Conducting a Participatory Situation Analysis of.pdf - Global HIV ...

establish a national or sub-national baseline, then the
scope of the sampling design will be more extensive than
in the examples presented in this Guide. For example,
instead of selecting several key data collection sites with
high HIV/AIDS prevalence, it may be necessary to consider
forming a sample based on total households in the
country. A key resource to help with decisions on sampling
with regard to vulnerable children is Guide to
Monitoring and Evaluation of the National Response
for Children Orphaned and Made Vulnerable by
HIV/AIDS (UNICEF, forthcoming).
Quick Look at Probability and Non-
Probability Sampling Methods
Sampling procedures generally fall into two broad
classes: (1) formal, or probability methods, and (2)
informal, or non-probability methods. In a probability
sample, every person in the defined universe
may be selected into the sample, with a known
(non-zero) and equal probability. Probability sampling
is characterized by using lists or sampling
frames (e.g., housing compounds) to select the
sample, and by clearly defined sample selection
procedures.When a sampling frame cannot be
constructed, the use of non-probability sampling
methods is the only alternative.
In probability sampling, the key element in the
sample selection process is randomization.This
means that units and/or respondents are randomly
selected from all those included in the sampling
frame, thus reducing potential bias.Which randomization
scheme is selected depends on the
acceptable level of error in the results as well as
on what is most feasible given the circumstances.
A key issue is credibility.A survey based on nonprobability
sampling methods may produce the
same results as a probability survey, but the
results will be harder to defend against skeptics
who suspect that the findings reflect poor sampling
rather than target population realities.
Probability methods produce data that can be
interpreted with much greater confidence than
data from non-probability methods. Probability
data in turn translate into a firmer basis for decision-making
when designing programs and allocating
resources.
Sample Sizes
Generalizing data findings to a larger population is
often desired, and requires representative sampling.
Representative sample sizes are calculated
using a formula that includes the number of children
identified or estimated to be OVC in a given
area and the number of interviews needed to
obtain the desired percent data confidence (e.g.,
95% confidence level). Quantitative approaches to
sampling are more likely to use sample sizes that
can represent the distribution of variables, such as
experiences, perspectives, and behaviors relevant
to the analysis; whereas, qualitative data collection
methods use sample sizes that can collect sufficient
information to represent the range of these
variables.
The logic for using purposeful sampling to collect
qualitative data is different from the logic for
using probability sampling to collect quantitative
data.A purposeful sample is a strategic approach,
not a single technique or calculation. Participants
are selected for their ability to provide experiences
and perspectives relating to the purpose of
study; but not because it is convenient or easy to
access them.
Many strategies or techniques are available to
determine sample sizes for both quantitative and
qualitative data collection.The Technical Lead
should explore with the Advisory Team the
strengths and weakness of the various approaches
to sampling.The final situation analysis report
must describe, justify, and explain the sample
selection process so that others can critique the
decision-making process (see Appendix A,
“Quantitative Methods” for resources that
address sampling).
If the sampling design is not to be carried out on a
national scale, it must be decided what geographic areas
will be covered. Setting geographic priorities requires taking
into account a variety of indicators, such as prevalence
of HIV/AIDS and proportion of an area’s children
who are orphans (from all causes). Other indicators such
as the dependency ratio and the rates of infant and child
mortality, maternal mortality, poverty, and primary
school attendance also reflect the vulnerability in an area.
Economic factors must also be considered, such as the
differences between rural and urban areas in the ability
of households to continue to produce income when a
parent becomes ill. Among rural areas, farming systems
vary in their vulnerability to HIV/AIDS morbidity and
mortality. HIV prevalence and orphan data can be combined
with such statistics to form a composite indicator
that can be used to compare geographic areas in their relative
vulnerability to the impacts of HIV/AIDS.
46
Guidelines and Tools