Reference. Moehring, A., Schroeders, U., Leichtmann, B., & Wilhelm, O. (2016). Ecological momentary assessment of digital literacy: Influence of fluid and crystallized intelligence, domain-specific knowledge, and computer usage. Intelligence, 59, 170–180. http://dx.doi.org/10.1016/j.intell.2016.10.003
Abstract. The ability to comprehend new information is closely related to the successful acquisition of new knowledge. With the ubiquitous availability of the Internet, the procurement of information online constitutes a key aspect in education, work, and our leisure time. In order to investigate individual differences in digital literacy, test takers were presented with health-related comprehension problems with task-specific time restrictions. Instead of reading a given text, they were instructed to search the Internet for the information required to answer the questions. We investigated the relationship between this newly developed test and fluid and crystallized intelligence, while controlling for computer usage, in two studies with adults (n1 = 120) and vocational high school students (n2 = 171). Structural equation modeling was used to investigate the amount of unique variance explained by each predictor. In both studies, about 85% of the variance in the digital literacy factor could be explained by reasoning and knowledge while computer usage did not add to the variance explained. In Study 2, prior health-related knowledge was included as a predictor instead of general knowledge. While the influence of fluid intelligence remained significant, prior knowledge strongly influenced digital literacy (β=.81). Together both predictor variables explained digital literacy exhaustively. These findings are in line with the view that knowledge is a major determinant of higher-level cognition. Further implications about the influence of the restrictiveness of the testing environment are discussed.
Reference. Schroeders, U., Schipolowski, S., Zettler, I., Golle, J., & Wilhelm, O. (2016). Do the smart get smarter? Development of fluid and crystallized intelligence in 3rd grade. Intelligence, 59, 84–95. https://doi.org/10.1016/j.intell.2016.08.003
Abstract. There are conflicting theoretical assumptions about the development of general cognitive abilities in childhood: On the one hand, a higher initial level of abilities has been suggested to facilitate ability improvement, for example, prior knowledge fosters the acquisition of new knowledge (Matthew effect). On the other hand, it has been argued that school education with its special focus on promoting less able students results in a compensation effect. A third hypothesis is that the development of cognitive abilities is—as an outcome of the opposing effects—overall independent of the initial state. In this study, 1,102 elementary students in 3rd Grade worked on two versions of the Berlin Test of Fluid and Crystallized Intelligence at two time points with an interval of five months. Beside the question of how initial state and growth are related (Matthew vs. compensation effect), we considered performance gains in fluid intelligence (gf) and crystallized intelligence (gc) as well as cross-lagged effects in a bivariate latent change score model. Both for gf and gc there was a strong compensation effect. Mean change was more pronounced in gf than in gc. We considered student characteristics (interest and self-concept), family background (socio-economic status, parental education) and classroom characteristics (teaching styles) in a series of prediction models to explain these changes in gf and gc. Although several predictors were included, only few had a significant contribution. Several methodological and content-related reasons are discussed to account for the unexpectedly negligible effects found for most of the covariates.
Reference. Schroeders, U., Schipolowski, S., & Böhme, K. (2015). Typical intellectual engagement and achievement in math and the sciences in secondary education. Learning and Individual Differences, 43, 31–38. doi:10.1016/j.lindif.2015.08.030
Abstract. Typical Intellectual Engagement (TIE) is considered a key trait in explaining individual differences in educational achievement in advanced academic or professional settings. Research in secondary education, however, has focused on cognitive and conative factors rather than personality. In the present large-scale study, we investigated the relation between TIE and achievement tests in math and science in Grade 9. A three-dimensional model (reading, contemplation, intellectual curiosity) provided high theoretical plausibility and satisfactory model fit. We quantified the predictive power of TIE with hierarchical regression models. After controlling for gender, migration background, and socioeconomic status, TIE contributed substantially to the explanation of math and science achievement. However, this effect almost disappeared after fluid intelligence and interest were added into the model. Thus,we found only limited support for the significance of TIE on educational achievement, at least for subjects more strongly relying on fluid abilities such as math and science.
Comment. You can also see the slides of a talk I will give on 14th September the „Fachgruppentagung Pädagogische Psychologie“ in Kassel:
Reference. Schroeders, U., Schipolowski, S., & Wilhelm, O. (2015). Age-related changes in the mean and covariance structure of fluid and crystallized intelligence in childhood and adolescence. Intelligence, 48, 15–29. doi:10.1016/j.intell.2014.10.006
Abstract. Evidence on age-related differentiation in the structure of cognitive abilities in childhood and adolescence is still inconclusive. Previous studies often focused on the interrelations or the g-saturation of broad ability constructs, neglecting abilities on lower strata. In contrast, we investigated differentiation in the internal structure of fluid intelligence/gf (with verbal, numeric, and figural reasoning) and crystallized intelligence/gc (with knowledge in the natural sciences, humanities, and social studies). To better understand the development of reasoning and knowledge during secondary education, we analyzed data from 11,756 students attending Grades 5 to 12. Changes in both the mean structure and the covariance structure were estimated with locally-weighted structural equation models that allow handling age as a continuous context variable. To substantiate a potential influence of school tracking (i.e., different learning environments), analyses were additionally conducted separated by school track (academic vs. nonacademic). Mean changes in gf and gc were approximately linear in the total sample, with a steeper slope for the latter. There was little indication of age-related differentiation for the different reasoning facets and knowledge domains. The results suggest that the relatively homogeneous scholastic learning environment in secondary education prevents the development of more pronounced ability or knowledge profiles.
Reference. Schipolowski, S., Wilhelm, O., & Schroeders, U. (2014). On the nature of crystallized intelligence: the relationship between verbal ability and factual knowledge. Intelligence, 46, 156-168. doi: 10.1016/j.intell.2014.05.014
Abstract. While crystallized intelligence (gc) is recognized in many contemporary intelligence frameworks, there is no consensus as to the nature and contents of the construct. Originally conceptualized as capturing acquired skills and declarative knowledge in different content domains, more recent definitions and typical indicators focus on verbal ability. We investigated the relationship between verbal ability and declarative knowledge under consideration of individual differences in fluid intelligence in a large-scale assessment study with 6,701 adolescents. Structural equation modeling was used to examine the factorial distinctness of verbal ability and declarative knowledge with three analytical strategies: (i) Estimating correlations between latent variables, (ii) estimating the amount of unique variance in each factor after accounting for differences in the other ability constructs, and (iii) investigating associations with covariates including school achievement, students’ characteristics, and psychological traits. The correlation between latent variables representing verbal ability, measured with items from six language domains, and knowledge in 16 content domains was very high (ρ = .91), but significantly different from unity. About 17% of the variance in the knowledge factor was independent of individual differences in verbal ability and fluid intelligence. Associations with covariates revealed unique correlational patterns for each ability construct. The findings suggest that verbal ability and knowledge are closely related, but empirically distinguishable facets of crystallized intelligence. The discussion focuses on the construct validity of verbal tests for the measurement of gc and the interpretation of the common factor of a broad knowledge assessment as a causal variable.