Discovering Careless Response Behavior in Psychometric Data

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dc.contributor.authorLehmann, Rene
dc.contributor.authorBengart, Paul
dc.contributor.authorVogt, Bodo
dc.date.accessioned2024-12-26T21:07:07Z
dc.date.available2024-12-26T21:07:07Z
dc.date.issued2025-01-07
dc.description.abstractIn psychological healthcare considering bipolar Likert scales data as compositional data can enhance statistical validity. Applying an isometric log-ratio transformation yields interval scaled real-valued data. It increases the normal approximation of item response means, reduces statistical biases and enhances the statistical power of the Pearson correlation test and two-sample t-tests (paired and unpaired) affecting linear regression, partial least squares path modeling and moderator analysis. Mental overload, missing attention, faking or social desirability can corrupt a test person's answers in a psychometric survey. As a result, the corresponding questionnaire data are useless affecting subsequent analyses and interpretations. Aiming to detect careless response behavior as statistical outliers we compare the well-known Mahalanobis-distance to a multivariate projection pursuit method. Performing outlier detections with traditional and with isometric log-ratio transformed data we point out the superiority of the compositional data interpretation of psychometric bipolar scales data.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.400
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other3d501b19-37ff-41ec-bdf8-a35b14948e50
dc.identifier.urihttps://hdl.handle.net/10125/109243
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Support for Healthcare Processes and Services
dc.subjectbipolar likert scale; isometric log-ratio transformation; outlier; careless response; mahalanobis-distance; projection pursuit
dc.titleDiscovering Careless Response Behavior in Psychometric Data
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage3316

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