4. Analysis and interpretation of data

The statistical analysis of data should take place in two stages, as shown in figure 11.

Figure 11. Overview of Data Entry and Calculation
1. Data entry, plausibility check and 
descriptive statistical analysis 
using the Nutrition Baseline software


2. Detailed analysis
of causes and determinants
using an advanced statistic software program

For preparing the questionnaire, entering and descriptive analysis of data the Nutrition Baseline Software is recommended. For further information please read the help in this program. The idea behind this program is to work only with one file for preparing the questionnaire, entering and analysing the data. It is also especially designed for performing a Nutrition Baseline survey. Compared to other procedures the computer work and possible errors are reduced.

For a more in-depth analysis of data a statistical program such as SPSS or SAS is recommended, since multivariate analyses such as multi-factorial analysis of variance or analysis of variance with a co-variant cannot be carried out with the Nutrition Baseline Software.

Note: One objective of nutrition surveys (see chapter 1.2) is that data should also be used for cross-sectional comparisons by country and measures. Consequently, data should be stored in a secure but accessible place.

An agreement has been reached with the Nutrition Unit of the WHO to professionally store the results of the anthropometric data from population-based nutritional surveys. It is highly recommended to send the results to the following address using a form attached in Annex 6.10.

Nutrition Unit
20 Avenue Appia
CH-1211 Geneva 27

4.1 Data analysis

4.1.1 Data entry

Data entry should also be carried out using the Nutrition Baseline software. This program is suitable for this purpose because it is user-friendly and tests the entered data for its validity. The most important reason, however, is that it can calculate, as already mentioned, anthropometric indicators and is useful for descriptive analysis.

Furthermore it is easy to export the data files into programs such as D-Base, Excel or SPSS so that the possibility exists for switching to other computer packages later on.

For data entry first a questionnaire has to be created. These file contain the variable names, the question, the description of the variable name and the possible answers. It is strongly recommended to use the variable names provided in this handbook

Note: It is extremely important that in each questionnaire a variable HOUSEHNO (a unique number given to each surveyed household) is included. This variable is the so-called "unique identifier".  Furthermore, the data from the child(ren) must contain a variable CHILDNO, which enables one to distinguish between two or more children from the same household.

4.1.2 Plausibility check

A plausibility check must be carried out immediately after data input in order to correct erroneous data. Despite meticulous preparation and data input, erroneous data always appears in the database. Before any further processing of raw data, the data must be checked for plausibility. In part this can be done with the Nutrition Baseline software. For further information about the plausibility check in this program please read the help in this program and check the option sheet. The following procedures are recommended:

  1. The frequency breakdown of all noncontinuous variables helps to identify possible errors due to inconsistency or non-homogeneity.
  2. Reliability and consistency can be measured by comparing the data entered from the household form with that entered from an individual form. For example, the age of the child should be the same on the household and the individual forms.
  3. The quality of age reporting of children and/or sampling can be checked by the age frequency expressed in months and presented as a bar histogram (i.e., were approximately the same number of infants studied at each age?).
  4. Comparison of data obtained by the enumerators with that obtained by the supervisors provides important information about the general reliability of the survey.
If unexplainable data entries are identified, these must be checked with the entries on the questionnaires. Erroneous data in the database should then be replaced by correct data obtained from the questionnaires. If no satisfactory explanation for a value can be derived from the information in the questionnaires, either additional information must be obtained from the household, or the information must be excluded. It is better to obtain a lesser amount of correct information than false information.

4.2 Anthropometric indices

The following are internationally used key indicators for determining the nutritional condition of children:

¯ Weight-for-age (wt/age),

¯ Height-for-age (ht/age),

¯ Weight-for-height (wt/ht).

A child lacking in energy or nutrients in relation to his height will not gain weight corresponding to his genetic potential. Conversely, an overfed child will be overweight. In addition, when a child is undernourished and/or suffers repeated infections over an extended period of time, the body growth is retarded and the genetic potential for height will not be reached.

In the population as a whole, there are always some individuals with unusual nutritional intakes who are heavier or lighter, or taller or shorter than others. If for example, the weight-for-height ratio for an adequately nourished community is determined, the ratio will have a biologically typical normal Gaussian distribution. Such a distribution has been derived for the weight-for-age, height-for-age and weight-for-height indicators for children in the USA aged from birth to 18 years of age. Separate curves have been developed for boys and for girls. These distributions are available from the National Center of Health Statistics (NCHS) for use as reference data.

If the distribution of the height-for-age ratios of the surveyed community group lies to the left of the corresponding distribution for the reference group, or if the distribution of the weight-for-height ratio lies to either side of that of the reference group, there is evidence of health or nutritional problems in the surveyed child population. Often this is estimated by determining the proportion of children at or below -2 Z-score units of the NCHS reference standard (or for weight-for-height ± 2 Z-score units). It must be kept in mind, however, that 2.3% of the children in the reference population fall naturally outside 2 Z-score units of the mean.

When plotting anthropometric measurements of groups of children, it is important to determine:

  1. the proportion of those below or above the "acceptable" thresholds,
e.g. in the case of weight-for-age, height-for-age, and weight-for-height for children under five years of age:
in the case of mid-upper-arm-circumference (MUAC) of children:
  1. whether in the observed children their proportion above or below thresholds is about equal throughout the five years or whether it changes perceptively for any of the age groups.
The following rules-of-thumb can be used to interpret anthropometric indices in children:

1) Height-for-age

Children with a Z-score below -2 compared to the NCHS height-for-age reference are considered stunted (-2 Z-score is the value -2 standard deviations below the median of the reference population. By definition 2.3% of the reference population is below -2 Z-score).

The proportion of stunted children relative to all children examined is a valid reflection of the prevalence of the combination of disease prevalence and dietary inadequacy in light of physiological requirements in the whole community. The proportion of "stunted" children in most developing countries usually ranges between 20% and 33%. Higher figures are indicative of an "abnormal" nutritional situation, such as may arise because of long-term civil disorders, severe and chronic food shortage, or may reflect the combination of genetic selection and an abnormal geographic location, e.g., life in the Himalayas or the Andes.

2) Weight-for-height

Children with a weight-for-height less than -2 Z-score of the NCHS reference population are probably badly malnourished. They are only probably so, because in the "healthy" North American reference population from which the NCHS standards were derived, 2.3% were found to have this weight-for-height. If one finds significantly more than 2.3% of the children below this line, the children of the community are suffering from wasting. Since "wasting" has clearly been found to be associated with a depressed metabolism and decreased functional capacity, it is a serious sign. Wasted children experience higher mortality, probably because of reduced immune competence, from higher morbidity and from substantially reduced physical and mental abilities.

3) Weight-for-age

The third index used internationally as an indicator of undernutrition is the weight-for-age (wt/age) index. Most children below the threshold (-2 Z-score units below the mean for NCHS standards) are undernourished, those at or above the threshold are of normal nutritional status. This indicator combines both the ht/age and wt/ht indices. A child weighing too little for his age is too small and/or too lean (Gomez classification).

In the US reference population, the proportion of children falling below -2 Z-score units is 2.3%; however, in most developing countries in the absence of a severe food crisis the proportion is 20-40%. Higher proportions warn of a "worse-than-usual" situation.

It should be emphasized that these three indices are very useful for the classification of populations, but must be interpreted with care and require additional information for the diagnosis of the nutritional status of an individual.

4) Combination height-for-age and weight-for-height

Figure 12 presents an overview of the thresholds of malnutrition using ht/age and wt/ht indices based on the Waterlow classification.

Figure 12. Thresholds of malnutrition based on the Waterlow classification (Z-scores height-for-age and weight-for-height)
s102.gif (6012 Byte)

Combination of indices 
Interpretation of nutritional status
  Height/age Weight/height  

stunted + wasted 
stunted + obese 

wasting: lean condition due to acute nutritional and/or health problems
stuntin: reduced linear growth due to chronic nutritional and/or health problems

5) Mid-upper-arm-circumference (MUAC)

The Mid-Upper-Arm-Circumference (MUAC) is easier to measure but suffers from a higher measurement error especially if the investigator has little experience or takes insufficient care during measurement. MUAC may be quite useful to rapidly screen for severely malnourished children in emergency situations, in refugee camps, under severe drought conditions, etc, or may serve in the context of a rapid assessment of the health situation of an area. Its sensitivity, i.e. the ability of the test to detect malnutrition, is quite high if the higher threshold value (13.5 cm) is used. At this level one may, however, miss borderline cases, i.e. those moderately malnourished or at risk of becoming severely malnourished. Raising the threshold to 14.5 cm will increase sensitivity but reduce specificity, i.e. the ability of the test to correctly identify those not at risk. Arm circumference has the advantage that it varies relatively little between one and three years of age, i.e. it is relatively age-independent, hence does not require knowledge of the precise age, as do weight-for-age, or height-for-age. Above three years and below one year of age, especially below 6 months, however, it is of limited value and should be used and interpreted only with caution.

4.3 Clustering

The forming of clusters or groups based on a variable, say age, can help to identify statistical variations or influences on nutritional indicators.
Clustering is carried out from

functional and


If clusters are to be formed, care must be taken to ensure that there is enough variation and that the number of individual cases in each group is roughly the same. If marital status is to be used as the basis for clustering and of all adults 42% are married, 30% single, 16% separated, 8% widowed and 4% divorced, the three smallest sample sizes are too small to build meaningful clusters since they are too small for statistical interpretation. Either these three groups should be excluded from the statistical analysis or they should be consolidated into one group. In this example that would be the group of the separated + widowed + divorced = 28%. In clustering it is first necessary to carry out a descriptive statistical analysis. This is easy to do with the Nutrition Baseline Software

With some variables, the basis for clusters can easily be selected. This allows the clusters to be established from the beginning of the project.
Variable Cluster Division

Age (Months)


male, female

0-5, 6-11, 12-17, 18-23, 24-35, 36-47, 48-59

before, after

Once the clusters have been formed, they can then be compared statistically with respect to the nutritional indicators. An example is shown below:
Male (%)
Female (%)


wasting + stunting








4.4 Food intake

In order to draw conclusions on the adequacy of nutrition, it is necessary to determine the individual requirements for a healthy nutrient intake. Such a relationship is, however, in practice very difficult to determine during a routine field survey because of

  1. the difficulty of measuring the exact quantities of nutrients consumed and
  2. the difficulty in determining the exact nutrient requirements of individual persons (see chapter 6.4).
Nutrient intake can be calculated from dietary intake data, using food composition tables. When these intake data are considered in relation to nutrient requirements, potential nutrient deficiencies can be identified. Food composition tables have been produced by international and regional organizations, and also in many countries.

With the Nutrition Baseline software it is possible to calculate the nutrient intake for the mother and the child but the food weighing method or the 24-hour-recall method are not recommended as part of the surveys discussed in these guidelines. Based on previous experience, the usability and accuracy of the findings do not justify the expense involved. Therefore, only the typical food customs in a country are documented via a food frequency recall. Consequently, only qualitative conclusions concerning nutrient supply can be drawn, but it is possible to derive qualitative statements on the possible nutrition problems within a family.

Before analyzing a survey on feeding practices, inquiries should be made to the health authorities concerning the nutrient value tables used in their country.

FAO/WHO recommendations for nutrient requirements are generally used for developing countries. Chapter 6.4 in the Appendix contains an overview of nutrient recommendations for children up to six years. However, authorities of many countries have published their own recommendations.

4.5 Breast-feeding, supplementary feeding, and weaning practices

A major cause of undernutrition and malnutrition in developing countries lies in improper breast-feeding, supplementary feeding, and weaning practices. The following principles are recommended:

The analysis and interpretation of breast-feeding and supplementary feeding practices therefore have particular significance. Important breast-feeding and supplementary feeding concepts are defined in table 8.

Table 8. Definition of breast-feeding categories according to WHO
Category A child must be given: A child may be given: A child may not be given:
Exclusive breast-feeding Human breast milk Medicines, vitamins, trace elements Anything else
Predominant breast-feeding Mostly human breast milk Fluids (water, or water based fluids) Anything else (including substitutes for human breast milk)
Complementary feeding Human breast milk and semi-solid or solid foods Usual foods and beverages  
Bottle feeding Usual fluids or semifluid foods from bottles with nipples Also human breast milk from a bottle  

The following indicators are generally used for breast-feeding, supplementary feeding, and weaning practices:

Exclusive colostrum feeding rate
The proportion of infants reported to have received exclusively colostrum during the first 12 hours after birth.

(Infants <12 months (<365 days) of age reported to have been exclusively breast-fed with colostrum in the first 12 hours after birth) /
(All surveyed infants <12 months (<365 days)of age)

Exclusive breast-feeding rate
The proportion of infants under 4 months of age exclusively breast-fed.

(Infants <4 months (<120 days) of age who were exclusively breast-fed in the last 24 hours) /
(All surveyed infants <4 months (<120 days) of age)

Predominant breast-feeding rate
The proportion of infants under 4 months of age predominantly breast-fed.

(Infants <4 months (<120 days) of age who were predominantly breast-fed in the last 24 hours) /
(All surveyed infants <4 months (<120 days) of age)

Timely, complementary feeding rate
Proportion of infants 6-9 months of age receiving breast milk and complementary foods

(Infants 6-9 months (180-299 days) of age receiving complementary foods in addition to breast milk in the last 24 hours) /
(All surveyed infants 6-9 months (180-299 days) of age)

Continued breast-feeding rate (1 year)
Proportion of children 12-15 months of age who are breast-feeding

(Children 12-15 months of age breast-fed in the last 24 hours) /
(All surveyed infants 12-15 months of age)

Continued breast-feeding rate (2 years)
Proportion of children 20-23 months of age who are breast-feeding

(Children 20-23 months of age breast-fed in the last 24 hours) /
(All surveyed infants 20-23 months of age)

Bottle-feeding rate
Proportion of infants less than 12 months of age who are receiving any food or drink from a bottle

(Infants <12 months (<365 days) bottle-fed during the last 24 hours) /
(All surveyed infants <12 months (<365 days) of age)

4.6 Reliability of a survey

There are three basic types of variables, and the method used to check the reliability of a survey depends on the type of variables. Reliability is a measure of the repeatability or reproducibility of the results.

Variables with continuous values of measurement

For variables with continuous values, such as height, weight, or hemoglobin levels, the mean and the standard deviation of the difference between the recorded measurements of the enumerators and the supervisor should be calculated. A small mean and small SD are desirable. In annex 6.7 an example of the calculation of an intra- and inter observation is described.

Variables with yes/no answers

For variables permitting only yes/no answers, such as the presence of goiter, anemia, or undernutrition (Z-score < 2), the inter and intra observer errors of the surveying of these variables should be calculated according to the following overview:

a: number of identified observations detected jointly by the enumerator and the supervisor
b: number of observations not detected by the enumerator but detected by the supervisor
c: number of observations detected by the enumerator but not detected by the supervisor
d: number of observations neither detected by the enumerator nor and the supervisor

Sensitivity of finding (proportion of true positive): a / (a + c) x 100

The sensitivity indicates the proportion of surveyed individuals actually possessing an observed attribute (undernourishment, belonging to a certain income group, etc.) who were correctly identified.

Specificity of finding (proportion of true negative): d / (b + d) x 100

The specificity indicates the proportion of surveyed individuals not possessing an observed attribute who were correctly identified.

Ideally, both sensitivity and specificity should be 100. The lower the value, the less precise was the surveying of the data.

Variables allowing more than two answers

In determining the reliability of answers to questions with more than two answers, the percentage deviation between the enumerators and the supervisor is calculated. The smaller the deviation, the better the survey.

% deviation = (Number of answers with differences between the responses obtained by enumerators and those obtained by the supervisor) /
(Number of all answers with responses) x 100

4.7 Analysis of causes and predictors

With statistical methods it is possible to identify the relationship between the variables investigated in the survey. If there is a statistically significant association between two variables, it is called a predictor. A predictor suggests a causal relationship between two variables, but does not prove that one exists.

Three goals can be achieved in the analysis of predictors.

  1. Identification of social risk groups
Significant associations between a nutritional indicator and a socioeconomic indicator can help to identify social risk groups, such as the landless and illiterates.

It is also possible to arrive at predictors based on observed socioeconomic data in which several individual predictors are included. Thus it was possible in the slums of a Brazilian city to find a higher risk of malnutrition in young children from families who obtain their electrical power supply from neighbors. Undoubtedly the cause of the malnutrition is not in the type of electrical power supply, and therefore an improvement in type of electricity supply would not result in an improved nutritional condition for the children. Rather, there are other causal factors involved in these predictors that are responsible for the poor nutritional conditions, such as low income and little education. Therefore, although the means of obtaining electrical power is not the cause of the malnutrition, defining these predictors can help the consultants to identify risk groups more quickly and at less cost. For example, in this way families who have no electrical supply for their households can be selected for individual discussion on health and nutrition.

  1. Identification on indicators for monitoring
Predictors can be helpful in identifying indicators suitable for use in monitoring the impact of intervention measures.
  1. Information of causal relationships
Predictors can also provide important information regarding causal relationships and form the first steps for analysis of causes.
Three procedures can be employed for analysis of causes:
Statistically significant relationships between variables do not automatically provide information concerning the cause of nutritional problems.  For this a separate analysis of causes is necessary.

1. Multiple statistical analysis of causes

In principle, in a statistical investigation of causes in order to obtain a clearer picture of a relationship, consideration should be given to the fact that a cause which cannot be eliminated has an integral part in the relationship. A check must therefore be made as to whether the available statistical information provides sufficient basis to be able to assume a relationship. Therefore data of all potential confounders must be available. Some important tests with the related assumptions are presented in the table of annex 6.8.

Special attention should be given to multiple regression analysis and multiple ANOVA. These most used statistical techniques allow among others the investigator to examine the effect of one factor while other factors in the regression equation are held constant mathematically.

2. Analyses of causes by comparison of literature
A second important source for analysis of causes is literature. A cause-effect relationship can only be discussed with greater certainty, if a statistically established connection has been defined in another similar situation.
3. Analysis of causes through specific investigations
The baseline survey must commence from the viewpoint that although nutritional problems have been solved in other locations more extensive research is needed to reveal causal relationships in the present location. The information gathered and relationships proposed in previous studies should be carefully considered during the planning for the baseline survey.
If there is no statistically significant relationship between variables, either no association exists or one or more of the following factors may have influenced the results: The last three points are applicable only to follow-up surveys.

If there is a statistically significant relationship between two variables, the following questions must be considered:

The following list summarizes possible confounding factors that could influence the results of a survey:

1. Differences in the survey teams and their equipment

A survey must be implemented by a several survey groups. Although the teams along with their equipment should randomly select households for the survey, in practice the surveys by a given team will be limited to one geographical area to save time. Thus, differences in the results can result from different teams and equipment.
2. Changes in demographic distribution
It is possible that during the project/program segments of the population in the designated region emigrate or immigrate. Those segments might not be representative of the general population. An example, they might be the poorest with the highest nutritional risk.
3. Decreases or increases in the beneficiary group
During the project members of the beneficiary group enter or exit. This shortens the time available for the intervention, and the project/program measures cannot be sustained long enough to take effect.
4. Excessive representation of socioeconomic data from families with more than one child under 5 years
Many households have more than one child under the age of 5 years. To secure the representation of all children under five years of age and to increase the coverage of sampling, a survey has to be taken of all children in a household. If the socioeconomic data of households is used in a statistical relationship to the socioeconomic data of the children, the data from the families with more than one child will be over-represented.
5. Changes in external conditions
It is not always possible to arrange for a comparison group to evaluate the impact of intervention measures on the nutritional condition of a target group (see sub-chapter Without data from a comparison group, the interpretation of the impact of nutritional intervention based on longitudinal comparisons of baseline and follow-up surveys can be exceedingly difficult and must be undertaken with extreme caution, as other factors outside the control of the project/program can also have a bearing on nutritional status.
6. Changes in the age distribution of the surveyed group
The nutritional status of babies from zero to five months is often better than that of babies from twelve to seventeen months. If the age distribution of the examined babies is different between the baseline and follow-up surveys, the impact of an intervention can be misrepresented.
The last two points are applicable only to follow-up surveys.

4.8 Evaluation of indicators

Nutrition surveys yield important information about the nutritional condition of an investigated population. Many conclusions can be drawn on the type and distribution of evident nutritional problems using statistical analyses of the observed nutritional indicators (anthropometric indices, xerophthalmia, anemia, goiter, breast-feeding practices, etc.). However, these do not provide answers about how these findings are to be interpreted if questions arise concerning

intervention should be undertaken.

There are no universally valid thresholds for nutritional indicators, which could then be used to decide which measures to apply. If one attempted to specify a certain set of economic and health conditions, a 10% stunting rate in one part of a country might be high, while in another part of the country, or in another country, it might seem low. It is therefore necessary to establish a list of criteria that can be used to evaluate the indicators.

The following criteria should be considered when evaluating nutritional indicators:

Only by considering these criteria conclusions can be drawn about the indicators and thereby about the nutritional situation.

5. Reporting of survey results

5.1 Format of a technical report

A technical report should have the following structure:

Title page


Table of contents

0. Abstract

1. Introduction

1.1 Project/program description and overall framework
1.2 Nutritional situation based on previous reports
1.3 Objectives of the survey
2. Methodology
2.1 Population and area surveyed
2.2 Study design
2.3 Sampling and data collection
2.4 Equipment utilized
2.5 Statistical methods
2.6 Sensitivity and specificity of the survey
2.7 Ethical considerations
3. Results
3.1 Demographic and socioeconomic data
3.2 Food habits
3.3 Infectious diseases
3.3 Anthropometric data
3.5 Nutritional deficiencies
3.6 Existing intervention programs in the project/program area
3.7 Predictor analysis
4. Discussion
4.1 Analysis of findings
4.2 Problem tree
5. Recommendations
5.1 Possibilities for intervention
5.2 Proposed in-depth studies
5.3 Proposed indicators for monitoring and evaluation
6. List of references

7. Appendix

7.1 Questionnaires utilized

The report should include the following information:

l Title page

The purpose of the title page is to present a concise statement of the subject of the survey and to identify the responsible personnel . The title page is the "main gate" of the survey report that invites the reader to engage him/herself to study the document. The title is the summary of the summary.

The title page should contain the following information:

- Title of the report
- Names of the principle enumerators and authors
- Date of submission of the report
- Name and address of the institution of the principle enumerators and authors
l Acknowledgment

All persons, institutions and groups that assisted in carrying out the survey and without whose support the survey would not have been possible should be acknowledged.

l Abstract

The purpose of the abstract is to summarize in less than 400 words all important parts of the survey.

The abstract should

- describe the general objective of the survey (justification);
- describe briefly the methodology used;
- report the main findings;
- discuss and analyze the main results;
- state the main conclusions and recommendations.
l Introduction

The purpose of the introduction is to put the survey in perspective.

The introduction includes several sections:

l Methodology

The description of the methodology should be so complete that it would be possible to repeat the survey using the same methodology.

l Results

When describing findings, data should be presented objectively without any comment or commentary.

The following tables, arranged according to communities or urban districts, should be included in the results section:

The results section should contain the following diagrams:
l Discussion

Finally, the survey results should be analyzed in the light of other findings. The discussion of the results must be separated from their presentation. The clear separation of the presentation from the discussion of the results allows readers to draw their own unbiased conclusions from the survey findings.

Concrete information must be given concerning the survey goals described in the Introduction, as follows:

l Recommendations l List of references

This section should contain a bibliography of the literature referred to in the report.

l Appendix section

The appendix section should include a copy of the actual questionnaire used in both the language of the investigators and the local language. The questionnaire is an important tool of a survey that has to be adapted to the local situation and must therefore be developed for each survey. The setting in which the data will be collected will influence the design and structure of the data recording form or questionnaire.

In addition, it is also possible to use the appendix section to include tables useful for clarification but too extensive for the main section of the report.

5.2 Considerations of style for writing the report

Tables and figures are important tools for information transfer in reports. However, there are some rules that should be considered when preparing a table or figure.

  1. The main difference between tables and figures is that in tables information is presented in digital form whereas in figures it is presented visually. Often visual information is easier to comprehend than digital. Therefore, the initial decision is which format is best for data presentation.
  2. Tables and figures must be self-explanatory, although they can be interpreted in the text. Each table or figure needs a header that includes the number of the table or figure followed by the title. The table or figure number must be referred to in the text. Tables and figures should be numbered separately in sequence through the report.
  3. The title of a table or figure should summarize briefly the information of the table or figure.
  4. Each column in a table has to have a header. The column headers should be concise so that they can be written horizontally. They may contain abbreviations. Tables can be laid out in several different formats, with lines separating columns, rows or headers, however, the same style should be used in all tables in the report.
  5. Immediately beneath the column heads the precise units of measurement of the data should be shown. Units of measurement should be written in brackets, e.g., (%) or (years). If in a table or a figure there is no room for longer units, (e.g., number of infant deaths per 1000 live births and stillbirths), the details should be put in a footnote to the table. Footnotes should be placed beneath the lower boundary of the table. However, the footnote should be at least two double spaced lines above the text to distinguish it clearly from the text.
  6. As a rule, tables are presented vertically on the page, although wide tables may be presented sideways (landscape). If a table is too long to fit on one page, then the table should be continued on the next page. At the bottom of the first page the word "continued..." should appear. The second page should begin with the words "Table ... continued," and the column headings should be repeated. Such larger tables should probably appear in an appendix because they will contain more detail than is necessary for the points made in the text.
  7. Various types of illustrations may accompany the report as figures, such as line drawings, graphs, maps, and photographs. Drawings should be presented clearly with Indian ink on white paper. Graphs developed by computer software should be printed with ink jet or laser printers. Photographs should be printed on glossy paper.
  8. If a table or figure is taken from another publication, the source must be identified.
  9. Wherever percentages are to be used in tables the raw figures must similarly be provided so that the reader can both check your results as well as verify the validity of you interpretation. In the table below, and examining only percentages, the difference in the prevalence between boys and girls is quite striking. Yet if one had only found one more boy with goiter, the percentage would have similarly reached 50%.
Table 9. Schoolchildren with visible goiter
    Boys        n = 4
    (n)           (%)
  Girls       n = 40 
   (n)            (%)
  Total       n = 44
    (n)            (%)
Visible goiter

Other style considerations include:

5.3 Information for the target groups

Once the survey is completed, the target group should quickly be informed of the findings. The presentation and contents should be tailored to the culture and educational level of the people.

A place and time for the meeting should be chosen so as to include as many people as possible. The questionnaire should include a question about when and where such a meeting should be held.

The following recommendations are given concerning the presentation and discussion of the results in a community: