access to that link, and I have only a vague understanding of what you wrote. I assume part of the issue is that normally, correlation can be negative or positive, but presumably with twin studies, it can never be negative (you can't expect people to have a negative correlation with their twin, e.g., for one twin to always vote left, the other to always vote right - they are either identical, similar, or uncorrelated). If "r" could be negative, presumably you must square it - you can't have a negative proportion of the variance explained by a certain factor. So perhaps with twin studies, the variable has already been (in some effective, not literal, sense) implicitly squared, making it naturally positive. But I don't get all this. Do you know of a website/ paper that I can access, or a book/search term that will help? thanks, IBE (talk) 09:21, 31 March 2017 (UTC)[reply]

Is this link the correct explanation: [2]? IBE (talk) 10:36, 31 March 2017 (UTC)[reply]

With Sci-hub, everyone has access. Use it to get the paper. See this one and this one, too.

Yes, familial correlations in biometric models are somewhat analogous to reliability coefficients. The correlation between two parallel forms of the same test equals the reliability (which is the ratio of true score variance to total variance) of the tests because that correlation is equal to the squared correlation between a test and its underlying true scores. An MZA twin correlation, for example, is equal to the squared correlation between genotype and phenotype (if the model assumptions hold).--Victor Chmara (talk) 11:44, 31 March 2017 (UTC)[reply]

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stereotype threat[edit]

This addition [3] by an IP adds some content that was removed later for not meeting POV and RS. They also added material on stereotype threat that uses only two sources - both advocacy material by educational groups pushing constructivist education - this does not meet RS. The pdf does not mention the word "stereotype" anywhere except for uncritically sourcing a single 2011 paper on stereotype threat - this is an argument from consequences pushed by advocates for increased educational spending.

The IP's edit tries to describe stereotype threat without naming it and without presenting any actual evidence for the claim - I'm going to remove the section.

-- Callinus (talk) 22:59, 12 March 2018 (UTC)[reply]

Egregious lack of criticism[edit]

There have been many criticisms of twin studies based on the view that they suffer from fundamental methodological flaws (especially classical twin studies); it is inexcusable that this criticism is not even mentioned in the lead of this article. I will try to address this problem soon. Every morning (there's a halo...) 21:30, 2 June 2018 (UTC)[reply]

I agree, and here is my criticism: it should be expected that most twins are going to have similar traits just as most people have similar traits due to regression to the mean. In other words, twins that share average traits are not good subjects for making claims about heritability, only outliers should be used to generate useful data. For example, the twin studies that show that twins have similar IQ levels should not be relevant if they have average IQs because any random sample of any population will show more people sharing average traits. So only twins that share high IQ scores or low IQ scores should be used to generate a heritability statistic, and that comes with it's own problem, that higher and lower IQ scores from the mean are less reliable than those at the mean. That means that twins who share average IQs should be ignored and twins who share rarer IQs (lower or higher than average) aren't very reliable. These two forces should act to drive the correlative value of twin studies down. 108.180.92.37 (talk) 05:44, 9 May 2022 (UTC)[reply]

Sexual Orientation in Twins[edit]

Sexual Orientation in Twins: Evidence That Human Sexual Identity May Be Determined Five Days Following Fertilization PMID: 38161549 PMCID: PMC10757681DOI: 10.7759/cureus.51346 Narraburra (talk) 05:01, 5 January 2024 (UTC)[reply]