Abstract: The role of response time in completing an item can have very different interpretations. Responding more slowly could be positively related to success as the item is answered more carefully. However, the association may be negative if working faster indicates higher ability. The objective of this study was to clarify the validity of each assumption for reasoning items considering the mode of processing. A total of 230 persons completed a computerized version of Raven’s Advanced Progressive Matrices test. Results revealed that response time overall had a negative effect. However, this effect was moderated by items and persons. For easy items and able persons the effect was strongly negative, for difficult items and less able persons it was less negative or even positive. The number of rules involved in a matrix problem proved to explain item difficulty significantly. Most importantly, a positive interaction effect between the number of rules and item response time indicated that the response time effect became less negative with an increasing number of rules. Moreover, exploratory analyses suggested that the error type influenced the response time effect.
Abstract: Bi-factor confirmatory factor models have been influential in research on cognitive abilities because they often better fit the data than correlated factors and higher-order models. They also instantiate a perspective that differs from that offered by other models. Motivated by previous work that hypothesized an inherent statistical bias of fit indices favoring the bi-factor model, we compared the fit of correlated factors, higher-order, and bi-factor models via Monte Carlo methods. When data were sampled from a true bi-factor structure, each of the approximate fit indices was more likely than not to identify the bi-factor solution as the best fitting. When samples were selected from a true multiple correlated factors structure, approximate fit indices were more likely overall to identify the correlated factors solution as the best fitting. In contrast, when samples were generated from a true higher-order structure, approximate fit indices tended to identify the bi-factor solution as best fitting. There was extensive overlap of fit values across the models regardless of true structure. Although one model may fit a given dataset best relative to the other models, each of the models tended to fit the data well in absolute terms. Given this variability, models must also be judged on substantive and conceptual grounds.
Abstract: This paper analyzes notions of culture and human intelligence. Drawing on implicit and explicit theory frameworks, I explore discourses about perceptions of intelligence and culture. These include cultural perceptions and meanings of intelligence in Asia, Africa and Western cultures. While there is little consensus on what intelligence really means from one culture to the next, the literature suggests that the culture or sub culture of an individual will determine how intelligence is conceived. In conclusion, the view is that culture and intelligence are interwoven.
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.