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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Parlett, Chelsea"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Erik Linstead","Elizabeth Stevens","Susanne M. Jaeggi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-05-01T07:00:00Z","date_published":"2021-05-01T07:00:00Z","updated_at":"2026-07-24T01:38:16Z","subjects":["Bayesian","metamemory","item response theory","clustering","judgement of learning","Data Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/19","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Erik Linstead","Elizabeth Stevens","Susanne M. 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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.
"]},{"key":"dc:source","label":"Dc Source","values":["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"]},{"key":"dc:title","label":"Title","values":["Novel Applications of Statistical and Machine Learning Methods to Analyze Trial-Level Data from Cognitive Measures"]}]}],"canonical_facts":{"dc:contributor":["Erik Linstead","Elizabeth Stevens","Susanne M. Jaeggi"],"dc:creator":["Parlett, Chelsea"],"dc:date.available":["2022-05-31T07:00:00Z"],"dc:description.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.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/19"],"dc:source":["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"],"dc:subject":["Bayesian","metamemory","item response theory","clustering","judgement of learning","Data Science"],"dc:title":["Novel Applications of Statistical and Machine Learning Methods to Analyze Trial-Level Data from Cognitive Measures"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:16Z"}