Speeded testing in the assessment of intelligence gives rise to a speed factor (original) (raw)
Related papers
2000
According to mental speed theory of intelligence, the speed of information processing constitutes an important basis for cognitive abilities. However, the question, how mental speed relates to real world criteria, like school, academic, or job performance, is still unanswered. The aim of the study is to test an indirect speed-factor model in comparison to rivaling models explaining the relationships between
Separating power and speed components of standardized intelligence measures
Intelligence, 2017
Gulliksen (1950) established the distinction between pure power and pure speed intelligence tests. Most standardized measures combine power and speed requirements. The speediness component affects test scores' reliability and validity, since it involves variance not due to the mental ability of interest. Here we propose the use of the Stafford's Speediness Quotient (SQ, 1971) for identifying items biased by the speed component. We developed two converging methods based on Structural Equation Modeling (SEM) to assess the validity of the SQ index. The methods concurrently identify items substantially affected by speededness in three standardized fluid intelligence tests with different speed requirements. Basing on the SQ at the item level, a simple strategy for separating the power and speed components of mental ability tests applied under time constraints is proposed. This strategy allows an estimation of the respondents' level uncontaminated by the speed unwanted variance. This procedure only requires right/wrong responses (e.g., does not need any information external to the test, such as response times) and it is appropriate for medium-small sized samples. A rule of thumb is suggested for identifying items affected by speediness. The simplicity of the proposed procedure allows its use in applied settings for detecting and controlling speed-related variance in tests' scores.
Cahiers de Psychologie Cognitive/Current Psychology …, 1991
Reviews research concerning biologically based intelligence. The relationship between elementary cognitive measurements considered as indexes of information-processing speed and psychometric intelligence is discussed. Certain results are discussed, including measurements of averaged evoked potentials, of basic information processing parameters, of reaction time (RT), and of inspection time. The underlying models are described, and the weakness of certain correlations is explained. Caution is advised in advancing a causal relationship between information-processing speed and intellectual competence. (French abstract) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Intelligence , 2014
We posit that fluid intelligence (Gf) develops in four cycles, with two phases in each, each distinctly connected with changes in processing speed and working memory. New representational units emerge in the first phase of each cycle at 2-4 (representations), 6-8 (inference based concepts), and 11-13 years (principles) and they are integrated into wider systems in the second phase, at 4-6, 8-11 and 13-16 years. We hypothesized that cycle transitions are better predicted by speed and phase transitions by working memory. To test this hypothesis several published studies were selected which measured speed, WM, and Gf at one or more of the age phases concerned. In structural equation models applied on each phase speed was regressed on age, working memory was regressed on age and speed, and Gf was regressed on all three. In line with the hypothesis, in the first phase of each cycle the speed-Gf relations were high and WM-Gf relations were low; this pattern was inverted in the second phase. The role of executive processes strengthened in the second phase of all cycles. The implications for developmental and differential theories of intelligence are discussed.
The present study examined the relations of general, fluid and crystallized intelligence with three cognitive functions – speed of processing, attention and working memory (WM) – in 158 7- to 18-year-old children and adolescents. Multiple measures of each of these cognitive functions were obtained. Intelligence was assessed using the Wechsler Abbreviated Scale of intelligence (WASI). Structural equation modeling was performed to determine which cognitive function served as the best predictor of intelligence. The results showed that only WM predicted general, fluid and crystallized intelligence when controlling for the other two cognitive functions. Neither processing speed nor attention significantly predicted intelligence. These findings indicate that WM is the main cognitive function underlying general, fluid and crystallized intelligence in children and adolescents. Moreover, results indicated that age-related changes in WM lead directly to developmental changes in intelligence (general, fluid and crystallized).
Intelligence, 2011
- used generalizability theory to test the reliability of general-factor loadings and to compare three different sources of error in them: the test battery size, the test battery composition, the factor-extraction technique, and their interactions. They found that their general-factor loadings were moderately to strongly dependable. We replicated the methods of Floyd et al. (2009) in a different sample of tests, from the Minnesota Study of Twins Reared Apart (MISTRA). Our first hypothesis was that, given the greater diversity of the tests in MISTRA, the general-factor loadings would be less dependable than in Floyd et al. (2009). Our second hypothesis, contrary to the positions of Floyd et al. (2009) and Jensen and Weng (1994), was that the general factors from the small, randomly-formed test batteries would differ substantively from the general factor from a wellspecified hierarchical model of all available tests. Subtests from MISTRA were randomly selected to form independent and overlapping batteries of 2, 4 and 8 tests in size, and the general-factor loadings of eight probe tests were obtained in each battery by principal components analysis, principal factor analysis and maximum likelihood estimation. Results initially indicated that the general-factor loadings were unexpectedly more dependable than in ; however, further analysis revealed that this was due to the greater diversity of our probe tests. After adjustment for this difference in diversity, and consideration of the representativeness of our probe tests versus those of , our first hypothesis of lower dependability was confirmed in the overlapping batteries, but not the independent ones. To test the second hypothesis, we correlated g factor scores from the random test batteries with g factor scores from the VPR model; we also calculated special coefficients of congruence on the same relation. Consistent with our second hypothesis, the general factors from small nonhierarchical models were found to not be reliable enough for the purposes of theoretical research. We discuss appropriate standards for the construction and factor analysis of intelligence test batteries.
Journal of Psychoeducational Assessment, 2009
This study investigated the factor structure of the Reynolds Intellectual assessment Scales (RIaS) using rigorous exploratory factor analytic and factor extraction procedures. The results of this study indicate that the RIaS is a single factor test. Despite these results, higher order factor analysis using the Schmid-Leiman procedure indicates that all subtests are aligned with their theoretically consistent factors. all analyses in this study, including the minimum average partial test, parallel analysis, the Schmid-Leiman procedure, as well as principal factors with orthogonal and oblique rotation, support interpretation at the composite intelligence index level and suggest caution when moving to interpretation at the verbal and nonverbal index levels. The memory subtests should continue to be separated from the main IQ battery because of poor g-loadings and contribution to cross loadings of the intelligence subtests.
Learning and Individual Differences, 2012
The purpose of this study was to replicate the structure of mental speed and relations evidenced with fluid intelligence (Gf) found in a number of recent studies. Specifically, a battery of computerized tasks examined whether results with paper-and-pencil assessments held across different test media. Participants (N = 186) completed the battery, which incorporated 20 elementary cognitive tasks, 4 broad speediness (Gs) measures, and 5 Gf markers. Competing measurement models were tested. A higher-order model, with a general mental speed factor and 7 task-class specific factors fit the data well. Gs could not be distinguished from general mental speed. Besides the general mental speed factor, two task-class specific factors were moderately related to Gf. These findings strengthen the evidence for a multifacted structure of mental speed, and highlight the importance of specific speed task-classes in accounting for meaningful outcomes.