Referrals for opportunities and persistence

Last registered on April 17, 2025

View Trial History

Pre-Trial

Trial Information

General Information

Title

Referrals for opportunities and persistence in education (ROPE)

RCT ID

AEARCTR-0015756

Initial registration date

April 07, 2025

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

April 17, 2025, 6:16 AM EDT

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

Locations

Country

Region

Country

Region

Primary Investigator

Name

Manuel Munoz

Affiliation

Luxembourg Institute of Socio-Economic Research

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

In development

Start date

2025-04-14

End date

2025-05-16

Keywords

Behavior, Education, Labor

Additional Keywords

Referrals, persistence, student motivation, higher education, application process

JEL code(s)

C93, D83, I21, I24

Secondary IDs

Prior work

This trial is based on or builds upon one or more prior RCTs.

Abstract

This study investigates whether the method of inviting students to apply for a university excellence award affects their persistence in completing the multi-stage application. Students at Universidad Autónoma de Bucaramanga (UNAB) in Colombia are invited via one of three channels: direct invitation (control), peer referral, or faculty referral. The study aims to uncover whether referral sources influence students' commitment, motivation, and overall completion rates (persistence). This study's findings will offer practical insights into optimizing engagement strategies in application processes.

External Link(s)

Registration Citation

Citation

Munoz, Manuel. 2025. "Referrals for opportunities and persistence in education (ROPE)." AEA RCT Registry. April 17. https://doi.org/10.1257/rct.15756-1.0

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Experimental Details

Interventions

Intervention(s)

Participants receive invitations to apply for the Student Excellence Award. They are invited through one of three channels:

1. Faculty Referral – Students are invited because a faculty member recommended them, and informed of this.
2. Peer Referral – Students are invited because a peer (considered for the award) recommended them, and informed of this.
3. Direct Invite (Control) – Students are invited based on their GPA.

All students will proceed through a multi-stage online application designed to mimic a competitive job application process. This includes a quick registration (basic demographics, beliefs, and peer nomination), psychometric assessments with strategic competition tasks, and a timed stress-test involving creative problem-solving.

Completion of each stage is recorded, and dropout rates across steps are analyzed. Data collection includes demographic and performance data (GPA, year of study), survey responses, and performance on psychometric and strategic tasks.

Intervention (Hidden)

Intervention Start Date

2025-04-14

Intervention End Date

2025-05-16

Primary Outcomes

Primary Outcomes (end points)

Completion rates across all application stages.

Primary Outcomes (explanation)

The main variable of interest is the proportion of students fully completing the multi-stage application.

Secondary Outcomes

Secondary Outcomes (end points)

- Making a referral
- Self-confidence
- Self-perception
- Performance in tests
- Entry into competition
- Creativity
- Time management

Secondary Outcomes (explanation)

- Making a referral: Choice to nominate a candidate for the award (only for faculty referrals and direct invites). This also includes the quality of the referral (being above median GPA) and whether the referred peer actually participates in the application process.
- Self-confidence: Accuracy in ranking of own GPA (incentivized)
- Self-perception: Text analysis of description of why the candidate believes he/she was nominated
- Performance in tests: score in ravens test, reading the eye in the mind test, and counting 1’s and 0’s in matrices test. I will look at a single score (combining all tests) and separate scores.
- Entry into competition: Choice of the tournament (instead of piece-rate) payment scheme for the matrices test.
- Creativity: Text analysis to the proposed solution to a problem.
- Time management: Ability to “press continue” on the timed-tests before the timer stops.

Experimental Design

Eligible undergraduate students are assigned to receive invitations through direct invitation (based on GPA), peer referral, or faculty referral. Outcomes measured include application completion rates, response quality, and overall engagement.

Participants are assigned to one of three invitation groups:

- Control (Direct Invitation): Receive a standard invitation based on academic criteria.
- Peer Referral Treatment: Receive the invitation and information that a peer recommended them.
- Faculty Referral Treatment: Receive the invitation with information that a faculty member recommended them.

Participants proceed through three application stages designed to assess persistence, engagement, and quality of responses. The experiment evaluates whether the referral channel affects student outcomes differently.

Primary Hypotheses:
H1: Students receiving referrals (peer or faculty) will show higher completion rates compared to students receiving direct invitations.
H2: Faculty referrals will lead to higher completion rates than peer referrals due to perceived authority and prestige.

Secondary Hypotheses:
H3: Referrals (peer or faculty) increase quality of engagement compared to direct invitations.
H4: Faculty referrals will increase application quality and depth of responses relative to peer referrals due to heightened social accountability and recognition.

Participants' completion rates, quality of engagement, and dropout patterns are tracked at each stage. Regression analyses control for baseline demographics, GPA, and year of study. Heterogeneity analysis by academic performance and year of study will also be conducted, as well as by demographics (including gender, social class and place of origin).

Experimental Design Details

Randomization Method

Randomization done by statistical software.

Randomization Unit

Individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

N/A

Sample size: planned number of observations

N = 3187 (number of students invited to apply for the award)

Sample size (or number of clusters) by treatment arms

A total of 3187 students will be invited to apply for the award. N = 198 were referred by faculty. N = 2,989 is the number of non-referred students that are eligible. A share N_p = 1000 (this is a maximum value) will be the students nominated by peers from this sample and the remaining share N_c = N – N_p = 1989 are students in the control who were directly invited to the study as they were not referred by anyone.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Assuming the control group is N_c = 1989, the peer-referred group is N_p = 1000, and the faculty referred group is N_f = 198. Also that the persistence rate in the control group is 0.15. The Minimum Detectable Effect (MDE) between control and peer referred students is 4-percentage-point increase (with 1000 peer-referrals using a two-sided chi-squared test). The MDE between control and faculty referrals is 7.9-percentage-point increase. And the MDE between peer and faculty referrals is 9.8-percentage-point increase (with 1000 peer-referrals using a two-sided chi-squared test).

Supporting Documents and Materials

IRB