This project will combine two types of data analysis strategies that are common in different fields. In cognitive psychology, the state of the art is cognitive process modeling. Data are analyzed by fitting mathematical representations of cognitive functions to data and interpreting the obtained parameter estimates. In psychometrics, the most common form of data analysis involves latent variable modeling. Batteries of small tests, each individual test imperfect, are jointly analyzed to uncover unobservable underlying factors, such as general intelligence or specific abilities. Cognitive modeling succeeds in extracting more information from data, whereas psychometrical methods are useful for pooling information across tasks or participants. Combining these two traditions involves the formal challenges of applying latent variable structure to cognitive model parameters, integrating the mathematical assumptions of both strategies, and investigating the effects of those combined assumptions. The project also involves technical challenges, such as implementing the methods in software. A new hybrid method called cognitive structural equation modeling will be applied in a retrospective analyses of data on cognitive executive functions and data on facets of working memory. Additionally, a cognitive structural equation model will be used to investigate the stability of participants' behavior in cognitive tasks over time.
The new method will be particularly well suited for the simultaneous analysis of different cognitive tasks in order to uncover underlying structure in participants' aptitude in the tasks. Improvements in psychological measurement are potentially useful in a variety of contexts, ranging from fundamental research in perception, cognition, memory, decision making, emotion, and development, to applied measurement in educational testing, job selection, and psychodiagnosis. Software developed as part of this project will be made freely available to researchers.
