Bayesian two-part multilevel model for longitudinal media use data

Shelley A. Blozis ()
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Shelley A. Blozis: University of California, Davis

Journal of Marketing Analytics, 2022, vol. 10, issue 4, No 3, 328 pages

Abstract: Abstract 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.

Keywords: TV; Repeated measures; Hierarchical models; Mixed-effects models; Nonlinear mixed-effects models; Diary data; Mixed-effects location scale models (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1057/s41270-022-00172-9

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