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Personal mindset and information processing in education: Experimental evidence from Cambodia
Initial registration date
February 16, 2020
February 18, 2020 2:17 PM EST
Wageningen University and Research
Other Primary Investigator(s)
University of Hannover
University of Goettingen
Additional Trial Information
We investigate if having a biased self-image affects how students take-up and process information that is in principle self-relevant. In particular, we aim to investigate if changing a student’s personal mindset (by allowing her to explore her own interests, and showing her that these interests can map into interesting careers), changes the way in which the student seeks and processes information about higher education (formal education and vocational training).
Gehrke, Esther, Friederike Lenel and Claudia Schupp. 2020. "Personal mindset and information processing in education: Experimental evidence from Cambodia." AEA RCT Registry. February 18.
In our intervention, students will go through an “interest exploration tool” on a tablet. At the end of the tool, students will be revealed their strongest personality types, as well as shown a list of 18 jobs (with description of typical tasks and required educational degree), ordered by their strongest type. After the students completed the tool, they participate in an information session on education paths, which provides detailed information on high schools and vocational training options as well as scholarship possibilities. This information session will be conducted in small groups.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
Primary outcomes are (1a) the accuracy of reported educational costs and (1b) retention of information on high school scholarship opportunities provided in the information session.
Primary Outcomes (explanation)
For (1a) students are asked at baseline and endline to estimate (i) the costs of transportation per month when going to high school (by motorbike), (ii) the cost of extra classes (i.e. informal tuition) per month, and (iii) the annual expenses for school material when going to high school. Additionally, students are asked to estimate the (iv) travel time (by motorbike) to high school.
The primary outcome for (1b) will be the accuracy of students' responses at endline when asked about non-governmental organizations that provide scholarships for the nearest high school.
Secondary Outcomes (end points)
Secondary outcomes measure (2a) interest in jobs that are outside the typical reference window and (2b) whether students are more likely to see themselves as being able to go to high-school.
Secondary Outcomes (explanation)
(2a) will be measured by the the number (and type) of jobs a student clicks on in the app when presented with a list of 18 jobs, and asked “Which of these jobs do you think could be interesting to you?” (elicited as part of the IET and the Placebo app). (2b) will be measured by (i) the weight that a student assigns to constraints that could keep her from pursuing higher education (measured at midline), and (ii) the interpretation of a graph that depicts average educational expenditures per grade in Cambodia (measured at midline).
We target students from grade 9 in northeast Cambodia. We construct a sample of lower secondary schools, and invite all students of one (randomly) selected class to participate in a workshop carried out at their school. All students that are willing to participate are randomly allocated into one of three arms: a) treatment group (40%), b) placebo group (40%), c) control group (20%).
Experimental Design Details
The assignment into treatment arms is done randomly in a two-stage process. First, we randomly assign one class of each school to receive our intervention. Randomization will be done by computer. Students within selected classes in treatment schools will then randomly be allocated to one of three treatment arms before the intervention begins (one the same day). Randomization will be done by having students blindly draw numbered badges from a bag.
Was the treatment clustered?
Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
a) 540 students in treatment, b) 540 students in placebo, c) 270 students in control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We calculate the smallest real effect sizes that we can detect at a 5% significance level with 80% power. We perform the calculation based on 540 students in treatment and 270 students in control (i.e. assuming the most conservative approach). When calculating the minimum detectable effect size (MDE), we take into account that we have baseline information on student age, gender, test scores, attendance and class size. We expect that these baseline characteristics can explain between 0% and 50% of the variation in the outcome variable.
The MDEs corresponding to the main treatment (i.e. comparing students in the treatments group to students in the control group) range between 0.15 of a standard deviation (SD) and 0.21 of a SD. MDEs are slightly smaller, when comparing students in treatment and placebo, and range between 0.12 (0.5 explained variance), and 0.17 (0.0 explained variance). These are the detectable effect sizes for outcomes with mean zero and a standard deviation of one (such as standardized cost estimates).
INSTITUTIONAL REVIEW BOARDS (IRBs)
Ethics Committee of the University of Goettingen
IRB Approval Date