Can Statistical Methods Reliably Detect Fraudulent Data? Examining The Utility Of P-Value Analyses, Extreme Effect Sizes, GRIM, And GRIMMER

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dc.contributor.advisorChristopher Green
dc.contributor.authorCrone, Gabriel
dc.date.accessioned2025-07-23T15:21:46Z
dc.date.available2025-07-23T15:21:46Z
dc.date.copyright2025-05-13
dc.date.issued2025-07-23
dc.date.updated2025-07-23T15:21:46Z
dc.degree.disciplinePsychology (Functional Area: Quantitative Methods)
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractData fraud occurs when one creates fake data (i.e., fabrication) or alters real data (i.e., falsification), often to support a desired research hypothesis. It is detrimental to science and occurs frequently, making it a pressing concern. Fortunately, there exist several statistical tools to detect it. Extant research, however, is largely inconsistent regarding which tools work well, and no research examines how well they differentiate fraudulent articles (containing fake data) from legitimate controls. The present thesis investigated how well four popular methods to detect data fraud differentiated retracted psychology articles from legitimate controls. I included the method of extreme effect sizes, p-value analysis, GRIM, and GRIMMER. Extreme effect sizes performed quite well: standardized effect sizes for retracted articles were noticeably larger than controls. The other methods performed at chance levels or worse. I contend that the method of extreme effect sizes could provide valuable information during investigations of potentially fraudulent studies.
dc.identifier.urihttps://hdl.handle.net/10315/43052
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectQuantitative psychology and psychometrics
dc.subjectPsychology
dc.subject.keywordsData fraud
dc.subject.keywordsFabrication
dc.subject.keywordsFalsification
dc.subject.keywordsStatistical Detection Tools
dc.subject.keywordsSummary data
dc.titleCan Statistical Methods Reliably Detect Fraudulent Data? Examining The Utility Of P-Value Analyses, Extreme Effect Sizes, GRIM, And GRIMMER
dc.typeElectronic Thesis or Dissertation

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