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How does students’ academic self-concept influence deceptive behavior?
Last registered on November 25, 2020


Trial Information
General Information
How does students’ academic self-concept influence deceptive behavior?
Initial registration date
November 25, 2020
Last updated
November 25, 2020 10:30 AM EST

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Primary Investigator
Research Center for Educational and Network Studies, Hungarian Academy of Sciences, Centre for Social Sciences; TÁRKI Social Research Institute, Budapest
Other Primary Investigator(s)
Additional Trial Information
On going
Start date
End date
Secondary IDs
Academic self-concept is long hypothesized to drive students’ deceptive behavior – though there are few experimental tests of this hypothesis. Our aim with this experiment is to fill this gap. We ask how students’ randomly induced academic self-concept affects their engagement with deception. We define deception as intended misreporting of data to obtain a more valuable outcome – a behavior that has relevant consequences in the context of education.
External Link(s)
Registration Citation
Keller, Tamas. 2020. "How does students’ academic self-concept influence deceptive behavior? ." AEA RCT Registry. November 25. https://doi.org/10.1257/rct.6818-1.0.
Experimental Details
We conduct our current experiment through an online application embedded in a ca 30 minutes long questionnaire.

The questionnaire contains a grade-specific ca 15 minutes long math tests with 6 multiple choice questions

In the treatment phase, students are randomized into two groups that receive either positive (treated group) or no feedback (control group) about their performance on the math test. Positive feedbacks affirm students’ initial self-concept and might boost it.

Students participate in the experiment in their schools. The participating schools agreed that the homeroom teacher supervises all students in the classroom during a standard school day, providing controlled conditions for the experiment.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
We measure students’ deceptive behavior or cheating with a binary variable = 1 if students fraudulently claimed to roll 6, and = 0 otherwise since students rolled with 5-sided dice, the variable measures willful deception.
Primary Outcomes (explanation)
Students are incentivized to cheat. They roll virtual dice in an external online application that is not connected to the survey program. After students have rolled the dice, they are asked to report the number they rolled. Students are prompted that if they rolled 6 they receive a more valuable gift than if they rolled 1, 2, 3, 4, or 5. Researchers will not know the number that students rolled. However, students roll a 5-sided dice that never produces 6 –information that is communicated with students.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
In the pre-treatment phase, students solve a grade-specific math test.

The treatment is receiving positive feedback. Students receive feedback concerning their performance, but they receive the feedback randomly, which is, therefore, not connected to their actual performance.

Students are incentivized to cheat in the post-treatment phase
Experimental Design Details
Not available
Randomization Method
Randomization was done by a computer
Randomization Unit
We randomize individual students
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
At the time of pre-registration, 102 classrooms from 20 schools agreed to participate in the experiment with approximately 1,900 students.
Sample size: planned number of observations
Approximately 1,900 students are in the participating school. We do not know the response rate yet, since the experiment is on the field.
Sample size (or number of clusters) by treatment arms
At the time of pre-registration, 102 classrooms from 20 schools agreed to participate in the experiment.
We do not have treatment arms, since individual students (within the classroom) are randomized to treated/control groups at the time they log in to the application.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Review Board of Centre for Social Sciences
IRB Approval Date
IRB Approval Number
Analysis Plan

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