Date of Award
Spring 5-2021
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computational and Data Sciences
First Advisor
Erik Linstead
Second Advisor
Elizabeth Stevens
Third Advisor
Susanne M. Jaeggi
Abstract
Many cognitive tasks and measures can benefit from trial-level analyses including Item Response Theory models as well as other Bayesian and Machine Learning models. Specifically, this dissertation focuses mainly on task-based measures of metamemory and how within-set variability as well as item-level characteristics can improve the inferences researchers make about these measures.First, a clustering analysis of judgements of learning across a task is examined in order to detect different participant strategies on a metamemory task and whether strategy use differs by age. Second, the benefits of using item response theory models to analyze both individual and item-level differences in metamemory tasks are discussed, and applications to multiple datasets are provided. Third, an extended, hierarchical item response theory model was applied to the Child Risk Utility Measure, a tablet-based lab measure used to measure risk taking in preschool aged children. Finally, multiple Bayesian logistic based regression models (including a cumulative logit model, logistic regression model, and zero-one-inflated beta regression model) are applied to the metamemory task described previously to demonstrate the benefits of performing item-level analyses especially as it pertains to differences in the variability of judgements of learning in addition to mean differences between groups. Item or trial-level analyses have many benefits when applied to cognitive tasks and measures and can provide deeper insight into observed effects.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
C. Parlett, "Novel applications of statistical and machine learning methods to analyze trial-level data from cognitive measures," Ph.D. dissertation, Chapman University, Orange, CA, 2021. https://doi.org/10.36837/chapman.000273