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# Sample size for IYCF assessment

This question was posted the Assessment and Surveillance forum area and has 9 replies.

### Mark Myatt

Frequent user

6 May 2009, 10:29

You will have a sample size in the anthropometry survey of slightly more than 900. Let us assume that it will be 900 children aged between 6 and 59 months. We can also assume that the distribution of ages of teh children will be pretty uniform. This means that you will have about: ((23 - 6) / (59 - 6)) * 900 = 289 children aged between 6 ande 23 months in the sample. Now you have to work out the required sample size for the IYCF indicators. You need (1) a guess of the indicator value, (2) an idea of the required precision, and (3) a guess of the population aged between 6 and 23 months. If you have no idea for (1) then assume 50%. For (2) you specify the required width of the 95% CI. 50% +/- 10% is OK but (e.g) 5% +/- 10% is meaningless. For (3) take the population of the survey area and multiply by: ((23 - 6) / (59 - 6)) * 0.2 = 0.064 This assumes that 20% (0.2) of the population are aged between 6 and 59 months. This is usually a pretty safe assumption. So, in a population of (e.g.) 87,000 there will be about: 87000 * 0.064 = 5568 childen aged between 6 and 23 months. Use a sample size calculator that allows for a "finite population correction". You can download SampleXS from: http://www.brixtonhealth.com/samplexs.html or use an on-line calculator such as GNU sampsize from: http://sampsize.sourceforge.net/iface/index.html#prev. Just enter your guesses for (1), (2), and (3). Using GNU sampsize with: Precision := 7.5% Prevalence := 50% (this is teh indicator value) Population := 5568 Level : = 95% I get a required sample size of 166. This assumes a simple random sample. You are using a cluster sample so you need to make a guess at the design effect. You can only know this from previous surveys. If you have no idea of the design effect then use 2. I'd guess that 1.5 should be OK for IYCF indicators. SampleXS does the calculation for you. If you use GNU sampsize then multiply the calculated sample size by the design effect. With the example (above) the required sample size is: n = 166 * 1.5 = 249 Since 289 >= 249 then (in this example) you will be able to estimate IYCF indicators with the required precision from the 30-by-30 sample. What if you need a bigger sample size? My preference would be to increase the main survey sample size using extra clusters. It is better to take more clusters than increase cluster size.

### Ali Maclaine

None

Normal user

7 May 2009, 07:34

Hi, Thanks for the answer but I am a bit confused: (a) You say that 87000 x 0.064 is 5568 children aged between 6 and 23 months, but above you have the 20% of the population between 6 and 59 months. Isn't the 5568 between 6 and 59m so you would have to recalculate for 6 to 23 months? (b) Also, does the population figure make much difference anyway to the final answer? (c) You don't cover what we should do for 0-5.9 months as we want to include them as well. (d) Sorry about the following question but I am confused. We want to get useful data for each IYCF indicator but we have very little data to base our estimates on. There are also 10 IYCF indicators now each looking at slightly different groups e.g. some take children 6-9months, some 6-23 months etc. Is this ok or do you have to deal with each indicator in a different way? What I basically want is to know is if we do a normal cluster survey (900 children 6-29 months and thinking add 100 aged 0-5.9m as is proportional to the amount in the population and we figure will be ok) will the results that we get mean anything statistically [so we can say X% (CI X - X) receive semi-solid foods] and therefore is it worth doing? Sorry for my ignorance. THANKS

### Nina Berry

IFE Consultant

Normal user

7 May 2009, 10:23

Hi Ali I am going to have a stab at answering your last question because I'd also like to 'practice' my statistical reasoning - and feeling reasonably thick-skinned at the moment. A cluster survey is a cross-sectional design. Assuming you are looking for a precision of p<0.05, you will need to have some baseline data from which to estimate the proportion of infants <6months old who display the outcome of interest in order to calculate the minimum sample size required to reach statistical significance. Let's say we want to know what proportion of the population of infants <6months old was exclusively breastfed in the past 24 hours. We have SOWC data estimating that 15% of infants are exclusively breastfed at 4 months. Assuming we require a precision p<0.05, then (according to the whizz bang table in my whizz bang textbook), we need to sample 139 infants. However, sample size has to be calculated on the basis of the unit of randomisation. So if your using a cluster design, you have to sample 139 clusters of infants rather than 139 infants, I think. That would significantly increase the number of infants required to reach significance. If you don't have any basis on which to estimate the proportion of the characteristic of interest then you must assume that it occurs at a rate of 50% because that estimate maximises your sample size. (To give you an idea, for the above scenario, an estimated proportion of 50% would require a sample size of 384!) Now stats is not my strength and I would be looking for a stat consult before committing to any quantitative design - hence I am more than happy to be corrected. Cheers Nina Berry Australia

### Mark Myatt

Frequent user

7 May 2009, 10:42

### Mark Myatt

Frequent user

7 May 2009, 10:51

Nina's calculation of n = 139 looks a bit small to me. GNU sampsize gives: Assumptions: Precision = 5.00 % Prevalence = 15.00 % Population size = 50000 95% Confidence Interval specified limits [ 10% -- 20% ] (these limits equal prevalence plus or minus precision) Estimated sample size: n = 196 This is for a simple random sample. With a cluster sample you have a design effect which (gross simplification) can be seen as a number by which you need to multiply your sample size to get the equivalent sample size from a cluster sample. With a design effect or (e.g.) 1.5 that 196 turns into 294. Nina's use of p < 0.05 is somewhat confusing since this applies to one type of error in a hypothesis test (AKA significance test) but is related to precision. Nina's n = 384 for 50% +/- 5% is correct.

### Nina Berry

IFE Consultant

Normal user

7 May 2009, 11:17

Thanks Mark - you're right. I misread my whizz-bang table. 196 it is. Now to get my head around the design factor. Cheers Nina

### Mark Myatt

Frequent user

7 May 2009, 11:39

Nina, Take a look at SampleXS (see link in post above). The help file contains some information on sampling, design effects, sample size, cluster selection &c. This was written for the epidemiology unit for the MSc Community Eye Health at UCL (now at LSHTM).

### Anonymous 360

Consultant

Normal user

8 Mar 2010, 09:27

Thank you so much for the survey information. I come across it while researching for KAP survey we plan to do soon covering Essential Nutrition Actions and WATSAN issues. Could I kindly request for sample questionnaires on KAP surveys covering mothers of children aged 0-24 months. Thank you in advance.

### Anonymous 3443

Health and nutrition manager

Normal user

27 Jan 2016, 18:11

Guys I have learnt a lot about what I plan to do soon going to implement, I plan to conduct KAP survey by next month and was looking around for who could guide me through, at least I have managed to gather much I hope you will not get tired if I requested for further assistance.
Can I have request for sample questionnaire please.