Bayesian two-part multilevel model for longitudinal media use data

• Published in: Journal of marketing analytics Vol. 10; no. 4; pp. 311 - 328
• Main Author: Blozis, Shelley A.
• Format: Journal Article
• Language: English
• Published: London Palgrave Macmillan UK 01.12.2022; Palgrave Macmillan
• Subjects: Bayesian analysis; Business and Management; Consumer behavior; Consumers; Flexibility; Growth models; Longitudinal studies; Market analysis; Marketing; Maximum likelihood estimates; Multilevel; Original Article; Scale models; Time use; Within-subjects design; Mixed-effects location scale models; TV; Repeated measures; Mixed-effects models; Nonlinear mixed-effects models; Hierarchical models; Diary data
• ISSN: 2050-3318; 2050-3326
• DOI: 10.1057/s41270-022-00172-9

Multilevel models are effective marketing analytic tools that can test for consumer differences in longitudinal data. A two-part multilevel model is a special case of a multilevel model developed for semi-continuous data, such as data that include a combination of zeros and continuous values. For repeated measures of media use data, a two-part multilevel model informs market research about consumer-specific likeliness to use media, level of use across time, and variation in use over time. These models are typically estimated using maximum likelihood. There are, however, tremendous advantages to using a Bayesian framework, including the ease at which the analyst can take into account information learned from previous investigations. This paper develops a Bayesian approach to estimating a two-part multilevel model and illustrates its use by applying the model to daily diary measures of television use in a large US sample.