If your cup is half full, you can correctly point out that. Is that a large correlation? It depends upon your perspective. That translates to a correlation coefficient of. The data show that AFQT scores explain 21% of the variation in income between survey respondents. 5 would mean that half of the variation in income could be explained by knowing someone’s AFQT scores (or, less scientifically, half the time you can predict someone’s income by knowing how they did on the AFQT). In this analysis, zero means AFQT has no predictive power, while one would mean that someone’s income can be perfectly predicted by knowing their AFQT score. So, what’s the unique contribution of AFQT scores? More precisely, how much of the variability in income can they explain? Statisticians often answer this question by reporting a statistic called r-squared that varies from zero to one. The dependent variable is the median annual family income for all 26 waves of NLSY data.īut a lot of other things also predict income. It’s top-coded at $300,000, logged for all regression analyses, and converted into 2018 dollars to account for inflation.ĭo higher AFQT scores predict a higher income? Of course they do, as this graphic shows: It’s a better measure of living standards than individual income is. My existing research looks at family income, so I’ll go with it as the dependent variable here. Analysis based on IQ produced essentially the same results. Accordingly, I’ll report results based on AFQT scores. AFQT and IQ measure essentially the same things and are highly correlated (r =. Only about 1,700 respondents got the IQ tests, while all were given the AFQT. This leaves me with a sample size of about 7,100, more than enough for population estimates.Įarly on in the panel, respondents were administered a variety of IQ tests, as well as the similar Armed Forces Qualifying Test. My NLSY-79 research looks at the economics of motherhood, so my analytic sample is limited to men and women with kids ( hey, this is a blog post, not a peer-reviewed article). This is a representative sample of over 10,000 Americans between the ages of 14 and 22 initially interviewed in 1979, then annually or biennially thereafter. I was motivated to do so when I realized I had the appropriate national data at hand: the 1979 cohort of the National Longitudinal Survey of Youth (NLSY-79). But it’s much more persuasive for me to respond with data. Racial differences? The intergenerational transmission of wealth? The effects of parenting or family structure? All of it must boil down to differences in IQ.Īs a social scientist, I’m naturally resistant to single-variable theories. I sometimes encounter people I’ve come to think of as “IQ truthers.” IQ is the magic social science variable that explains virtually every observed correlation. It includes additional methodological details for the statistically curious. Author’s Note: This is a revised version of a post that originally appeared on my personal blog.
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