Annex 4. Statistical procedures for nutritional surveys

Introduction

This annex provides guidelines for statistical procedures, including sampling methods and determination of sample size, to be used in nutritional surveys. It fulfils the need - unmet by most handbooks, which deal more with surveys of communicable diseases - for guidance on the type of community-based survey essential for nutritional assessment.

The essential procedures for anthropometric surveys are covered in Chapter 3 and Annex 3; Chapter 2 outlines the parameters and criteria (mostly clinical and biochemical) used in assessing micronutrient deficiencies. In practice, a survey that combines clinical, anthropometric, and biochemical elements is required. Different types of nutrient are usually assessed in different age groups or among individuals of different physiological status, and few manuals provide guidance on how such assessments should be combined or integrated. Table A 4.1 shows the suggested age/sex groups to be examined - usually on the basis of the household-selection procedure described in this annex.

Involvement of a statistician right at the start of the survey design process is important, to ensure that sample sizes are appropriate (neither too large nor too small) and will produce results from which valid comparisons can be made between different populations and in the same population over time. The sample size is usually similar for anthropometry and for assessment of the different types of nutrient, but the design factor (increase in size of cluster sample required because of patchy distribution of the deficiency) is generally recommended to be larger for micronutrient surveys (3) than for anthropometric surveys (2).

The first part of the annex deals with the principles of random sampling and with sample size, and the second part presents various sampling procedures.

Principles of random sample surveys

Basic concepts

When dealing with large population groups it is not feasible to survey all individuals. However, valid conclusions can be drawn from measurements made on only a limited number of individuals within the population, provided that this "sample" is representative of the population as a whole.

The sampling techniques described in this annex are designed to ensure this essential representativeness through randomization in selection and elimination of observer bias. Data obtained only from health services, for example, are unlikely to be representative of the population as a whole; data collected only in the most accessible villages, or in camps that are reported to be in a bad state, will be similarly unrepresentative. Strict procedures must be followed in selecting individuals to be included in a sample to ensure that it is representative. Moreover, if the objective of a survey is to compare the nutritional status of two groups, representative data must be collected from the two groups separately.

Table A4.1 Examples of appropriate age/sex groups for nutritional assessments

Age/sex group	Type of assessment
Children <5 years	Anthropometry Anaemia, vitamin A deficiency, beriberi, scurvy (if any cases seen)
Children of school age (6-12 years) and adolescents	Goitre prevalence; urinary iodine; anaemia/iron deficiency
Women of reproductive age, or pregnant women	Anaemia/iron deficiency, beriberi, scurvy
Adults	Anthropometry Beriberi, pellagra, scurvy (if any cases seen)

The techniques, and the methods of analysing the results, recognize and allow for the fact that there may be some inaccuracy. Data gathered from a sample of a population provide only an estimate of what the results would be if measurements were made on the entire population. Whenever a sample is drawn, there is a risk that it may not be truly representative and therefore yield data that do not reflect the true situation. Inevitably, therefore, if a second sample is drawn from the same population, slightly different results are likely be obtained.

From a sample it is possible to calculate not only an estimate of malnutrition (or other variable of interest) but also the range of values within which the actual rate of malnutrition in the entire population almost certainly lies. The confidence interval is strictly not symmetrical, but as the sample size increases it becomes more and more symmetrical. For example, the 95% confidence limits for a 10% estimate of malnutrition based on a randomly selected sample of 30 children are 2% and 26%. However the confidence limits for a 10% estimate based on a sample size of 2000 are 9% and 11%. See Table A4.3.

A 95% confidence level¹ is usually considered to be appropriate for nutritional surveys. The precision of the result and the size of the confidence interval depend on the sample size and the actual prevalence of malnutrition (or other variable of interest) in the population.

¹ A 95% confidence level represents an error risk of 5%, meaning that, out of 100 surveys, as many as 5 may give results that do not reflect the true situation purely by chance.

Basic sampling procedure

Three main sampling methods can be used - random, systematic, and cluster. Cluster sampling is the most widely used and often the only feasible method in emergencies involving large population groups. In all cases, estimates are required of the total population and of any subgroups to be distinguished within the total. The essential steps in obtaining a sample are as follows:

1. Obtain available population data. Census data and a list of all settlements in the area might be obtained from departments of planning, statistics, or malaria control, for example. If no data are available, as may be the case for refugees or displaced persons, a rough population estimate should be made by counting the dwellings and estimating the number of people in each dwelling.

2. Divide the total population into groups relevant to the information to be collected. In the case of camp populations, it may be desirable to distinguish between different camps, different sections of camps, or between long-term residents and new arrivals. Among rural populations it is generally appropriate to distinguish pastoralists (such as nomadic herders), subsistence farmers, and others (including artisans and traders). If different groups are not distinguished, the survey findings may be difficult to interpret.

3. Choose the sampling methodology to be used. The required precision should be identified and the necessary sample size determined accordingly.

4. Select the households or individuals to be examined. The relevant sampling procedures should be followed carefully.

Defining sample size

The sample size is the number of individuals to be included in the survey to "represent" each population of interest. The sample size required depends on the following factors:

· Required precision and confidence level. The greater the precision required, the larger the sample needed.

· Expected prevalence of malnutrition (or other variable being estimated). The smaller the expected proportion of people presenting malnutrition, the greater the size of the sample required for a particular level of precision.

· Time and resources available. The time, personnel, equipment, transport, and funds available for the survey may limit the number of individuals or households that can be visited.

In practice, selection of sample size almost always involves a trade-off between the ideal and the feasible. A sample that is too small gives results of limited precision and therefore of questionable usefulness. For example, a result of 10% wasting (below median - 2SD weight-for-height) in a sample of 100 children would give a confidence interval ranging from approximately 4% to 16% - a result that cannot be interpreted usefully. Beyond a certain level, however, increases in sample size produce only small improvements in precision but involve disproportionate increases in costs. The formulae for calculating sample size (re) are as follows

· for simple random sampling

· for cluster sampling

where:

n = sample size required

p = expected prevalence of malnutrition in the population; as the prevalence of malnutrition is not known before the survey is done, an estimate must be used - this is usually an experienced guess, or derived from a small pilot survey

e = relative precision required

1.96 is a statistical parameter corresponding to the confidence level of 95% (an error risk of 5%).

k = "clustering" factor, or design factor, which is a measure of the clustering of the characteristic being measured.¹

¹ According to studies analysed by CDC, the design factor k usually has a value of approximately 2 in anthropometric studies among children under 5 years of age, with 30 clusters.

p and e can be expressed either as percentages or as fractions of 1 (10% = 0.10), but must both be expressed in the same terms.

The sample size for a cluster survey is likely to be larger than that for a random sample for the same precision. This is because the units within a cluster tend to be similar in their characteristics. Poor (and therefore malnourished) people, for instance, are likely to be found living together in the same areas.

Example

Expected prevalence of malnutrition 15%: p = 0.15

Relative precision required (e) 20% of the estimated prevalence

Design factor k = 2.

For random sampling:

For cluster sampling:

Table A 4.2 shows the sample sizes required for particular levels of expected prevalence and required precision with a fixed error risk of 5%. To take another example, if the expected malnutrition rate is 15%, and a relative precision of 3% is required, a sample size of 24188 obtained by simple random sampling will be needed. For cluster samples, the figures in Table A 4.2 should be multiplied by the appropriate design factor for the "clustering" of the characteristic being measured within sample clusters.

Table A 4.3 shows confidence intervals at the 95% level (5% error risk) corresponding to various sample sizes and observed rates when random sampling is used. For cluster sampling, the sample sizes must be multiplied by the appropriate design factor to take into account the clustering of the characteristic being measured.

Table A4.2 Sample sizes for estimating a population proportion with specified relative precision (95% confidence level)^a

Îc	pb
	0.05	0.10	0.15	0.20	0.25	0.30	0.35	0.40	0.45	0.50	0.55	0.60	0.65	0.70	0.75	0.80	0.85	0.90	0.95
0.01	729904	345744	217691	153664	115248	89637	71344	57624	46953	38416	31431	25611	20686	16464	12805	9604	6779	4268	2022
0.02	182476	86436	54423	38416	28812	22409	17836	14406	11738	9604	7858	6403	5171	4116	3201	2401	1695	1067	505
0.03	81100	38416	24188	17074	12805	9960	7927	6403	5217	4268	3492	2846	2298	1829	1423	1067	753	474	225
0.04	45619	21609	13606	9604	7203	5602	4459	3602	2935	2401	1964	1601	1293	1029	800	600	424	267	126
0.05	29196	13830	8708	6147	4610	3585	2854	2305	1878	1537	1257	1024	827	659	512	384	271	171	81
0.06	20275	9604	6047	4268	3201	2490	1982	1601	1304	1067	873	711	575	457	356	267	188	119	56
0.07	14896	7056	4443	3136	2352	1829	1456	1176	958	784	641	523	422	336	261	196	138	87	41
0.08	11405	5402	3401	2401	1801	1401	1115	900	734	600	491	400	323	257	200	150	106	67	32
0.09	9011	4268	2688	1897	1423	1107	881	711	580	474	388	316	255	203	158	119	84	53	25
0.10	7299	3457	2177	1537	1152	896	713	576	470	384	314	256	207	165	128	96	68	43	20
0.15	3244	1537	968	683	512	398	317	256	209	171	140	114	92	73	57	43	30	19	9
0.20	1825	864	544	384	288	224	178	144	117	96	79	64	52	41	32	24	17	11	5
0.25	1168	553	348	246	184	143	114	92	75	61	50	41	33	26	20	15	11	7	-d
0.30	811	384	242	171	128	100	79	64	52	43	35	28	23	18	14	11	8	5	-d
0.35	596	282	178	125	94	73	58	47	38	31	26	21	17	13	10	8	6	-d	-d
0.40	456	216	136	96	72	56	45	36	29	24	20	16	13	10	8	6	-d	-d	-d
0.50	292	138	87	61	46	36	29	23	19	15	13	10	8	7	5	-d	-d	-d	-d

^a
, where Z_1-a represents the number of standard errors from the mean, and a is the significance level of a test.

^b P= anticipated population proportion (prevalence)

^c Î = relative precision.

^d Sample size less than 5.

Table A4.3 Confidence intervals at 95% probability level corresponding to various sample sizes and sample percentages

Sample size	Percentage observed in sample
	5%	10%	20%	30%	40%	50%
30	1-18	2-26	8-39	15-49	23-59	31-69
40	1-17	3-24	9-36	17-47	25-57	34-66
50	1-15	3-22	10-34	18-45	26-55	36-65
60	1-14	4-20	11-32	19-43	28-54	37-63
80	1-12	4-19	12-31	20-41	29-52	39-61
100	2-11	5-18	13-29	21-40	30-50	40-60
200	2-9	6-15	15-26	24-37	33-47	43-57
300	3-8	7-14	16-25	25-36	35-46	44-56
400	3-8	7-13	16-24	26-35	35-45	45-55
500	3-7	8-13	17-24	26-34	36-45	46-55
1000	4-7	8-12	18-23	27-33	37-43	47-53
2000	4-6	9-11	18-22	28-32	38-42	48-52

If, for example, the observed malnutrition rate is about 20%, a total sample size of 100 will make it possible to estimate the true rate somewhere between 13% and 29%, assuming random sampling. If greater accuracy is required, for instance 18-22%, a sample size of 2000 would be needed.

In nutrition surveys in emergencies, the expected prevalence of severe malnutrition usually ranges between 5% and 20%, and the precision must be defined accordingly; a relative precision of 20-25% is generally appropriate.

The size of the total population does not normally affect the size of the sample required. However, if the population is small and the calculated sample size turns out to be greater than 10% of the total population, a correcting factor (finite population factor) can be applied as follows:

where

n_f = adjusted sample size for small (finite) population
n = sample size for large (infinite) population (for example, as set out in Table A 4.2)
N = population size
f = n/N.

Calculating results and confidence intervals

When results have been calculated, the corresponding confidence interval, d, should be calculated as follows and reported:

· for random sampling:

· for cluster sampling the following formula can be used to give an approximate result:

Using a random number table

A set of random numbers is presented in Table A4.4. Numbers can be read in any direction - from left to right, right to left, top to bottom, or bottom to top.

Table A4.4 Random numbers

13 118	50 901	57 493	96 647	46 146	65 512	97 571	49 679	92 251	36 599
81 111	33 653	61 544	90 072	61 635	94 254	98 222	49 594	99 403	56 952
07 124	56 894	00 475	09 815	05 299	17 082	80 775	11 320	98 562	68 957
55 155	23 168	83 063	80 324	51 450	68 094	71 844	68 302	49 552	12 682
46 406	44 641	45 461	75 174	33 268	86 032	40 355	58 288	05 532	29 419
10 616	17 092	76 614	04 950	67 982	28 515	16 782	86 129	44 391	64 449
38 497	57 435	46 124	37 302	10 783	93 043	06 903	77 158	49 638	26 211
83 203	45 840	75 843	75 843	74 567	75 971	97 779	98 047	68 916	35 038
19 236	62 703	12 863	14 452	72 228	55 022	07 024	43 615	74 802	02 110
79 024	60 592	93 692	29 737	09 314	26 191	52 484	11 588	14 078	85 947
76 073	57 252	52 795	67 673	62 267	29 552	68 244	49 280	58 583	42 190
50 568	66 590	38 807	30 061	26 336	46 147	04 554	44 562	72 604	63 031
11 838	73 906	55 981	23 668	22 627	88 438	96 686	73 645	81 410	10 942
57 618	30 523	16 757	11 956	58 411	41 647	67 884	30 084	14 500	66 958
61 846	47 265	09 508	11 030	10 462	93 922	17 022	71 031	07 827	94 722
60 935	25 351	11 687	07 679	73 455	58 617	24 415	56 921	88 450	50 471
63 328	21 749	74 262	77 143	55 995	50 707	91 516	38 002	60 552	00 634
75 937	07 127	11 014	00 738	46 159	09 866	87 587	41 648	36 538	24 398
11 981	89 485	54 965	08 300	67 724	24 919	65 682	50 101	45 470	07 232
12 311	17 067	42 758	64 557	46 297	28 414	93 801	81 180	12 176	08 536
45 160	76 932	00 433	42 228	73 696	27 478	65 321	22 979	30 198	86 708
26 427	48 280	53 441	44 543	95 231	39 939	09 251	09 755	26 671	89 392
54 568	17 774	95 705	28 018	26 507	63 504	98 872	22 449	56 423	59 133
80 855	94 883	08 969	16 949	86 045	68 398	46 164	57 147	35 104	37 262
96 203	73 918	77 875	48 444	08 167	58 460	87 945	52 145	20 330	77 172
91 210	89 152	93 904	27 666	51 080	00 487	12 073	41 639	28 717	33 909
37 808	11 431	03 351	82 979	96 677	41 588	17 592	51 11x	84 657	25 427
47 738	40 686	00 948	46 598	99 095	67 011	05 786	05 642	26 282	97 486
03 255	71 561	78 549	15 611	49 097	58 375	70 087	10 066	83 530	26 684
92 658	11 755	39 005	72 386	20 601	49 630	85 266	78 939	89 931	99 674
86 040	48 908	88 153	05 616	91 381	88 378	28 263	34 725	80 739	15 251
87 806	60 615	14 520	04 557	72 939	71 060	10 650	58 769	07 497	00 808
46 138	03 111	47 053	89 391	83 636	05 877	17 980	63 940	23 003	23 737
81 514	46 994	77 869	72 054	22 819	89 316	77 195	20 194	65 043	27 706
28 419	60 216	07 640	80 670	84 427	98 368	99 656	10 214	04 023	39 899
99 109	64 711	06 962	56 790	96 313	54 470	18 568	04 319	31 680	39 507
15 045	85 129	03 531	06 107	93 785	38 290	00 911	68 388	68 686	53 357
61 398	94 861	90 462	09 438	53 920	59 996	91 957	39 255	86 563	20 781
58 455	18 205	39 389	18 286	22 994	78 421	22 241	04 228	86 679	47 840
81 025	70 374	79 493	39 386	41 707	57 491	35 647	43 409	37 182	73 435

Numbers can be read off with any required total number of digits. The steps involved in using this, or any other, set of random numbers are:

1. Decide on the direction in which numbers will be read; e.g. left to right going down the page.

2. Specify the required number of digits. If a random number is required in the interval 0001 to 1342, 4 digits are needed (any of which may be zero).

3. Close your eyes and stick a pin (or other sharply pointed object) in the table. Read off the required number of digits in the direction chosen in step 1, starting with the first digit to the left of the point. If the resulting number falls within the required interval, use this number. If not, repeat the process until an eligible number is drawn or move to the next number.

Sampling methods

All sampling methods involve a highly ordered form of selection designed to eliminate observer bias; each can be adapted in various ways depending on the situation. The paragraphs that follow provide a general description of each method and how it can be applied.

In all cases, each selected individual, or every child under 5 years old belonging to each selected household, must be seen and (for an anthropometric survey) measured. The survey team, with the help of the community, must find the individuals concerned, wherever they are. If necessary, the team must return later to see and measure an individual missed on the first visit. No substitutions can be allowed and no one can be missed (unless they have died or left the community being surveyed).

Random sampling

Random sampling is the best method - when it can be used - since it is the only one that ensures representativeness. An up-to-date list of all individuals in the population is needed, with enough information to allow them to be located. Individuals are randomly drawn from the list using a random number table (see above and Table A 4.4). For a nutritional survey the sample would be restricted to children aged 6-59 months or 65-110 cm in length or height.

In practice, a reliable population list is rarely available, and it is sometimes practical to use the following alternative procedure:

1. Go to the area and make a list of all households included in the area of interest.

2. Assign each household on the list an identification number.

3. Select the required number of households using a random number table. Otherwise, pick household identification numbers out of a hat or a large box. (If this type of selection is done in public, the community can see how households are selected.) A number corresponding to each household is written on a small piece of paper, which is placed in the hat or box. The pieces of paper are shuffled and the required number of papers are then picked out (blindly). The households selected in this way become the sample for the survey.

4. Visit all of these (and only these) households. No households may be excluded or substituted for any reason. In a nutritional survey, all children in the specified age group belonging to each selected household must be measured.

Systematic sampling

Systematic sampling eliminates the need for complete, up-to-date population registers, but requires:

· a reasonably accurate plan or map showing all households; and

· an orderly layout, or site plan, which makes it possible to go systematically through the whole site.

This technique has been used in well-organized refugee camps, where households are arranged in blocks and lines. The procedure is as follows:

1. Either list all households and assign each one an identification number, or trace a continuous route on the map, which passes in front of every household.

2. Calculate the number of households to be visited in order to obtain the required sample. If the required sample size is 544 and there are, on average, 15 children (aged 6-59 months) per 10 households, the number of households to be visited is 544/1.5 = 362.6, or 363 (round up to the nearest whole number in this calculation).

3. Calculate the "sampling interval" by dividing the total number of households by the number that must be visited. If the total number of households is 5000, and 363 are to be visited, the sampling interval is 5000/363 = 13.8, or 13 (round down to the nearest whole number in this calculation).

4. Select the first household to be visited within the first sampling interval at the beginning of the list (or route) by drawing a random number which is smaller than the sampling interval. If the number drawn is 7, start with the seventh house.

5. Select the next household by adding the sampling interval to the first household identification number (or counting that number of households along the prescribed route), e.g. 7 + 13 = 20.

6. Continue in this way (e.g. 7, 20, 33, 46, etc.) until the number of households required for the survey has been systematically selected.

7. Visit all of these (and only these) households. No selected household may be excluded or substituted for any reason.

Two-stage cluster sampling

Two-stage cluster sampling is used in large populations, when no register is available and households cannot be visited systematically. Sampling is done in two stages:

1. Clusters, or sampling sites, within the total population are selected randomly. (Clusters may be natural groupings such as villages or, in a camp, blocks of a few houses. Where natural groupings do not exist, artificial clusters may be defined by imposing a grid on a map of the area.)

2. Within each selected cluster, an appropriate number of individuals or households are randomly selected.

This process is applied separately to each population of interest. For instance, if a comparison is to be made between two separate, large refugee camps, the same number of clusters must be surveyed in each camp.

The larger the number of clusters, the higher is the probability of good representativeness of the population under study. In practice, physical constraints will limit the number of subjects who can be conveniently studied in a cluster; 30 subjects may often be the maximum to which easy access is possible in a community. The number of clusters to be examined is then derived by dividing the desired sample size, as determined below, by 30. It should be remembered that the sample size for clusters is larger than that for simple random samples.

Stage 1: selecting the clusters

Where feasible, the population is divided into a large number of clusters (at least 100) containing similar numbers of people using administrative, physical, or geographical boundaries. For this purpose, a map and a list of all separate identifiable units will be needed. Well defined villages of similar size are examples of possible clusters. Larger villages can be divided into two or more clusters. In a refugee camp, existing or imposed "sections" can be used. These clusters are numbered and then, using a random number table or systematic sampling, 30 are selected.

Alternatively, and more usually, the following procedure can be used:

1. Prepare a list of all existing units or zones with their estimated populations. (A unit or zone may comprise a village, camp, defined neighbourhood, or "section" within a camp.)

2. Add two more columns. In the first, record the cumulative population figures obtained by adding the population of each unit or zone to the combined population of all the preceding units or zones on the list, as shown in Table A 4.5.

3. Calculate the sampling interval by dividing the total population by the number of clusters required (30). For example, if the population is 18 600, the interval will be 18 600/30 = 620.

4. Using a random number table, obtain a number between 1 and the sampling interval to define the unit or zone where the first cluster will be drawn. In the example in Table A 4.5, a random number of 510 places the first cluster in unit 1.

5. Add the sampling interval repeatedly to the original random number (e.g. 510, 1130, 1750, 2370...) to locate additional clusters up to the required total of 30, as shown in Table A 4.5. Note that large population units are likely to be assigned more than one cluster; small units (with populations less than the sampling interval) may have none.

6. Within each unit to which more than one cluster is assigned (e.g. unit 3 in Table A 4.5) further sampling is undertaken to locate the required number of clusters within the unit. Make a sketch map of the unit or zone and subdivide the whole into subunits of roughly equal population (or numbers of households), as illustrated in Fig. A 4.1. Randomly select from these the required number of clusters using a random number table or by drawing numbers out of a hat.

Table A4.5 Example of first stage of cluster sampling

Geographical units/zones	Estimated population	Cumulative population	Attributed numbers	Location of clusters
Unit 1	800	800	1-800	1
Unit 2	310	1 110	801-1110
Unit 3	1 220	2 330	1111-2330	2, 3
Unit 4	550	2 880	2331-2880	4
etc...	...	...	...	...
...	...	...	...	...
Total	18 600	18 600	18 600	(30)

Note: See fig. A4.1 for an explanation.

Never change a sampling site because it is too remote or is close to a bigger and "worse affected" place that someone feels should be surveyed in preference to the randomly selected "unimportant" site.

Strictly speaking, clusters for nutritional surveys should be defined on the basis of the numbers of children aged 6-59 months. In most situations, the proportion of children is relatively uniform, and figures for the population as a whole can be used, as indicated above. However, if there are known to be wide variations in the proportion of children in the populations of different areas, the numbers of children aged 6-59 months should be estimated and used as a basis for defining clusters. On the other hand, where reliable population figures are not available, clusters may have to be defined on the basis of estimates of the numbers of households in different units or zones.

Stage 2: selecting individuals within each cluster

Once the survey team is on site, the required number of children (usually 30) can be selected by systematic sampling, as described above, if the site layout permits. Alternatively, a sketch map of the area should be drawn, the houses numbered, and households selected using a random number table. In many situations, neither of these methods is feasible and the following procedure is adopted:

1. Go to the centre of the selected unit or cluster.

2. Randomly choose a direction by spinning a pencil (pen, bottle) on the ground (or a flat surface) and noting the direction in which it points when it stops.

3. Walk in that direction from the centre to the outer perimeter of the unit or cluster, counting the number of households along this line.

4. Using a random number table, obtain a number between 1 and the number of households counted.

5. Go to the household indicated and examine all children belonging to that household (e.g. if the number is 5, go to the fifth household along the randomly chosen line).

6. Go to the next nearest house, the one with the door nearest to the last house surveyed.

7. Continue the process until the required number of children (probably 30) has been completed.

Fig. A4.1 Division of a unit or zone for the selection of clusters

Note: In most cases a population will be divided into at least 100 clusters, of which 30 will be selected.

The method to be used must be decided in advance and used consistently throughout the survey. It is important that there be no element of deliberate choice by the survey team in selecting the sample houses.

All children belonging to each selected household should be surveyed, including those in the last household (even if this means exceeding the number "required"). No substitutions can be made.

Thirty separate clusters should be surveyed if at all possible. If the number of clusters is reduced, the reliability of the estimate obtained may be poor and provide an inaccurate picture of the true nutritional status of the population being surveyed. A greater number of children per cluster does not compensate for a reduced number of clusters.¹

¹ More than 30 clusters may be surveyed, but this will not significantly increase the accuracy or reliability of the results.