“Intelligence and General Psychopathology in the Vietnam Experience Study: A Closer Look”, 2021 (; backlinks; similar):
Prior research has indicated that one can summarize the variation in psychopathology measures in a single dimension, labeled P by analogy with the g factor of intelligence. Research shows that this P factor has a weak to moderate negative relationship to intelligence.
We used data from the Vietnam Experience Study to reexamine the relations between psychopathology assessed with the MMPI (Minnesota Multiphasic Personality Inventory) and intelligence (total n = 4,462: 3,654 whites, 525 blacks, 200 Hispanics, and 83 others).
We show that the scoring of the P factor affects the strength of the relationship with intelligence. Specifically, item response theory-based scores correlate more strongly with intelligence than sum-scoring or scale-based scores: r’s = −0.35, −0.31, and −0.25, respectively.
We furthermore show that the factor loadings from these analyses show moderately strong Jensen patterns such that items and scales with stronger loadings on the P factor also correlate more negatively with intelligence (r = −0.51 for 566 items= −0.60 for 14 scales).
Finally, we show that training an elastic net model on the item data allows one to predict intelligence with extremely high precision, r = 0.84. We examined whether these predicted values worked as intended with regards to cross-racial predictive validity, and relations to other variables. We mostly find that they work as intended, but seem slightly less valid for blacks and Hispanics (r’s = 0.85, 0.83, and 0.81, for whites, Hispanics, and blacks, respectively).
[Keywords: Vietnam Experience Study, MMPI, general psychopathology factor, intelligence, cognitive ability, machine learning, elastic net, LASSO, random forest, crud factor]
…To further examine predictive accuracy, we trained a lasso model to see if a relatively sparse model could be obtained. The validity of the lasso model, however, was essentially identical to the elastic net one, and the optimal lasso fit was not very sparse (363⁄556 items used)…It is seen that about 90 items are needed to reach a correlation accuracy of 0.80, whereas only 3 items are needed to reach 0.50. This may be surprising, but some items have absolute correlations to g of around 0.40, so it is unsurprising that combining 3 of them yields a model accuracy at 0.50.
…Finally, we fit a random forest model. This performed slightly worse than the elastic net (r = 0.78). The failure of the random forest model to do better than the elastic net indicates that nonlinear and interaction effects are not important in a given dataset for the purpose of prediction. In other words, the additive assumption is supported for this dataset and outcome variable