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.
Highlights
The Flynn effect (FE) is the well-documented generational increase of mean IQ scores found in many countries [1,2]
This study investigated the utility of commonly used indices of model fit in growth mixture modeling for examining the Flynn effect
We fit a series of two- to five-class models to the simulated data and examined eight fit indices (i.e., Akaike information criterion (AIC), AICc, consistent Akaike information criterion (CAIC), Bayesian information criterion (BIC), size adjusted BIC (SSBIC), Draper’s information criterion (DIC), ICL-BIC and LMR) and the mean entropy for the two-class models to determine the relative accuracy in identifying a correct solution
Summary
The Flynn effect (FE) is the well-documented generational increase of mean IQ scores found in many countries [1,2]. The FE was observed as early as the 1930s, but only began to be systematically studied in the 1980s [3]. While the typical FE is between three to five IQ points per decade, the effect’s magnitude and direction have shown considerable variation across time and location [4]. Many of the original FE studies were done comparing mean scores without paying attention to the scores’. Another criticism is that the compared scores were often from instruments normed at different time points, with no investigation into whether the scores were comparable [6,7]
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