Linear regression applied to agricultural data to model dry matter intake. Examination of researcher degrees of freedom and impact of choices: a multiverse analysis

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dc.contributor.advisorO'Sullivan, Kathleen (Catherine)
dc.contributor.advisorO'Mullane, John J.
dc.contributor.advisorFitzgerald, Tony
dc.contributor.advisorBuckley, Frank
dc.contributor.authorO'Shea Ryan, Fintanen
dc.contributor.funderUniversity College Corken
dc.date.accessioned2025-05-12T15:38:30Z
dc.date.available2025-05-12T15:38:30Z
dc.date.issued2024
dc.date.submitted2024
dc.description.abstractMultiverse analysis offers a transparent and systematic reporting of alternative results that could be obtained from plausible decisions made during the entire data analysis pipeline from data collection to reporting of results. These decisions are also referred to as the researcher degrees of freedom (DF), the flexibility with which data are collected, analysed and reported. This thesis answers the question what impact do researcher degrees of freedom have on the development of a linear regression model. A mini scoping review of the literature on multiverse analyses of regression modelling was conducted which highlighted a set of frequently investigated researcher DFs and identified previously overlooked researcher DFs. Multiverse analysis first emerged in response to the statistical crisis in psychology, but since then it has been successfully used in the fields of neuroscience, economics and education. This thesis presents the first use of multiverse analysis in agricultural science. The work in this thesis represents a collaboration between researchers at University College Cork and Teagasc, who provided the dataset used in this multiverse analysis. This data was originally collected as part of previous study by Teagasc in which researchers developed and validated a linear regression model for predicting dry matter intake in grazing cows. The potential researcher DFs in this study were identified and used with the set of researchers DFs from the mini scoping review to design this multiverse analysis. Reasonable alternative choices were proposed for each of the researcher DFs included in this multiverse analysis. A total of 2,592 different universes of analysis were included in this multiverse analysis, each the result of a unique combination of choices made at each researcher DF. The choices made at all stages of the data analysis pipeline were found to have had an affect on the results. The results of this multiverse analysis demonstrate the importance of expert knowledge in developing a linear regression model. The range of different results observed highlight the potential dangers of using black box style methods for developing models, which prioritise predictive ability over real world interpretation.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO'Shea Ryan, F. 2024. Linear regression applied to agricultural data to model dry matter intake. Examination of researcher degrees of freedom and impact of choices: a multiverse analysis. MRes Thesis, University College Cork.
dc.identifier.endpage182
dc.identifier.urihttps://hdl.handle.net/10468/17424
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectUniversity College Cork (Boole Fellowship in Mathematical Sciences)
dc.rights© 2024, Fintan O'Shea Ryan.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMultiverse analysis
dc.subjectRegression
dc.subjectResearcher degrees of freedom
dc.subjectDry matter intake
dc.titleLinear regression applied to agricultural data to model dry matter intake. Examination of researcher degrees of freedom and impact of choices: a multiverse analysis
dc.typeMasters thesis (Research)en
dc.type.qualificationlevelMastersen
dc.type.qualificationnameMRes - Master of Researchen
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