Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

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

MPG-Autoren
/persons/resource/persons134596

Greb,  Fabian
Department of Music, Max Planck Institute for Empirical Aesthetics, Max Planck Society;

/persons/resource/persons130461

Schlotz,  Wolff
Scientific Services, Max Planck Institute for Empirical Aesthetics, Max Planck Society;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Greb, F., Steffens, J., & Schlotz, W. (2018). Understanding music-selection behavior via statistical learning: Using the percentile-Lasso to identify the most important factors. Music & Science. doi:10.1177/2059204318755950.


Zitierlink: https://hdl.handle.net/21.11116/0000-0000-DDC6-8
Zusammenfassung
Music 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.