Determinants of Norm Compliance: Moral Similarity and Group Identification (Second Additional Data Collection)

Last registered on May 21, 2024

Pre-Trial

Trial Information

General Information

Title
Determinants of Norm Compliance: Moral Similarity and Group Identification (Second Additional Data Collection)
RCT ID
AEARCTR-0013299
Initial registration date
May 15, 2024

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
May 21, 2024, 10:56 AM EDT

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

Locations

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Primary Investigator

Affiliation
University of Cologne

Other Primary Investigator(s)

PI Affiliation
University of Michigan

Additional Trial Information

Status
In development
Start date
2024-05-19
End date
2024-06-30
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
What determines whether someone complies with a social norm? The social identity approach offers a mechanism for norm compliance: a person who feels similar to a group identifies more with that group and, in turn, complies with the group's norms. We used an economics experiment to test this mechanism. We manipulated the similarity between an individual and a social group by exogenously changing their similarity in moral values. Moral values were identified using a survey developed in conjunction with moral foundations theory. In one treatment, the subject and social group's moral values were similar, and in another, they were dissimilar. Subsequently, we measured group identification and behavior. To measure behavior, we used a modified rule-following task in which the social group expressed a normative expectation that subjects follow "the rule". We found that moral similarity increased group identification, and group identification increased rule compliance. We show that this behavior change was due to increased group norm sensitivity rather than changes in the group norms. We advance the study of social identity by establishing a causal pathway between group identification and behavior change.
External Link(s)

Registration Citation

Citation
Krupka, Erin and Alexander Schneeberger. 2024. "Determinants of Norm Compliance: Moral Similarity and Group Identification (Second Additional Data Collection)." AEA RCT Registry. May 21. https://doi.org/10.1257/rct.13299-1.0
Experimental Details

Interventions

Intervention(s)
We randomly assign participants to one of two treatment conditions. In each treatment, we introduce the participants to the moral position of a different social group. Through this treatment variation, we exogenously alter the moral similarity between our subjects and the social group (high or low moral similarity) and, consequently, the group identification towards the social group.
Intervention Start Date
2024-05-19
Intervention End Date
2024-06-30

Primary Outcomes

Primary Outcomes (end points)
Norm compliance (the number of balls in the blue bucket of the modified rule-following task in the Choice Experiment).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Group identification (the rating of the "Inclusion of Ingroup in the Self Scale" in the Choice Experiment).
Secondary Outcomes (explanation)
In our confirmatory analysis, we utilize the distance measure of this scale rather than the overlap measure.

Experimental Design

Experimental Design
The additional data collection includes one survey and one experiment:

The Pre-Screening Survey: In this survey, participants answer the Moral Foundations Questionnaire (Graham et al., 2011). Based on their responses, we calculate the progressivism index (Clark et al., 2017).

The Choice Experiment: We re-invite participants with a positive progressivism index. In this experiment, we introduce participants to the moral position of either social group, ask for a rating in a continuous version of the "Inclusion of Ingroup in the Self Scale" (Tropp & Wright, 2001) and let them play a modified version of the rule-following task. In this modified version, the rule "The rule is to put the balls in the blue bucket" is replaced by the rule "According to the members of Group A, the rule is to put the balls in the blue bucket".
Experimental Design Details
Not available
Randomization Method
When subjects participate in the Choice experiment, the experimental server randomly assigns them to either treatment. To achieve this, we use the Python module NumPy.
Randomization Unit
Individual level
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Choice Experiment: 2 treatments with 233 participants each.
Sample size: planned number of observations
Choice Experiment: 2 treatments with 233 participants each.
Sample size (or number of clusters) by treatment arms
Choice Experiment: 2 treatments with 233 participants each.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The sample size in the Choice Experiment is based on the following power calculation: We used the data from the original data to calculate an expected effect size of 0.23. Subsequently, we used G-Power (Faul, Erdfelder, Lang, & Buchner, 2007) to calculate the minimal required sample size for a one-tailed t-test with, with an effect size of 0.23, an alpha-error probability of 0.05, a power of 0.80 and an even allocation between treatments. Based on this power calculation, we require at least 233 subjects per treatment.
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Michigan - Health Sciences and Behavioral Sciences Institutional Review Board
IRB Approval Date
2020-03-02
IRB Approval Number
HUM00163304
Analysis Plan

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