Previous literature has shown that music preferences (and thus preferred musical features) differ depending on the listening context and reasons for listening (RL). Yet, no research has investigated how features of music that people dance or move to relate to particular RL. Consequently, in two online surveys, participants (N=173) were asked to name songs they move to (‘dance music’, or DM). A subset of the same participants (N=105) additionally provided RL for their selected songs. To investigate relationships between the two, we first extracted audio features from DM using the Spotify API and compared those features with a baseline dataset representative of music in general (‘general music’, or GM). Analysis revealed that, compared to GM, DM has significantly higher levels of energy, danceability, valence, and loudness, and lower speechiness, instrumentalness and acousticness. Second, to identify potential subgroups of DM, a cluster analysis was performed on its audio features. Results of this cluster analysis suggested five subgroups of DM with varying combinations of audio features: “fast-lyrical”, “sad-instrumental”, “soft-acoustic”, “sad-energy”, and “happy-energy”. Third, a factor analysis revealed three main RL categories: “achieving self-awareness”, “regulation of arousal and mood”, and “expression of social relatedness”. Finally, we identified variations in people’s RL ratings for each subgroup of DM. This suggests that certain characteristics of DM are more suitable for listeners’ particular RL, which shape their music preferences. Importantly, the highest-rated RL items for dance music belonged to the “regulation of mood and arousal” category. This might be interpreted as the main function of DM. We hope that future research will elaborate on connections between musical qualities of DM and particular music listening functions.