Reducing Prejudice Against Muslims: A Randomized Control Trial

Last registered on June 24, 2020
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Pre-Trial

Trial Information

General Information

Title
Reducing Prejudice Against Muslims: A Randomized Control Trial
RCT ID
AEARCTR-0004962
Initial registration date
December 03, 2019

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
December 04, 2019, 1:28 PM EST

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

Last updated
June 24, 2020, 10:34 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
United States of America
Region

Primary Investigator

Name
Mohamed Abbadi
Affiliation
University of Michigan - Ann Arbor
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Kentaro Toyama
PI Affiliation
Contact Investigator
PI Name
Ann Lin
PI Affiliation
Contact Investigator
PI Name
Yan Chen
PI Affiliation
Contact Investigator

Additional Trial Information

Status
In development
Start date
2020-06-25
End date
2020-07-31
Keywords
Other
Additional Keywords
Intergroup Relationships, Muslims, Debiasing, Trust Game
JEL code(s)
C7, C93, J7
Secondary IDs
Abstract
Evidence from different disciplines, including behavioral economics, psychology and political science, suggests that conversation-based interventions may reduce prejudice against out-groups, at least in the short term (Broockman and Kalla 2016). In this study, we plan to compare the effects of two interventions in a randomized online field experiment, using a representative sample of the United States population.
We will implement three experimental conditions in an online platform (Dynata), randomizing participants into a perspective-taking treatment, a value-consistency treatment, and a control condition. Participants will be asked to watch a video, do a writing exercise, play a trust game, and complete a survey. Results will be used to test the effectiveness of the two debiasing techniques.
External Link(s)

Registration Citation

Citation
Abbadi, Mohamed et al. 2020. "Reducing Prejudice Against Muslims: A Randomized Control Trial." AEA RCT Registry. June 24. https://doi.org/10.1257/rct.4962-1.2000000000000002
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Experimental Details

Interventions

Intervention(s)
We adapt two conversation-based interventions to an online setting and compare their effectiveness in reducing anti-Muslim bias. The perspective-taking intervention is inspired by Broockman and Kalla (2016), who illustrate that having short conversations about experiences of discrimination reduces prejudice against the transgender community. The value-consistency intervention follows Fein and Spencer (1997) and others, who find that participants writing about positive moral values taking leads to more positive ratings of job candidates from minority groups.
See section 2 in the attachment.
Intervention Start Date
2020-06-25
Intervention End Date
2020-07-31

Primary Outcomes

Primary Outcomes (end points)
Investment amount (measuring trust behaviors) and investor guess (measuring trust beliefs).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Response to the list elicitation questions (measuring aggregate bias against immigrants and Muslims).
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We implement an online randomized field experiment to evaluate two de-biasing techniques: perspective-taking and value-consistency. Participants from an online surveying platform (Dynata) are randomly assigned to one of three experimental conditions: a control condition, the perspective-taking, or the value-consistency treatments. Participants in the study are a representative sample of the United States population.
Our experimental protocol consists of five components; 1) an introductory section, 2) a short demographic survey, 3) a 10-minute interactive session varying across experimental conditions,4) an investment game (Berg et al., 1995) measuring potential bias at the individual level, and5) a post-experiment survey, which includes the list randomization measure of bias at the group level (Kuklinski et al., 1997; Glynn, 2013).

See section 2 in the attachment for details.
Experimental Design Details
Randomization Method
have two levels of randomization
1. We use the built-in randomization feature in Qualtrics survey platform (randomly assigns participants to one of the treatment arms)
2. The participants will select one of ten randomly positioned icons that have second-players (responders) names associated with them.
3. We use the built-in randomization feature in the web platform to randomly assign participants to one of three list elicitation questions.
Randomization Unit
Individuals
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A.
Sample size: planned number of observations
See section 2 of the attachment (pages 4, and 5)
Sample size (or number of clusters) by treatment arms
See section 2 of the attachment (pages 4, and 5)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See Power Calculation in the attachment (pages 4, and 5). Our power calculation is based on door-to-door canvassing data.
Supporting Documents and Materials

Documents

Document Name
Additional COVID-19 related Survey Questions
Document Type
survey_instrument
Document Description
We added a few questions related to the COVID-19 pandemic. We are trying to observe whether our interventions have spillover effects on other contexts.
File
Additional COVID-19 related Survey Questions

MD5: b5353a23fdbb4641242e809cd255c507

SHA1: dffe6c7c3308cbeb4bf3f2ebb3940c08edc29185

Uploaded At: April 23, 2020

Document Name
Pre-Anlysis Plan
Document Type
other
Document Description
The document contains the experimental design, power calculation, analysis plan and protocols for the three experimental conditions.
File
Pre-Anlysis Plan

MD5: 397dc8005a4804562b4bf902b8724120

SHA1: 0585b9380b565b8a7e58025b0255d5066ebdd289

Uploaded At: December 03, 2019

IRB

Institutional Review Boards (IRBs)

IRB Name
Health Sciences and Behavioral Sciences Institutional Review Board (IRB-HSBS) - University of Michigan
IRB Approval Date
2019-06-03
IRB Approval Number
HUM00163822
Analysis Plan

Analysis Plan Documents

Pre-Analysis Plan

MD5: 397dc8005a4804562b4bf902b8724120

SHA1: 0585b9380b565b8a7e58025b0255d5066ebdd289

Uploaded At: December 03, 2019

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