Measure of Emotional Episodes with Music (MEEM): Development and psychometric evaluation of a modular instrument

Emotional episodes experienced with music are embedded within the milieu of everyday life, yet most existing psychometric instruments only capture trait-like tendencies rather than state-like contextual factors. In this article, we detail the development of the Measure of Emotional Episodes with Music (MEEM) as a modular solution to capture situational and contextual dimensions of music-related emotional experiences. Building upon prior content-validity evaluation and grounded in the Episode Model, we tested the hypothesised dimensionality for the five constructs across two online vignette experiments. Evaluative confirmatory factor analyses (CFA) in the first experiment supported the assumed latent structure for each of five constructs, showing good model fit and clear differentiation among sub-constructs. Several sub-constructs were refined to more aptly reflect the specific facets captured by the best-performing items. Taking a three item model for each construct forward into a second experiment, CFA again supported the first experiment results and the original hypothesised structure. Associations between the five MEEM scales and two types of emotion ratings (HAAS and GEMIAC) were gathered as an initial indicator of convergent validity. Analysis of a factor structure combining all five constructs did not favour a hierarchical model specified by a single higher-order latent factor but instead a multidimensional interpretation, consistent with Episode Model assumptions. MEEM consists of five separate scales, each representing a theoretically derived construct—with two or three sub-constructs—measured with 35 items in total. Concurrent but distinct development of five constructs permits individualised applications for each of the five scales. This modular design of MEEM allows researchers to administer either the full battery or selected scales depending on their research aims. By targeting emotional states as dynamic, situated, and functionally grounded processes, MEEM provides a meaningful alternative to trait-based instruments, and offers a foundation for future validation and cross-contextual applications.