Date of Award

Spring 5-2021

Document Type


Degree Name

Doctor of Philosophy (PhD)


Computational and Data Sciences

First Advisor

Erik Linstead

Second Advisor

Elizabeth Stevens

Third Advisor

Susanne M. Jaeggi


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

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Included in

Data Science Commons



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.