Journal of Data Science

Abstract: We apply model-based cluster analysis to data concerning types of democracies, creating an instrument for typologies. Noting several ad vantages of model-based clustering over traditional clustering methods, we fit a normal mixture model for types of democracy in the context of the majoritarian-consensus contrast using Lijphart’s (1999) data on ten variables for 36 democracies. The model for the full period (1945-1996) finds four types of democracies: two types representing a majoritarian-consensus contrast, and two mixed ones lying between the extremes. The four-cluster solution shows that most of the countries have high cluster membership probabilities, and the solution is found to be quite stable with respect to possible measurement error in the variables included in the model. For the recent-period (1971-1996) data, most countries remain in the same clusters as for the full-period data.

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.