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close this bookThe Management of Nutrition in Major Emergencies (WHO - OMS, 2000, 250 p.)
View the document(introduction...)
View the documentPreface
View the documentAcknowledgements
Open this folder and view contentsChapter 1. Meeting nutritional requirements
Open this folder and view contentsChapter 2. Major nutritional deficiency diseases in emergencies
Open this folder and view contentsChapter 3. Assessment and surveillance of nutritional status
Open this folder and view contentsChapter 4. Nutritional relief: general feeding programmes
Open this folder and view contentsChapter 5. Nutritional relief: selective feeding programmes
Open this folder and view contentsChapter 6. Prevention, treatment, and control of communicable diseases
Open this folder and view contentsChapter 7. The context: emergency preparedness and response programmes
View the documentAnnex 1. Nutritional requirements
View the documentAnnex 2. Basic facts about food and nutrition
View the documentAnnex 3. Nutritional anthropometry in emergencies
View the documentAnnex 4. Statistical procedures for nutritional surveys
View the documentAnnex 5. Use of particular foods in emergencies
View the documentAnnex 6. Guiding principles for feeding infants and young children in emergencies
View the documentAnnex 7. Programme indicators
View the documentAnnex 8. Biochemical assessment of micronutrients
View the documentAnnex 9. Human resource development for the management of nutrition in major emergencies: outline of an educational programme
View the documentBack Cover

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 Z1-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

nf = 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.