What Kind of Music Do You Like? A Statistical Analysis of Music Genre Popularity Over Time
Volume 20, Issue 2 (2022), pp. 168–187
Pub. online: 20 April 2022 Type: Data Science In Action Open Access
9 December 2021
9 December 2021
6 March 2022
6 March 2022
20 April 2022
20 April 2022
Popular music genre preferences can be measured by consumer sales, listening habits, and critics’ opinions. We analyze trends in genre preferences from 1974 through 2018 presented in annual Billboard Hot 100 charts and annual Village Voice Pazz & Jop critics’ polls. We model yearly counts of appearances in these lists for eight music genres with two multinomial logit models, using various demographic, social, and industry variables as predictors. Since the counts are correlated over time, we use a partial likelihood approach to fit the models. Our models provide strong fits to the observed genre proportions and illuminate trends in the popularity of genres over the sampled years, such as the rise of country music and the decline of rock music in consumer preferences, and the rise of rap/hip-hop in popularity among both consumers and critics. We forecast the genre proportions (for consumers and critics) for 2019 using fitted multinomial probabilities constructed from forecasts of 2019 predictor values and compare our Hot 100 forecasts to observed 2019 Hot 100 proportions. We model over time the association between consumer and critics’ preferences using Cramér’s measure of association between nominal variables and forecast how this association might trend in the future.
Supplementary materialSupplementary Material
This includes the raw data files for both Hot 100 and Pazz & Jop listing singles and albums and genre assignments and all R code used in our analysis along with an explanatory README.txt file. Other items include: Full tables of genre counts for years 1974-2018; Tables of estimated multinomial logit model coefficients; Scatterplot matrix of candidate predictors; Various residual plots; Diagnostic plots for association analysis; Boxplots of 2019 Hot 100 forecasts from the model with the cubic time trend; and the cross-correlation function graph.
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