A lot of questions ... lest us start by addressing a few of them and see where that takes us ... Sample size calculations usually assume a simple random sample. Cluster samples usually have a lower effective sample size than a simple random sample. This means that you should take a larger sample size than calculated. It is usually to take a sample size twice that calculated for a simple random sample. Using GNU sampsize I get: Estimated sample size for two-sample comparison of percentages Test H: p1 = p2, where p1 is the percentage in population 1 and p2 is the percentage in population 2 Assumptions: alpha = 5% (two-sided) power = 80% p1 = 90% p2 = 80% Estimated sample size: n1 = 199 n2 = 199 So your sample size in each district should be about 398. You might consider using a single-tailed hypothesis test as you are expected and are interested in a difference in one direction. Using GNU sampsize I get: Estimated sample size for two-sample comparison of percentages Test H: p1 = p2, where p1 is the percentage in population 1 and p2 is the percentage in population 2 Assumptions: alpha = 5% (one-sided) power = 80% p1 = 90% p2 = 80% Estimated sample size: n1 = 157 n2 = 157 Giving a sample size of about 314 in each group. If you have baseline data you may want to use a one-sample test (i.e. for 80% rather than 90%) This would need a sample size of about 140 in one (i.e. the intervention) group only. I would probably go for a one-sample test in one sample from the intervention district with 90% or 95% power. Sample sizes would be 204 and 266 respectively. This would be safe if you think that a secular trend in reduction of stigma is not operating and so will not need a "control" district. A general rule is to prefer many small clusters over a few large clusters. using m = 30 clusters is usually a safe choice but you could go for fewer. This would be cheaper. With n = 266 you might (e.g.) go for 24 clusters of 12 (that come to 264 ... close enough). Picking clusters can be done using the PPS sampling approach as used in SMART surveys or the spatially stratified approach as used in RAM type surveys. Households within each cluster could be selected as described in the SMART survey manual. You do not need to sample all 200 villages. See above ... 24 would probably do you well, 30 might be better. Data analysis would require specifying the sample design. This can be done in packages such as STATA, SPSS, EpiInfo, SUDAAN, SAS, &c. WRT the effect size. If you knew this in advance then you would not need to do a survey. Select the level of effect that you deem to be usefully or substantively significant. If you think a drop from 90% to 80% to be a success then use that. I would avoid a before-after paired study as this often proved to be a lot of work (best to reserve these to interventions in (e.g.) schools where follow-up is simple). In summary ... I think you can do a more powerful and cheaper on-sample study with a single tailed test (or a 95% CI approach). I hope this helps. Please do not hesitate to ask follow-up questions.

Dear Friend Thank you so much first of all; I must say that you are doing an excellent job to put sincere efforts and share your knowledge with those who are unknown to you! Please accept my sincere thanks for this! I answer within your replies (para with ### are the answers): A lot of questions ... lest us start by addressing a few of them and see where that takes us ... Sample size calculations usually assume a simple random sample. Cluster samples usually have a lower effective sample size than a simple random sample. This means that you should take a larger sample size than calculated. It is usually to take a sample size twice that calculated for a simple random sample. Using GNU sampsize I get: Estimated sample size for two-sample comparison of percentages Test H: p1 = p2, where p1 is the percentage in population 1 and p2 is the percentage in population 2 Assumptions: alpha = 5% (two-sided) power = 80% p1 = 90% p2 = 80% Estimated sample size: n1 = 199 n2 = 199 So your sample size in each district should be about 398. ###I agree with the design effect. But do we add design effect even when there is two sample comparison? Does it not to be applied in only one sample i.e. e.g. like prevalence surveys where you don’t aim for a change from time 1 to time 2? You might consider using a single-tailed hypothesis test as you are expected and are interested in a difference in one direction. Using GNU sampsize I get: Estimated sample size for two-sample comparison of percentages Test H: p1 = p2, where p1 is the percentage in population 1 and p2 is the percentage in population 2 Assumptions: alpha = 5% (one-sided) power = 80% p1 = 90% p2 = 80% Estimated sample size: n1 = 157 n2 = 157 Giving a sample size of about 314 in each group. ###This is also a strong argument. But, is it still OK to take single-tailed hypothesis, even when it is possible that the mass-media may increase the knowledge of the population; or may reduce (strange!) or may not have any effect at all from the baseline level? If you have baseline data you may want to use a one-sample test (i.e. for 80% rather than 90%) This would need a sample size of about 140 in one (i.e. the intervention) group only. I would probably go for a one-sample test in one sample from the intervention district with 90% or 95% power. Sample sizes would be 204 and 266 respectively. This would be safe if you think that a secular trend in reduction of stigma is not operating and so will not need a "control" district. ###Thank you again for this good argument as well. But we are testing not just knowledge; we are also testing a strategy for treatment coverage as well, so I guess two districts would be taken. We also need to know what level of knowledge control district has. A general rule is to prefer many small clusters over a few large clusters. using m = 30 clusters is usually a safe choice but you could go for fewer. This would be cheaper. With n = 266 you might (e.g.) go for 24 clusters of 12 (that come to 264 ... close enough). ###As for I understood, according to you, there is no specific mathematical formula to calculate the number of clusters; it just depends on convenience and desirable cluster size? I have seen surveys that have taken >40 or >50 surveys, do they also not use any particular mathematical formula and decide on number of clusters as per convenience and desirable cluster size? 30 clusters are generally for immunization programs, generally, and our program is on neurology, would that be no issue? 24812=288, and not 264 Picking clusters can be done using the PPS sampling approach as used in SMART surveys or the spatially stratified approach as used in RAM type surveys. Households within each cluster could be selected as described in the SMART survey manual. You do not need to sample all 200 villages. See above ... 24 would probably do you well, 30 might be better. ###Yes, I would use PPS; and plan to sample households by simple random sampling, what do you think?. I would read SMART survey manual as well. Thank you for this link. Data analysis would require specifying the sample design. This can be done in packages such as STATA, SPSS, EpiInfo, SUDAAN, SAS, &c. WRT the effect size. If you knew this in advance then you would not need to do a survey. Select the level of effect that you deem to be usefully or substantively significant. If you think a drop from 90% to 80% to be a success then use that. ###sorry, but I didn't understand "If you knew this in advance then you would not need to do a survey." I would avoid a before-after paired study as this often proved to be a lot of work (best to reserve these to interventions in (e.g.) schools where follow-up is simple). ###Thank you. Since we employ radio for mass-media, don’t you think that we wouldn’t need to go to the same participants, as those of the baseline survey, for the final-line survey? Meaning, a random sample of public for baseline and then another random sample of public for final-line, participants may or may not be same? In summary ... I think you can do a more powerful and cheaper on-sample study with a single tailed test (or a 95% CI approach). I hope this helps. Please do not hesitate to ask follow-up questions. ###Thank you so much for your time and patience. Also, I would be glad if you may share your insights on the second approach I had mentioned in my original message:- Since assuming a change from 90% to 80% is only an assumption and something may not be obtained in reality. Could these limitations in the two-sample comparison of proportions be overcome by doing two independent surveys (that use the formula as below that gives maximum sample size and we do not assume anything that our intervention mass-media would bring this much of improvement) that takes into account 95% CI but doesn’t take into account statistical power (80% or 90/95% power). Could this also be one of the alternative methods according to you? Project cost is not a limitation factor. Since power is an important component to show that the difference existed in reality; below formulas and above second approach doesn’t take into account this power aspect. Power could be estimated after two surveys (baseline and final-line) have been conducted but not pre-assumed. N=2* 1.962 (p-(1-p))/d2 (for survey baseline) p=0.9 (because of 90%); d=0.1 N=2* 1.962 (p-(1-p))/d2 (for survey final-line) p=0.8 (because of 80% or whatever is obtained in baseline survey); d=0.1 Best regards!

1. Nutrition Surveillance Specialist

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We are looking for a consultant to support the countries of the region in their planning and implementation of nutritional surveys. Also, the work includes an important aspect of collaboration with regional platforms such as the Cadre Harmonisé.

2. Nutrition Routine information

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We are looking for a consultant to support countries in the region in their routine nutrition information systems. The person should have advanced knowledge of DHIS2 software.

 

Thank you so much again for all your help and timely answers!, I have no pending queries now, I have got the answers I was looking for, Thank you so much! Just to be sure on one last para "Statistical tests vs. CIs", as far as I have understood, you do not support the second approach that I had mentioned (two prevalence surveys without assuming that a particular effect size would be obtained, 10% in our example), but prefer to pre-assume that an intervention would bring x% of change (10% in our example) and then according to this assumption, we calculate the sample size. Have I understood correctly? Nonetheless, thank you so much for all your help!, Best regards.

The point I was trying to make is that the two approaches are equivalent to each other. The only difference between them is the mechanics of the testing. With two surveys you will still want to ask whether the two prevalences differ from each other and you will then fall back on a significance test and that is where we started from. Best, IMO, to decide what is the smallest effect worth detecting and then calculate the sample size sufficient to detect that with acceptable levels of error. The sample size calculation (and the sample size required) for either approach will be the same.

Thank you so much, sir. Sorry, I misunderstood initially your point. Best regards!!