Novel Applications of Statistical and Machine Learning Methods to Analyze Trial-Level Data from Cognitive Measures

<p>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.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computational and Data Sciences

Year dc:date.available

2021

Author dc:creator

• Parlett, Chelsea

Contributors dc:contributor

• Erik Linstead
• Elizabeth Stevens
• Susanne M. Jaeggi

• Bayesian
• metamemory
• item response theory
• clustering
• judgement of learning
• Data Science

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_dissertations/19

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_dissertations-1019