Replication data for: Measuring and Bounding Experimenter Demand

Principal Investigator(s): View help for Principal Investigator(s) Jonathan de Quidt; Johannes Haushofer; Christopher Roth

Version: View help for Version V1

Name	File Type	Size	Last Modified
figures			10/20/2021 10:58:AM
tables			10/20/2021 10:58:AM

Project Description

Summary: We propose a technique for assessing robustness to demand effects of findings from experiments and surveys. The core idea is that by deliberately inducing demand in a structured way we can bound its influence. We present a model in which participants respond to their beliefs about the researcher's objectives. Bounds are obtained by manipulating those beliefs with "demand treatments." We apply the method to 11 classic tasks, and estimate bounds averaging 0.13 standard deviations, suggesting that typical demand effects are probably modest. We also show how to compute demand-robust treatment effects and how to structurally estimate the model.

Scope of Project

Subject Terms: [Demand Effects, Experimental design, Experimenter demand, Demand Characteristics]

JEL Classification:
      C83 Survey Methods; Sampling Methods
      C90 Design of Experiments: General
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
      D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

Geographic Coverage: United States

Universe: Online respondents in the United States. One sample is recruited from a representative panel (by gender, income, age, and region)

Data Type(s): experimental data

Methodology

Data Source: Experimental data

Unit(s) of Observation: Individual,

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Published Versions

V1 [2019-10-12]

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