Psychology Ph.D. Dissertations

Specific Cognitive Abilities: Exploring the Use of Psychometric Network Analysis for Predicting Occupational and Educational Outcomes

Date of Award

2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.)

Department

Psychology/Industrial-Organizational

First Advisor

Samuel McAbee (Committee Chair)

Second Advisor

Melissa Keith (Committee Member)

Third Advisor

Jari Willing (Committee Member)

Fourth Advisor

Jeanne Novak (Other)

Abstract

The current study bridges the gap between the intelligence literature and applied psychology fields such as industrial-organizational psychology and educational psychology by examining the practical utility of psychometric network models of intelligence. Using data from Project TALENT, this study first demonstrated that a psychometric network provided a better fit to the data than common confirmatory factor models such as a g model, correlated factors models, hierarchical model, and a bifactor model. Second, the study demonstrated that specific ability scores based on the network may provide a marginal increase in the variance explained in outcomes of interest compared to g and factor scores; however, scores informed by the network may not provide much advantage in terms of subgroup differences over factor scores. For both validity and subgroup differences, the g composite was consistently the worst performing method in the study. The results of the study underline the importance of considering specific abilities in selection systems. In addition, researchers and practitioners should continue to explore psychometric networks.

Link to Full Text

Share

COinS