1. Food and Nutrition Surveillance and
Response in Emergencies
Session 12
Data Collection, Analysis
and Interpretation
2. Introduction
• Assessing the impact on food and nutrition and
understanding the coping mechanisms of
different affected groups is needed to:
Target
design and
implement appropriate strategies
To protect and promote good nutrition and
household food security throughout relief and
rehabilitation responses.
3. Introduction
• Population in crisis may be moving or
living in camps, towns or villages or
dispersed in the rural environment
• Design of the assessment depends mainly
on the practical crisis conditions
4. Typical survey designs include:
• Longitudinal survey: data is collected for the
same population over a long period of time.
Longitudinal studies are useful in establishing
trends over a long period of time
• Cross-sectional surveys: This is one of the
commonly used survey designs that looks into
population issues at a given point in time.
• In emergency: Cross-sectional surveys mainly
used.
6. Survey Planning
• Collect the following information, if available,
before the rapid assessment
Previous nutrition surveys
Demographic information
Mortality and morbidity
Socio-economic situation
Administrative structure
7. Survey Planning
• CHECKLIST FOR PLANNING SURVEY
Which population is to be assessed
What is the smallest unit to be assessed (camp, village, district)
Which sampling methods will be used (systematic, cluster)
Which age group
Which indicators will be used (Weight for Height, oedema)
What personnel, equipment, transport, number of teams and
resources will be needed
How many clusters/children per day per team
9. Simple Random Sampling
• The survey subjects are chosen at random
from a list of all those eligible in the
sampling population.
• This is the ideal procedure but not
practicable in emergency situation
10. Systematic Random
Sampling
• Survey subjects are selected systematically e.g.
every 10th child from a list of all households. If
the average number of preschool children is
known, a sample of every 10th house or tent may
be taken systematically and all eligible children
examined
• Sample size for systematic random sampling is
450 children
11. Systematic Random
Sampling
Recommended where:
• the population is concentrated in an
organised or structured urban setting or in
refugee camp.
• The total number of households is less
than 10,000
12. Systematic Random
Sampling
Information required for this sampling method:
Total number of households.
Total population
Average number of children 6 months to 5 years age
(100 cm) bracket per household
• In camps and permanent settlements, the
sampling unit – household or dwelling (tent)
13. Systematic Random
Sampling
Calculation of the number of households to
obtain the required number of eligible children
No. of Households = 450/ (A x P)
where: A= Average household size
P= Proportion of children right age/height
14. Systematic Random
Sampling
No. of Households to be visited
Example: If average hh size is 6 persons and the
percentage of children under 5 years is 15%
(0.15)
450 / ( 6 x 0.15) = 500 households
15. Systematic Random
Sampling
No. of Households to be visited
Example: If the sampling area consists of 9000
household the sampling interval is:
9000/ 500 = 18.
Visit every 18th household
16. Cluster Sampling
• Sampling method used for large populations and
populations spread over large area for which
estimates of the number of people are available.
• It may also be useful in large or newly
established camps where numbers and ages of
people are not fully known
• The sample size needed to obtain the same
precision is about twice that of the systematic
random sample = 900 children
17. Cluster Sampling
• To obtain 900 children, the sample size for
cluster sampling is 30 clusters of 30 children.
• The sampling method is referred to as
30 by 30 cluster method
• For reliability of results, it is important to
examine not less than 30 clusters and not less
than a total of 900 children.
18. Cluster Sampling
Sampling procedure:
• Map out area of study following existing
geographic or administrative boundaries
• Obtain best available census data for each
division/location
• Prepare a list with three columns: Column 1:
Name of each geographic unit ( e.g. District,
Division, Location.
19. Cluster Sampling
• Column 2: Population of each unit,
• Column 3: cumulative population of the units.
• Each unit should have at least 300 inhabitants
• Draw a systematic sample of 30 clusters from
the list and their population estimates
20. Cluster Sampling
• Obtain sampling interval by dividing the total
population by number of clusters-usually 30
• Example: Suppose there is a total of 183 sections,
the sampling interval = 183/30=6.1
• Every 6th section/unit is then drawn randomly until
30 survey sections – the clusters - are selected
• The 30 children are obtained from these 30
clusters
21. Design of Survey Tools
Main Indicator
• Weight for height is recommended as the main
indicator of malnutrition by most guidelines
Independent of age
Has internationally accepted reference population
Interpretation based on wide experiences from many
parts of the world.
22. Design of Survey Tools
Questionnaire Design Consideration:
Surveys are two communication:
AUDIENCE + PURPOSE=DESIGN
Respondents prefer shorter surveys
Keep questions clear and concise
Contents should not be controversial or sensitive
23. Rapid Assessment
Mainly carried out on adhoc bases.
Useful when:
• When nutrition information are fast needed
• When resources of carrying out Nutrition survey are
limited.
• MUAC is usually used
• Additional methods include: FGD, Key informant
interview, observation (transect walks), seasonal
calendars and Case study.
24. Type of information in RA
• MUAC measurements: adults (women), <5yr
• Food availability and accessibility
• Water sources
• Common diseases- how are recent trends
• Access to health services/ other interventions
• Livestock and population movement- destinations/
origin of emigrants
• Type of food consumed/freq. of feeding
• Security situation
25. What is Data analysis?
• The way information and results are
interpreted and assessed
– Assigning meaning to figures, stories,
observations, etc that have been gathered and
recorded.
– Conceptual frameworks (i.e., UNICEF) guide data
analysis.
– Data analysis possible by hand or computer
(various packages, e.g., EPINFO; EPINUT; SPSS;
etc.)
26. Handling data before analysis …
• Clearly identify source (by name or code)
• Keep track of those who have not responded and
follow up
• Indicate the date and file data securely
• Review responses for completeness
• Translate into code (if necessary) or summarise
using key words
• Decide on how to record missing data
• Transfer data to blank copies of the original
monitoring sheet or a spreadsheet programme in
preparation for analysis.
27. Types of Data
• Numerical: values for which a numeric magnitude has
meaning
– discreet
• Restricted to certain values that differ in fixed amounts. No intermediate
values are possible, i.e., number of times a woman has given birth or
number of beds available in a hospital
– Continuous
• Not restricted to whole number values, i.e., height, weight
• Non-numerical: values for which magnitude has no meaning.
– Nominal/categorical class
• Values are arbitrary codes with no inherent meaning. The order and
magnitude are unimportant, i.e., sex (1=male, 2=female)
– Ordinal
• Values have inherent meaning based on order but not magnitude, i.e.,
ratings of quality (1=high, 2=low or 2=high, 1=low)
28. Steps in data analysis and
interpretation
1. Review the questions that generated the
information.
• Why was the particular information necessary? What
kind of decisions are to be made based on this
information?
2. Collate the relevant data:
– Baseline info and previous surveys or assessments
undertaken
– Background info e.g. morbidity data, food security info,
health facilities data, ongoing interventions, security
situation.
– Sort information into parts that belong together.
29. Steps in data analysis and
interpretation continued …
3. Data preparation and cleaning
– Before starting the analysis, the data needs to be
prepared and “cleaned”. Issues to look out for
include:-
– Missing data
– Data out of the required range.
– Extreme (biologically unlikely) weight for height data –
outliers
4. Analyze qualitative data
5. Analyze quantitative data
6. Integrate the information
30. Analysing Qualitative Data
• Describe the phenomena
– transcribe all interviews/observations
• thorough and comprehensive (‘thick’ description), i.e., information about
the context of an act, the intentions of the actor and the process in which
action is embedded.
describe the sample population,
– who were the key informants, what made them qualify as such? Who took part
in the FGDs? How representative were the participants of the groups they
represented? Under what circumstances were observations carried out? Who
was observed (and who was not)?
• Classification of the data
– look for and code key words and phrases that are similar in meaning
– categorize issues by topics
• Identify and group (categorise) pieces of data together, i.e., separate
similar or related data
31. Analysing Qualitative Data continued ...
• Interconnect the concepts
– compare responses from different groups
– determine patterns and trends in the responses from different groups or
individual respondents
– make summary statements of the patterns or trends and responses
– cite key quotations, statements and phrases from respondents to give added
meaning to the text.
– re-check with key informants to verify the responses and the generalization of
the findings.
Display summaries of data in such a way that interpretation becomes easy,
• list the data that belong together – may be followed by further summarization
graphically in some chart (i.e., a matrix – most common form of graphic display of
qualitative data) or a figure (i.e., diagram, flow chart). These help visualize possible
relationships between certain variables.
32. Analysing Qualitative Data continued ...
• draw conclusions, and (remember…)
• collection, processing, analysis and reporting of qualitative data are
closely intertwined, and not (as is the case with quantitative data)
distinct successive steps. One searches for evidence, purposively
looks for associations during the fieldwork by intertwining data
collection and analysis, verifies findings by looking for independent
supporting evidence.
• develop strategies for testing or confirming findings
to prove their validity.
• Check for representativeness of data (since informants are
selected systematically & according to previously established rules)
--- are all categories of informants been interviewed? Cross-check
data with evidence from other, independent sources (informants,
informant categories or different research techniques)
33. Analysing quantitative data
• First thing to do to analyse quantitative data is
convert raw data into useful summaries
– Descriptive measures
• Proportions, frequencies and ratios
– Measures of central tendency
• Mean/average, median, mode
– Measures of dispersion
• Range, standard deviation, percentiles.
34. Measures of Central Tendency
• A fundamental task in many statistical analyses is to estimate a location
parameter for the distribution; i.e., to find a typical or central value that
best describes the data.
• Interval estimates
– Parameter estimated from a sample data (point estimate or sample estimate)
as opposed to population (true value) parameter.
• Mean – the true mean is the sum of all the members of the given population divided
by the number of members in the population. Impractical to measure every member
a random sample is drawn gives the point estimate of the population mean.
– Interval estimate expand on point estimates by incorporating the uncertainty of
the point estimate.
• For example, different samples from the same population will generate different
values for the sample mean.
• An interval estimate quantifies this uncertainty in the sample estimate by computing
lower and upper values of an interval which will, with a given level of confidence
(i.e., probability) contain the population parameter.
35. Measures of central tendency continued…
• Why different measures
– Normal distribution
• Symmetric distribution – single peak, well-behaved tails
(estimates for mean, median & mode similar) - use mean as the
locator estimate.
– Exponential distribution
• Skewed distribution – mean & median not the same – mean pulled
to one side (direction of skewness).
Use all three central measures.
– Cauchy distribution
• Symmetric distribution – single peak with heavy tails
extreme values in the tails distort the mean - use median as the
locator estimate.
36. Quantitative techniques continued…
• Hypothesis test
– Also addresses the uncertainty of the sample estimate.
However, instead of providing an interval, a hypothesis test
attempts to refute a specific claim about a population
parameter based on the sample data.
• To reject a hypothesis is to conclude that it is false.
• To accept a hypothesis does not mean that it is true, only that we
not have evidence to believe otherwise.
– Hypothesis tests are usually stated in terms of both a
condition that is doubted (null hypothesis) and a condition
that is believed (alternative hypothesis).
37. Quantitative techniques continued…
• Common format for a hypothesis test:
– H0: a statement of the null hypothesis, e.g., two population
means are equal.
– Ha: a statement of the alternative hypothesis, e.g., two population
means are not equal.
• Test statistic: the test statistic is based on the specific hypothesis test.
• Significance level: the significance level, α, defines the sensitivity of
the test (i.e., 0.1, 0.05, 0.001) and denotes that we inadvertently reject the
null hypothesis by that percentage (i.e., 10,5 or 1%) of the time when it is
in fact true. The probability of rejecting the null hypothesis when it is in fact
false is called the power of the test and is denoted by 1-ß. Its compliment,
the probability of accepting the null hypothesis when the alternative
hypothesis is, in fact, true is called ß, and can only be computed for a
specific alternative hypothesis.
38. Quantitative techniques continued…
• Two-sample t-test for Equal Means
– Used to determine if two population means are equal, i.e., tests if a
new process or treatment is superior to a current process or treatment.
– Data may either be paired or not paired.
• One-factor ANOVA
– One factor analysis of variance is a special case of ANOVA for one
factor of interest and a generalization of the two-sample t-test.
• Multi-factor ANOVA
– Used to detect significant factors in a multi-factor model. A response
(dependent) variable and one or more factor (independent) variables
as is the case in designed experiments where the experimenter sets
the values for each of the factor variables and then measures the
response variable.
39. Data interpretation
• Summaries of data interpretation of results.
– What tools are used for interpretation?
– Logic
– Knowledge of the programme
– Experience.
• Ascription
• Pre- and post-measures of change.
• After-the-fact statements of change
• Explicit statements of cause/motivation of change
• Evidence ruling out plausible alternative explanation for the change
• Independence evidence attesting to the program’s likelihood of
effecting change.
40. Data interpretationcontinued…
• Assessment
• Comparison with past project performance
• Comparison with accepted target levels
• Comparison with other programmes or general norms
• Comparison with constituents needs
• With some standards, cost-benefit comparison
41. Data interpretationcontinued…
• Description of the sample
– Describe the study population by producing tables showing the
distribution of important variables e.g. sex, age, sex by age,
morbidity, nutritional status, nutritional status and age, nutritional
status and sex, nutritional status and morbidity, etc.
• Establish the links and association among the various
variables and the nutritional status
– Statistical analysis could be used to determine links or associations
between various quantitative data.
– Further links between qualitative data and the resulting nutritional
status could be established guided by the conceptual framework.
42. Data interpretationcontinued…
• Variables to look into in establishing
associations/links:-
• Socio-economic and political environment
• Food security situation (food availability and access)
• Health and sanitation
• Care practices for mothers and children
• Food consumption
• Food utilization by the body
• Mortality
43. Data interpretationcontinued…
• Identify areas requiring interventions
• Are the interventions that contribute positively to nutritional
status available and accessible to all or sustainable?
• Identify factors contributing negatively to nutritional status. Have
these been sufficiently addressed?
• Compare the current, nutrition situation and the previous rates.
Is it acceptable, poor, serious or critical (WHO classification)?
• Prepare study findings or results
• Prepare study results highlighting the key findings
• Discuss study findings with study population and
partners
• Provides an opportunity for further comprehensive discussion
and analysis of the results especially with the study population.
44. Cut off points for indicators of
Malnutrition
Indicator Weight for Height
% of the Median
Weight for Height Z
Score (SD)
MUAC
Severe Acute
Malnutrition
<70% or oedema <-3 Z scores or
oedema
<11 cm or oedema
Moderate Acute
Malnutrition
≥70% and <80% ≥-3 Z-scores and <-2
Z-scores
≥11 cm and <12.5
cm
Global / Total Acute
malnutrition.
<80% or oedema <-2 Z scores or
oedema
<12.5 cm or oedema
Normal ≥ 80% ≥-2 Z-scores ≥13.5 cm
At risk ≥12.5 cm and <13.5
cm
45. % median and Z scores
• Percentage of Median – the ratio of a child’s weight to the median weight of
a child of the same height in the reference data, expressed as a percentage,
e.g., if the median weight of the reference data for a particular height is
10kgs then to say that the child is 80% weight for height means that the
child is 8kgs.
WFH Percent median = Individual weight x 100
Median reference weight
• Z-scores: by describing how far in units (units called SD’s) a child’s weight is
from the median weight of a child at the same height in the reference data.
The “distance” is called a Z-score. It is expressed in multiples of the
standard deviation and is derived as follows:
WFH Z-score = Observed weight – median weight
Standard Deviation
46. WHO Classification of Global Acute
Malnutrition Using Z- Scores
Global /Total Acute malnutrition WFH Z
Scores
Interpretation
<5% Acceptable level
5 – 9.9% Poor
10 – 14.9% Serious
>15% Critical
47. Quality control measures
• Thorough training of staff plus pre-testing of tools
(interpretation of the questionnaires, if necessary)
• Standardization tests- Intra-personal/ interpersonal errors
• Close monitoring of the field work by qualified persons
• Cross-checking of the field questionnaires for anomaly daily
• Daily review of enumerator experiences and problems
• Progress review per plan and by checklist
• Data cleaning: collection, entry,
• Integrity of equipments: maintain accuracy using known
weights
