Predicting school achievement rather than intelligence: does metacognition matter?

This paper investigates the role of specific and general metacognitive ability on specific and general academic achievement, controlling for the effects of intelligence. Four hypotheses were elaborated and empirically tested through structural equation modelling. The sample was composed by 684 students (6th to 12th graders) from a private Brazilian school, which answered to three intelligence tests and three metacognitive tests. The modeled hypotheses presented a good data-fit (χ² = 51.18; df = 19; CFI = 1.00; RMSEA = 0.05), showing that the general metacognitive ability explained general academic achievement rather than intelligence, but did not explain specific academic achievement. On the other hand, specific metacognitive ability explained specific academic achievement rather than intelligence, but did not explain general academic achievement. The predictive power of the general metacognitive ability was greater than fluid intelligence in the explanation of general academic achievement. In the same line, specific metacognitive ability had a greater predictive power than intelligence and specific knowledge in the explanation of specific academic achievement. Finally, a new structural model of metacognition and its role in academic achievement are proposed

Is Small Still Beautiful for the Strengths and Difficulties Questionnaire? Novel Findings Using Exploratory Structural Equation Modeling

Article first published online: June 17, 2018During the present decade a large body of research has employed confirmatory factor analysis (CFA) to evaluate the factor structure of the Strengths and Difficulties Questionnaire (SDQ) across multiple languages and cultures. However, because CFA can produce strongly biased estimations when the population cross-loadings differ meaningfully from zero, it may not be the most appropriate framework to model the SDQ responses. With this in mind, the current study sought to assess the factorial structure of the SDQ using the more flexible exploratory structural equation modeling approach. Using a large-scale Spanish sample composed of 67,253 youths aged between 10 and 18 years (M = 14.16, SD = 1.07), the results showed that CFA provided a severely biased and overly optimistic assessment of the underlying structure of the SDQ. In contrast, exploratory structural equation modeling revealed a generally weak factorial structure, including questionable indicators with large cross-loadings, multiple error correlations, and significant wording variance. A subsequent Monte Carlo study showed that sample sizes greater than 4,000 would be needed to adequately recover the SDQ loading structure. The findings from this study prevent recommending the SDQ as a screening tool and suggest caution when interpreting previous results in the literature based on CFA modeling.The author(s) received no financial support for the research, authorship, and/or publication of this articl

El Diario de Pontevedra : periódico liberal: Ano XVII Número 4805 - 1900 maio 19

<p>VSS = Very Simple Structure; BIC = Bayesian Information Criteria; EBIC = Extended Bayesian Information Criteria; MAP = Minimum Average Partial procedure; Kaiser = Kaiser-Guttman eigenvalue greater than one rule; PA = Parallel Analysis; EGA = Exploratory Graph Analysis. Low correlation = .2; Moderate Correlation = .5; High Correlation = .7.</p

Statistics by each method, from 1 to 10 factors.

<p>VSS = Very Simple Structure; BIC = Bayesian Information Criteria; EBIC = Extended Bayesian Information Criteria; MAP = Minimum Average Partial procedure; Kaiser = Kaiser-Guttman eigenvalue rule. The number of factors is chosen as follows: the highest value of the VSS statistic, the lowest value of the MAP, BIC and EBIC statistics, and the last observed eigenvalue greater than the simulated eigenvalue in the parallel analysis.</p

Psychology data from the “BAFACALO project: The Brazilian Intelligence Battery based on two state-of-the-art models – Carroll’s Model and the CHC model”

The BAFACALO’s dataset contains the answers from 292 Brazilian high-school students from a public school to 18 cognitive tests developed to assess different hierarchical levels of intelligence structure. Most of the participants were girls (53.40%), with ages ranging from 14 to 20 years old (Mean = 15.71, Standard Deviation = 1.15) and the average monthly household income varying from R$1,751 to R$ 3,500 (Reais). The BAFACALO’s tests were constructed by Gomes [1,2] based on the Educational Testing Service’s Kit of Factor-Referenced Cognitive Tests [3]

Network of partial correlations estimated during the exploratory graph analysis procedure showing seven latent dimensions in data from the Inductive Reasoning Developmental Test.

<p>Network of partial correlations estimated during the exploratory graph analysis procedure showing seven latent dimensions in data from the Inductive Reasoning Developmental Test.</p

ANOVA’s Partial eta squared effect sizes.

<p>VSS = Very Simple Structure; BIC = Bayesian Information Criteria; EBIC = Extended Bayesian Information Criteria; MAP = Minimum Average Partial procedure; Kaiser = Kaiser-Guttman eigenvalue greater than one rule; PA = Parallel Analysis; EGA = Exploratory Graph Analysis. In bold and underlined are the large effect sizes [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174035#pone.0174035.ref061" target="_blank">61</a>].</p

Mean accuracy and its standard deviation, for each method and each condition, for the two factor structure.

<p>VSS = Very Simple Structure; BIC = Bayesian Information Criteria; EBIC = Extended Bayesian Information Criteria; MAP = Minimum Average Partial procedure; Kaiser = Kaiser-Guttman eigenvalue greater than one rule; PA = Parallel Analysis; EGA = Exploratory Graph Analysis. Low correlation = .2; Moderate Correlation = .5; High Correlation = .7. The rows show the aggregate mean and standard deviation for each level of correlation (bold), sample size (bold and italicized) and number of items per factor (non-italicized).</p

Mean accuracy and its standard deviation, for each method and each condition, for the four factor structure.

<p>VSS = Very Simple Structure; BIC = Bayesian Information Criteria; EBIC = Extended Bayesian Information Criteria; MAP = Minimum Average Partial procedure; Kaiser = Kaiser-Guttman eigenvalue greater than one rule; PARAN = Parallel Analysis; EGA = Exploratory Graph Analysis. Low correlation = .2; Moderate Correlation = .5; High Correlation = .7. The rows show the aggregate mean and standard deviation for each level of correlation (bold), sample size (bold and italicized) and number of items per factor (non-italicized).</p