Sensing psychological situations: Applying machine learning techniques on smartphone-sensed data to predict perceived characteristics of situations in daily life
Author(s) / Creator(s)
Bergmann, Maximilian
Kunz, Fiona
Schödel, Ramona
Abstract / Description
This study is conducted as part of a master thesis at the Department of Psychology of the Ludwig Maximilian University Munich. It investigates whether behavioral and situational data collected via smartphone sensing in daily life can predict individuals' psychological situation. For this purpose, this study applies a machine learning approach to predict individuals’ in situ ratings of perceived situational characteristics (DIAMONDS; Rauthmann et al., 2014) based on smartphone sensing data. Note that all independent variables (or features) defined in our preregistration protocol are also included in another study aimed for publication. All data used in this study is retrieved from the Smartphone Sensing Panel Study (SSPS; Basic Protocol of the SSPS is available under: http://dx.doi.org/10.23668/psycharchives.2901
Keyword(s)
Psychological Situation Situational characteristics DIAMONDS Mobile Sensing Smartphone Sensing Machine Learning PredictionPersistent Identifier
PsychArchives acquisition timestamp
2021-06-17 11:34:14 UTC
Publisher
PsychArchives
Citation
Bergmann, M., Kunz, F., & Schödel, R. (2021). Sensing psychological situations: Applying machine learning techniques on smartphone-sensed data to predict perceived characteristics of situations in daily life. PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.4928
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PreregistrationProtocol_SensingPsychologicalSituations.pdfAdobe PDF - 130.6KBMD5: 34b49384b85c8f22d00b755255c2a68b
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There are no other versions of this object.
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Author(s) / Creator(s)Bergmann, Maximilian
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Author(s) / Creator(s)Kunz, Fiona
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Author(s) / Creator(s)Schödel, Ramona
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PsychArchives acquisition timestamp2021-06-17T11:34:14Z
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Made available on2021-06-17T11:34:14Z
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Date of first publication2021-06-16
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Abstract / DescriptionThis study is conducted as part of a master thesis at the Department of Psychology of the Ludwig Maximilian University Munich. It investigates whether behavioral and situational data collected via smartphone sensing in daily life can predict individuals' psychological situation. For this purpose, this study applies a machine learning approach to predict individuals’ in situ ratings of perceived situational characteristics (DIAMONDS; Rauthmann et al., 2014) based on smartphone sensing data. Note that all independent variables (or features) defined in our preregistration protocol are also included in another study aimed for publication. All data used in this study is retrieved from the Smartphone Sensing Panel Study (SSPS; Basic Protocol of the SSPS is available under: http://dx.doi.org/10.23668/psycharchives.2901en
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Publication statusotheren
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Review statusunknownen
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CitationBergmann, M., Kunz, F., & Schödel, R. (2021). Sensing psychological situations: Applying machine learning techniques on smartphone-sensed data to predict perceived characteristics of situations in daily life. PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.4928en
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/4356
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.4928
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Language of contenteng
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PublisherPsychArchivesen
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Is related tohttps://doi.org/10.23668/psycharchives.2901
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Is related tohttps://doi.org/10.23668/psycharchives.12706
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Keyword(s)Psychological Situationen
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Keyword(s)Situational characteristicsen
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Keyword(s)DIAMONDSen
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Keyword(s)Mobile Sensingen
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Keyword(s)Smartphone Sensingen
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Keyword(s)Machine Learningen
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Keyword(s)Predictionen
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Dewey Decimal Classification number(s)150
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TitleSensing psychological situations: Applying machine learning techniques on smartphone-sensed data to predict perceived characteristics of situations in daily lifeen
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DRO typepreregistrationen
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Visible tag(s)Smartphone Sensing Panel Studyen