More than g : Evidence for the Incremental Validity of Performance‐Based Assessments for Predicting Training Performance

Exploring the Relationship between Cognitive Ability Tilt and Job Performance

Most of the work examining the relationship between intelligence and job performance has conceptualized intelligence as g. Recent findings, however, have supported the claim that more specific factors of intelligence contribute to the prediction of job performance. The present study builds upon prior work on specific cognitive abilities by investigating the relationship between ability tilt, a measure representing differential strength between two specific abilities, and job performance. It was hypothesized that ability tilt would differentially relate to job performance based on whether or not the tilt matched the ability requirements of the job, and that ability tilt would provide incremental validity over g and specific abilities for predicting performance when the tilt matched job requirements. Hypotheses were tested using a large sample from the General Aptitude Test Battery (GATB) database. Ability tilt related with job performance in the expected direction for 27 of the 36 tilt-job combinations examined, with a mean effect size of .04 when the tilt matched job requirements. The mean incremental validities for ability tilt were .007 over g and .003 over g and specific abilities, and, on average, tilt explained 7.1% of the total variance in job performance. The results provide limited evidence that ability tilt may be a useful predictor in addition to ability level, and contribute to our understanding of the role of specific abilities in the workplace.

Using Bifactor Models to Examine the Predictive Validity of Hierarchical Constructs: Pros, Cons, and Solutions

The use of bifactor models has increased substantially in the past decade. However, bifactor models are prone to a nonidentification problem in the context of prediction that is not well recognized in the general research community. Moreover, the practical consequences of adopting different conceptualizations of hierarchical constructs when examining their predictive validity has received little attention. Therefore, Study 1 examined the statistical performance of bifactor models and investigated the effectiveness of an augmentation strategy to remedy the nonidentification problem. Monte Carlo simulations showed that the augmentation strategy is effective. The second simulation study demonstrated that researchers may arrive at different conclusions regarding the predictive validity of hierarchical constructs depending on their choice of models. In general, augmented bifactor models, which are restricted variants of the more general bifactor-(S·I-1) model, reasonably recovered the overall predictive validity ( R 2) of hierarchical constructs and led to correct substantive conclusions regarding the incremental validity of facets regardless of the true data-generation model given a sufficiently large sample ( n ≥ 600). The authors discussed implications of those findings and made practical recommendations for further users of bifactor models.