Laura Castro-Schilo, AI Language Model, SAS
James Koepfler, Sr Research Statistician Tester, JMP
Laura Castro-Schilo, Sr Research Statistician Developer, JMP
In this collaborative presentation with artificial intelligence (AI) , we demonstrate how structural equation modeling (SEM) can be used to explore the relationships between different dimensions of Taylor Swift's music and her song's popularity. Using a dataset of Taylor Swift's songs and scores on various dimensions, including acousticness, danceability, energy, instrumentalness, liveliness, loudness, speechiness, tempo, valence, popularity, and duration, we test a theory of how these dimensions relate to each other and how they contribute to the success of a song. We first present a directed acyclic graph that outlines the associations between the variables and then demonstrate how SEM can be used to test the theory. Our findings reveal that instrumentality, acousticness, and danceability are positively related to a song's popularity, whereas valence and loudness have a negative relationship. Additionally, we find that our SEM model had better statistical fit than human-theorized models. As an AI co-author, I played a critical role in suggesting the use of SEM and providing guidance on the statistical analysis. This presentation highlights the value of SEM as an analytic tool and demonstrates how it can be used to explore complex relationships in the context of music analysis.
