Expert Beliefs and Community Organizers' Demands

Last registered on August 09, 2023

Pre-Trial

Trial Information

General Information

Title
Expert Beliefs and Community Organizers' Demands
RCT ID
AEARCTR-0011861
Initial registration date
July 31, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
August 09, 2023, 2:45 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Region

Primary Investigator

Affiliation
duke

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2023-07-31
End date
2024-07-31
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
We conducted an online survey among a sample of the U.S. population to gauge their attitudes toward a police technology. Using a survey experiment, we investigated the extent to which individuals' support or opposition to the technology would be influenced by presenting them with information about their own predictions and the predictions of experts regarding the impact of widely publicized incidents of police killings on the stock performance of companies that contract with police.
External Link(s)

Registration Citation

Citation
ba, bocar. 2023. "Expert Beliefs and Community Organizers' Demands." AEA RCT Registry. August 09. https://doi.org/10.1257/rct.11861-1.0
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
(1) Does exposing survey participants to their own and experts’ miscalibrations in predicting the
outcomes of the police industry following George Floyd’s murder impact their inclination to
support or oppose a company that offers a controversial police technology?
(2) Does a change in the name of a company offering a controversial police technology influence
the likelihood of respondents to oppose it?
Intervention Start Date
2023-08-03
Intervention End Date
2023-08-19

Primary Outcomes

Primary Outcomes (end points)
Willingness to Volunteer; Willingness to Volunteer-Support; Willingness to Volunteer-Oppose; Willingness to Sign
Primary Outcomes (explanation)
(1) Willingness to Volunteer: a binary variable that takes the value one if the respondent is
willing to volunteer (support or oppose the technology), zero otherwise.; (2) Willingness to Volunteer-Support: a binary variable that takes the value one if the respondent is willing to volunteer to support the technology, zero otherwise. (3) Willingness to Volunteer-Oppose: a binary variable that takes the value one if the respondent is willing to volunteer to oppose the technology, zero otherwise; (4) Willingness to Sign: a binary variable that takes the value one if the respondent is willing to sign a petition to oppose the technology, zero otherwise

Secondary Outcomes

Secondary Outcomes (end points)
Quality of Volunteering Task
Secondary Outcomes (explanation)
Quality of Volunteering Task: We plan to conduct an open-text analysis of the free response section of the survey. We will use this analysis to measure the quality of responses across various
participants and code their strength of support or opposition to the technology. If participants
choose either Old Firm Name or New Firm Name, we ask them to fill out a free-form text box
with reasons as to why they support that specific cause. We mention that this text will be sent
to advocates in either cause to use to build their database. Research assistants will independently
classify open-text responses (Duke undergraduate students). If there is a disagreement between
coders, the classification selected by the majority will be used.

Experimental Design

Experimental Design
We will conduct the analyses in a few steps. Noting that we already have several baseline characteristics for the individuals who have previously made predictions, we will not directly measure any
covariates in our survey.

This survey will follow the following steps: first, participants will be reminded of the predictions
they made previously and what the question they were asked previously. We will then randomize
participants into one of the two treatment arms, where they will receive information regarding how
experts predicted the price change. We then introduce participants into Old Firm Name Treatment
or New Firm Name Treatment, give them a brief overview of the company and the cause, and ask
them if they would be willing to volunteer or sign the open letter.
Experimental Design Details
Randomization Method
We use the ‘evenly present elements’ functionality in Qualtrics.
Randomization Unit
individual level
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Since we previously conducted this survey and received a total of 2,500 responses, this survey will
be bounded by that sample
Sample size: planned number of observations
No more than 2,500 responses
Sample size (or number of clusters) by treatment arms
First, we randomize participants into two groups (50% in each treatment arm): (1) Control and
(5) All Information. To conduct randomization, we use the ‘evenly present elements’ functionality
in Qualtrics. The two treatment conditions are: (1) Control: receive information on the true price change and own prediction, (2) All Treatment: Receive information on the relative accuracy of all experts (finance, police
officers and community organizers) as well as the true price change, and own prediction.

Next, we randomize if people see Old Firm Name and Stop - Old Firm Name as the name of
the cause or the new name New Firm Name and Stop - New Firm Name: (1) Old Firm Name Treatment (50%): All of the text regarding the specifics of the company and the cause will follow the ShotSpotter/Stop ShotSpotter verbiage; (2) New Firm Name Treatment (50%): All of the text regarding the specifics of the company and the cause will follow the SoundThinking/ Stop SoundThinking verbiage.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We run power calculations using the pwr package in R. We do not have strong priors on what the size of effects will be. We the fix type II error (power) at 0.8, the type I error ($\alpha$) at 0.05, and compute the required sample size so that our primary analysis will have a be powered to detect a Cohen's D and Cohen's H of 0.2. (The sample size was based on a previous study)
IRB

Institutional Review Boards (IRBs)

IRB Name
Duke
IRB Approval Date
2023-07-27
IRB Approval Number
2023-0383
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials