Impact of Aquaculture Training in Ghana: Cluster Randomized Trial
Last registered on February 11, 2020

Pre-Trial

Trial Information
General Information
Title
Impact of Aquaculture Training in Ghana: Cluster Randomized Trial
RCT ID
AEARCTR-0005445
Initial registration date
February 10, 2020
Last updated
February 11, 2020 1:48 PM EST
Location(s)
Region
Primary Investigator
Affiliation
IFPRI
Other Primary Investigator(s)
Additional Trial Information
Status
In development
Start date
2019-06-03
End date
2021-11-30
Secondary IDs
Abstract
As part of the Ghana Tilapia Seed Project (2019-2022), a cluster randomized experiment is being planned to test the impact of aquaculture training provision among small-scale tilapia pond farmers in 4 major tilapia producing regions in Ghana.The intervention is the provision of information to farmers. This is combined practical face-to-face training + access to mobile app. Information to be provided in mainly on good aquaculture practices.

To minimize risk of contamination, the unit of randomization will be clusters (that is, districts). Due to small sample size and few geographical locations, we plan to use to pair-matching method first and then randomization (that is, assigning randomly 1 to the treatment group and the other to the control group). We produced 19 district pairs based on cluster size (number of active tilapia farmers in each district) and region. In each pair, we randomly assigned 1 in the Treatment and the other in the Control group based on a lottery among the research team.
External Link(s)
Registration Citation
Citation
Ragasa, Catherine. 2020. "Impact of Aquaculture Training in Ghana: Cluster Randomized Trial." AEA RCT Registry. February 11. https://doi.org/10.1257/rct.5445-1.0.
Sponsors & Partners
Sponsor(s)
Partner(s)
Type
private_company
Experimental Details
Interventions
Intervention(s)
The intervention is the provision of information to farmers. This is combined practical face-to-face training + access to mobile app. Information to be provided in mainly on good aquaculture practices.
Intervention Start Date
2020-03-30
Intervention End Date
2021-05-28
Primary Outcomes
Primary Outcomes (end points)
Productivity (harvest in kg per m2)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Adoption of good aquaculture practices (dummy, count, index/score)
Secondary Outcomes (explanation)
Dummy, count Index or score based on pre-determined list of good aquaculture practices, including record-keeping, hygiene and sanitation
Experimental Design
Experimental Design
To minimize risk of contamination, the unit of randomization will be clusters (that is, districts).
Due to small sample size and few geographical locations, we plan to use to pair-matching method first and then randomization (that is, assigning randomly 1 to the treatment group and the other to the control group). Within the resulting pairs, the intervention is randomized, and the outcomes measured at the end of follow-up survey. We consider this design to be adaptive, because the construction of the matched pairs depends on the baseline covariates of all candidate units.
We produced 19 district pairs based on cluster size (number of farmers in each district) and region. We used mahascores and mahaselectunique commands in Stata to select the optimal pairing based on the covariates. In each pair, we randomly assigned 1 in the Treatment and the other in the Control group using a coin toss by the research team.




Experimental Design Details
Not available
Randomization Method
Randomization using coin flip by the research team
Randomization Unit
district
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
38 districts (groups of villages)
Sample size: planned number of observations
384 small-scale tilapia pond farmers
Sample size (or number of clusters) by treatment arms
192 farmers per treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We used clustersampsi in Stata to compute for the minimum number of clusters needed and the number of sample per cluster. The mean of the outcome indicator (productivity (kg/m2)) is 1.14, and the standard deviation is 0.52. Using complete randomization, minimum sample size per arm would have been 67 with 80% power and 5% level of significance. Given the randomization done at cluster level, we have to compute for the intraclass correlation (ICC), which is 0.10. Given that the districts have different number of farmers (therefore different cluster size), we have to further adjust the sample size to detect the minimum effect we are interested in. The cluster sizes range from 3-37, and the coefficient of variation (of cluster sizes) is 0.82. The design effect should be inflated by 2.57, accounting for the ICC and varying cluster sizes. The study is limited by the number of districts at least 3 small-scale active tilapia pond farmers. On the 4 major-producing regions (Brong Ahafo, Ashanti, Eastern and Volta), there are only 38 districts with at least 3 small-scale active tilapia pond farmers. The total number of farmers in these districts is 384. For 1 treatment (aquaculture training), we have 19 clusters and 192 farmers available for the study. With this design effect inflator, the sample size of 192 farmers in 19 clusters in each treatment arm can detect a minimum of 18% increase in the productivity (kg per m2). Local partners predict and aims an increase in 20%, so we have enough power to detect the predicted outcome increase. Last, given that we are doing a pair matching method before randomization, we can further deflate the design effect to capture this increase in efficiency and power.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IFPRI
IRB Approval Date
2019-04-09
IRB Approval Number
IRB #00007490
Analysis Plan

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