Understanding music-selection behavior via statistical learning: Using the percentile-Lasso to identify the most important factors

dc.contributor.authorGreb, Fabian
dc.contributor.authorSteffens, Jochen
dc.contributor.authorSchlotz, Wolff
dc.date.accessioned2018-12-19T07:02:36Z
dc.date.available2018-12-19T07:02:36Z
dc.date.issued2018
dc.description.abstractMusic psychological research has either focused on individual differences of music listening behavior or investigated situational influences. The present study addresses the question of how much of people's listening behavior in daily life is due to individual differences and how much is attributable to situational effects. We aimed to identify the most important factors of both levels (i.e., person-related and situational) driving people's music selection behavior. Five hundred eighty-seven participants reported three self-selected typical music listening situations. For each situation, they answered questions on situational characteristics, functions of music listening, and characteristics of the music selected in the specific situation (e.g., fast - slow, simple - complex). Participants also reported on several person-related variables (e.g., musical taste, Big Five personality dimensions). Due to the large number of variables measured, we implemented a statistical learning method, percentile-Lasso, for variable selection, which prevents overfitting and optimizes models for the prediction of unseen data. Most of the variance in music selection behavior was attributable to differences between situations, while individual differences accounted for much less variance. Situation-specific functions of music listening most consistently explained which kind of music people selected, followed by the degree of attention paid to the music. Individual differences in musical taste most consistently accounted for person-related differences in music selection behavior, whereas the influence of Big Five personality was very weak. These results show a detailed pattern of factors influencing the selection of music with specific characteristics. They clearly emphasize the importance of situational effects on music listening behavior and suggest shifts in widely-used experimental designs in laboratory-based research on music listening behavior.en
dc.identifier.eissn2059-2043
dc.identifier.issn2059-2043
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/8715
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-7844
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc500 Naturwissenschaften und Mathematikde
dc.subject.otherdaily lifeen
dc.subject.otherstatistical learningen
dc.subject.otherlasso regressionen
dc.subject.othersituational influencesen
dc.subject.othermusic listening behavioren
dc.subject.othermusic selection behavioren
dc.subject.otherpercentile-lassoen
dc.titleUnderstanding music-selection behavior via statistical learning: Using the percentile-Lasso to identify the most important factorsen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1177/2059204318755950
dcterms.bibliographicCitation.journaltitleMusic & Scienceen
dcterms.bibliographicCitation.originalpublishernameSAGE Publicationsen
dcterms.bibliographicCitation.originalpublisherplaceWashington, DCen
dcterms.bibliographicCitation.pageend17
dcterms.bibliographicCitation.pagestart1
dcterms.bibliographicCitation.volume1
tub.accessrights.dnbfree
tub.affiliationFak. 1 Geistes- und Bildungswissenschaften::Inst. Sprache und Kommunikation::FG Audiokommunikationde
tub.affiliation.facultyFak. 1 Geistes- und Bildungswissenschaftende
tub.affiliation.groupFG Audiokommunikationde
tub.affiliation.instituteInst. Sprache und Kommunikationde
tub.publisher.universityorinstitutionTechnische Universität Berlinde

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