Editor’s note: Trevor Kvaran is vice president, analytics at marketing research firm Communicus, Tucson, Ariz.  

In advertising research, delivering great insight begins with asking the right questions, collecting the right data and applying the right analytical methods. Sometimes the most effective methods are also some of the most obscure.

I have been using a longitudinal research design for many years but have been surprised by how many researchers are unfamiliar with it. In this article, I will look at a simplified example that highlights why longitudinal data is so useful in understanding advertising effectiveness.

When trying to determine the impact of advertising on shifting purchasing behavior, as well as evaluate precisely how a campaign had effected this change, many analytical teams use a structural equation modeling (SEM) approach.

Providing insights into the ways purchasing behavior changes over time requires data that speaks to how people change and an analytical plan that accommodates this. This originates with a longitudinal research design measuring individual consumers at multiple points in time. Failing to implement this longitudinal component can lead to wildly misleading conclusions about the effects of advertising.

SEM is a multivariate analytical technique that can be used to model complex relationships between latent variables. In this simplified case, I suggest a causal model composed of three latent constructs:

These latent variables are constructed using a confirmatory factor analysis. This model is tightly focused on only three latent constructs but in practice, SEM models can be used to provide a clear picture of how numerous factors can both affect consumer behavior and be affected by marketing efforts.

Once researchers define the variables, they can build an SEM model to determine the causal relationships between advertising engagement, brand pe...