Modeling individual subtests of the WAIS IV with multiple latent factors

Full-information item bifactor model for mathematical ability assessment in Chinese compulsory education quality monitoring

This study focuses on the measurement of mathematical ability in the Chinese Compulsory Education Qualification Monitoring (CCEQM) framework using bifactor theory. First, we propose a full-information item bifactor (FIBF) model for the measurement of mathematical ability. Second, the performance of the FIBF model is empirically studied using a data set from three representative provinces were selected from CCEQM 2015-2017. Finally, Monte Carlo simulations are conducted to demonstrate the accuracy of the model evaluation indices and parameter estimation methods used in the empirical study. The obtained results are as follows: (1) The results for the four used model selection indices (AIC, SABIC, HQ, BIC) consistently showed that the fit of the FIBF model is better than that of the UIRT; (2) All of the estimated general and domain-specific abilities of the FIBF model have reasonable interpretations; (3) The model evaluation indices and parameter estimation methods exhibit excellent accuracy, indicating that the application of the FIBF model is technically feasible in large-scale testing projects.

Evaluation of multidimensional models of WAIS-IV subtest performance

OBJECTIVE

The present study examined the extent to which the covariance structure of the WAIS-IV is best accounted for by models that assume that test performance is the result of group-level factors and multiple independent general factors.

METHOD

Structural models with one to four general factors were evaluated with either four or five group-level factors. Simulations based on four general factors were run to clarify the adequacy of the estimates of the allocation of covariance by the models.

RESULTS

Four independent general factors provided better fit than a single general factor for either model with four or five group-level factors. While one of the general factors had much larger loadings than all other factors, simulation results suggested that this might be an artifact of the statistical procedure rather than a reflection of the nature of individual differences in cognitive abilities.

CONCLUSIONS

These results argue against the contention that clinical interpretation of cognitive test batteries should primarily be at the level of general intelligence. It is a fallacy to assume that factor analysis can reveal the structure of human abilities. Test validity should not be based solely on the results of modeling the covariance of test batteries.

Modeling General and Specific Abilities

The present study examined issues related to structural modeling of abilities by the use of simulated data as well as analysis of the standardization data from the Woodcock–Johnson-III. In both cases, results were evaluated with cross-validation. Simulation results showed that cross-validation with an independent data set was more successful in identifying the model that was used to generate test scores than were several fit indices. Analysis of the Woodcock–Johnson-III standardization data with cross-validation showed that bifactor models provided better fit than hierarchical or correlated factor models. This was true considering both fit indices and cross-validation. General and specific factors shared a considerable amount of variance as evaluated by using the bifactor models to partition variance. The results of the present study suggest that there is a certain degree of ambiguity in determining the exact amount of covariance in test performance accounted for by general and specific factors. This calls in to question the practice of adjusting or controlling for general abilities when evaluating measures of specific abilities. Evidence for the validity of a construct should not be limited to factor analysis of tests purported to measure that construct.

Representation of the Cattell–Horn–Carroll Theory of Cognitive Abilities in the Factor Structure of the Dutch-Language Version of the WAIS-IV

The Cattell–Horn–Carroll (CHC) theory of cognitive abilities has been guiding in the revision of the Wechsler Adult Intelligence Scale–Fourth edition (WAIS-IV). Especially the measurement of fluid reasoning (Gf) is improved. A total of five CHC abilities are included in the WAIS-IV subtests. Using confirmatory factor analysis, a five-factor model based on these CHC abilities is evaluated and compared with the four index scores in the Dutch-language version of the WAIS-IV. Both models demonstrate moderate fit, preference is given to the five-factor CHC model both on statistical and theoretical grounds. Evaluation of the WAIS-IV according to CHC terminology enhances uniformity, and can be important when interpreting possible sources of index discrepancies. To optimally assemblage CHC and WAIS-IV, more knowledge of the interaction of abilities is needed. This can be done by incorporating intelligence testing in neuropsychological assessment. Using this functional approach contributes to a better understanding of an individual’s cognitive profile.