| Evaluation | Effect | Average Age | Observations | Experimental Evaluation | Country | Intervention |
|---|---|---|---|---|---|---|
| Santibanez 2014 | -7 | Grade 5 | 36,420 | No | Mexico | Schools received training and a grant of $4980 |
| Aturupane 2014 i | 5 | Grade 4 | 3,107 | No | Sri Lanka | Schools established committees to improve management and results |
| Barr 2012 ii | 22 | Grade 3 | 2,153 | Yes | Uganda | School committees were trained on how to implement school monitoring |
| Barr 2012 i | 11 | Grade 3 | 2,153 | Yes | Uganda | School committees were trained on how to implement school monitoring |
| Blimpo 2011 i | -4 | Grade 4 | 1,629 | Yes | Gambia | Schools received training for capacity building and a grant of $553 |
Graph of Effects
This graph shows the effects of [Number of Evaluations] evaluations of [Program Type] programs. The first evaluation, [Evaluation name 1], estimated an effect of [Estimated effect Eval. 1] points (graphed with the top grey circle). Because this is an estimate, there is uncertainty about what the true effect is. But we know that the true effect will probably lie between a low value of [True effect lie between – Low value, Eval. 1] and a high value of [True effect lie between – High value, Eval. 1] (graphed with the top horizontal line). We can combine the estimates from all the evaluations and compute an average effect of [Estimated effect, Average] (graphed with the black circle). There is also uncertainty about where the true average effect is. But we know that the true average effect probably lies between [True effect lie between – Low value, Average] and [True effect lie between – High value, Average] points (graphed with the bottom horizontal line).
| Evaluation | Effects | Low Value | High Value | Weight in Average Effect (%) |
|---|---|---|---|---|
| Aturupane 2014 i | 5 | -11 | 21 | 20 |
| Barr 2012 i | 11 | -9 | 30 | 9 |
| Barr 2012 ii | 22 | 4 | 40 | 10 |
| Blimpo 2011 i | -4 | -13 | 6 | 44 |
| Santibanez 2014 | -7 | -25 | 11 | 17 |
| Overall |