Personal music listening on mobile phones is rapidly growing as a popular means of everyday engagement with music. This portable and flexible style of listening allows for the immediate selection of music to fulfil emotional needs, presenting it as a powerful resource for emotion regulation. The experience sampling method (ESM) is ideal for observing music listening behavior, as it assesses current subjective experience during natural everyday music episodes. The current study aimed to develop a comprehensive model of personal music listening, and to determine the interaction of variables that produce various emotional outcomes. Data were collected from 195 participants using the MuPsych app: a mobile ESM designed for the real-time and ecologically valid measurement of personal music listening. Multilevel structural equation modelling was utilized to determine predictors of emotional outcomes on both experience and listener levels. Results revealed that music generally returns affect to a neutral state, but this is counteracted through the selection of mood-congruent music. Emotional reasons for listening, along with critical ranges of initial mood, were found to put listeners at risk of potentially undesirable outcomes. Finally, it was revealed that emotional outcomes are determined almost entirely within situations, which emphasizes the importance of accounting for contextual variables in all music and emotion research. This model has provided valuable insight into personal music listening, and the variables that are influential in producing desired emotional outcomes.
The measurement of everyday music use remains a challenge for researchers, with many of the available methodologies limited by intrusiveness or lack of ecological validity. The Experience Sampling Method (ESM) addresses such limitations by assessing current subjective experience at various times throughout participants’ everyday functioning. The aim of the current project was to develop and trial a mobile ESM (m-ESM) capable of collecting event-related data during natural listening episodes. This methodology was designed to maintain a natural and familiar listening experience for participants, and to collect real-time data on personal music listening. An application (app) was created which utilized mobile-device technology, and allowed combination of experience sampling with a personal music player. Analyses were performed on trial data from 101 participants to determine the efficacy of the m-ESM. Results indicated that this methodology would maintain ecological validity and cause minimal intrusion into everyday activities of the listener. Questionnaires were answered immediately at the time of listening, minimizing the problem of retrospective recall biases. This innovative methodology allows for the collection of a wealth of listening data that will advance the accurate measurement of everyday, personal music listening.