Factors affecting university application decisions

Last registered on September 23, 2024

Pre-Trial

Trial Information

General Information

Title
Factors affecting university application decisions
RCT ID
AEARCTR-0013680
Initial registration date
June 27, 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
July 01, 2024, 12:14 PM EDT

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

Last updated
September 23, 2024, 3:28 PM EDT

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

Locations

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Primary Investigator

Affiliation
MIT

Other Primary Investigator(s)

PI Affiliation
MIT

Additional Trial Information

Status
In development
Start date
2024-10-01
End date
2026-06-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study looks into the different factors that influence student decisions on university application and attendance.
External Link(s)

Registration Citation

Citation
Tadjfar, Nagisa and Kartik Vira. 2024. "Factors affecting university application decisions." AEA RCT Registry. September 23. https://doi.org/10.1257/rct.13680-2.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
The intervention will involve providing secondary school students in the UK with information, mentorship, and subsidized in-person visits to universities, with the treatment arms varying the content and nature of each.
Intervention Start Date
2024-10-01
Intervention End Date
2026-01-01

Primary Outcomes

Primary Outcomes (end points)
Short-run outcomes (collected at midline and endline surveys):
- Interest in mentorship / further exposure to researcher-assigned universities*
- Stated interest in applying to researcher-assigned universities*

Long-run outcomes (collected by schools after university applications are submitted):
- Applications / attendance to researcher-assigned universities*
- Applications / attendance to mentor's university
- Applications / attendance to university with subsidized visit

*Note: researcher-assigned universities are sets of universities that are suitable for the student based on their grade profiles (universities to which students with similar grades have attended based on national data). Researcher-assigned universities will exclude universities already familiar to the students such as the universities their parents and/or siblings have attended as well as universities that are commonly attended by students at their school.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We will look into heterogeneity of treatment effects on the primary outcomes by demographic similarity between participating students and mentors (where demographics include socioeconomic status, ethnicity, gender, and location). For instance, we will examine whether female students matched with female mentors were more likely to apply to their mentor's university compared to female students matched to male mentors.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design consists of two treatment arms and a control group with randomization at the individual student level. All students will complete baseline, midline, and endline surveys before and after the intervention.
Experimental Design Details
Not available
Randomization Method
Randomization will be conducted on a computer. Within each participating school, we will receive a list of participating students, and then randomly assign students from this list to either the control arm C, treatment T1, or treatment T2.
Randomization Unit
Individual, stratified by school
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
As the randomization is at the individual level, the number of clusters at the unit of randomization level is the same as the number of individuals. However, we will be sampling particular schools, and our central estimate is that we will recruit 20 schools.
Sample size: planned number of observations
Our central estimate is that we will have a sample size of 2000 individuals, but we may get fewer or more as we are still in the process of recruiting schools to the study. We present MDEs as a function of sample size below to give a sense of how power will depend on the sample size.
Sample size (or number of clusters) by treatment arms
Given budgetary constraints, we plan to assign 100 students to treatment arm T2, 400 to arm T1, and the remainder to arm C. Under our central estimate of 2000 students, we will thus have 1500 students in arm C.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our primary outcomes are all binary / probability outcomes, so for power calculations we use the standard formula for the variance of a proportion. The minimum detectable effect size then depends on the baseline probability of the outcome and the sample size in each arm. We have limited information about the baseline probabilities for most primary outcomes, but we expect many to fall between 0.05 and 0.2. Below we present power under baseline probabilities of 0.05, 0.1, and 0.2. We assume assignment of 1500 students to C, 400 to T1, and 100 to T2, as in our central estimate for sample size described above. For comparisons of arm T1 against C, we have the following MDEs: Baseline 0.05: 3.4 pp Baseline 0.1: 4.7 pp Baseline 0.2: 6.3 pp For comparisons of arm T2 against C, we have the following MDEs: Baseline 0.05: 6.3 pp Baseline 0.1: 8.7 pp Baseline 0.2: 11.6 pp
IRB

Institutional Review Boards (IRBs)

IRB Name
MIT Committee on the Use of Humans as Experimental Subjects (COUHES)
IRB Approval Date
2024-01-26
IRB Approval Number
2306001025