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


Trial Information
General Information
The Effects of Information Provision to Parents on Student Outcomes – A Randomised Controlled Trial
Initial registration date
November 06, 2019
Last updated
November 06, 2019 9:04 AM EST
Primary Investigator
KU Leuven
Other Primary Investigator(s)
PI Affiliation
KU Leuven, UNU-MERIT Maastricht University
Additional Trial Information
On going
Start date
End date
Secondary IDs
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
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
Schools are assigned to the four experimental conditions: control and three treatment conditions.
Intervention Start Date
Intervention End Date
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
Students in the control group completed a pre-test at the start of the experiment and a post-test after four weeks, but did not receive any treatment. Students in all treatment groups completed the same pre-test before the intervention and the same post-test at the end of the classroom intervention. Students in all treatment groups received a homework assignment before the classroom intervention. Students in the first treatment group (‘class’) received a standard homework assignment without parental involvement. Students in the second treatment group (‘parental involvement’) and in the third treatment group (‘information’) received a modified version of the homework assignment with parental involvement. Students in the third treatment group (‘information’) were given before the class an information brochure for their parents. Students in all treatment groups followed a 4-hour financial education class on saving and investing. The course material was designed as an adaptive online learning path that students completed in groups of two. All course material was developed by high school teachers, tailored to the age and the ability of the target group. After the classroom intervention and the post-test at school, the intervention ended for the first treatment group. In the second and third treatment group, students received a second homework assignment which consisted of a family task to be completed with a parent. Parents of the students in all treatment groups received a pre-test after the student completed the pre-test at school. A post-test for parents was given to parents of students in all treatment groups after students completed the post-test at school.
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?
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 Name
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)