Conducting Research with Quasi-Experiments: A Guide for Marketers
SSRN Electronic Journal
2022
- 46Citations
- 10,741Usage
- 127Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
This paper aims to broaden the understanding of quasi-experimental methods among marketing scholars and those who read their work by describing the underlying logic and set of actions that make their work convincing. The purpose of quasi-experimental methods is, in the absence of experimental variation, to determine the presence of a causal relationship. First, the paper explores how to identify settings and data where it is interesting to understand whether an action causally affects a marketing outcome. Second, the paper outlines how to structure an empirical strategy that allows the author to identify a causal empirical relationship. The paper details the application of various methods to identify how an action affects an outcome in marketing, including difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. The paper emphasizes the importance of clearly communicating the identifying assumptions underlying the assertion of causality. Last, the paper explains how exploring the behavioral mechanism— whether individual, organizational, or market-level— can actually reinforce arguments of causality.
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