Abstract: The Flynn effect (FE) is the well-documented generational increase of mean IQ scores over time, but a methodological issue that has not received much attention in the FE literature is the heterogeneity in change patterns across time. Growth mixture models (GMMs) offer researchers a flexible latent variable framework for examining the potential heterogeneity of change patterns. The article presents: (1) a Monte Carlo investigation of the performance of the various measures of model fit for GMMs in data that resemble previous FE studies; and (2) an application of GMM to the National Intelligence Tests. The Monte Carlo study supported the use of the Bayesian information criterion (BIC) and consistent Akaike information criterion (CAIC) for model selection. The GMM application study resulted in the identification of two classes of participants that had unique change patterns across three time periods. Our studies show that GMMs, when applied carefully, are likely to identify homogeneous subpopulations in FE studies, which may aid in further understanding of the FE.
Abstract: After nearly thirty years of concerted effort by many investigators, the cause or causes of the secular gains in IQ test scores, known as the Flynn effect, remain elusive. In this target article, I offer six suggestions as to how we might proceed in our efforts to solve this intractable mystery. The suggestions are as follows: (1) compare parents to children; (2) consider other traits and conditions; (3) compare siblings; (4) conduct more and better intervention programs; (5) use subtest profile data in context; and (6) quantify the potential contribution of heterosis. This last section contains new simulations of the process of heterosis, which provide a plausible scenario whereby rapid secular changes in multiple genetically influenced traits are possible. If there is any theme to the present paper, it is that future study designs should be simpler and more highly focused, coordinating multiple studies on single populations.
Abstract: Hunt and Jaeggi  nicely summarize the point that within the academic field of intelligence, we do not have a commonly understandable definition of what intelligence is. Still the term is used extensively and with consensus to the effect that humans are the most intelligent species. An example is given of this problem, and a definition and solution are suggested.
Abstract: In the present paper published data and new analyses are presented and discussed in order to demonstrate the power of family data (siblings and parents to military conscripts with IQ data) in the study of the Flynn effect (FE). In particular, it is shown how studies of the mean intelligence changes in sibships of different sizes and changing proportions of sibship sizes can enhance our understanding how these factors may influence FE. Some new analyses of correlations between intelligence and sibship sizes illustrate how family data can be used to investigate changes in the correlation pattern across generations. It is shown that comparison of the secular trends in the general population and in sibling pairs can be a powerful method in the exploration of the relative influence of between-families and within-families factors in the FE. Surprising connections between the birth order effect on intelligence and the FE are demonstrated.
Abstract: The Dickens and Flynn model of the Flynn effect (generational increases in mean IQ) assigns an important role to genotype-environment covariance (GE-cov). We quantify GE-cov in a longitudinal simplex model by modeling it as phenotype to environment (Ph->E) transmission in twin data. The model fits as well as the standard genetic simplex model, which assumes uncorrelated genetic and environmental influences. We use the results to explore numerically the possible role of GE-cov in amplifying increases in environmental means. Given the estimated Ph->E transmission parameters, GE-cov resulted in an amplification (in std units) of a factor 1.57 (full scale IQ) to 1.7 (performance IQ). The results lend credence to the role of GE-cov in the Flynn effect.
Abstract: Previous research shows that perceived intelligence judgments significantly correlate with measured intelligence scores. The present study investigated the developmental trajectory of the association between perceived intelligence and measured intelligence. Using the Block and Block longitudinal dataset, we examined the relationship between a single rating of “high intellectual ability” made in early childhood by targets’ preschool teachers with future intellectual ability and scholastic outcome measures, including IQ scores, grade-point average, SAT scores, and educational attainment. Even when controlling for variables including attractiveness, parental education levels, the General Factor of Personality, and socioeconomic status, correlations between early childhood intelligence perceptions and later intellectual measures were significant, large, and robust. Results are discussed in terms of potential mechanisms and cues in early childhood that may reveal future intellectual abilities.