What Kind of Music Do You Like? A Statistical Analysis of Music Genre Popularity Over Time

Petitbon, Aimée M.; Hitchcock, David B.

doi:10.6339/22-JDS1040

Journal of Data Science

What Kind of Music Do You Like? A Statistical Analysis of Music Genre Popularity Over Time

Volume 20, Issue 2 (2022), pp. 168–187

Aimée M. Petitbon David B. Hitchcock

https://doi.org/10.6339/22-JDS1040

Pub. online: 20 April 2022 Type: Data Science In Action

Open Access

Received
9 December 2021

Accepted
6 March 2022

Published
20 April 2022

Abstract

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 material

Supplementary 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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2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

baseline-category logit consumer preferences Cramér’s V forecasting multinomial logit model partial likelihood

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