DDRIG in DRMS: Measuring Persuasion Without Measuring a Prior Belief: A New Application of Planned Missing Data Techniques

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Experiments on advice taking typically require a person receiving advice (an “advisee”) express their belief about the topic they are being advised about before receiving the actual advice. Then, after advice is given, the advisee reports a new belief. Researchers can then study the effects of advice by looking at how the advisee’s belief changed. However, there is a potential problem with this setup. Making the advisee explicitly consider their expectation before receiving advice can change its effects, but there is no way to study how the advisee’s belief changes without knowing what they believed beforehand. This research uses cutting edge statistical methods to estimate advisees’ prior beliefs based on other factors that are known about them, allowing researchers to understand the influence of advice under more realistic conditions, where advisees don’t have to directly think about what they believe before being advised. The scientific term for situations where measurement affects subsequent behavioral processes is “measurement reactivity”. This project’s approach to curing this measurement reactivity is to treat advisees’ prior beliefs as missing data. Advances in statistical methods allow researchers to impute missing data with without bias under the right set of circumstances. These methods are typically utilized when data are missing incidentally, but this project will experimentally randomize when data are missing, ensuring the correct conditions for the imputation to work effectively. Because these advisees never directly report their prior beliefs, the researchers can study how advice utilization occurs in the absence of measurement reactivity. This work has important implications for how advisors can help advisees optimize their expertise in many real-life scenarios. The methodological innovation of using a planned missing data experimental design to cure measurement reactivity also has the potential to generalize to other scientific domains where it is a potential confound.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.