Quasi-experimental methods have been widely applied in marketing to explain changes in consumer behavior, firm behavior, and market-level outcomes. The purpose of these methods is to determine the presence of a causal relationship in the absence of experimental variation. This Journal of Marketing article offers guidance on how to successfully conduct research in marketing with quasi-experiments, and this webinar offers insights on key topics important to their use.
The webinar examines the use and usefulness of quasi-experimental methods for marketing by identifying the marketing settings and data where quasi-experimental methods are useful. It outlines how to structure an empirical strategy that allows researchers or analysts to identify a causal relationship between a marketing action and an outcome using methods such as difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. The importance of understanding and communicating the assumptions underlying the assertion of causality and establishing the generalizability of the findings is also discussed. Finally, the webinar explores how identification of the behavioral mechanism—whether individual, organizational, or market-level—can reinforce arguments of causality.
Featured speakers: Avi Goldfarb (University of Toronto), Catherine Tucker (Massachusetts Institute of Technology), and Yanwen Wang (University of British Columbia).
Full Journal of Marketing article: https://doi.org/10.1177/00222429221082977
Read the Scholarly Insight for this study here.
