Minimum detectable effect size for main outcomes (accounting for sample
design and clustering)
School will be the unit of randomization and total 25 school will be selected in the Treatment group. Power calculations follow multi-level clustering design. In each Treatent school, students from grades 6 to 8 will get benefit from the Intervention. Each grade would consist of 35 students on average. Similarly, Control group would consist of 50 schools and students from grades 6 to 8 from each school would take part in the study.
The main estimation equation would be Yigs=Tigs+Xic+eigst , where Yigs is a standardized math test score for individual i in grade g from school s, Tigs is a treatment dummy, Xic is a set of individual covariates, including previous achievement, which I anticipate having access to. We aim to randomize at the school level. Hedges and Hedberg (2007) reports intraclass correlation coefficients across schools of about 0.20 without covariates and about 0.15 with covariates.
The research study would have an MDE of 0.23 to 0.27 depending on the effective ICC range of 0.1 to 0.15 for comparing the treatment group versus control. This is for the program’s Intent To Treat (ITT) effect. We will also estimate classroom and student participation levels for both treated and control classrooms to further estimate program impacts based on amount of practice time.
We expect the takeup rate in the Treatment group to be 75% or more given that supervisors will visit schools twice and schools will have access to digital devices and internet connectivity. The takeup rate in the control group is taken to be 5% or less based on current field implementation.