Latent Class Analysis: A Probabilistic Approach to Uncovering Latent Classes of TV Advertisements
Abstract:
In media measurement, examinations of relationships between audience behaviors (e.g., viewership of TV advertisements) and demographics (e.g., age and gender) are common practice. These examinations can follow a variable-centered approach that differentiates variables into dependent and independent variables (e.g., linear regression) or a person-centered approach which groups observations together according to their configural profile of characteristics (e.g., K-means; Morris & Kavussanu, 2008). This paper extends the boundaries of media research by using a latent clustering method — Latent Class Analysis (LCA) for analyzing advertisement viewership data. LCA is a probabilistic mixture modeling technique used to identify a set of discrete latent classes of observations, in this case advertisements, based on the values of a set of categorical indicators. The technique focuses on generating classes of advertisements that share similar patterns of responses and comparing with other classes to subsequently determine how they are differentially related to predictors and outcomes (Wang & Hanges, 2011).
The dataset used in our analysis is characterized by extreme volume (7M rows) and a variety of variable types (variables are a combination of continuous, categorical, and count). Preliminary findings indicate the classification of advertisements into two latent classes as defined by a set of categorical indicators of interest. The most salient features of Class 1 in comparison to Class 2 are a greater likelihood to air within News/Information and Sports TV genres as well as a greater likelihood to air on a weekday as opposed to a weekend or weeknight. We conduct additional analyses to understand how these latent classifications are related to a measure of what the media world refers to as co-viewing. This paper concludes with a discussion of the results and their implications along with recommendations for future research. R code and a sample dataset is provided for interested readers and practitioners.
Recommended Citation:
Craft, M., & Rao, K. (2023). Latent Class Analysis: A Probabilistic Approach to Uncovering Latent Classes of TV Advertisements. American Association for Public Opinion Research, Philadelphia, PA.
Attached Documents/Links:
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