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The Effects of Information Provision to Parents on Student Outcomes – A Randomised Controlled Trial
Last registered on November 06, 2019

Pre-Trial

Trial Information
General Information
Title
The Effects of Information Provision to Parents on Student Outcomes – A Randomised Controlled Trial
RCT ID
AEARCTR-0004990
Initial registration date
November 06, 2019
Last updated
November 06, 2019 9:04 AM EST
Location(s)

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Primary Investigator
Affiliation
KU Leuven
Other Primary Investigator(s)
PI Affiliation
KU Leuven, UNU-MERIT Maastricht University
Additional Trial Information
Status
On going
Start date
2018-08-15
End date
2020-07-01
Secondary IDs
Abstract
Parental involvement is increasingly used to improve student performance at school, yet, it remains unclear if the diverging background of parents could reinforce disparities among students. This study provides causal evidence on the effects on student performance of information provision to parents in a financial education course in which parents are involved by means of a homework assignment. Based on a randomised controlled trial with three different treatment groups and in total 1,253 students in 8th and 9th grade in Flanders, we identify the effects of a classroom intervention without parental involvement, parental involvement through homework and the provision of information to parents. In addition, effects of an interactive family task on students’ and parents’ learning are evaluated.
External Link(s)
Registration Citation
Citation
De Witte, Kristof and Joana Elisa Maldonado. 2019. "The Effects of Information Provision to Parents on Student Outcomes – A Randomised Controlled Trial." AEA RCT Registry. November 06. https://doi.org/10.1257/rct.4990-1.0.
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Experimental Details
Interventions
Intervention(s)
Schools are assigned to the four experimental conditions: control and three treatment conditions.
Intervention Start Date
2018-10-01
Intervention End Date
2018-11-30
Primary Outcomes
Primary Outcomes (end points)
We measure financial literacy by a test based on nine questions. The questions measure financial knowledge as well as financial behaviour.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
The financial literacy test also measures financial attitudes and intertemporal preferences.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Schools that registered for participation were randomised to the aforementioned four experimental conditions. We assessed the level of financial literacy of all students before as well as after followed the course. Students assigned to the control group completed the same tests at the same time as students in the treatment groups, without receiving any treatment between the tests.
Experimental Design Details
Not available
Randomization Method
Schools were randomly assigned to the different experimental conditions by a random number generator in STATA.
Randomization Unit
The treatment was clustered at school-level.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
28 schools
Given that the intervention has already been completed and data have been collected for both the pre-test and the post-test, the numbers reported refer to the observations for which both the pre-test and the post-test are available, i.e. the final sample for the analysis. The calculation of the minimal detectable effect size is also based on this final sample.

Sample size: planned number of observations
1,253 students
Sample size (or number of clusters) by treatment arms
Control group = 305 pupils, 9 schools
Treatment group 1 = 418 pupils, 10 schools
Treatment group 2 = 209 pupils, 4 schools
Treatment group 3 = 321 pupils, 5 schools
Average number of schools per condition = 7
Average number of pupils per condition = 313.25
Average number of pupils per school = 44.75
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The computation is based on List et al. (2011) and accounts for intracluster correlation in the calculation of the minimal detectable effect size. In our experimental setting, there are on average 7 schools in each experimental condition. Each school has on average 44.75 students. Computation in Stata based on the post-test shows that the intracluster correlation in the final sample equals 0.1. In the analysis, this intracluster correlation can be reduced by controlling for baseline characteristics of schools and students. Using the conventional power of 0.8 and a significance level of 0.1, the calculation results in a minimal detectable effect size of 0.52 standard deviations in case we would not control for students’ characteristics. Details of the calculation: According to List et al. (2011), in a clustered design, the minimum number of observations in each experimental group can be computed as follows: n=2(t_(α/2)+t_β)²(σ/δ)²(1+(m-1)ρ) This implies that the minimum detectable effect size is equal to: δ=σ/√(n/(2(t_(α/2)+t_β)²(1+(m-1)ρ))) Or the minimum detectable effect size expressed as a fraction of a standard deviation is equal to: δ/σ=1/√(n/(2(t_(α/2)+t_β)²(1+(m-1)ρ))) δ/σ=1/√(313.25/(2(1.96+0.84)²(1+(44.75-1)0.1)))=0.52 Reference List, J., Sadoff, S. and Wagner, M. (2011), So you want to run an experiment, now what? Some simple rules of thumb for optimal experimental design, Experimental Economics 14, 439-457
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number