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The Effectiveness of Parental Tutoring Compared to Teaching at School
Last registered on September 26, 2019

Pre-Trial

Trial Information
General Information
Title
The Effectiveness of Parental Tutoring Compared to Teaching at School
RCT ID
AEARCTR-0004707
Initial registration date
September 13, 2019
Last updated
September 26, 2019 11:14 AM EDT
Location(s)

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Primary Investigator
Affiliation
KU Leuven
Other Primary Investigator(s)
PI Affiliation
KU Leuven, UNU-MERIT Maastricht University
PI Affiliation
IRES, UCLouvain and FNRS
Additional Trial Information
Status
In development
Start date
2019-09-15
End date
2020-12-31
Secondary IDs
Abstract
This intervention seeks to measure the effects of parental tutoring at home compared to traditional teaching in the class at school. The experiment will evaluate the effects of an intervention in economics education in grade 9 and 10 of Flemish secondary schools in Belgium. The study will measure knowledge, attitudes and behaviour of students, parents and teachers. In particular, it will assess economic and political knowledge, financial literacy, political preferences on economic issues, trust in institutions as well as family communication. Randomisation will be done at school level. The expected sample size is 60 schools with 2,400 participating students.
External Link(s)
Registration Citation
Citation
Maldonado, Joana Elisa, Kristof De Witte and Koen Declercq. 2019. "The Effectiveness of Parental Tutoring Compared to Teaching at School." AEA RCT Registry. September 26. https://doi.org/10.1257/rct.4707-2.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
Participating schools will be randomly assigned to the following three experimental conditions:
Control group: Students do not receive any treatment.
Treatment group 1: Students follow three class periods of a digital learning path about the role of the government on the labour market in class and one class period with an interactive discussion game in class.
Treatment group 2: Students follow three class periods of a digital learning path about the role of the government on the labour market in class and receive a homework assignment that has to be completed with a parent and includes an interactive discussion game.
Intervention Start Date
2019-10-07
Intervention End Date
2019-11-30
Primary Outcomes
Primary Outcomes (end points)
We measure the knowledge acquired during the intervention in a post-test based on a set of 16 questions. Three of these questions will measure general financial literacy. Three questions will measure specific knowledge acquired during the interactive discussion game. The remaining questions will measure the knowledge on the specific topics of the digital learning path. In addition, the post-test measures attitudes concerning political preferences and trust in institutions. Finally, the post-test also enquires about family communication patterns.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
An open call for schools to participate was launched at the beginning of September 2019. Participating students are in grade 9 and 10, i.e. the third or fourth year of secondary education in Flanders and on average aged 15-16. Schools that register for participation will be randomised to the aforementioned three experimental conditions. Before and after the intervention, knowledge, attitudes and behaviour of students, parents and teachers will be assessed in online surveys. Students assigned to the control group will complete the same tests at the same time as students in the treatment groups, without receiving any intervention. Approximately four months after the intervention, students in the treatment groups will be given a second post-test to measure long-term effects.
Experimental Design Details
Not available
Randomization Method
Schools will be randomly assigned to the three experimental conditions by a computer, using a random number generator in Stata.
Randomization Unit
Randomisation will be done at the school level. All students and teachers in the same school will be assigned to the same experimental condition. In this way, all teachers in the same school will receive the same instructions in order to minimize spill-over effects and contamination of the different treatment conditions.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
The treatment will be clustered at school-level. The expected number of clusters is 60.
Sample size: planned number of observations
The expected number of observations is 2,400 students.
Sample size (or number of clusters) by treatment arms
The expected 60 clusters will be randomly assigned to the three experimental conditions, i.e. approximately 20 schools in each condition.
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 20 schools expected in each experimental condition. Each school could have on average approximately 40 participating students. Based on previous experiments, the intracluster correlation in the final sample can be assumed to equal 0.1. This intracluster correlation can be reduced by controlling for characteristics of schools and students. Using the conventional power of 0.8 and a significance level of 0.05, the calculation results in a minimal detectable effect size of 0.31 standard deviations. 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/√(800/(2(1.96+0.84)²(1+(40-1)0.1)))=0.31 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
Analysis Plan

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